WorldWideScience

Sample records for forecast system cfs

  1. Assessment of simulation of radiation in NCEP Climate Forecasting System (CFS V2)

    Science.gov (United States)

    Goswami, Tanmoy; Rao, Suryachandra A.; Hazra, Anupam; Chaudhari, Hemantkumar S.; Dhakate, Ashish; Salunke, Kiran; Mahapatra, Somnath

    2017-09-01

    The objective of this study is to identify and document the radiation biases in the latest National Centers for Environment Prediction (NCEP), Climate Forecasting System (CFSv2) and to investigate the probable reasons for these biases. This analysis is made over global and Indian domain under all-sky and clear-sky conditions. The impact of increasing the horizontal resolution of the atmospheric model on these biases is also investigated by comparing results of two different horizontal resolution versions of CFSv2 namely T126 and T382. The difference between the top of the atmosphere and surface energy imbalance in T126 (T382) is 3.49 (2.78) W/m2. This reduction of bias in the high resolution model is achieved due to lesser low cloud cover, resulting more surface insolation, and due to more latent heat fluxes at the surface. Compared to clear sky simulations, all sky simulations exhibit larger biases suggesting that the cloud covers are not simulated well in the model. The annual mean high level cloud cover is over estimated over the global as well as the Indian domain. This overestimation over the Indian domain is also present during JJAS. There is also evidence that both of the models have insufficient water vapour in their atmosphere. This study suggests that in order to improve the model's mean radiation climatology, simulation of clouds in the model also needs to be improved, and future model development activities should focus on this aspect.

  2. Recalibration of CFS seasonal precipitation forecasts using statistical techniques for bias correction

    Science.gov (United States)

    Bliefernicht, Jan; Laux, Patrick; Siegmund, Jonatan; Kunstmann, Harald

    2013-04-01

    The development and application of statistical techniques with a special focus on a recalibration of meteorological or hydrological forecasts to eliminate the bias between forecasts and observations has received a great deal of attention in recent years. One reason is that retrospective forecasts are nowadays available which allows for a proper training and validation of this kind of techniques. The objective of this presentation is to propose several statistical techniques with different degree of complexity and to evaluate and compare their performance for a recalibration of seasonal ensemble forecasts of monthly precipitation. The techniques selected in this study range from straightforward normal score and quantile-quantile transformation, local scaling, to more sophisticated and novel statistical techniques such as Copula-based methodology recently proposed by Laux et al. (2011). The seasonal forecasts are derived from the Climate Forecast System Version 2. This version is the current coupled ocean-atmosphere general circulation model of the U.S. National Centers for Environmental Prediction used to provide forecasts up to nine months. The CFS precipitation forecasts are compared to monthly precipitation observations from the Global Precipitation Climatology Centre. The statistical techniques are tested for semi-arid regions in West Africa and the Indian subcontinent focusing on large-scale river basins such as the Ganges and the Volta basin. In both regions seasonal precipitation forecasts are a crucial source of information for the prediction of hydro-meteorological extremes, in particular for droughts. The evaluation is done using retrospective CFS ensemble forecast from 1982 to 2009. The training of the statistical techniques is done in a cross-validation mode. The outcome of this investigation illustrates large systematic differences between forecasts and observations, in particular for the Volta basin in West Africa. The selection of straightforward

  3. Understanding the Impact of Ground Water Treatment and Evapotranspiration Parameterizations in the NCEP Climate Forecast System (CFS) on Warm Season Predictions

    Science.gov (United States)

    Ek, M. B.; Yang, R.

    2016-12-01

    Skillful short-term weather forecasts, which rely heavily on quality atmospheric initial conditions, have a fundamental limit of about two weeks owing to the chaotic nature of the atmosphere. Useful forecasts at sub-seasonal to seasonal time scales, on the other hand, require well-simulated large-scale atmospheric response to slowly varying lower boundary forcings from both the ocean and land surface. The critical importance of ocean has been recognized, where the ocean indices have been used in a variety of climate applications. In contrast, the impact of land surface anomalies, especially soil moisture and associated evaporation, has been proven notably difficult to demonstrate. The Noah Land Surface Model (LSM) is the land component of NCEP CFS version 2 (CFSv2) used for seasonal predictions. The Noah LSM originates from the Oregon State University (OSU) LSM. The evaporation control in the Noah LSM is based on the Penman-Monteith equation, which takes into account the solar radiation, relative humidity, air temperature, and soil moisture effects. The Noah LSM is configured with four soil layers with a fixed depth of 2 meters and free drainage at the bottom soil layer. This treatment assumes that the soil water table depth is well within the specified range, and also potentially misrepresents the soil moisture memory effects at seasonal time scales. To overcome the limitation, an unconfined aquifer is attached to the bottom of the soil to allow the water table to move freely up and down. In addition, in conjunction with the water table, an alternative Ball-Berry photosynthesis-based evaporation parameterization is examined to evaluate the impact from using a different evaporation control methodology. Focusing on the 2011 and 2012 intense summer droughts in the central US, seasonal ensemble forecast experiments with early May initial conditions are carried out for the two years using an enhanced version of CFSv2, where the atmospheric component of the CFSv2 is

  4. cFE/CFS (Core Flight Executive/Core Flight System)

    Science.gov (United States)

    Wildermann, Charles P.

    2008-01-01

    This viewgraph presentation describes in detail the requirements and goals of the Core Flight Executive (cFE) and the Core Flight System (CFS). The Core Flight Software System is a mission independent, platform-independent, Flight Software (FSW) environment integrating a reusable core flight executive (cFE). The CFS goals include: 1) Reduce time to deploy high quality flight software; 2) Reduce project schedule and cost uncertainty; 3) Directly facilitate formalized software reuse; 4) Enable collaboration across organizations; 5) Simplify sustaining engineering (AKA. FSW maintenance); 6) Scale from small instruments to System of Systems; 7) Platform for advanced concepts and prototyping; and 7) Common standards and tools across the branch and NASA wide.

  5. The Core Flight System (cFS) Community: Providing Low Cost Solutions for Small Spacecraft

    Science.gov (United States)

    McComas, David; Wilmot, Jonathan; Cudmore, Alan

    2016-01-01

    In February 2015 the NASA Goddard Space Flight Center (GSFC) completed the open source release of the entire Core Flight Software (cFS) suite. After the open source release a multi-NASA center Configuration Control Board (CCB) was established that has managed multiple cFS product releases. The cFS was developed and is being maintained in compliance with the NASA Class B software development process requirements and the open source release includes all Class B artifacts. The cFS is currently running on three operational science spacecraft and is being used on multiple spacecraft and instrument development efforts. While the cFS itself is a viable flight software (FSW) solution, we have discovered that the cFS community is a continuous source of innovation and growth that provides products and tools that serve the entire FSW lifecycle and future mission needs. This paper summarizes the current state of the cFS community, the key FSW technologies being pursued, the development/verification tools and opportunities for the small satellite community to become engaged. The cFS is a proven high quality and cost-effective solution for small satellites with constrained budgets.

  6. Core Flight System (cFS) a Low Cost Solution for SmallSats

    Science.gov (United States)

    McComas, David; Strege, Susanne; Wilmot, Jonathan

    2015-01-01

    The cFS is a FSW product line that uses a layered architecture and compile-time configuration parameters which make it portable and scalable for a wide range of platforms. The software layers that defined the application run-time environment are now under a NASA-wide configuration control board with the goal of sustaining an open-source application ecosystem.

  7. World Area Forecast System (WAFS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The World Area Forecast System (WAFS) is a worldwide system by which world area forecast centers provide aeronautical meteorological en-route forecasts in uniform...

  8. A Forecasting Decision Support System

    OpenAIRE

    Sayed, Hanaa E.; Hossam A. Gabbar; Fouad, Soheir A.; Ahmed, Khalil M.; Miyazaki, Shigeji

    2008-01-01

    Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters', simp...

  9. Chronic Fatigue Syndrome (CFS): Diagnosis

    Science.gov (United States)

    ... multiple sclerosis, fibromyalgia, primary sleep disorders, and major depressive disorder. Medications can also cause side effects that mimic the symptoms of CFS. Because CFS can resemble many other ...

  10. Cognitive Impairments Associated with CFS and POTS

    Directory of Open Access Journals (Sweden)

    Leonard A. Jason

    2013-05-01

    Full Text Available Chronic fatigue syndrome (CFS is characterized by fatigue, sleep dysfunction, and cognitive deficits (Fukuda et al., 1994. Research surrounding cognitive functioning among patients with CFS has found difficulty with memory, attention, and information processing. A similar disorder, postural tachycardia syndrome (POTS, is characterized by increased heart rate, fatigue, and mental cloudiness (Raj et al., 2009. Potential implications of cognitive deficits for patients with CFS and/or POTS are discussed, including difficulties with school and/or employment. A few biological theories (i.e., kindling, impairments in the central nervous system, and difficulty with blood flow have emerged as potential explanations for the cognitive deficits reported in both CFS and POTS Future research should continue to examine possible explanations for cognitive impairments in CFS and POTS, and ultimately use this information to try and reduce cognitive impairments for these patients.

  11. Subseasonal features of the Asian summer monsoon in the NCEP climate forecast system

    Institute of Scientific and Technical Information of China (English)

    Song YANG; WEN Min; R Wayne HIGGINS

    2008-01-01

    The operational climate forecast system (CFS) of the US National Centers for Environmental Prediction provides climate predic-tions over the world, and CFS products are becoming an important source of information for regional climate predictions in many Asian countries where monsoon climate dominates. Recent studies have shown that, on monthly-to-seasonal time-scales, the CFS is highly skillful in simulating and predicting the variability of the Asian monsoon. The higher-frequency variability of the Asian summer monsoon in the CFS is analyzed, using output from a version with a spectral triangular truncation of 126 waves in horizon-tal and 64 sigma layers in vertical, focusing on synoptic, quasi-biweekly, and intraseasonal time-scales. The onset processes of different regional monsoon components were investigated within Asia. Although the CFS generally overestimates variability of mon-soon on these time-scales, it successfully captures many major features of the variance patterns, especially for the synoptic time-scale. The CFS also captures the timing of summer monsoon onsets over India and the Indo-China Peninsula. However, it encoun-ters difficulties in simulating the onset of the South China Sea monsoon. The success and failure of the CFS in simulating the onset of monsoon precipitation can also be seen from the associated features of simulated atmospheric circulation processes. Overall, the CFS is capable of simulating the synoptic-to-intraseasonal variability of the Asian summer monsoon with skills. As for seasonal-to-interannual time-scales shown previously, the model is expected to possess a potential for skillful predictions of the high-frequencyvariability of the Asian monsoon.

  12. Chronic Fatigue Syndrome (CFS): Symptoms

    Science.gov (United States)

    ... CDC.gov . Chronic Fatigue Syndrome (CFS) Share Compartir Symptoms On this Page Primary Symptoms Other Symptoms What's ... a doctor distinguish CFS from other illnesses. Primary Symptoms As the name chronic fatigue syndrome suggests , fatigue ...

  13. Black Sea coastal forecasting system

    Directory of Open Access Journals (Sweden)

    A. I. Kubryakov

    2012-03-01

    Full Text Available The Black Sea coastal nowcasting and forecasting system was built within the framework of EU FP6 ECOOP (European COastalshelf sea OPerational observing and forecasting system project for five regions: the south-western basin along the coasts of Bulgaria and Turkey, the north-western shelf along the Romanian and Ukrainian coasts, coastal zone around of the Crimea peninsula, the north-eastern Russian coastal zone and the coastal zone of Georgia. The system operates in the real-time mode during the ECOOP project and afterwards. The forecasts include temperature, salinity and current velocity fields. Ecosystem model operates in the off-line mode near the Crimea coast.

  14. Weather Forecasting Systems and Methods

    Science.gov (United States)

    Mecikalski, John (Inventor); MacKenzie, Wayne M., Jr. (Inventor); Walker, John Robert (Inventor)

    2014-01-01

    A weather forecasting system has weather forecasting logic that receives raw image data from a satellite. The raw image data has values indicative of light and radiance data from the Earth as measured by the satellite, and the weather forecasting logic processes such data to identify cumulus clouds within the satellite images. For each identified cumulus cloud, the weather forecasting logic applies interest field tests to determine a score indicating the likelihood of the cumulus cloud forming precipitation and/or lightning in the future within a certain time period. Based on such scores, the weather forecasting logic predicts in which geographic regions the identified cumulus clouds will produce precipitation and/or lighting within during the time period. Such predictions may then be used to provide a weather map thereby providing users with a graphical illustration of the areas predicted to be affected by precipitation within the time period.

  15. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  16. Crime Forecasting System (An exploratory web-based approach

    Directory of Open Access Journals (Sweden)

    Yaseen Ahmed Meenai

    2011-08-01

    Full Text Available With the continuous rise in crimes in some big cities of the world like Karachi and the increasing complexity of these crimes, the difficulties the law enforcing agencies are facing in tracking down and taking out culprits have increased manifold. To help cut back the crime rate, a Crime Forecasting System (CFS can be used which uses historical information maintained by the local Police to help them predict crime patterns with the support of a huge and self-updating database. This system operates to prevent crime, helps in apprehending criminals, and to reduce disorder. This system is also vital in helping the law enforcers in forming a proactive approach by helping them in identifying early warning signs, take timely and necessary actions, and eventually help stop crime before it actually happens. It will also be beneficial in maintaining an up to date database of criminal suspects includes information on arrest records, communication with police department, associations with other known suspects, and membership in gangs/activist groups. After exploratory analysis of the online data acquired from the victims of these crimes, a broad picture of the scenario can be analyzed. The degree of vulnerability of an area at some particular moment can be highlighted by different colors aided by Google Maps. Some statistical diagrams have also been incorporated. The future of CFS can be seen as an information engine for the analysis, study and prediction of crimes.

  17. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  18. Climate Forecast System Version 2 (CFSv2) Operational Forecasts

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

  19. Global Forecast System (GFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and...

  20. Magnetogram Forecast: An All-Clear Space Weather Forecasting System

    Science.gov (United States)

    Barghouty, Nasser; Falconer, David

    2015-01-01

    Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

  1. Common chromosomal fragile sites (CFS) may be involved in normal and traumatic cognitive stress memory consolidation and altered nervous system immunity.

    Science.gov (United States)

    Gericke, G S

    2010-05-01

    Previous reports of specific patterns of increased fragility at common chromosomal fragile sites (CFS) found in association with certain neurobehavioural disorders did not attract attention at the time due to a shift towards molecular approaches to delineate neuropsychiatric disorder candidate genes. Links with miRNA, altered methylation and the origin of copy number variation indicate that CFS region characteristics may be part of chromatinomic mechanisms that are increasingly linked with neuroplasticity and memory. Current reports of large-scale double-stranded DNA breaks in differentiating neurons and evidence of ongoing DNA demethylation of specific gene promoters in adult hippocampus may shed new light on the dynamic epigenetic changes that are increasingly appreciated as contributing to long-term memory consolidation. The expression of immune recombination activating genes in key stress-induced memory regions suggests the adoption by the brain of this ancient pattern recognition and memory system to establish a structural basis for long-term memory through controlled chromosomal breakage at highly specific genomic regions. It is furthermore considered that these mechanisms for management of epigenetic information related to stress memory could be linked, in some instances, with the transfer of the somatically acquired information to the germline. Here, rearranged sequences can be subjected to further selection and possible eventual retrotranscription to become part of the more stable coding machinery if proven to be crucial for survival and reproduction. While linkage of cognitive memory with stress and fear circuitry and memory establishment through structural DNA modification is proposed as a normal process, inappropriate activation of immune-like genomic rearrangement processes through traumatic stress memory may have the potential to lead to undesirable activation of neuro-inflammatory processes. These theories could have a significant impact on the

  2. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  3. Assessment of reservoir system variable forecasts

    Science.gov (United States)

    Kistenmacher, Martin; Georgakakos, Aris P.

    2015-05-01

    Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.

  4. Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate

    Directory of Open Access Journals (Sweden)

    Mark R. Jury

    2014-01-01

    Full Text Available This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3. Early season forecasts from the coupled forecast system (CFS are steadier than European community medium range forecast (ECMWF. CFS and ECMWF April forecasts of June–August (JJA rainfall achieve significant fit (r2=0.27, 0.25, resp., but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.

  5. Core Flight Software (CFS) Maturation Towards Human Rating Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The Core Flight Software (CFS) system developed by Goddard Space Flight Center, through experience on Morpheus, has proven to be a quality product and a viable...

  6. Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

    Science.gov (United States)

    Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

    2017-04-01

    ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i

  7. Involving human forecasters in numerical prediction systems

    Directory of Open Access Journals (Sweden)

    V. Homar

    2006-01-01

    Full Text Available Human forecasters routinely improve upon the output from numerical weather prediction models and often have keen insight to model biases and shortcomings. This wealth of knowledge about model performance is largely untapped, however, as it is used only at the end point in the forecast process to interpret the model-predicted fields. Yet there is no reason why human forecasters cannot intervene at other earlier times in the numerical weather prediction process, especially when an ensemble forecasting system is in use. Human intervention in ensemble creation may be particularly helpful for rare events, such as severe weather events, that are not predicted well by numerical models. The USA/NOAA SPC/NSSL Spring Program 2003 tested an ensemble generation method in which human forecasters were involved in the ensemble creation process. The forecaster highlighted structures of interest and, using an adjoint model, a set of perturbations were obtained and used to generate a 32-member ensemble. The results show that this experimental ensemble improves upon the operational numerical forecasts of severe weather. The human-generated ensemble is able to provide improved guidance on high-impact weather events, but lacks global dispersion and produces unreliable forecasts for non-hazardous weather events. Further results from an ensemble constructed by combining the operational ensemble perturbations with the human-generated perturbations shows promising skill for the forecast of severe weather while avoiding the problem of limited global dispersion. The value of human beings in the creation of ensembles designed to target specific high- impact weather events is potentially large. Further investigation of the value of forecasters being part of the ensemble creation process is strongly recommended. There remains a lot to learn about how to create ensembles for short-range forecasts of severe weather, and we need to make better use of the skill and experience of

  8. Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States

    Science.gov (United States)

    De Sales, Fernando; Xue, Yongkang

    2013-07-01

    This study evaluates the UCLA-ETA regional model's dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day-1, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement.

  9. The Invasive Species Forecasting System

    Science.gov (United States)

    Schnase, John; Most, Neal; Gill, Roger; Ma, Peter

    2011-01-01

    The Invasive Species Forecasting System (ISFS) provides computational support for the generic work processes found in many regional-scale ecosystem modeling applications. Decision support tools built using ISFS allow a user to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of management concern, such as a national park, monument, forest, or refuge. This type of decision product helps resource managers plan invasive species protection, monitoring, and control strategies for the lands they manage. Until now, scientists and resource managers have lacked the data-assembly and computing capabilities to produce these maps quickly and cost efficiently. ISFS focuses on regional-scale habitat suitability modeling for invasive terrestrial plants. ISFS s component architecture emphasizes simplicity and adaptability. Its core services can be easily adapted to produce model-based decision support tools tailored to particular parks, monuments, forests, refuges, and related management units. ISFS can be used to build standalone run-time tools that require no connection to the Internet, as well as fully Internet-based decision support applications. ISFS provides the core data structures, operating system interfaces, network interfaces, and inter-component constraints comprising the canonical workflow for habitat suitability modeling. The predictors, analysis methods, and geographic extents involved in any particular model run are elements of the user space and arbitrarily configurable by the user. ISFS provides small, lightweight, readily hardened core components of general utility. These components can be adapted to unanticipated uses, are tailorable, and require at most a loosely coupled, nonproprietary connection to the Web. Users can invoke capabilities from a command line; programmers can integrate ISFS's core components into more complex systems and services. Taken together, these

  10. Global Ensemble Forecast System (GEFS) [2.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  11. WOD - Weather On Demand forecasting system

    Science.gov (United States)

    Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

    2017-04-01

    The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

  12. Solar Energy Forecast System Development and Implementation

    Science.gov (United States)

    Jascourt, S. D.; Kirk-Davidoff, D. B.; Cassidy, C.

    2012-12-01

    Forecast systems for predicting real-time solar energy generation are being developed along similar lines to those of more established wind forecast systems, but the challenges and constraints are different. Clouds and aerosols play a large role, and for tilted photovoltaic panels and solar concentrating plants, the direct beam irradiance, which typically has much larger forecast errors than global horizontal irradiance, must be utilized. At MDA Information Systems, we are developing a forecast system based on first principles, with the well-validated REST2 clear sky model (Gueymard, 2008) at its backbone. In tuning the model and addressing aerosol scattering and surface albedo, etc., we relied upon the wealth of public data sources including AERONET (aerosol optical depth at different wavelengths), Suominet (GPS integrated water vapor), NREL MIDC solar monitoring stations, SURFRAD (includes upwelling shortwave), and MODIS (albedo in different wavelength bands), among others. The forecast itself utilizes a blend of NWP model output, which must be brought down to finer time resolution based on the diurnal cycle rather than simple interpolation. Many models currently do not output the direct beam irradiance, and one that does appears to have a bias relative to its global horizontal irradiance, with equally good performance attained by utilizing REST2 and the model global radiation to estimate the direct component. We will present a detailed assessment of various NWP solar energy products, evaluating forecast skill at a range of photovoltaic installations.

  13. Evaporation-precipitation variability over Indian Ocean and its assessment in NCEP Climate Forecast System (CFSv2)

    Energy Technology Data Exchange (ETDEWEB)

    Pokhrel, Samir; Parekh, Anant; Saha, Subodh Kumar; Dhakate, Ashish; Chaudhari, Hemantkumar S. [Indian Institute of Tropical Meteorology, Pune (India); Rahaman, Hasibur [Indian National Centre for Ocean Information Services, Hyderabad (India); Gairola, Rakesh Mohan [Space Applications Centre, ISRO, Ahmedabad (India)

    2012-11-15

    An attempt has been made to explore all the facets of Evaporation-Precipitation (E-P) distribution and variability over the Indian Ocean (IO) basin using Objectively Analyzed air-sea Fluxes (OAFlux) data and subsequently a thorough assessment of the latest version of National Centers for Environment Prediction (NCEP) Climate Forecast System (CFS) version-2 is done. This study primarily focuses on two fundamental issues, first, the core issue of pervasive cold SST bias in the CFS simulation in the context of moisture flux exchange between the atmosphere and the ocean and second, the fidelity of the model in simulating mean and variability of E-P and its elemental components associated with the climatic anomalies occurring over the Indian and the Pacific ocean basin. Valuation of evaporation and precipitation, the two integral component of E-P, along with the similar details of wind speed, air-sea humidity difference ({Delta}Q) and Sea Surface Temperature (SST) are performed. CFS simulation is vitiated by the presence of basin wide systematic positive bias in evaporation, {Delta}Q and similar negative bias in wind speed and SST. Bifurcation of the evaporation bias into its components reveals that bias in air humidity (Q{sub a}) is basically responsible for the presence of pervasive positive evaporation bias. The regions where CFS does not adhere to the observed wind-evaporation and Q{sub a} -evaporation relation was found to lie over the northern Arabian Sea (AS), the western Bay of Bengal (BoB) and the western Equatorial IO. Evaporation bias is found to control a significant quantum of cold SST bias over most of the basin owing to its intimate association with SST in a coupled feedback system. This area is stretched over the almost entire north IO, north of 15 {sup circle} S excluding a small equatorial strip, where the evaporation bias may essentially explain 20-100 % of cold SST bias. This percentage is maximum over the western IO, central AS and BoB. The CFS

  14. Forecasting hurricane tracks using a complex adaptive system

    OpenAIRE

    Lear, Matthew R.

    2005-01-01

    Forecast hurricane tracks using a multi-model ensemble that is comprised by linearly combining the individual model forecasts have greatly reduced the average forecast errors when compared to individual dynamic model forecast errors. In this experiment, a complex adaptive system, the Tropical Agent Forecaster (TAF), is created to fashion a 'smart' ensemble forecast. The TAF uses autonomous agents to assess the historical performance of individual models and model combinations, called predicto...

  15. Evaluation of a probabilistic hydrometeorological forecast system

    Directory of Open Access Journals (Sweden)

    B. Ahrens

    2009-07-01

    Full Text Available Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF. QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m.

    Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006. It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

  16. Evaluation of a probabilistic hydrometeorological forecast system

    Directory of Open Access Journals (Sweden)

    S. Jaun

    2009-03-01

    Full Text Available Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF. QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m.

    Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006. It is shown that the ensemble covers the uncertainty during different weather situations with an appropriate spread-skill relationship. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

  17. Forecasting Models in the State Education System

    Directory of Open Access Journals (Sweden)

    Gintautas DZEMYDA

    2003-04-01

    Full Text Available This paper presents model-based assessment and forecasting of the Lithuanian education system in the period of 2001-2010. In order to obtain satisfactory forecasting results, constructing of models used for these aims should be grounded on some interactive data mining. Data mining of data stored in the system of the Lithuanian teacher's database and of data from other sources representing the state of education system and the demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, the analysis of flow of teachers and pupils, the clustering of schools, the model of dynamics of pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.

  18. Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast system

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Zeng-Zhen [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Huang, Bohua; Schneider, Edwin K. [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); George Mason University, Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, Fairfax, VA (United States); Hou, Yu-Tai; Yang, Fanglin [NCEP/NWS/NOAA, Environmental Modeling Center, Camp Springs, MD (United States); Wang, Wanqiu [NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Stan, Cristiana [Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States)

    2011-05-15

    In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics. (orig.)

  19. Climate Prediction Center (CPC) NCEP-Global Forecast System (GFS) Precipitation Forecast Product

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) forecast precipitation data at 37.5km resolution is created at the NOAA Climate Prediction Center for the purpose of near real-time...

  20. The NASA GEOS-5 Aerosol Forecasting System

    Science.gov (United States)

    Colarco, Peter; daSilva, Arlindo; Darmenov, Anton

    2011-01-01

    The NASA Goddard Earth Observing System modeling and data assimilation environment (GEOS-5) is maintained by the Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center. Near-realtime meteorological forecasts are produced to support NASA satellite and field missions. We have implemented in this environment an aerosol module based on the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model. This modeling system has previously been evaluated in the context of hindcasts based on assimilated meteorology. Here we focus on the development and evaluation of the near-realtime forecasting system. We present a description of recent efforts to implement near-realtime biomass burning emissions derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) fire radiative power products. We as well present a developing capability for improvement of aerosol forecasts by assimilation of aerosol information from MODIS.

  1. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    Science.gov (United States)

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-06-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

  2. Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system

    Science.gov (United States)

    Sigmond, M.; Reader, M. C.; Flato, G. M.; Merryfield, W. J.; Tivy, A.

    2016-12-01

    The need for skillful seasonal forecasts of Arctic sea ice is rapidly increasing. Technology to perform such forecasts with coupled atmosphere-ocean-sea ice systems has only recently become available, with previous skill evaluations mainly limited to area-integrated quantities. Here we show, based on a large set of retrospective ensemble model forecasts, that a dynamical forecast system produces skillful seasonal forecasts of local sea ice retreat and advance dates - variables that are of great interest to a wide range of end users. Advance dates can generally be skillfully predicted at longer lead times ( 5 months on average) than retreat dates ( 3 months). The skill of retreat date forecasts mainly stems from persistence of initial sea ice anomalies, whereas advance date forecasts benefit from longer time scale and more predictable variability in ocean temperatures. These results suggest that further investments in the development of dynamical seasonal forecast systems may result in significant socioeconomic benefits.

  3. The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales

    OpenAIRE

    J. Schellekens; Weerts, A. H.; Moore, R J; Pierce, C.E.; S. Hildon

    2011-01-01

    Operational flood forecasting systems share a fundamental challenge: forecast uncertainty which needs to be considered when making a flood warning decision. One way of representing this uncertainty is through employing an ensemble approach. This paper presents research funded by the Environment Agency in which ensemble rainfall forecasts are utilised and tested for operational use. The form of ensemble rainfall forecast used is the Met Office short-range product called MOGRE...

  4. Predictability during active break phases of Indian summer monsoon in an ensemble prediction system using climate forecast system

    Science.gov (United States)

    Abhilash, S.; Sahai, A. K.; Pattnaik, S.; De, S.

    2013-08-01

    This study examines the phase dependant temporal and spatial error evolution and prediction of active break spells of Indian summer monsoon rainfall in an ensemble prediction system (EPS) on a pentad time scale using climate forecast system (CFS). The EPS system shows systematic wet bias (overestimation) over west coast over the Arabian Sea and Myanmar coast and dry bias (underestimation) over Indian land mass even at pentad 1 lead and these biases consistently increase up to 4 pentad lead and saturate thereafter. Irrespective of the phases of the monsoon, the lower bound of predictability is 2 pentads, while upper bound of predictability for initial conditions starting from active phase saturates at 3 pentads and for break and transition phases predictability error saturates at a later stage at about 5 pentad. Initial conditions started from transition phase shows higher potential predictability followed by break phase and then active phase.

  5. Road icing forecasting and detecting system

    Science.gov (United States)

    Xu, Hongke; Zheng, Jinnan; Li, Peiqi; Wang, Qiucai

    2017-05-01

    Regard for the facts that the low accuracy and low real-time of the artificial observation to determine the road icing condition, and it is difficult to forecast icing situation, according to the main factors influencing the road-icing, and the electrical characteristics reflected by the pavement ice layer, this paper presents an innovative system, that is, ice-forecasting of the highway's dangerous section. The system bases on road surface water salinity measurements and pavement temperature measurement to calculate the freezing point of water and temperature change trend, and then predicts the occurrence time of road icing; using capacitance measurements to verdict the road surface is frozen or not; This paper expounds the method of using single chip microcomputer as the core of the control system and described the business process of the system.

  6. Evaluating the Cloud Cover Forecast of NCEP Global Forecast System with Satellite Observation

    CERN Document Server

    Ye, Quanzhi

    2011-01-01

    To assess the quality of daily cloud cover forecast generated by the operational global numeric model, the NCEP Global Forecast System (GFS), we compose a large sample with outputs from GFS model and satellite observations from the International Satellite Cloud Climatology Project (ISCCP) in the period of July 2004 to June 2008, to conduct a quantitative and systematic assessment of the performance of a cloud model that covers a relatively long range of time, basic cloud types, and in a global view. The evaluation has revealed the goodness of the model forecast, which further illustrates our completeness on understanding cloud generation mechanism. To quantity the result, we found a remarkably high correlation between the model forecasts and the satellite observations over the entire globe, with mean forecast error less than 15% in most areas. Considering a forecast within 30% difference to the observation to be a "good" one, we find that the probability for the GFS model to make good forecasts varies between...

  7. Big Software for SmallSats: Adapting CFS to CubeSat Missions

    Science.gov (United States)

    Cudmore, Alan P.; Crum, Gary; Sheikh, Salman; Marshall, James

    2015-01-01

    Expanding capabilities and mission objectives for SmallSats and CubeSats is driving the need for reliable, reusable, and robust flight software. While missions are becoming more complicated and the scientific goals more ambitious, the level of acceptable risk has decreased. Design challenges are further compounded by budget and schedule constraints that have not kept pace. NASA's Core Flight Software System (cFS) is an open source solution which enables teams to build flagship satellite level flight software within a CubeSat schedule and budget. NASA originally developed cFS to reduce mission and schedule risk for flagship satellite missions by increasing code reuse and reliability. The Lunar Reconnaissance Orbiter, which launched in 2009, was the first of a growing list of Class B rated missions to use cFS. Large parts of cFS are now open source, which has spurred adoption outside of NASA. This paper reports on the experiences of two teams using cFS for current CubeSat missions. The performance overheads of cFS are quantified, and the reusability of code between missions is discussed. The analysis shows that cFS is well suited to use on CubeSats and demonstrates the portability and modularity of cFS code.

  8. North American Mesoscale Forecast System (NAM) [12 km

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The North American Mesoscale Forecast System (NAM) is one of the major regional weather forecast models run by the National Centers for Environmental Prediction...

  9. Global Forecast System (GFS) [0.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and...

  10. Development of an oil spill forecast system for offshore China

    Science.gov (United States)

    Wang, Yonggang; Wei, Zexun; An, Wei

    2016-07-01

    An oil spill forecast system for offshore China was developed based on Visual C++. The oil spill forecast system includes an ocean environmental forecast model and an oil spill model. The ocean environmental forecast model was designed to include timesaving methods, and comprised a parametrical wind wave forecast model and a sea surface current forecast model. The oil spill model was based on the "particle method" and fulfills the prediction of oil particle behavior by considering the drifting, evaporation and emulsification processes. A specific database was embedded into the oil spill forecast system, which contained fundamental information, such as the properties of oil, reserve of emergency equipment and distribution of marine petroleum platform. The oil spill forecast system was successfully applied as part of an oil spill emergency exercise, and provides an operational service in the Research and Development Center for Offshore Oil Safety and Environmental Technology.

  11. UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM

    Energy Technology Data Exchange (ETDEWEB)

    Werth, D.; Garrett, A.

    2009-04-15

    We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.

  12. Grey System Forecast for Firing Accuracy of Gun

    Institute of Scientific and Technical Information of China (English)

    CHENG Qi-yue; QIU Wan-hua

    2001-01-01

    In this paper, the system and subsystem forecast models for firing accuracy have been built by means of theory of Grey System Forecast. It has provided a scientific forecasting method for micro-errorcontrol and macro-error-control and improving the firing accuracy.

  13. Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World

    Science.gov (United States)

    Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.

    2011-12-01

    The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly

  14. A Decision Support System for effective use of probability forecasts

    Science.gov (United States)

    De Kleermaeker, Simone; Verkade, Jan

    2013-04-01

    Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.

  15. Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

    Science.gov (United States)

    Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

    2016-12-01

    Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

  16. The Discriminant Analysis Flare Forecasting System (DAFFS)

    Science.gov (United States)

    Leka, K. D.; Barnes, Graham; Wagner, Eric; Hill, Frank; Marble, Andrew R.

    2016-05-01

    The Discriminant Analysis Flare Forecasting System (DAFFS) has been developed under NOAA/Small Business Innovative Research funds to quantitatively improve upon the NOAA/SWPC flare prediction. In the Phase-I of this project, it was demonstrated that DAFFS could indeed improve by the requested 25% most of the standard flare prediction data products from NOAA/SWPC. In the Phase-II of this project, a prototype has been developed and is presently running autonomously at NWRA.DAFFS uses near-real-time data from NOAA/GOES, SDO/HMI, and the NSO/GONG network to issue both region- and full-disk forecasts of solar flares, based on multi-variable non-parametric Discriminant Analysis. Presently, DAFFS provides forecasts which match those provided by NOAA/SWPC in terms of thresholds and validity periods (including 1-, 2-, and 3- day forecasts), although issued twice daily. Of particular note regarding DAFFS capabilities are the redundant system design, automatically-generated validation statistics and the large range of customizable options available. As part of this poster, a description of the data used, algorithm, performance and customizable options will be presented, as well as a demonstration of the DAFFS prototype.DAFFS development at NWRA is supported by NOAA/SBIR contracts WC-133R-13-CN-0079 and WC-133R-14-CN-0103, with additional support from NASA contract NNH12CG10C, plus acknowledgment to the SDO/HMI and NSO/GONG facilities and NOAA/SWPC personnel for data products, support, and feedback. DAFFS is presently ready for Phase-III development.

  17. Management earnings forecasts and analyst forecasts:Evidence from mandatory disclosure system

    Institute of Scientific and Technical Information of China (English)

    Yutao; Wang; Yunsen; Chen; Juxian; Wang

    2015-01-01

    Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analyst forecasts, this study explores the effects of selective disclosure of MEFs.Under China’s mandatory disclosure system, this study proposes that managers issue frequent forecasts to take advantage of opportune changes in predicted earnings. The argument herein is that this selective disclosure of MEFs increases information asymmetry and uncertainty, negatively influencing analyst earnings forecasts. Empirical evidence shows that firms that issue more frequent forecasts and make significant changes in MEFs are less likely to attract an analyst following, which can lead to less accurate analyst forecasts. The results imply that the selective disclosure of MEFs damages information transmission and market efficiency, which can enlighten regulators seeking to further enhance disclosure policies.

  18. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov

    2006-01-01

    For operational planning it is important to provide information about the situation-dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind...... speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field....... An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used...

  19. Revised cloud processes to improve the mean and intraseasonal variability of Indian summer monsoon in climate forecast system: Part 1

    Science.gov (United States)

    Abhik, S.; Krishna, R. P. M.; Mahakur, M.; Ganai, Malay; Mukhopadhyay, P.; Dudhia, J.

    2017-06-01

    The National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFS) is being used for operational monsoon prediction over the Indian region. Recent studies indicate that the moist convective process in CFS is one of the major sources of uncertainty in monsoon predictions. In this study, the existing simple cloud microphysics of CFS is replaced by the six-class Weather Research Forecasting (WRF) single moment (WSM6) microphysical scheme. Additionally, a revised convective parameterization is employed to improve the performance of the model in simulating the boreal summer mean climate and intraseasonal variability over the Indian summer monsoon (ISM) region. The revised version of the model (CFSCR) exhibits a potential to improve shortcomings in the seasonal mean precipitation distribution relative to the standard CFS (CTRL), especially over the ISM region. Consistently, notable improvements are also evident in other observed ISM characteristics. These improvements are found to be associated with a better simulation of spatial and vertical distributions of cloud hydrometeors in CFSCR. A reasonable representation of the subgrid-scale convective parameterization along with cloud hydrometeors helps to improve the convective and large-scale precipitation distribution in the model. As a consequence, the simulated low-frequency boreal summer intraseasonal oscillation (BSISO) exhibits realistic propagation and the observed northwest-southeast rainband is well reproduced in CFSCR. Additionally, both the high and low-frequency BSISOs are better captured in CFSCR. The improvement of low and high-frequency BSISOs in CFSCR is shown to be related to a realistic phase relationship of clouds.type="synopsis">type="main">Plain Language SummaryThis study attempts to demonstrate the impact of better representation of cloud processes on simulating the mean and intraseasonal variability of Indian summer monsoon in a revised version of CFSv2 called CFSCR. The CFSCR shows

  20. The Red Sea Modeling and Forecasting System

    KAUST Repository

    Hoteit, Ibrahim

    2015-04-01

    Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

  1. Advances in Global Flood Forecasting Systems

    Science.gov (United States)

    Thielen-del Pozo, J.; Pappenberger, F.; Burek, P.; Alfieri, L.; Kreminski, B.; Muraro, D.

    2012-12-01

    A trend of increasing number of heavy precipitation events over many regions in the world during the past century has been observed (IPCC, 2007), but conclusive results on a changing frequency or intensity of floods have not yet been established. However, the socio-economic impact particularly of floods is increasing at an alarming trend. Thus anticipation of severe events is becoming a key element of society to react timely to effectively reduce socio-economic damage. Anticipation is essential on local as well as on national or trans-national level since management of response and aid for major disasters requires a substantial amount of planning and information on different levels. Continental and trans-national flood forecasting systems already exist. The European Flood Awareness System (EFAS) has been developed in close collaboration with the National services and is going operational in 2012, enhancing the national forecasting centres with medium-range probabilistic added value information while at the same time providing the European Civil Protection with harmonised information on ongoing and upcoming floods for improved aid management. Building on experiences and methodologies from EFAS, a Global Flood Awareness System (GloFAS) has now been developed jointly between researchers from the European Commission Joint Research Centre (JRC) and the European Centre for Medium-Range Weather Forecast (ECWMF). The prototype couples HTESSEL, the land-surface scheme of the ECMWF NWP model with the LISFLOOD hydrodynamic model for the flow routing in the river network. GloFAS is set-up on global scale with horizontal grid spacing of 0.1 degree. The system is driven with 51 ensemble members from VAREPS with a time horizon of 15 days. In order to allow for the routing in the large rivers, the coupled model is run for 45 days assuming zero rainfall after day 15. Comparison with observations have shown that in some rivers the system performs quite well while in others the hydro

  2. Skill assessment for an operational algal bloom forecast system.

    Science.gov (United States)

    Stumpf, Richard P; Tomlinson, Michelle C; Calkins, Julie A; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T

    2009-02-20

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast

  3. Skill assessment for an operational algal bloom forecast system

    Science.gov (United States)

    Stumpf, Richard P.; Tomlinson, Michelle C.; Calkins, Julie A.; Kirkpatrick, Barbara; Fisher, Kathleen; Nierenberg, Kate; Currier, Robert; Wynne, Timothy T.

    2010-01-01

    An operational forecast system for harmful algal blooms (HABs) in southwest Florida is analyzed for forecasting skill. The HABs, caused by the toxic dinoflagellate, Karenia brevis, lead to shellfish toxicity and to respiratory irritation. In addition to predicting new blooms and their extent, HAB forecasts are made twice weekly during a bloom event, using a combination of satellite derived image products, wind predictions, and a rule-based model derived from previous observations and research. These forecasts include: identification, intensification, transport, extent, and impact; the latter being the most significant to the public. Identification involves identifying new blooms as HABs and is validated against an operational monitoring program involving water sampling. Intensification forecasts, which are much less frequently made, can only be evaluated with satellite data on mono-specific blooms. Extent and transport forecasts of HABs are also evaluated against the water samples. Due to the resolution of the forecasts and available validation data, skill cannot be resolved at scales finer than 30 km. Initially, respiratory irritation forecasts were analyzed using anecdotal information, the only available data, which had a bias toward major respiratory events leading to a forecast accuracy exceeding 90%. When a systematic program of twice-daily observations from lifeguards was implemented, the forecast could be meaningfully assessed. The results show that the forecasts identify the occurrence of respiratory events at all lifeguard beaches 70% of the time. However, a high rate (80%) of false positive forecasts occurred at any given beach. As the forecasts were made at half to whole county level, the resolution of the validation data was reduced to county level, reducing false positives to 22% (accuracy of 78%). The study indicates the importance of systematic sampling, even when using qualitative descriptors, the use of validation resolution to evaluate forecast

  4. Skill of global hydrological forecasting system FEWS GLOWASIS using climatic ESP forecasts

    Science.gov (United States)

    Weerts, A. H.; Candogan, N.; Winsemius, H. C.; van Beek, R.; Westerhoff, R.

    2012-04-01

    Forecasting of water availability and scarcity is a prerequisite for the management of hydropower reservoirs, basin-scale management of water resources, agriculture and disaster relief. The EU 7th Framework Program project Global Water Scarcity Information Service (GLOWASIS) aims to pre-validate a service that provides real-time global-scale information on water scarcity. In this contribution, we demonstrate what skill (compared to a climatology) may be reached with a global seasonal ensemble forecasting system consisting of: a) a global hydrological model PCR-GLOBWB; b) an updating procedure for initial forecasting states, based on the best-guess global rainfall information. As best guess, a combination of ERA-Interim Reanalysis rainfall and Global Precipitation Climatology Project (GPCP) observations is being used; c) a forecast based on Ensemble Streamflow Prediction (ESP)procedure and reverse ESP procedure (Wood and Lettenmaier, 2008). In the ESP procedure, a meteorological forecast ensemble is generated based on past years of observation series (i.e. climatological observations). As observations, the combination of ERA-Interim and GPCP is used. In reverse ESP, an ensemble is generated by using a set of initial states from a large sample of updates at the specific month of interest, and forecasts are produced using one observed set. This analysis allows us to measure how much skill may be expected from hydrological inertia and climatology alone, leaving aside for the moment potential skill improvement by using seasonal meteorological forecasts. In future work, we will measure how much skill improvement compared to the forecasts mentioned above may be reached, when ECMWF Seasonal forecasts are used. This will allow us to measure the contributions to skill of each component (initial state inertia, hydrology and meteorological inputs) of the forecast system.

  5. A complex adaptive system approach to forecasting hurricane tracks

    OpenAIRE

    Lear, Matthew R.

    2005-01-01

    , for the life of the storm, perform the best in terms of the distance between forecast and best-track positions. A TAF forecast is developed using a linear combination of the highest weighted predictors. When applied to the 2004 Atlantic hurricane season, the TAF system with a requirement to contain a minimum of three predictors, consistently outperformed, although not statistically significant, the CONU forecast at 72 and 96 hours for a homogeneous data set. At 120 hours, the TAF system s...

  6. The GOCF/AWAP system - forecasting temperature extremes

    Energy Technology Data Exchange (ETDEWEB)

    Fawcett, Robert [National Climate Centre, Australian Bureau of Meteorology, Docklands, Victoria 3008 (Australia); Hume, Timothy, E-mail: r.fawcett@bom.gov.a, E-mail: t.hume@bom.gov.a [Centre for Australian Weather and Climate Research, Australian Bureau of Meteorology, Docklands, Victoria 3008 (Australia)

    2010-08-15

    Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, 'forecast - highest on record' and 'forecast - lowest on record'. Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both {sup 0}C and standard deviations.

  7. The GOCF/AWAP system - forecasting temperature extremes

    Science.gov (United States)

    Fawcett, Robert; Hume, Timothy

    2010-08-01

    Gridded hourly temperature forecasts from the Bureau of Meteorology's Gridded Operational Consensus Forecasting (GOCF) system are combined in real time with the Australian Water Availability Project (AWAP) gridded daily temperature analyses to produce gridded daily maximum and minimum temperature forecasts with lead times from one to five days. These forecasts are compared against the historical record of AWAP daily temperature analyses (1911 to present), to identify regions where record or near-record temperatures are predicted to occur. This paper describes the GOCF/AWAP system, showing how the daily maximum and minimum temperature forecasts are prepared from the hourly forecasts, and how they are bias-corrected in real time using the AWAP analyses, against which they are subsequently verified. Using monthly climatologies of long-term daily mean, standard deviation and all-time highest and lowest on record, derived forecast products (for both maximum and minimum temperature) include ordinary and standardised anomalies, "forecast - highest on record" and "forecast - lowest on record". Compensation for the climatological variation across the country is achieved in these last two products, which provide the necessary guidance as to whether or not record-breaking temperatures are expected, by expressing the forecast departure from the previous record in both °C and standard deviations.

  8. System Science approach to Space Weather forecast

    Science.gov (United States)

    Balikhin, Michael A.

    There are many dynamical systems in nature that are so complex that mathematical models of their behaviour can not be deduced from first principles with the present level of our knowledge. Obvious examples are organic cell, human brain, etc often attract system scientists. A example that is closer to space physics is the terrestrial magnetosphere. The system approach has been developed to understand such complex objects from the observation of their dynamics. The systems approach employs advanced data analysis methodologies to identify patterns in the overall system behaviour and provides information regarding the linear and nonlinear processes involved in the dynamics of the system. This, in combination with the knowledge deduced from the first principles, creates the opportunity to find mathematical relationships that govern the evolution of a particular physical system. Advances and problems of systems science applications to provide a reliable forecasts of space weather phenomena such as geomagnetic storms, substorms and radiation belts particle fluxes are reviewed and compared with the physics based models.

  9. Forecasting the Performance of Agroforestry Systems

    Science.gov (United States)

    Luedeling, E.; Shepherd, K.

    2014-12-01

    Agroforestry has received considerable attention from scientists and development practitioners in recent years. It is recognized as a cornerstone of many traditional agricultural systems, as well as a new option for sustainable land management in currently treeless agricultural landscapes. Agroforestry systems are diverse, but most manifestations supply substantial ecosystem services, including marketable tree products, soil fertility, water cycle regulation, wildlife habitat and carbon sequestration. While these benefits have been well documented for many existing systems, projecting the outcomes of introducing new agroforestry systems, or forecasting system performance under changing environmental or climatic conditions, remains a substantial challenge. Due to the various interactions between system components, the multiple benefits produced by trees and crops, and the host of environmental, socioeconomic and cultural factors that shape agroforestry systems, mechanistic models of such systems quickly become very complex. They then require a lot of data for site-specific calibration, which presents a challenge for their use in new environmental and climatic domains, especially in data-scarce environments. For supporting decisions on the scaling up of agroforestry technologies, new projection methods are needed that can capture system complexity to an adequate degree, while taking full account of the fact that data on many system variables will virtually always be highly uncertain. This paper explores what projection methods are needed for supplying decision-makers with useful information on the performance of agroforestry in new places or new climates. Existing methods are discussed in light of these methodological needs. Finally, a participatory approach to performance projection is proposed that captures system dynamics in a holistic manner and makes probabilistic projections about expected system performance. This approach avoids the temptation to take

  10. DMI's Baltic Sea Coastal operational forecasting system

    Science.gov (United States)

    Murawski, Jens; Berg, Per; Weismann Poulsen, Jacob

    2017-04-01

    Operational forecasting is challenged with bridging the gap between the large scales of the driving weather systems and the local, human scales of the model applications. The limit of what can be represented by local model has been continuously shifted to higher and higher spatial resolution, with the aim to better resolve the local dynamic and to make it possible to describe processes that could only be parameterised in older versions, with the ultimate goal to improve the quality of the forecast. Current hardware trends demand a str onger focus on the development of efficient, highly parallelised software and require a refactoring of the code with a solid focus on portable performance. The gained performance can be used for running high resolution model with a larger coverage. Together with the development of efficient two-way nesting routines, this has made it possible to approach the near-coastal zone with model applications that can run in a time effective way. Denmarks Meteorological Institute uses the HBM(1) ocean circulation model for applications that covers the entire Baltic Sea and North Sea with an integrated model set-up that spans the range of horizontal resolution from 1nm for the entire Baltic Sea to approx. 200m resolution in local fjords (Limfjord). For the next model generation, the high resolution set-ups are going to be extended and new high resolution domains in coastal zones are either implemented or tested for operational use. For the first time it will be possible to cover large stretches of the Baltic coastal zone with sufficiently high resolution to model the local hydrodynamic adequately. (1) HBM stands for HIROMB-BOOS-Model, whereas HIROMB stands for "High Resolution Model for the Baltic Sea" and BOOS stands for "Baltic Operational Oceanography System".

  11. Value assessment of a global hydrological forecasting system

    Science.gov (United States)

    Candogan Yossef, N.; Winsemius, H.; van Beek, L. P. H.; van Beek, E.; Bierkens, M. F. P.

    2012-04-01

    The inter-annual variability in streamflow presents risks and opportunities in the management of water resources systems. Reliable hydrological forecasts, effective communication and proper response allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. A global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value for these regions. FEWS-World system is developed for this purpose. It is based on the Delft-FEWS (flood early warning system) developed by Deltares and incorporates the global hydrological model PCR-GLOBWB. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value as its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. For the assessment in historical simulation mode a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) was used. For the assessment in retroactive forecasting mode the model was forced with ensemble forecasts from the seasonal forecast archives of ECMWF. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world. The results of the preliminary assessment show that although forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily decrease. The forecast requirements and response options of several water related sectors was

  12. First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

    Science.gov (United States)

    Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

    2017-04-01

    Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for

  13. Analyzing the Core Flight Software (CFS) with SAVE

    Science.gov (United States)

    Ganesan, Dharmalingam; Lindvall, Mikael; McComas, David

    2008-01-01

    This viewgraph presentation describes the SAVE tool and it's application to Core Flight Software (CFS). The contents include: 1) Fraunhofer-a short intro; 2) Context of this Collaboration; 3) CFS-Core Flight Software?; 4) The SAVE Tool; 5) Applying SAVE to CFS -A few example analyses; and 6) Goals.

  14. Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

    Science.gov (United States)

    Nageswararao, M. M.; Mohanty, U. C.; Nair, Archana; Ramakrishna, S. S. V. S.

    2016-06-01

    The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model's precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP-NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model's inability to simulate the observed remote response of sea surface

  15. Grey forecasting model for active vibration control systems

    Science.gov (United States)

    Lihua, Zou; Suliang, Dai; Butterworth, John; Ma, Xing; Dong, Bo; Liu, Aiping

    2009-05-01

    Based on the grey theory, a GM(1,1) forecasting model and an optimal GM(1,1) forecasting model are developed and assessed for use in active vibration control systems for earthquake response mitigation. After deriving equations for forecasting the control state vector, design procedures for an optimal active control method are proposed. Features of the resulting vibration control and the influence on it of time-delay based on different sampling intervals of seismic ground motion are analysed. The numerical results show that the forecasting models based on the grey theory are reliable and practical in structural vibration control fields. Compared with the grey forecasting model, the optimal forecasting model is more efficient in reducing the influences of time-delay and disturbance errors.

  16. Assimilation scheme of the Mediterranean Forecasting System: operational implementation

    Directory of Open Access Journals (Sweden)

    E. Demirov

    Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements.

    Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

  17. Maintenance of Chronic Fatigue Syndrome (CFS in Young CFS Patients Is Associated with the 5-HTTLPR and SNP rs25531 A > G Genotype.

    Directory of Open Access Journals (Sweden)

    Benedicte Meyer

    Full Text Available Earlier studies have shown that genetic variability in the SLC6A4 gene encoding the serotonin transporter (5-HTT may be important for the re-uptake of serotonin (5-HT in the central nervous system. In the present study we investigated how the 5-HTT genotype i.e. the short (S versus long (L 5-HTTLPR allele and the SNP rs25531 A > G affect the physical and psychosocial functioning in patients with chronic fatigue syndrome (CFS. All 120 patients were recruited from The Department of Paediatrics at Oslo University Hospital, Norway, a national referral center for young CFS patients (12-18 years. Main outcomes were number of steps per day obtained by an accelerometer and disability scored by the Functional Disability Inventory (FDI. Patients with the 5-HTT SS or SLG genotype had a significantly lower number of steps per day than patients with the 5-HTT LALG, SLA or LALA genotype. Patients with the 5-HTT SS or SLG genotype also had a significantly higher FDI score than patients with the 5-HTT LALG, SLA or LALA genotype. Thus, CFS patients with the 5-HTT SS or SLG genotype had worse 30 weeks outcome than CFS patients with the 5-HTT LALG, SLA or LALA genotype. The present study suggests that the 5-HTT genotype may be a factor that contributes to maintenance of CFS.

  18. Climate Forecast System Reforecast (CFSR), for 1981 to 2011

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP Climate Forecast System Reanalysis (CFSR) was designed and executed as a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system to...

  19. Design of Intelligent Network Performance Analysis Forecast Support System

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A system designed for supporting the network performance analysis and forecast effort is pre sented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance mea surements. The online real-time network performance forecast will be based on one so-called hybrid predic tion modeling approach for short-term network performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal sup port platform for network performance analysis and forecast effort.

  20. Improvements in medium range weather forecasting system of India

    Indian Academy of Sciences (India)

    V S Prasad; Saji Mohandas; Surya Kanti Dutta; M Das Gupta; G R Iyengar; E N Rajagopal; Swati Basu

    2014-03-01

    Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.

  1. An Electrical Energy Consumption Monitoring and Forecasting System

    Directory of Open Access Journals (Sweden)

    J. L. Rojas-Renteria

    2016-10-01

    Full Text Available Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.

  2. Radar Based Flow and Water Level Forecasting in Sewer Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.

    2009-01-01

    This paper describes the first radar based forecast of flow and/or water level in sewer systems in Denmark. The rainfall is successfully forecasted with a lead time of 1-2 hours, and flow/levels are forecasted an additional ½-1½ hours using models describing the behaviour of the sewer system. Both...... radar data and flow/water level model are continuously updated using online rain gauges and online in-sewer measurements, in order to make the best possible predictions. The project show very promising results, and show large potentials, exploiting the existing water infrastructure in future climate...

  3. NOAA/NCEP Global Forecast System (GFS) Atmospheric Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — U.S. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) numerical weather...

  4. Skills and occupational needs: labour market forecasting systems in Italy

    NARCIS (Netherlands)

    Castiglioni, C.; Tijdens, K.

    2014-01-01

    The development of forecasting systems for occupational needs represents, for different actors, such as enterprises, employers’ associations, trade unions, an opportunity to receive information and to anticipate labour market trends. Also, this information could be used to develop an effective

  5. Climate Forecast System Version 2 (CFSv2) Operational Analysis

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the...

  6. Elements of a coastal ocean forecasting system for India

    Digital Repository Service at National Institute of Oceanography (India)

    Shetye, S.R.; Radhakrishnan, K.

    After about four decades of investment in infrastructure for ocean research, an appropriate initiative for India now would be to build a coastal ocean forecasting system to support the country's myriad activities in its Exclusive Economic Zone...

  7. Climate Forecast System Reanalysis (CFSR), for 1979 to 2011

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP Climate Forecast System Reanalysis (CFSR) was initially completed for the 31-year period from 1979 to 2009, in January 2010. The CFSR was designed and...

  8. An experimental system for flood risk forecasting at global scale

    Science.gov (United States)

    Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

    2016-12-01

    Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

  9. SOFT project: a new forecasting system based on satellite data

    Science.gov (United States)

    Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim

    2002-01-01

    The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.

  10. Self-Organizing Maps-based ocean currents forecasting system

    Science.gov (United States)

    Vilibić, Ivica; Šepić, Jadranka; Mihanović, Hrvoje; Kalinić, Hrvoje; Cosoli, Simone; Janeković, Ivica; Žagar, Nedjeljka; Jesenko, Blaž; Tudor, Martina; Dadić, Vlado; Ivanković, Damir

    2016-03-01

    An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

  11. Forecasting Demand for Weapon System Items

    Science.gov (United States)

    1994-07-01

    level at quarter n. SL(n) was set using the Presutti- Trepp model that DLA currently uses. Forecast error is required by the model to set the safety level...of inventory investment versus response time, we varied the safety level by changing the "lambda factor" or backorder cost used in the Presutti- Trepp

  12. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  13. Benchmark analysis of forecasted seasonal temperature over different climatic areas

    Science.gov (United States)

    Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.

    2015-12-01

    From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

  14. Demonstrating the value of larger ensembles in forecasting physical systems

    Directory of Open Access Journals (Sweden)

    Reason L. Machete

    2016-12-01

    Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by

  15. Impacts of Land Process on the Onset and Evolution of Asian Summer Monsoon in the NCEP Climate Forecast System

    Institute of Scientific and Technical Information of China (English)

    Song YANG; WEN Min; Rongqian YANG; Wayne HIGGINS; ZHANG Renhe

    2011-01-01

    Impacts of land models and initial land conditions (ICs) on the Asian summer monsoon,especially its onset,were investigated using the NCEP Climate Forecast System (CFS).Two land models,the Oregon State University (OSU) land model and the NCEP,OSU,Air Force,and Hydrologic Research Laboratory (Noah) land model,were used to get parallel experiments.The experiments also used land ICs from the NCEP/Department of Energy (DOE) Global Reanalysis 2 (GR2) and the Global Land Data Assimilation System (GLDAS).Previous studies have demonstrated that,a systematic weak bias appears in the modeled monsoon,and this bias may be related to a cold bias over the Asian land mass.Results of the current study show that replacement of the OSU land model by the Noah land model improved the model's cold bias and produced improved monsoon precipitation and circulation patterns.The CFS predicted monsoon with greater proficiency in El Ni(n)o years,compared to La Ni(n)a years,and the Noah model performed better than the OSU model in monsoon predictions for individual years.These improvements occurred not only in relation to monsoon onset in late spring but also to monsoon intensity in summer.Our analysis of the monsoon features over the India peninsula,the Indo-China peninsula,and the South Chinese Sea indicates different degrees of improvement.Furthermore,a change in the land models led to more remarkable improvement in monsoon prediction than did a change from the GR2 land ICs to the GLDAS land ICs.

  16. Short-Term Wind Speed Forecasting for Power System Operations

    KAUST Repository

    Zhu, Xinxin

    2012-04-01

    The emphasis on renewable energy and concerns about the environment have led to large-scale wind energy penetration worldwide. However, there are also significant challenges associated with the use of wind energy due to the intermittent and unstable nature of wind. High-quality short-term wind speed forecasting is critical to reliable and secure power system operations. This article begins with an overview of the current status of worldwide wind power developments and future trends. It then reviews some statistical short-term wind speed forecasting models, including traditional time series approaches and more advanced space-time statistical models. It also discusses the evaluation of forecast accuracy, in particular, the need for realistic loss functions. New challenges in wind speed forecasting regarding ramp events and offshore wind farms are also presented. © 2012 The Authors. International Statistical Review © 2012 International Statistical Institute.

  17. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  18. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  19. Ensemble Bayesian forecasting system Part I: Theory and algorithms

    Science.gov (United States)

    Herr, Henry D.; Krzysztofowicz, Roman

    2015-05-01

    The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of

  20. The DMU-ATMI THOR Air Pollution Forecast System

    DEFF Research Database (Denmark)

    Brandt, J.; Christensen, J. H.; Frohn, L. M.

    A new operational air pollution forecast system, THOR, has been developed at the National Environmental Research Institute, Den-mark. The integrated system consists of a series of different air pollu-tion models, which cover a wide range of scales (from European scale to street scale in cities......) and applications. The goal of the system is, on continuous basis, to produce 3 days air pollution forecasts of the most important air pollution species on different scales. Furthermore, the system will be an integrated part of the national urban and rural monitoring programmes and will be used for emission...... reduction scenarios supporting decision-makers. Currently, the THOR system consists of a numerical weather forecast model, ETA, a long-range air pollution chemistry-transport model, DEOM, an urban background model, BUM, and an operational street pollution model, OSPM. The ETA model is initialized...

  1. Influences of tropical-extratropical interaction on the multidecadal AMOC variability in the NCEP climate forecast system

    Science.gov (United States)

    Huang, Bohua; Hu, Zeng-Zhen; Schneider, Edwin K.; Wu, Zhaohua; Xue, Yan; Klinger, Barry

    2012-08-01

    We have examined the mechanisms of a multidecadal oscillation of the Atlantic Meridional Overturning Circulation (AMOC) in a 335-year simulation of the Climate Forecast System (CFS), the climate prediction model developed at the National Centers for Environmental Prediction (NCEP). Both the mean and seasonal cycle of the AMOC in the CFS are generally consistent with observation-based estimates with a maximum northward volume transport of 16 Sv (106 m3/s) near 35°N at 1.2 km. The annual mean AMOC shows an intermittent quasi 30-year oscillation. Its dominant structure includes a deep anomalous overturning cell (referred to as the anomalous AMOC) with amplitude of 0.6 Sv near 35°N and an anomalous subtropical cell (STC) of shallow overturning spanning across the equator. The mechanism for the oscillation includes a positive feedback between the anomalous AMOC and surface wind stress anomalies in mid-latitudes and a negative feedback between the anomalous STC and AMOC. A strong AMOC is associated with warm sea surface temperature anomaly (SSTA) centered near 45°N, which generates an anticyclonic easterly surface wind anomaly. This anticyclonic wind anomaly enhances the regional downwelling and reinforces the anomalous AMOC. In the mean time, a wind-evaporation-SST (WES) feedback extends the warm SSTA to the tropics and induces a cyclonic wind stress anomaly there, which drives a tropical upwelling and weakens the STC north of the equator. The STC anomaly, in turn, drives a cold upper ocean heat content anomaly (HCA) in the northern tropical Atlantic and weakens the meridional heat transport from the tropics to the mid-latitude through an anomalous southward western boundary current. The anomalous STC transports cold HCA from the subtropics to the mid-latitudes, weakening the mid-latitude deep overturning.

  2. Influences of tropical-extratropical interaction on the multidecadal AMOC variability in the NCEP climate forecast system

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Bohua; Schneider, Edwin K.; Klinger, Barry [Gorge Mason University, Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, Fairfax, VA (United States); Institute of Global Environment and Society, Center for Ocean-Land-Atmosphere Studies, Calverton, MD (United States); Hu, Zeng-Zhen; Xue, Yan [National Centers for Environmental Prediction/NOAA, Climate Prediction Center, Camp Springs, MD (United States); Wu, Zhaohua [Florida State University, Department of Earth, Ocean, and Atmospheric Science, Center for Ocean-Atmospheric Prediction Studies, Tallahassee, FL (United States)

    2012-08-15

    We have examined the mechanisms of a multidecadal oscillation of the Atlantic Meridional Overturning Circulation (AMOC) in a 335-year simulation of the Climate Forecast System (CFS), the climate prediction model developed at the National Centers for Environmental Prediction (NCEP). Both the mean and seasonal cycle of the AMOC in the CFS are generally consistent with observation-based estimates with a maximum northward volume transport of 16 Sv (10{sup 6} m{sup 3}/s) near 35 N at 1.2 km. The annual mean AMOC shows an intermittent quasi 30-year oscillation. Its dominant structure includes a deep anomalous overturning cell (referred to as the anomalous AMOC) with amplitude of 0.6 Sv near 35 N and an anomalous subtropical cell (STC) of shallow overturning spanning across the equator. The mechanism for the oscillation includes a positive feedback between the anomalous AMOC and surface wind stress anomalies in mid-latitudes and a negative feedback between the anomalous STC and AMOC. A strong AMOC is associated with warm sea surface temperature anomaly (SSTA) centered near 45 N, which generates an anticyclonic easterly surface wind anomaly. This anticyclonic wind anomaly enhances the regional downwelling and reinforces the anomalous AMOC. In the mean time, a wind-evaporation-SST (WES) feedback extends the warm SSTA to the tropics and induces a cyclonic wind stress anomaly there, which drives a tropical upwelling and weakens the STC north of the equator. The STC anomaly, in turn, drives a cold upper ocean heat content anomaly (HCA) in the northern tropical Atlantic and weakens the meridional heat transport from the tropics to the mid-latitude through an anomalous southward western boundary current. The anomalous STC transports cold HCA from the subtropics to the mid-latitudes, weakening the mid-latitude deep overturning. (orig.)

  3. Changes in Gut and Plasma Microbiome following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS.

    Directory of Open Access Journals (Sweden)

    Sanjay K Shukla

    Full Text Available Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS is a disease characterized by intense and debilitating fatigue not due to physical activity that has persisted for at least 6 months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of secondary symptoms, including sore throat, memory and concentration impairment, headache, and muscle/joint pain. In patients with post-exertional malaise, significant worsening of symptoms occurs following physical exertion and exercise challenge serves as a useful method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal dysbiosis and systemic responses to gut microorganisms may play a role in the symptomology of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symptoms could be due to increased bacterial translocation from the intestine into the systemic circulation. To test this hypothesis, we collected symptom reports and blood and stool samples from ten clinically characterized ME/CFS patients and ten matched healthy controls before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Microbiomes of blood and stool samples were examined. Stool sample microbiomes differed between ME/CFS patients and healthy controls in the abundance of several major bacterial phyla. Following maximal exercise challenge, there was an increase in relative abundance of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005. There was also a significant difference in clearance of specific bacterial phyla from blood following exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in ME/CFS patients versus clearance in the controls. These results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon exercise challenge, there

  4. Reduction of NCEP Global Forecast System 2-m Temperature Forecast Errors

    Science.gov (United States)

    Zheng, W.; Ek, M. B.; Wei, H.; Meng, J.

    2015-12-01

    In this study the systematic deficiencies and cause of errors in 2-m temperature forecasts in the NCEP Global Forecast System (GFS) are identified by investigating the physics of the Noah land surface model and land-atmosphere interactions, and a practical solution is found to reduce this kind of forecast errors. This presentation focuses on further evaluation of the proposed modifications with two one-month experiments for summer and winter seasons through the verification of GFS forecasts against surface and sounding observations. It was found that the modifications can substantially avoid late afternoon rapidly dropping 2-m temperature and decoupling when a cessation of turbulent transport between the surface and the atmosphere due to high near surface atmospheric stability happens, and reduce the cold bias of 2-m temperature during nighttime. Furthermore, the surface dew point temperature, surface wind speed and scores for light and medium precipitation are also improved. In the future, new land data sets such as vegetation and soil types, near real-time green vegetation fraction and snow albedo will be updated and we expect to further reduction of 2-m temperature bias in the GFS model.

  5. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

    Directory of Open Access Journals (Sweden)

    G. Thirel

    2010-08-01

    Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm

  6. INFERNO: a system for early outbreak detection and signature forecasting.

    Science.gov (United States)

    Naumova, Elena N; O'Neil, E; MacNeill, I

    2005-08-26

    Public health surveillance systems that monitor daily disease incidence provide valuable information about threats to public health and enable public health authorities to detect enteric outbreaks rapidly. This report describes the INtegrated Forecasts and EaRly eNteric Outbreak (INFERNO) detection system of algorithms for outbreak detection and forecasting. INFERNO incorporates existing knowledge of infectious disease epidemiology into adaptive forecasts and uses the concept of an outbreak signature as a composite of disease epidemic curves. Four main components comprise the system: 1) training, 2) warning and flagging, 3) signature forecasting, and 4) evaluation. The unifying goal of the system is to gain insight into the nature of temporal variations in the incidence of infection. Daily collected records are smoothed initially by using a loess-type smoother. Upon receipt of new data, the smoothing is updated; estimates are made of the first two derivatives of the smoothed curve, which are used for near-term forecasting. Recent data and near-term forecasts are used to compute a five level, color-coded warning index to quantify the level of concern. Warning algorithms are designed to balance false detection of an epidemic (Type I errors) with failure to correctly detect an epidemic (Type II errors). If the warning index signals a sufficiently high probability of an epidemic, the fitting of a gamma-based signature curve to the actual data produces a forecast of the possible size of the outbreak. Although the system is under development, its potential has been demonstrated through successful use of emergency department records associated with a substantial waterborne outbreak of cryptosporidiosis that occurred in Milwaukee, Wisconsin, in 1993. Prospects for further development, including adjustment for seasonality and reporting delays, are also outlined.

  7. Big Software for SmallSats: Adapting cFS to CubeSat Missions

    Science.gov (United States)

    Cudmore, Alan P.; Crum, Gary Alex; Sheikh, Salman; Marshall, James

    2015-01-01

    Expanding capabilities and mission objectives for SmallSats and CubeSats is driving the need for reliable, reusable, and robust flight software. While missions are becoming more complicated and the scientific goals more ambitious, the level of acceptable risk has decreased. Design challenges are further compounded by budget and schedule constraints that have not kept pace. NASA's Core Flight Software System (cFS) is an open source solution which enables teams to build flagship satellite level flight software within a CubeSat schedule and budget. NASA originally developed cFS to reduce mission and schedule risk for flagship satellite missions by increasing code reuse and reliability. The Lunar Reconnaissance Orbiter, which launched in 2009, was the first of a growing list of Class B rated missions to use cFS.

  8. Short-term load forecasting of power system

    Science.gov (United States)

    Xu, Xiaobin

    2017-05-01

    In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

  9. Research and Development for Technology Evolution Potential Forecasting System

    Science.gov (United States)

    Gao, Changqing; Cao, Shukun; Wang, Yuzeng; Ai, Changsheng; Ze, Xiangbo

    Technology forecasting is a powerful weapon for many enterprises to gain an animate future. Evolutionary potential radar plot is a necessary step of some valuable methods to help the technology managers with right technical strategy. A software system for Technology Evolution Potential Forecasting (TEPF) with automatic radar plot drawing is introduced in this paper. The framework of the system and the date structure describing the concrete evolution pattern are illustrated in details. And the algorithm for radar plot drawing is researched. It is proved that the TEPF system is an effective tool during the technology strategy analyzing process with a referenced case study.

  10. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

    Directory of Open Access Journals (Sweden)

    G. Thirel

    2010-04-01

    Full Text Available The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF 10-day Ensemble Prediction System (EPS. Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc., especially

  11. Developing a model of forecasting information systems performance

    Directory of Open Access Journals (Sweden)

    G. N. Isaev

    2017-01-01

    Full Text Available Research aim: to develop a model to forecast the performance ofinformation systems as a mechanism for preliminary assessment of the information system effectiveness before the beginning of financing the information system project.Materials and methods: the starting material used the results of studying the parameters of the statistical structure of information system data processing defects. Methods of cluster analysis and regression analysis were applied.Results: in order to reduce financial risks, information systems customers try to make decisions on the basis of preliminary calculations on the effectiveness of future information systems. However, the assumptions on techno-economic justification of the project can only be obtained when the funding for design work is already open. Its evaluation can be done before starting the project development using a model of forecasting information system performance. The model is developed using regression analysis in the form of a multiple linear regression. The value of information system performance is the predicted variable in the regression equation. The values of data processing defects in the classes of accuracy, completeness and timeliness are the forecast variables. Measurement and evaluation of parameters of the statistical structure of defects were done through programmes of cluster analysis and regression analysis. The calculations for determining the actual and forecast values of the information system performance were conducted.Conclusion: in terms of implementing the model, a research of information systems was carried out, as well as the development of forecasting model of information system performance. The conducted experimental work showed the adequacy of the model. The model is implemented in the complex task of designing information systems in education and industry.

  12. Optimal Control and Forecasting of Complex Dynamical Systems

    CERN Document Server

    Grigorenko, Ilya

    2006-01-01

    This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance. The theory presented here can be efficiently applied to various problems, such as the determination of the optimal shape of a laser pulse to induce certain excitations in quantum systems, the optimal design of nanostructured materials and devices, or the control of chaotic systems and minimization of the forecast error for a given forecasting model (for example, artificial neural networks). Starting from a brief review of the history of variational calcul

  13. Interpretations of systematic errors in the NCEP Climate Forecast System at lead times of 2, 4, 8, ..., 256 days

    Directory of Open Access Journals (Sweden)

    Siwon Song

    2012-09-01

    Full Text Available The climatology of mean bias errors (relative to 1-day forecasts was examined in a 20-year hindcast set from version 1 of the Climate Forecast System (CFS, for forecast lead times of 2, 4, 8, 16, ... 256 days, verifying in different seasons. Results mostly confirm the simple expectation that atmospheric model biases should be evident at short lead (2–4 days, while soil moisture errors develop over days-weeks and ocean errors emerge over months. A further simplification is also evident: surface temperature bias patterns have nearly fixed geographical structure, growing with different time scales over land and ocean. The geographical pattern has mostly warm and dry biases over land and cool bias over the oceans, with two main exceptions: (1 deficient stratocumulus clouds cause warm biases in eastern subtropical oceans, and (2 high latitude land is too cold in boreal winter. Further study of the east Pacific cold tongue-Intertropical Convergence Zone (ITCZ complex shows a possible interaction between a rapidly-expressed atmospheric model bias (poleward shift of deep convection beginning at day 2 and slow ocean dynamics (erroneously cold upwelling along the equator in leads > 1 month. Further study of the high latitude land cold bias shows that it is a thermal wind balance aspect of the deep polar vortex, not just a near-surface temperature error under the wintertime inversion, suggesting that its development time scale of weeks to months may involve long timescale processes in the atmosphere, not necessarily in the land model. Winter zonal wind errors are small in magnitude, but a refractive index map shows that this can cause modest errors in Rossby wave ducting. Finally, as a counterpoint to our initial expectations about error growth, a case of non-monotonic error growth is shown: velocity potential bias grows with lead on a time scale of weeks, then decays over months. It is hypothesized that compensations between land and ocean errors may

  14. Performance evaluation of NCEP climate forecast system for the prediction of winter temperatures over India

    Science.gov (United States)

    Nageswararao, M. M.; Mohanty, U. C.; Kiran Prasad, S.; Osuri, Krishna K.; Ramakrishna, S. S. V. S.

    2016-11-01

    The surface air temperature during the winter season (December-February) in India adversely affects agriculture as well as day-to-day life. Therefore, the accurate prediction of winter temperature in extended range is of utmost importance. The National Center for Environmental Prediction (NCEP) has been providing climatic variables from the fully coupled global climate model, known as Climate Forecast System version 1 (CFSv1) on monthly to seasonal scale since 2004, and it has been upgraded to CFSv2 subsequently in 2011. In the present study, the performance of CFSv1 and CFSv2 in simulating the winter 2 m maximum, minimum, and mean temperatures ( T max, T min, and T mean, respectively) over India is evaluated with respect to India Meteorological Department (IMD) 1° × 1° observations. The hindcast data obtained from both versions of CFS from 1982 to 2009 (27 years) with November initial conditions (lead-1) are used. The analyses of winter ( T max, T min, and T mean) temperatures revealed that CFSv1 and CFSv2 are able to replicate the patterns of observed climatology, interannual variability, and coefficient of variation with a slight negative bias. Of the two, CFSv2 is appreciable in capturing increasing trends of winter temperatures like observed. The T max, T min, and T mean correlations from CFSv2 is significantly high (0.35, 0.53, and 0.51, respectively), while CFSv1 correlations are less (0.29, 0.15, and 0.12) and insignificant. This performance of CFSv2 may be due to the better estimation of surface heat budget terms and realistic CO2 concentration, which were absent in CFSv1. CFSv2 proved to have a high probability of detection in predicting different categories (below, near, and above normal) for winter T min, which are required for crop yield and public utility services, over north India.

  15. Flood forecasting and alert system for Arda River basin

    Science.gov (United States)

    Artinyan, Eram; Vincendon, Beatrice; Kroumova, Kamelia; Nedkov, Nikolai; Tsarev, Petko; Balabanova, Snezhanka; Koshinchanov, Georgy

    2016-10-01

    The paper presents the set-up and functioning of a flood alert system based on SURFEX-TOPODYN platform for the cross-border Arda River basin. The system was built within a Bulgarian-Greek project funded by the European Territorial Cooperation (ETC) Programme and is in operational use since April 2014. The basin is strongly influenced by Mediterranean cyclones during the autumn-winter period and experiences dangerous rapid floods, mainly after intensive rain, often combined with snow melt events. The steep mountainous terrain leads to floods with short concentration time and high river speed causing damage to settlements and infrastructure. The main challenge was to correctly simulate the riverflow in near-real time and to timely forecast peak floods for small drainage basins below 100 km2 but also for larger ones of about 1900 km2 using the same technology. To better account for that variability, a modification of the original hydrological model parameterisation is proposed. Here we present the first results of a new model variant which uses dynamically adjusted TOPODYN river velocity as function of the computed partial streamflow discharge. Based on historical flooding data, river sections along endangered settlements were included in the river flow forecasting. A continuous hydrological forecast for 5 days ahead was developed for 18 settlements in Bulgaria and for the border with Greece, thus giving enough reaction time in case of high floods. The paper discusses the practical implementation of models for the Arda basin, the method used to calibrate the models' parameters, the results of the calibration-validation procedure and the way the information system is organised. A real case of forecasted rapid floods that occurred after the system's finalisation is analysed. One of the important achievements of the project is the on-line presentation of the forecasts that takes into account their temporal variability and uncertainty. The web presentation includes a

  16. Climate Prediction Center (CPC) NCEP-Global Forecast System (GFS) 0-10cm Soil-Moisture Forecast Product

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Forecast System (GFS) forecast 0-10cm soil-moisture data at 37.5km resolution is created at the NOAA Climate Prediction Center for the purpose of near...

  17. Operational Forecasting and Warning systems for Coastal hazards in Korea

    Science.gov (United States)

    Park, Kwang-Soon; Kwon, Jae-Il; Kim, Jin-Ah; Heo, Ki-Young; Jun, Kicheon

    2017-04-01

    Coastal hazards caused by both Mother Nature and humans cost tremendous social, economic and environmental damages. To mitigate these damages many countries have been running the operational forecasting or warning systems. Since 2009 Korea Operational Oceanographic System (KOOS) has been developed by the leading of Korea Institute of Ocean Science and Technology (KIOST) in Korea and KOOS has been operated in 2012. KOOS is consists of several operational modules of numerical models and real-time observations and produces the basic forecasting variables such as winds, tides, waves, currents, temperature and salinity and so on. In practical application systems include storm surges, oil spills, and search and rescue prediction models. In particular, abnormal high waves (swell-like high-height waves) have occurred in the East coast of Korea peninsula during winter season owing to the local meteorological condition over the East Sea, causing property damages and the loss of human lives. In order to improve wave forecast accuracy even very local wave characteristics, numerical wave modeling system using SWAN is established with data assimilation module using 4D-EnKF and sensitivity test has been conducted. During the typhoon period for the prediction of sever waves and the decision making support system for evacuation of the ships, a high-resolution wave forecasting system has been established and calibrated.

  18. A short-term ensemble wind speed forecasting system for wind power applications

    Science.gov (United States)

    Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

    2011-12-01

    This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

  19. Weather modeling and forecasting of PV systems operation

    CERN Document Server

    Paulescu, Marius; Gravila, Paul; Badescu, Viorel

    2012-01-01

    In the past decade, there has been a substantial increase of grid-feeding photovoltaic applications, thus raising the importance of solar electricity in the energy mix. This trend is expected to continue and may even increase. Apart from the high initial investment cost, the fluctuating nature of the solar resource raises particular insertion problems in electrical networks. Proper grid managing demands short- and long-time forecasting of solar power plant output. Weather modeling and forecasting of PV systems operation is focused on this issue. Models for predicting the state of the sky, nowc

  20. Dynamic downscaling of CFS winter seasonal simulations over the United States using the ETA/SSIB-3 model

    Science.gov (United States)

    de Sales, F.; Xue, Y.

    2010-12-01

    The NCEP ETA/SSiB-3 regional circulation model (RCM) was 1-way nested in the NCEP Climate Forecast System (CFS) for a series of 22-year downscaling simulations of the winter season (December through April) over North America from 1982 through 2003. Each year’ simulation has 5 ensemble members producing a total of 110 winter hindcasts. These simulations are part of the Multi-RCM Ensemble Downscaling (MRED), which aims to explore the utility and value of RCMs in operational seasonal to interannual climate prediction. The Eta/SSiB-3 shows good downscaling ability for seasonal mean precipitation. The model reproduced well the winter precipitation pattern over the country, especially the high precipitation regions; over the East, the Northwest, and central California, with a large drier region in between. The RCM, however, did not simulate the precipitation maximum over the Southeast, which was well captured by the global model. The December-January-February-March-April (DJFMA) of 22-year mean bias averaged over the whole country for the CFS is 1.52 mm day-1, while for the ETA-SSIB-3 model it is -0.1 mm day-1. Similar comparison yielded a 66.7% reduction in RMSE of precipitation with downscaling. The analysis of average precipitation time series indicates that overall the RCM improved the simulation by reducing excessive rainfall produced by the GCM, especially over the western states. RCM reduced the countrywide CFS’ RMSE of time series from 1.60 to 0.33 mm day-1. The error reduction was larger over the western states (nearly 83%) than over the eastern states (approximately 67%). However, the temporal correlation with observation shows little difference between GCM and RCM, indicating the dominant role of lateral boundary forcing from CFS in producing the temporal variability. The simulation of seasonal snow water equivalent was also improved by the regional model. Comparison between models simulations and the Rutgers University observational data shows that the

  1. Global Ocean Forecast System 3.1 Validation Test

    Science.gov (United States)

    2017-05-04

    the water column. GOFS nowcasts/forecasts the ocean’s “ weather ”, which includes the three-dimensional ocean temperature, salinity and current...C. DeLuca, V. Balaji, M. Suarez, and A. da Silva, 2004: The Architecture of the Earth System Modeling Framework. Computing in Science and

  2. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    Science.gov (United States)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  3. Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

    Science.gov (United States)

    Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

    2015-12-01

    In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

  4. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, Thomas Hoff [Clean Power Research, L.L.C., Napa, CA (United States); Kankiewicz, Adam [Clean Power Research, L.L.C., Napa, CA (United States)

    2016-02-26

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest

  5. Weather forecast-based optimization of integrated energy systems.

    Energy Technology Data Exchange (ETDEWEB)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  6. The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

    Science.gov (United States)

    Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2017-04-01

    Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users

  7. A High-Order CFS Algorithm for Clustering Big Data

    OpenAIRE

    Fanyu Bu; Zhikui Chen; Peng Li; Tong Tang; Ying Zhang

    2016-01-01

    With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learn...

  8. Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

    Science.gov (United States)

    Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

    2015-01-01

    A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

  9. Solar Storm GIC Forecasting: Solar Shield Extension Development of the End-User Forecasting System Requirements

    Science.gov (United States)

    Pulkkinen, A.; Mahmood, S.; Ngwira, C.; Balch, C.; Lordan, R.; Fugate, D.; Jacobs, W.; Honkonen, I.

    2015-01-01

    A NASA Goddard Space Flight Center Heliophysics Science Division-led team that includes NOAA Space Weather Prediction Center, the Catholic University of America, Electric Power Research Institute (EPRI), and Electric Research and Management, Inc., recently partnered with the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) to better understand the impact of Geomagnetically Induced Currents (GIC) on the electric power industry. This effort builds on a previous NASA-sponsored Applied Sciences Program for predicting GIC, known as Solar Shield. The focus of the new DHS S&T funded effort is to revise and extend the existing Solar Shield system to enhance its forecasting capability and provide tailored, timely, actionable information for electric utility decision makers. To enhance the forecasting capabilities of the new Solar Shield, a key undertaking is to extend the prediction system coverage across Contiguous United States (CONUS), as the previous version was only applicable to high latitudes. The team also leverages the latest enhancements in space weather modeling capacity residing at Community Coordinated Modeling Center to increase the Technological Readiness Level, or Applications Readiness Level of the system http://www.nasa.gov/sites/default/files/files/ExpandedARLDefinitions4813.pdf.

  10. Real-time drought forecasting system for irrigation managment

    Science.gov (United States)

    Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco

    2013-04-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in in European areas traditionally rich of water such as the Po Valley. In dry periods, the problem of water shortage can be enhanced by conflictual use of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective on this issue is increasing due to climate change and global warming scenarios which come out from the last IPCC Report. The increased frequency of dry periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought forecasting system Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin. The studied area covers 74,000 ha in the middle of the Po Valley, near the city of Lodi. The hydrological ensemble forecasts are based on 20 meteorological members of the non-hydrostatic WRF model with 30 days as lead-time, provided by Epson Meteo Centre, while the hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The hydrological model was validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station. Reliability of the forecasting system and its benefits was assessed on some cases-study occurred in the recent years.

  11. The Forecasting and Warning System of Geological Disasters in China

    Institute of Scientific and Technical Information of China (English)

    YaoXuexiang; XuJing

    2004-01-01

    Geological disasters such as landslide and mudslide can be caused by many factors. Collaborations among different governmental agencies and multi-disciplines are necessary to establish a forecasting and warning system of geological disasters ( FWSGD). A FWSGD in China has been in operation since June 1 , 2003 as a joint project between the China Meteorological Administration (CMA) and the Ministry of Land and Resources ( MLR). This system has successfully shown very good social and economic benefits. The temporal-spatial distributions of China geological disasters and their causes have been analyzed in this paper. The FWSGD is described and its possible existing issues are also discussed. Authors suggest a new approach to study these disasters from interactions of the earth systems. Finally, a monitoring, forecasting, warning and preventing system for geological disasters in China is proposed.

  12. Development of KASI Geomagnetic Storm Forecast System using Coronagraph Data

    Science.gov (United States)

    Baek, Ji-Hye; Choi, SeongHwan; Park, Jongyeob; Kim, Roksoon; Kim, Sujin; Kim, Jihun

    2017-08-01

    We present Korea Astronomy and Space Science Institute (KASI) Geomagnetic Storm Forecast System. The aim of the system is to calculate the CME arrival time and predict the geoeffectiveness of the CME. To implement the system, we use the Large Angle and Spectrometric Coronagraph (LASCO) C2 and C3 data, the HMI magnetogram data of Solar Dynamics Observatory(SDO), and CACTUS CME list. The system consists of servers, which are to download, process, and publish data, data handling programs and web service. We apply an image differencing technique on LASCO data to determine speed and earthward direction parameters of CMEs. KASI Geomagnetic Storm Forecast Model has installed and being tested at Community Coordinated Modeling Center (CCMC) of NASA/GSFC. We expect that users can predict CME arrival time and geoeffectiveness of the CME easily and fast using the system. In order to improve the forecast performance of the system, we plan to incorporate advanced coronagraph data which will be developed and installed on ISS by KASI and NASA in collaboration.

  13. A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

    Science.gov (United States)

    Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

    2017-04-01

    Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of

  14. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days

    Science.gov (United States)

    Bennett, James C.; Robertson, David E.; Shrestha, Durga Lal; Wang, Q. J.; Enever, David; Hapuarachchi, Prasantha; Tuteja, Narendra K.

    2014-11-01

    This study describes a System for Continuous Hydrological Ensemble Forecasting (SCHEF) designed to forecast streamflows to lead times of 9 days. SCHEF is intended to support optimal management of water resources for consumptive and environmental purposes and ultimately to support the management of impending floods. Deterministic rainfall forecasts from the ACCESS-G numerical weather prediction (NWP) model are post-processed using a Bayesian joint probability model to correct biases and quantify uncertainty. Realistic temporal and spatial characteristics are instilled in the rainfall forecast ensemble with the Schaake shuffle. The ensemble rainfall forecasts are then used as inputs to the GR4H hydrological model to produce streamflow forecasts. A hydrological error correction is applied to ensure forecasts transit smoothly from recent streamflow observations. SCHEF forecasts streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in forecasts of streamflows at lead times of 1-6 days. Forecasts perform best in temperate perennially flowing rivers, while forecasts are poorest in intermittently flowing rivers. The poor streamflow forecasts in intermittent rivers are primarily the result of poor rainfall forecasts, rather than an inadequate representation of hydrological processes. Forecast uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow forecasts at all lead times. Additionally, we show that the choice of forecast time-step can influence forecast accuracy: forecasts generated at a 1-h time-step tend to be more accurate than at longer time-steps (e.g. 1-day). This is largely because at shorter time-steps the hydrological error correction is able to correct streamflow forecasts with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the

  15. A decision support system for use of probability forecasts

    NARCIS (Netherlands)

    De Kleermaeker, S.; Verkade, J.S.

    2013-01-01

    Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers’ increased awareness of forecasting uncert

  16. A decision support system for use of probability forecasts

    NARCIS (Netherlands)

    De Kleermaeker, S.; Verkade, J.S.

    2013-01-01

    Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers’ increased awareness of forecasting

  17. Seasonal rainfall prediction skill over South Africa: one- versus two-tiered forecasting systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-04-01

    Full Text Available Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface...

  18. Fuzzy neural network technique for system state forecasting.

    Science.gov (United States)

    Li, Dezhi; Wang, Wilson; Ismail, Fathy

    2013-10-01

    In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

  19. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    Science.gov (United States)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  20. Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

    2016-12-01

    The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

  1. Optimal Power Flow for Distribution Systems under Uncertain Forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

    2016-12-29

    The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

  2. Earthquake Forecasting as a System-Science Problem

    Science.gov (United States)

    Jordan, T. H.

    2012-12-01

    The increasing exposure of society to natural hazards has made the forecasting of extreme events a pressing scientific concern. No aspect of this problem has been more vexing than earthquake prediction. The century-long failure to identify observable precursory signals diagnostic of impending events has led to an alternative approach, in which a variety of constraints on earthquake location, magnitude, and long-term frequency are synthesized into probabilistic seismic hazard models, such as those produced by the USGS National Seismic Hazard Mapping Project. This presentation will describe how recent progress in earthquake system science is improving hazard and risk forecasting. These system-level problems can be partitioned according to causal sequences described in terms of conditional probabilities. For example, the exceedance probabilities of shaking intensities at geographically distributed sites conditional on a particular fault rupture (a ground motion prediction model or GMPM) can be combined with the probabilities of different ruptures (an earthquake rupture forecast or ERF) to create a seismic hazard map. Deterministic simulations of ground motions from very large suites (millions) of ruptures, now feasible through high-performance computational platforms such as SCEC's CyberShake, are allowing seismologists to replace empirical GMPMs with physics-based models that more accurately represent wave propagation through heterogeneous geologic structures, such as sedimentary basins that amplify seismic shaking. A notable advance is the development of ERFs conditioned on preceding seismic activity, such as the Uniform California Earthquake Rupture Forecasts produced by the Working Groups on California Earthquake Probabilities. These time-dependent probability models account for the stress-renewal processes of elastic rebound, and they are beginning to capture aftershock triggering. However, they have not fully reconciled the long-term phase modulation of stress

  3. An Electrical Energy Consumption Monitoring and Forecasting System

    OpenAIRE

    Rojas-Renteria, J. L.; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; Gonzalez-Perez, J. L.; Lozano-Dorantes, R.

    2016-01-01

    Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecastin...

  4. A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

    Directory of Open Access Journals (Sweden)

    Yiqi Chu

    2016-10-01

    Full Text Available Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

  5. Oceanic stochastic parametrizations in a seasonal forecast system

    CERN Document Server

    Andrejczuk, M; Juricke, S; Palmer, T N; Weisheimer, A; Zanna, L

    2015-01-01

    We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.

  6. Global crop production forecasting data system analysis

    Science.gov (United States)

    Castruccio, P. A. (Principal Investigator); Loats, H. L.; Lloyd, D. G.

    1978-01-01

    The author has identified the following significant results. Findings led to the development of a theory of radiometric discrimination employing the mathematical framework of the theory of discrimination between scintillating radar targets. The theory indicated that the functions which drive accuracy of discrimination are the contrast ratio between targets, and the number of samples, or pixels, observed. Theoretical results led to three primary consequences, as regards the data system: (1) agricultural targets must be imaged at correctly chosen times, when the relative evolution of the crop's development is such as to maximize their contrast; (2) under these favorable conditions, the number of observed pixels can be significantly reduced with respect to wall-to-wall measurements; and (3) remotely sensed radiometric data must be suitably mixed with other auxiliary data, derived from external sources.

  7. Anvil Forecast Tool in the Advanced Weather Interactive Processing System

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.

  8. Sales Forecasting System for Newspaper Distribution Companies in Turkey

    Directory of Open Access Journals (Sweden)

    Gencay İncesu

    2012-07-01

    Full Text Available Normal 0 false false false EN-US X-NONE X-NONE st1\\:*{behavior:url(#ieooui } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Newspapers are like goods with a shelf life of one day and they have to be distributed daily basis to the sales points. A problem that most newspaper companies encounter daily is how to predict the right number of newspapers to print and distribute among distinct sales points. The aim is to predict newspaper demand as accurately as possible to meet customer need with minimum number of returns, missed sales and oversupply. This makes it necessary to develop a short-term forecasting system. The data taken from one of the largest distribution companies in Turkey is time dependent. Therefore, time series analysis is used to forecast newspaper circulation. In this paper, the newspaper sales system is examined for Turkey. Various types of forecasting techniques which are applicable to newspaper circulation planning are compared and a nonlinear approach for returns is applied.

  9. Minor Component Analysis-based Landing Forecast System for Ship-borne Helicopter

    Institute of Scientific and Technical Information of China (English)

    ZHOU Bo,; SHI Ai-guo; WAN Lin; YANG Bao-zhang

    2005-01-01

    The general structure of ship-borne helicopter landing forecast system is presented, and a novel ship motion prediction model based on minor component analysis (MCA) is built up to improve the forecast effectiveness. To validate the feasibility of this landing forecast system, time series for the roll, pitch and heave are generated by simulation and then forecasted based on MCA. Simulation results show that ship-borne helicopters can land safely in higher sea condition while carrying on rescue or replenishment tasks at sea in terms of the landing forecast system.

  10. Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

    Science.gov (United States)

    Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.; Pi, X.; Mannucci, A. J.; Butala, M.; Wilson, B. D.; Komjathy, A.; Wang, C.; Rosen, G.

    2016-07-01

    The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over-the-horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics-based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere-Thermosphere-Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and middle to low latitude ionosphere-electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

  11. Road landslide information management and forecasting system base on GIS.

    Science.gov (United States)

    Wang, Wei Dong; Du, Xiang Gang; Xie, Cui Ming

    2009-09-01

    Take account of the characters of road geological hazard and its supervision, it is very important to develop the Road Landslides Information Management and Forecasting System based on Geographic Information System (GIS). The paper presents the system objective, function, component modules and key techniques in the procedure of system development. The system, based on the spatial information and attribute information of road geological hazard, was developed and applied in Guizhou, a province of China where there are numerous and typical landslides. The manager of communication, using the system, can visually inquire all road landslides information based on regional road network or on the monitoring network of individual landslide. Furthermore, the system, integrated with mathematical prediction models and the GIS's strongpoint on spatial analyzing, can assess and predict landslide developing procedure according to the field monitoring data. Thus, it can efficiently assists the road construction or management units in making decision to control the landslides and to reduce human vulnerability.

  12. Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

    OpenAIRE

    Pappenberger, F.; K. J. Beven; N. M. Hunter; Bates, P. D.; B. T. Gouweleeuw; Thielen, J.; A. P. J. De De Roo

    2005-01-01

    International audience; The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessa...

  13. Flow Forecasting in Drainage Systems with Extrapolated Radar Rainfall Data and Auto Calibration on Flow Observations

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Grum, M.; Rasmussen, Michael R.;

    2011-01-01

    in a small urban catchment has been developed. The forecast is based on application of radar rainfall data, which by a correlation based technique, is extrapolated with a lead time up to two hours. The runoff forecast in the drainage system is based on a fully distributed MOUSE model which is auto......Forecasting of flows, overflow volumes, water levels, etc. in drainage systems can be applied in real time control of drainage systems in the future climate in order to fully utilize system capacity and thus save possible construction costs. An online system for forecasting flows and water levels......-calibrated on flow measurements in order to produce the best possible forecast for the drainage system at all times. The system shows great potential for the implementation of real time control in drainage systems and forecasting flows and water levels....

  14. Nivmar: a storm surge forecasting system for Spanish waters

    Directory of Open Access Journals (Sweden)

    Enrique Álvarez Fanjul

    2001-07-01

    Full Text Available In this paper, a storm surge prediction system for the Spanish Waters is presented. The system, named Nivmar, is based on the ocean circulation Hamsom model and on the harmonical prediction of tides computed from data measured by the tide gauge network Redmar, managed by Puertos del Estado. Nivmar is executed twice a day, running Hamsom forced by meteorological fields derived from the INM (Instituto Nacional de Meteorología operational application of Hirlam atmospheric model. Data from Redmar tide gauges is used to to forecast the tidal elevations, to validate the system and to perform data assimilation, correcting systematic errors in the mean sea level due to physicals processes that are not included in the ocean model (i. e. steric height. The forecast horizon is 48 hours. In order to validate the system with measured data from Redmar a very stormy 5 months period was selected. Results from this test (November 95 to March 96 are presented. Data from this experiment shown that Nivmar is able to correctly predict sea level in the region. A simple data assimilation scheme for sea level is described and results from its application are studied. Finally, special focus is made in future plans and potential developments and applications of the system.

  15. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    Science.gov (United States)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the

  16. The Establishment of an Operational Earthquake Forecasting System in Italy

    Science.gov (United States)

    Marzocchi, Warner; Lombardi, Anna Maria; Casarotti, Emanuele

    2014-05-01

    Just after the Mw 6.2 earthquake that hit L'Aquila, on April 6 2009, the Civil Protection nominated an International Commission on Earthquake Forecasting (ICEF) that paved the way to the development of the Operational Earthquake Forecasting (OEF), defined as the "procedures for gathering and disseminating authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes". In this paper we introduce the first official OEF system in Italy that has been developed by the new-born Centro di Pericolosità Sismica at the Istituto Nazionale di Geofisica e Vulcanologia. The system provides every day an update of the weekly probabilities of ground shaking over the whole Italian territory. In this presentation, we describe in detail the philosophy behind the system, the scientific details, and the output format that has been preliminary defined in agreement with Civil Protection. To our knowledge, this is the first operational system that fully satisfies the ICEF guidelines. Probably, the most sensitive issue is related to the communication of such a kind of message to the population. Acknowledging this inherent difficulty, in agreement with Civil Protection we are planning pilot tests to be carried out in few selected areas in Italy; the purpose of such tests is to check the effectiveness of the message and to receive feedbacks.

  17. Multiplexed FBG Monitoring System for Forecasting Coalmine Water Inrush Disaster

    Directory of Open Access Journals (Sweden)

    B. Liu

    2012-01-01

    Full Text Available This paper presents a novel fiber-Bragg-grating- (FBG- based system which can monitor and analyze multiple parameters such as temperature, strain, displacement, and seepage pressure simultaneously for forecasting coalmine water inrush disaster. The sensors have minimum perturbation on the strain field. And the seepage pressure sensors adopt a drawbar structure and employ a corrugated diaphragm to transmit seepage pressure to the axial strain of FBG. The pressure sensitivity is 20.20 pm/KPa, which is 6E3 times higher than that of ordinary bare FBG. The FBG sensors are all preembedded on the roof of mining area in coalmine water inrush model test. Then FBG sensing network is set up applying wavelength-division multiplexing (WDM technology. The experiment is carried out by twelve steps, while the system acquires temperature, strain, displacement, and seepage pressure signals in real time. The results show that strain, displacement, and seepage pressure monitored by the system change significantly before water inrush occurs, and the strain changes firstly. Through signal fusion analyzed it can be concluded that the system provides a novel way to forecast water inrush disaster successfully.

  18. An Operational Coastal Forecasting System in Galicia (NW Spain)

    Science.gov (United States)

    Balseiro, C. F.; Carracedo, P.; Pérez, E.; Pérez, V.; Taboada, J.; Venacio, A.; Vilasa, L.

    2009-09-01

    The Galician coast (NW Iberian Peninsula coast) and mainly the Rias Baixas (southern Galician rias) are one of the most productive ecosystems in the world, supporting a very active fishing and aquiculture industry. This high productivity lives together with a high human pressure and an intense maritime traffic, which means an important environmental risk. Besides that, Harmful Algae Blooms (HAB) are common in this area, producing important economical losses in aquiculture. In this context, the development of an Operational Hydrodynamic Ocean Forecast System is the first step to the development of a more sophisticated Ocean Integrated Decision Support Tool. A regional oceanographic forecasting system in the Galician Coast has been developed by MeteoGalicia (the Galician regional meteorological agency) inside ESEOO project to provide forecasts on currents, sea level, water temperature and salinity. This system is based on hydrodynamic model MOHID, forced with the operational meteorological model WRF, supported daily at MeteoGalicia . Two grid meshes are running nested at different scales, one of ~2km at the shelf scale and the other one with a resolution of 500 m at the rias scale. ESEOAT (Puertos del Estado) model provide salinity and temperature fields which are relaxed at all depth along the open boundary of the regional model (~6km). Temperature and salinity initial fields are also obtained from this application. Freshwater input from main rivers are included as forcing in MOHID model. Monthly mean discharge data from gauge station have been provided by Aguas de Galicia. Nowadays a coupling between an hydrological model (SWAT) and the hydrodynamic one are in development with the aim to verify the impact of the rivers discharges. The system runs operationally daily, providing two days of forecast. First model verifications had been performed against Puertos del Estado buoys and Xunta de Galicia buoys network along the Galician coast. High resolution model results

  19. Towards an integrated forecasting system for pelagic fisheries

    DEFF Research Database (Denmark)

    Christensen, Asbjørn; Butenschön, Momme; Gürkan, Zeren;

    2012-01-01

    for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher...... trophic level, in this case the sandeel population, demonstrated here on the basis of hindcast data. The coupled forecasting system has been tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local‐and basin...

  20. A quality assessment of the MARS crop yield forecasting system for the European Union

    Science.gov (United States)

    van der Velde, Marijn; Bareuth, Bettina

    2015-04-01

    Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

  1. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  2. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

    2008-02-15

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

  3. A nonlinear combination forecasting method based on the fuzzy inference system

    Institute of Scientific and Technical Information of China (English)

    董景荣; YANG; Jun; 等

    2002-01-01

    It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones,However,the literature on combining forecasts has almost exclusively focused on linear combining forecasts.In this paper,a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series.Furthermore,the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system.Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.

  4. Forecasting Model of Coal Requirement Quantity Based on Grey System Theory

    Institute of Scientific and Technical Information of China (English)

    孙继湖

    2001-01-01

    The generally used methods of forecasting coal requirement quantity include the analogy method, the outside-push method and the cause-effect analysis method. However, the precision of forecasting results using these methods is lower. This paper uses the grey system theory, and sets up grey forecasting model GM (1, 3) to coal requirement quantity. The forecasting result for the Chinese coal requirement quantity coincides with the actual values, and this shows that the model is reliable. Finally, this model are used to forecast Chinese coal requirement quantity in the future ten years.

  5. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Science.gov (United States)

    Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

    2017-09-01

    Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast

  6. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2017-09-01

    Full Text Available Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts from the GloSea5 model (1996 to 2009 are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region. Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 % in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows, whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70

  7. A High-Order CFS Algorithm for Clustering Big Data

    Directory of Open Access Journals (Sweden)

    Fanyu Bu

    2016-01-01

    Full Text Available With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i an adaptive dropout deep learning model to learn features from each type of data, (ii a feature tensor model to capture the correlations of heterogeneous data, and (iii a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

  8. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    Science.gov (United States)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  9. Better Forecasting for Better Planning: A Systems Approach.

    Science.gov (United States)

    Austin, W. Burnet

    Predictions and forecasts are the most critical features of rational planning as well as the most vulnerable to inaccuracy. Because plans are only as good as their forecasts, current planning procedures could be improved by greater forecasting accuracy. Economic factors explain and predict more than any other set of factors, making economic…

  10. Incompressible Modes Excited by Supersonic Shear in Boundary Layers: Acoustic CFS Instability

    Science.gov (United States)

    Belyaev, Mikhail A.

    2017-02-01

    We present an instability for exciting incompressible modes (e.g., gravity or Rossby modes) at the surface of a star accreting through a boundary layer. The instability excites a stellar mode by sourcing an acoustic wave in the disk at the boundary layer, which carries a flux of energy and angular momentum with the opposite sign as the energy and angular momentum density of the stellar mode. We call this instability the acoustic Chandrasekhar–Friedman–Schutz (CFS) instability, because of the direct analogy to the CFS instability for exciting modes on a rotating star by emission of energy in the form of gravitational waves. However, the acoustic CFS instability differs from its gravitational wave counterpart in that the fluid medium in which the acoustic wave propagates (i.e., the accretion disk) typically rotates faster than the star in which the incompressible mode is sourced. For this reason, the instability can operate even for a non-rotating star in the presence of an accretion disk. We discuss applications of our results to high-frequency quasi-periodic oscillations in accreting black hole and neutron star systems and dwarf nova oscillations in cataclysmic variables.

  11. MOCASSIM - an operational forecast system for the Portuguese coastal waters.

    Science.gov (United States)

    Vitorino, J.; Soares, C.; Almeida, S.; Rusu, E.; Pinto, J.

    2003-04-01

    An operational system for the forecast of oceanographic conditions off the Portuguese coast is presently being implemented at Instituto Hidrográfico (IH), in the framework of project MOCASSIM. The system is planned to use a broad range of observations provided both from IH observational networks (wave buoys, tidal gauges) and programs (hydrographic surveys, moorings) as well as from external sources. The MOCASSIM system integrates several numerical models which, combined, are intended to cover the relevant physical processes observed in the geographical areas of interest. At the present stage of development the system integrates a circulation module and a wave module. The circulation module is based on the Harvard Ocean Prediction System (HOPS), a primitive equation model formulated under the rigid lid assumption, which includes a data assimilation module. The wave module is based on the WaveWatch3 (WW3) model, which provides wave conditions in the North Atlantic basin, and on the SWAN model which is used to improve the wave forecasts on coastal or other specific areas of interest. The models use the meteorological forcing fields of a limited area model (ALADIN model) covering the Portuguese area, which are being provided in the framework of a close colaboration with Instituto de Meteorologia. Although still under devellopment, the MOCASSIM system has already been used in several operationnal contexts. These included the operational environmental assessment during both national and NATO navy exercises and, more recently, the monitoring of the oceanographic conditions in the NW Iberian area affected by the oil spill of MV "Prestige". The system is also a key component of ongoing research on the oceanography of the Portuguese continental margin, which is presently being conducted at IH in the framework of national and European funded projects.

  12. Hydrological-oriented verification for ensemble forecasting systems: the case of the PIT diagram

    Science.gov (United States)

    Bourgin, François; Ramos, Maria-Helena; Perrin, Charles; Renard, Benjamin

    2017-04-01

    The most common way to communicate uncertainty in streamflow predictions for water resources and risk management is through the use of ensemble scenarios or prediction intervals. While the advantages of probabilistic flow forecasting for decision-making are recognized, the evaluation of the quality of ensemble-based or probabilistic forecasts remains a challenge. Reliability is a fundamental attribute when evaluating the quality of probabilistic flow predictions. It is related to the statistical coherence of the associated uncertainty estimates. Reliable predictions are thus important for users who take actions based on prediction intervals (e.g., reservoir inflow volume forecasts) or on the forecast probability of a given critical event (e.g., exceedance of a flood threshold). However, forecast systems are usually developed to serve many users and, in general, they are evaluated without considering the user's specific decision-making problem. This means that a forecasting system must be reliable in all situations (for normal, high or low flows; for peak flow probabilities or volume probabilities of occurrence), regardless of the event of interest for the user. At the same time, users are often interested in knowing if a forecasting system performs well for their case of application. Application-focused evaluations of the quality of a forecast are thus also important to enhance the usefulness of a forecasting system. Here, we investigate the specificities of hydrological-oriented verification of reliability that is commonly assessed with the Probability Integral Transform (PIT) diagram. We applied an ensemble forecasting system to a large set of catchments in France to assess the impact of conditioning strategies used to stratifying the data on the evaluation of forecast performance. For example, we considered separating low and high flows, or focusing on rainfall-driven or recession parts of the hydrographs. We show that the use of conditioning strategies can

  13. An Intercomparison of Predicted Sea Ice Concentration from Global Ocean Forecast System & Arctic Cap Nowcast/Forecast System

    Science.gov (United States)

    Rosemond, K.

    2015-12-01

    The objective of this research is to provide an evaluation of improvements in marginal ice zone (MIZ) and pack ice estimations from the Global Ocean Forecast System (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/Forecast System (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational ice concentration data from the National Ice Center (NIC) MIZ analysis and the ice concentration estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models compares best to the NIC operational data stream needed for vessel support. It will also provide a quantitative assessment of GOFS and ACNFS performance and be used in the Operational Evaluation (OPEVAL) report from the NIC to NRL. The intercomparison results are based on statistical evaluations through a series of map overlays from both models ACNFS, GOFS with the NIC's MIZ data. All data was transformed to a common grid and difference maps were generated to determine which model had the greatest difference compared to the MIZ ice concentrations. This was provided daily for both the freeze-up and meltout seasons. Results indicated the GOFS model surpassed the ACNFS model, however both models were comparable. These results will help US Navy and NWS Anchorage ice forecasters understand model biases and know which model guidance is likely to provide the best estimate of future ice conditions.The objective of this research is to provide an evaluation of improvements in marginal ice zone (MIZ) and pack ice estimations from the Global Ocean Forecast System (GOFS) model compared to the current operational model, the Arctic Cap Nowcast/Forecast System (ACNFS). This will be determined by an intercomparison between the subjectively estimated operational ice concentration data from the National Ice Center (NIC) MIZ analysis and the ice concentration estimates from GOFS and ACNFS. This will help ascertain which nowcast from the models

  14. Verification of Global Radiation Forecasts from the Ensemble Prediction System at DMI

    DEFF Research Database (Denmark)

    Lundholm, Sisse Camilla

    consumption of the house and the amount of available solar energy. In order to make the most of this solar heating unit, accurate forecasts of the available solar radiation are esstential. However, because of its sensitivity to local meteorological conditions, the solar radiation received at the surface......To comply with an increasing demand for sustainable energy sources, a solar heating unit is being developed at the Technical University of Denmark. To make optimal use — environmentally and economically —, this heating unit is equipped with an intelligent control system using forecasts of the heat...... of the Earth can be highly fluctuating and challenging to forecast accurately. To comply with the accuracy requirements to forecasts of both global, direct, and diffuse radiation, the uncertainty of these forecasts is of interest. Forecast uncertainties can become accessible by running an ensemble of forecasts...

  15. Design and Application of an Expert System for Equipment Maintenance and Forecast

    Institute of Scientific and Technical Information of China (English)

    WANG Jian; PAN Kai-long; SHEN Yun-feng; LI Jie

    2007-01-01

    The maintenance and forecast expert system of equipment based on Artificial Neural Network is composed of control, measure, failure forecast, execution, data processing module and database. The data processing module obtains the change of the controlled objects′ structure and parameters, then takes correspondent measures according to the examination and diagnosis information. The failure forecast module finds the control system fault, separates the fault symptom location, tells the fault kind, estimates the magnitude and time of the fault, and finally makes evaluation and decision.

  16. Forecasting and observability: critical technologies for system operations with high PV penetration

    DEFF Research Database (Denmark)

    Alet, Pierre-Jean; Efthymiou, Venizelos; Graditi, Giorgio

    2016-01-01

    Forecasting and monitoring technologies for photovoltaics are required on different spatial and temporal scales by multiple actors, from the owners of PV systems to transmission system operators. In this paper the Grid integration working group of the European Technology and Innovation Platform...... – Photovoltaics (ETIP PV) reviews the different use cases for these technologies, their current status, and the need for future developments. Power system operations require a real-time view of PV production for managing power reserves and for feeding shortterm forecasts. They also require forecasts on all...... for a cost/benefit analysis since the forecasting error can be linked to the prices charged for energy imbalance...

  17. Evaluation of CFSV2 Forecast Skill for Indian Summer Monsoon Sub-Seasonal Characteristics

    Science.gov (United States)

    S, S. A.; Ghosh, S.

    2015-12-01

    Prediction of sub seasonal monsoon characteristics of Indian Summer Monsoon (ISM) is highly crucial for agricultural planning and water resource management. The Climate forecast System version 2 (CFS V2), the state of the art coupled climate model developed by NCEP, is currently being employed for the seasonal and extended range forecasts of ISM. Even though CFSV2 is a fully coupled ocean- atmosphere- land model with advanced physics, increased resolution and refined initialisation, its ISM forecasts, in terms of seasonal mean and variability needs improvement. Numerous works have been done for verifying the CFSV2 forecasts in terms of the seasonal mean, its mean and variability, active and break spells, and El Nino Southern Oscillation (ENSO) - monsoon interactions. Most of these works are based on either rain fall strength or rainfall based indices. Here we evaluate the skill of CFS v2 model in forecasting the various sub seasonal features of ISM, viz., the onset and withdrawal days of monsoon that are determined using circulation based indices, the Monsoon Intra Seasonal Oscillations (MISO), and Indian Ocean and Pacific Ocean sea surface temperatures. The MISO index, we use here, is based on zonal wind at 850 hPa and Outgoing Long wave Radiation (OLR) anomalies. With this work, we aim at assessing the skill of the model in simulating the large scale circulation patterns and their variabilities within the monsoon season. Variabilities in these large scale circulation patterns are primarily responsible for the variabilities in the seasonal monsoon strength and its temporal distribution across the season. We find that the model can better forecast the large scale circulation and than the actual precipitation. Hence we suggest that seasonal rainfall forecasts can be improved by the statistical downscaling of CFSV2 forecasts by incorporating the established relationships between the well forecasted large scale variables and monsoon precipitation.

  18. Operational flood forecasting system of Umbria Region "Functional Centre

    Science.gov (United States)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

  19. Towards an integrated forecasting system for pelagic fisheries

    DEFF Research Database (Denmark)

    Christensen, Asbjørn; Butenschön, Momme; Gürkan, Zeren

    First results of a coupled modelling and forecasting system for pelagic fisheries are presented. The system consists of three mathematically fundamentally different model subsystems: POLCOMSERSEM provides the physical–biogeochemical environment in the northwest European shelf, the Sandeel...... Population Analysis Model describes sandeel stocks in the North Sea, and the Sandeel Larval Analysis Model connects POLCOMS‐ERSEM and SPAM by computing the physical–biological interaction. Our main findings by coupling model subsystems is that well‐defined and generic model interfaces are very important...... for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher...

  20. Towards an integrated forecasting system for pelagic fisheries

    DEFF Research Database (Denmark)

    Christensen, Asbjørn; Butenschön, Momme; Gürkan, Zeren

    2012-01-01

    First results of a coupled modelling and forecasting system for pelagic fisheries are presented. The system consists of three mathematically fundamentally different model subsystems: POLCOMSERSEM provides the physical–biogeochemical environment in the northwest European shelf, the Sandeel...... Population Analysis Model describes sandeel stocks in the North Sea, and the Sandeel Larval Analysis Model connects POLCOMS‐ERSEM and SPAM by computing the physical–biological interaction. Our main findings by coupling model subsystems is that well‐defined and generic model interfaces are very important...... for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher...

  1. Nonparametric forecasting of low-dimensional dynamical systems.

    Science.gov (United States)

    Berry, Tyrus; Giannakis, Dimitrios; Harlim, John

    2015-03-01

    This paper presents a nonparametric modeling approach for forecasting stochastic dynamical systems on low-dimensional manifolds. The key idea is to represent the discrete shift maps on a smooth basis which can be obtained by the diffusion maps algorithm. In the limit of large data, this approach converges to a Galerkin projection of the semigroup solution to the underlying dynamics on a basis adapted to the invariant measure. This approach allows one to quantify uncertainties (in fact, evolve the probability distribution) for nontrivial dynamical systems with equation-free modeling. We verify our approach on various examples, ranging from an inhomogeneous anisotropic stochastic differential equation on a torus, the chaotic Lorenz three-dimensional model, and the Niño-3.4 data set which is used as a proxy of the El Niño Southern Oscillation.

  2. Hydrologic Severity-based Forecast System for Road Infrastructure Monitoring

    Science.gov (United States)

    Hernandez, F.; Li, L.; Lochan, S.; Liang, X.; Liang, Y.; Teng, W. L.

    2013-12-01

    The state departments of transportation in the U.S. are responsible for responding to weather- and hydrology-related emergencies affecting the transportation infrastructure, such as heavy rain, flooding, scouring of bridge structures, icing, and fog. These emergency response actions often require significant amount of effort to identify, inspect, and manage, e.g., potentially compromised bridges due to scouring. An online Hydrologic Disaster Forecasting and Response (HDFR) system is being developed for the Pennsylvania Department of Transportation (PennDOT), to provide more accurate estimates on current road infrastructure conditions. The HDFR system can automatically access satellite data from NASA data centers, NOAA radar rainfall measurements, and meteorological and hydrometeorological station observations. The accessed data can be fused, using an extended multi-scale Kalman smoother-based (MKS-based) algorithm to provide enhanced data products. The fused information is then contrasted with historical data, to assess the severity of the weather and hydrological conditions and to provide more accurate estimates of those areas with a high likelihood of being affected by similar emergencies. The real- and near-real-time data, as well as weather forecasts, are input to a multi-scale hydrological simulator. The HDFR system will be able to generate stream flow predictions at road-level scales, allowing for the monitoring of a complex and distributed infrastructure, with less computational resources than those previously required. Preliminary results will be presented that show the advantages of the HDFR system over PennDOT's current methods for identifying bridges in need of inspection.

  3. Satellite data assimilation in global forecast system in India

    Science.gov (United States)

    Basu, Swati

    2014-11-01

    Satellite data is very important for model initialization and verification. A large number of satellite observations are currently assimilated into the Numerical Weather Prediction (NWP) systems at the National Centre for Medium Range Weather Forecasting (NCMRWF). Apart from Global meteorological observations from GTS, near-real time satellite observations are received at NCMRWF from other operational centres like ISRO, NOAA/NESDIS, EUMETCAST, etc. Recently India has become member of Asia-Pacific Regional ATOVS Retransmission Service (APRARS) for faster access to high resolution global satellite data useful for high resolution regional models. Indian HRPT at Chennai covers the APRARS data gap region over South East Asia. A robust data monitoring system has been implemented at NCMRWF to assess the quantity and quality of the data as well as the satellite sensor strength, before getting assimilated in the models. Validation of new satellite observations, especially from Indian satellites are being carried out against insitu observations and similar space borne platforms. After establishing the quality of the data, Observation System Experiments (OSEs) are being conducted to study their impact in the assimilation and forecast systems. OSEs have been carried out with the Oceansat-2 scatterometer winds and radiance data from Megha-Tropiques SAPHIR sensor. Daily rainfall analysis dataset is being generated by merging satellite estimates and in-situ observations. ASCAT soil wetness measurements from METOP satellite is being assimilated into the global model. Land surface parameters (LuLc and albedo) retrieved from Indian satellites are being explored for its possible usage in the global and regional models. OLR from Indian satellites are used for validating model outputs. This paper reviews the efforts made at NCMRWF in (i) assimilating the data from Indian/International satellites and (ii) generating useful products from the satellite data.

  4. Rough Precipitation Forecasts based on Analogue Method: an Operational System

    Science.gov (United States)

    Raffa, Mario; Mercogliano, Paola; Lacressonnière, Gwendoline; Guillaume, Bruno; Deandreis, Céline; Castanier, Pierre

    2017-04-01

    In the framework of the Climate KIC partnership, has been funded the project Wat-Ener-Cast (WEC), coordinated by ARIA Technologies, having the goal to adapt, through tailored weather-related forecast, the water and energy operations to the increased weather fluctuation and to climate change. The WEC products allow providing high quality forecast suited in risk and opportunities assessment dashboard for water and energy operational decisions and addressing the needs of sewage/water distribution operators, energy transport & distribution system operators, energy manager and wind energy producers. A common "energy water" web platform, able to interface with newest smart water-energy IT network have been developed. The main benefit by sharing resources through the "WEC platform" is the possibility to optimize the cost and the procedures of safety and maintenance team, in case of alerts and, finally to reduce overflows. Among the different services implemented on the WEC platform, ARIA have developed a product having the goal to support sewage/water distribution operators, based on a gradual forecast information system ( at 48hrs/24hrs/12hrs horizons) of heavy precipitation. For each fixed deadline different type of operation are implemented: 1) 48hour horizon, organisation of "on call team", 2) 24 hour horizon, update and confirm the "on call team", 3) 12 hour horizon, secure human resources and equipment (emptying storage basins, pipes manipulations …). More specifically CMCC have provided a statistical downscaling method in order to provide a "rough" daily local precipitation at 24 hours, especially when high precipitation values are expected. This statistical technique consists of an adaptation of analogue method based on ECMWF data (analysis and forecast at 24 hours). One of the most advantages of this technique concerns a lower computational burden and budget compared to running a Numerical Weather Prediction (NWP) model, also if, of course it provides only this

  5. Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.

    1995-08-01

    In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.

  6. Towards an integrated forecasting system for pelagic fisheries

    Directory of Open Access Journals (Sweden)

    A. Christensen

    2012-03-01

    Full Text Available First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2–6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

  7. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

    Science.gov (United States)

    2016-06-07

    terms of the standard deviations (computed over 10 ensemble members) of sea surface height ( SSH ; top panel) and sea surface temperature (SST; bottom...mean SSH forecast (top panel) and SSH standard deviation of the ensemble forecast (bottom panel) on day 10 of the forecast. The concentration of...spread for SSH (top panel; cm) and SST (bottom panel; ° C). The standard deviation of the SSH and SST are computed at day 14 of the data assimilation

  8. Severe versus Moderate Criteria for the New Pediatric Case Definition for ME/CFS

    Science.gov (United States)

    Jason, Leonard; Porter, Nicole; Shelleby, Elizabeth; Till, Lindsay; Bell, David S.; Lapp, Charles W.; Rowe, Kathy; De Meirleir, Kenny

    2009-01-01

    The new diagnostic criteria for pediatric ME/CFS are structurally based on the Canadian Clinical Adult case definition, and have more required specific symptoms than the (Fukuda et al. Ann Intern Med 121:953-959, 1994) adult case definition. Physicians specializing in pediatric ME/CFS referred thirty-three pediatric patients with ME/CFS and 21…

  9. Living with ME/CFS. challenge for scientists?

    Science.gov (United States)

    Weiler, John J A

    2015-01-01

    A Graded Exercise Therapy (GET) - Myalgic Encephalomyelitis (ME)/Chronic Fatigue Syndrome (CFS) Running Anomaly. Imagine you have been disabled with ME/CFS's cluster of symptoms for 19 years. Yet, this morning you just ran an easy 10K with no flare up of your exercise intolerance symptoms during the run or post-exertional malaise after the run. Then later in the day you go browsing for books and after 30 minutes or so your exercise intolerance and post-exertional malaise symptoms flare up. You experience a wave of exhaustion, achy muscles and additional cognitive fog, all of which carry into the next day. To me, this is a confusing anomaly that needs an explanation.

  10. Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Madsen, Henrik; Nielsen, Torben Skov

    2006-01-01

    speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field...

  11. Development of multimodel ensemble based district level medium range rainfall forecast system for Indian region

    Indian Academy of Sciences (India)

    S K Roy Bhowmik; V R Durai

    2012-04-01

    India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25° × 0.25° utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

  12. A Pilot Tsunami Inundation Forecast System for Australia

    Science.gov (United States)

    Allen, Stewart C. R.; Greenslade, Diana J. M.

    2016-12-01

    The Joint Australian Tsunami Warning Centre (JATWC) provides a tsunami warning service for Australia. Warnings are currently issued according to a technique that does not include explicit modelling at the coastline, including any potential coastal inundation. This paper investigates the feasibility of developing and implementing tsunami inundation modelling as part of the JATWC warning system. An inundation model was developed for a site in Southeast Australia, on the basis of the availability of bathymetric and topographic data and observations of past tsunamis. The model was forced using data from T2, the operational deep-water tsunami scenario database currently used for generating warnings. The model was evaluated not only for its accuracy but also for its computational speed, particularly with respect to operational applications. Limitations of the proposed forecast processes in the Australian context and areas requiring future improvement are discussed.

  13. Inferential, non-parametric statistics to assess the quality of probabilistic forecast systems

    NARCIS (Netherlands)

    Maia, A.H.N.; Meinke, H.B.; Lennox, S.; Stone, R.C.

    2007-01-01

    Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must al

  14. Inferential, non-parametric statistics to assess the quality of probabilistic forecast systems

    NARCIS (Netherlands)

    Maia, A.H.N.; Meinke, H.B.; Lennox, S.; Stone, R.C.

    2007-01-01

    Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must al

  15. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    NARCIS (Netherlands)

    Candogan Yossef, Naze; Beek, Van Rens; Weerts, Albrecht; Winsemius, Hessel; Bierkens, Marc F.P.

    2017-01-01

    In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS).

  16. Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea

    Directory of Open Access Journals (Sweden)

    Chihyun Jung

    2016-03-01

    Full Text Available We present a case study of the development of an adaptive forecasting system for a leading personal computer (PC manufacturer in South Korea. It is widely accepted that demand forecasting for products with short product life cycles (PLCs is difficult, and the PLC of a PC is generally very short. The firm has various types of products, and the volatile demand patterns differ by product. Moreover, we found that different departments have different requirements when it comes to the accuracy, point-of-time and range of the forecasts. We divide the demand forecasting process into three stages depending on the requirements and purposes. The systematic forecasting process is then introduced to improve the accuracy of demand forecasting and to meet the department-specific requirements. Moreover, a newly devised short-term forecasting method is presented, which utilizes the long-term forecasting results of the preceding stages. We evaluate our systematic forecasting methods based on actual sales data from the PC manufacturer, where our forecasting methods have been implemented.

  17. Modelled operation of the Shetlands Islands power system comparing computational and human operators` load forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Hill, D.C. [University Coll. of North Wales, Menai Bridge (United Kingdom). School of Ocean Science; Infield, D.G. [Loughborough Univ. of Technology (United Kingdom). Dept. of Electronic and Electrical Engineering

    1995-11-01

    A load forecasting technique, based upon an autoregressive (AR) method is presented. Its use for short term load forecasting is assessed by direct comparison with real forecasts made by human operators of the Lerwick power station on the Shetland Islands. A substantial improvement in load prediction, as measured by a reduction of RMS error, is demonstrated. Shetland has a total installed capacity of about 68 MW, and an average load (1990) of around 20 MW. Although the operators could forecast the load for a few distinct hours better than the AR method, results from simulations of the scheduling and operation of the generating plant show that the AR forecasts provide increased overall system performance. A detailed model of the island power system, which includes plant scheduling, was run using the AR and Lerwick operators` forecasts as input to the scheduling routine. A reduction in plant cycling, underloading and fuel consumption was obtained using the AR forecasts rather than the operators` forecasts in simulations over a 28 day study period. It is concluded that the load forecasting method presented could be of benefit to the operators of such mesoscale power systems. (author)

  18. Short-term wind-speed forecasting system for wind power applications

    Science.gov (United States)

    Traiteur, J. J.; Baidya Roy, S.

    2010-12-01

    Accurate short-term wind speed forecasts for utility-scale large wind farms will be crucial for the U.S. Department of Energy's goal of providing 20% of total electricity from wind by 2030. Communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate system for short-term (0-60 mins) forecasting of wind speed is developed. This system uses a 27 member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM) to generate a probability density function (pdf) of daytime forecasts at 90m height for a location in Chalmers Township in West/Central Illinois. The WRF-SCM ensemble is initialized by the 20km Rapid Update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The pdf is calibrated using Bayesian Model Averaging (BMA) where the individual forecasts are weighted according to their performance. This combination of a numerical weather prediction ensemble system and Bayesian statistics allows for accurate and computationally efficient prediction of 1 hour wind speed and the level of uncertainty in the forecasts.

  19. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  20. Forecasting and observability: critical technologies for system operations with high PV penetration

    DEFF Research Database (Denmark)

    Alet, Pierre-Jean; Efthymiou, Venizelos; Graditi, Giorgio

    2016-01-01

    Forecasting and monitoring technologies for photovoltaics are required on different spatial and temporal scales by multiple actors, from the owners of PV systems to transmission system operators. In this paper the Grid integration working group of the European Technology and Innovation Platform –...... for a cost/benefit analysis since the forecasting error can be linked to the prices charged for energy imbalance......Forecasting and monitoring technologies for photovoltaics are required on different spatial and temporal scales by multiple actors, from the owners of PV systems to transmission system operators. In this paper the Grid integration working group of the European Technology and Innovation Platform...... – Photovoltaics (ETIP PV) reviews the different use cases for these technologies, their current status, and the need for future developments. Power system operations require a real-time view of PV production for managing power reserves and for feeding shortterm forecasts. They also require forecasts on all...

  1. Mediterranea Forecasting System: a focus on wave-current coupling

    Science.gov (United States)

    Clementi, Emanuela; Delrosso, Damiano; Pistoia, Jenny; Drudi, Massimiliano; Fratianni, Claudia; Grandi, Alessandro; Pinardi, Nadia; Oddo, Paolo; Tonani, Marina

    2016-04-01

    The Mediterranean Forecasting System (MFS) is a numerical ocean prediction system that produces analyses, reanalyses and short term forecasts for the entire Mediterranean Sea and its Atlantic Ocean adjacent areas. MFS became operational in the late 90's and has been developed and continuously improved in the framework of a series of EU and National funded programs and is now part of the Copernicus Marine Service. The MFS is composed by the hydrodynamic model NEMO (Nucleus for European Modelling of the Ocean) 2-way coupled with the third generation wave model WW3 (WaveWatchIII) implemented in the Mediterranean Sea with 1/16 horizontal resolution and forced by ECMWF atmospheric fields. The model solutions are corrected by the data assimilation system (3D variational scheme adapted to the oceanic assimilation problem) with a daily assimilation cycle, using a background error correlation matrix varying seasonally and in different sub-regions of the Mediterranean Sea. The focus of this work is to present the latest modelling system upgrades and the related achieved improvements. In order to evaluate the performance of the coupled system a set of experiments has been built by coupling the wave and circulation models that hourly exchange the following fields: the sea surface currents and air-sea temperature difference are transferred from NEMO model to WW3 model modifying respectively the mean momentum transfer of waves and the wind speed stability parameter; while the neutral drag coefficient computed by WW3 model is passed to NEMO that computes the turbulent component. In order to validate the modelling system, numerical results have been compared with in-situ and remote sensing data. This work suggests that a coupled model might be capable of a better description of wave-current interactions, in particular feedback from the ocean to the waves might assess an improvement on the prediction capability of wave characteristics, while suggests to proceed toward a fully

  2. Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

    Science.gov (United States)

    Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

    2016-12-01

    Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

  3. Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems

    DEFF Research Database (Denmark)

    Löwe, Roland; Vezzaro, Luca; Mikkelsen, Peter Steen

    2016-01-01

    This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer...... overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy...... smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only....

  4. The Eruption Forecasting Information System (EFIS) database project

    Science.gov (United States)

    Ogburn, Sarah; Harpel, Chris; Pesicek, Jeremy; Wellik, Jay; Pallister, John; Wright, Heather

    2016-04-01

    The Eruption Forecasting Information System (EFIS) project is a new initiative of the U.S. Geological Survey-USAID Volcano Disaster Assistance Program (VDAP) with the goal of enhancing VDAP's ability to forecast the outcome of volcanic unrest. The EFIS project seeks to: (1) Move away from relying on the collective memory to probability estimation using databases (2) Create databases useful for pattern recognition and for answering common VDAP questions; e.g. how commonly does unrest lead to eruption? how commonly do phreatic eruptions portend magmatic eruptions and what is the range of antecedence times? (3) Create generic probabilistic event trees using global data for different volcano 'types' (4) Create background, volcano-specific, probabilistic event trees for frequently active or particularly hazardous volcanoes in advance of a crisis (5) Quantify and communicate uncertainty in probabilities A major component of the project is the global EFIS relational database, which contains multiple modules designed to aid in the construction of probabilistic event trees and to answer common questions that arise during volcanic crises. The primary module contains chronologies of volcanic unrest, including the timing of phreatic eruptions, column heights, eruptive products, etc. and will be initially populated using chronicles of eruptive activity from Alaskan volcanic eruptions in the GeoDIVA database (Cameron et al. 2013). This database module allows us to query across other global databases such as the WOVOdat database of monitoring data and the Smithsonian Institution's Global Volcanism Program (GVP) database of eruptive histories and volcano information. The EFIS database is in the early stages of development and population; thus, this contribution also serves as a request for feedback from the community.

  5. A production throughput forecasting system in an automated hard disk drive test operation using GRNN

    Energy Technology Data Exchange (ETDEWEB)

    Samattapapong, N.; Afzulpurkar, N.

    2016-07-01

    The goal of this paper is to develop a pragmatic system of a production throughput forecasting system for an automated test operation in a hard drive manufacturing plant. The accurate forecasting result is necessary for the management team to response to any changes in the production processes and the resources allocations. In this study, we design a production throughput forecasting system in an automated test operation in hard drive manufacturing plant. In the proposed system, consists of three main stages. In the first stage, a mutual information method was adopted for selecting the relevant inputs into the forecasting model. In the second stage, a generalized regression neural network (GRNN) was implemented in the forecasting model development phase. Finally, forecasting accuracy was improved by searching the optimal smoothing parameter which selected from comparisons result among three optimization algorithms: particle swarm optimization (PSO), unrestricted search optimization (USO) and interval halving optimization (IHO). The experimental result shows that (1) the developed production throughput forecasting system using GRNN is able to provide forecasted results close to actual values, and to projected the future trends of production throughput in an automated hard disk drive test operation; (2) An IHO algorithm performed as superiority appropriate optimization method than the other two algorithms. (3) Compared with current forecasting system in manufacturing, the results show that the proposed system’s performance is superior to the current system in prediction accuracy and suitable for real-world application. The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. Because of the production throughput forecasting result is useful information for management team to respond to any changing in production processes and resources allocation. However, a practically forecasting system for

  6. A production throughput forecasting system in an automated hard disk drive test operation using GRNN

    Directory of Open Access Journals (Sweden)

    Nara Samattapapong

    2016-04-01

    Originality/value: The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. Because of the production throughput forecasting result is useful information for management team to respond to any changing in production processes and resources allocation. However, a practically forecasting system for production throughput has not been described in detail yet. The experiments were conducted on a real data set from the final testing operation of hard disk drive manufacturing factory by using Visual Basics Application on Microsoft Excel© to develop preliminary forecasting system on testing and verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system.

  7. An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    Directory of Open Access Journals (Sweden)

    J. A. Velázquez

    2009-07-01

    Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

    In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  8. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    Directory of Open Access Journals (Sweden)

    F. Anctil

    2009-11-01

    Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

    In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  9. Intelligent forecasting compensatory control system for profile machining

    Science.gov (United States)

    Fung, Eric H. K.; Chuen, C. W.; Lee, L. M.

    2000-10-01

    Precision machining is becoming increasingly important in modern industry because many modern products require high form accuracy. An affordable approach to improve the accuracy of the surface profile of a workpiece is to adopt the on-line error forecasting and compensation control (FCC) techniques. In the present study, the consideration of variation of cutting force as a result of piezoactuator movement requires the formulation of ARMAX models. The time-series analysis based on ARMAX technique has an advantage over the traditional spectral method in that the latter can lead to the over-parameterization of the accompanying model. The roundness measurement results obtained from the practical experiments and the derived improvement percentages are grouped under one or more of the system parameters which include the ARMAX orders, feed rate, depth of cut, material, and forgetting factor. An expert system has been successfully developed to implement the rules using the Prolog language for helping the users to select suitable parameters for the FCC system of the lathe machine. Based on the measurement data, it can be shown that the lathe machine, when equipped with the ARMAX-based FCC system, can yield a minimum value of average improvement of 26% under the testing conditions.

  10. Forecasting of Hourly Photovoltaic Energy in Canarian Electrical System

    Science.gov (United States)

    Henriquez, D.; Castaño, C.; Nebot, R.; Piernavieja, G.; Rodriguez, A.

    2010-09-01

    The Canarian Archipelago face similar problems as most insular region lacking of endogenous conventional energy resources and not connected to continental electrical grids. A consequence of the "insular fact" is the existence of isolated electrical systems that are very difficult to interconnect due to the considerable sea depths between the islands. Currently, the Canary Islands have six isolated electrical systems, only one utility generating most of the electricity (burning fuel), a recently arrived TSO (REE) and still a low implementation of Renewable Energy Resources (RES). The low level of RES deployment is a consequence of two main facts: the weakness of the stand-alone grids (from 12 MW in El Hierro up to only 1 GW in Gran Canaria) and the lack of space to install RES systems (more than 50% of the land protected due to environmental reasons). To increase the penetration of renewable energy generation, like solar or wind energy, is necessary to develop tools to manage them. The penetration of non manageable sources into weak grids like the Canarian ones causes a big problem to the grid operator. There are currently 104 MW of PV connected to the islands grids (Dec. 2009) and additional 150 MW under licensing. This power presents a serious challenge for the operation and stability of the electrical system. ITC, together with the local TSO (Red Eléctrica de España, REE) started in 2008 and R&D project to develop a PV energy prediction tool for the six Canarian Insular electrical systems. The objective is to supply reliable information for hourly forecast of the generation dispatch programme and to predict daily solar radiation patterns, in order to help program spinning reserves. ITC has approached the task of weather forecasting using different numerical model (MM5 and WRF) in combination with MSG (Meteosat Second Generation) images. From the online data recorded at several monitored PV plants and meteorological stations, PV nominal power and energy produced

  11. Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

    Science.gov (United States)

    Chowdary, J. S.; Chaudhari, H. S.; Gnanaseelan, C.; Parekh, Anant; Suryachandra Rao, A.; Sreenivas, P.; Pokhrel, S.; Singh, P.

    2014-04-01

    This study investigates the El Niño Southern Oscillation (ENSO) teleconnections to tropical Indian Ocean (TIO) and their relationship with the Indian summer monsoon in the coupled general circulation model climate forecast system (CFS). The model shows good skill in simulating the impact of El Niño over the Indian Oceanic rim during its decay phase (the summer following peak phase of El Niño). Summer surface circulation patterns during the developing phase of El Niño are more influenced by local Sea Surface Temperature (SST) anomalies in the model unlike in observations. Eastern TIO cooling similar to that of Indian Ocean Dipole (IOD) is a dominant model feature in summer. This anomalous SST pattern therefore is attributed to the tendency of the model to simulate more frequent IOD events. On the other hand, in the model baroclinic response to the diabatic heating anomalies induced by the El Niño related warm SSTs is weak, resulting in reduced zonal extension of the Rossby wave response. This is mostly due to weak eastern Pacific summer time SST anomalies in the model during the developing phase of El Niño as compared to observations. Both eastern TIO cooling and weak SST warming in El Niño region combined together undermine the ENSO teleconnections to the TIO and south Asia regions. The model is able to capture the spatial patterns of SST, circulation and precipitation well during the decay phase of El Niño over the Indo-western Pacific including the typical spring asymmetric mode and summer basin-wide warming in TIO. The model simulated El Niño decay one or two seasons later, resulting long persistent warm SST and circulation anomalies mainly over the southwest TIO. In response to the late decay of El Niño, Ekman pumping shows two maxima over the southern TIO. In conjunction with this unrealistic Ekman pumping, westward propagating Rossby waves display two peaks, which play key role in the long-persistence of the TIO warming in the model (for more than a

  12. Evaluation of ECMWF System 4 product for ensemble streamflow forecast in Upper Hanjiang River Basin

    Science.gov (United States)

    Li, Yilu; Tian, Fuqiang

    2017-04-01

    This study attempts to investigate the application of ECMWF System 4 forecast dataset for long term streamflow forecasts with the lead time of 0-2 months in China. The case study is Upper Hanjiang River Basin (UHRB), where forecast results are essential for the central route of South to North Water Diversion Project (SNWDP) in China. A semi-distributed hydrological model (THREW) was applied to simulate the rainfall-runoff processes over the UHRB during the period of 2001-2008. The accuracy of streamflow prediction decreases with lead time, while it is no significant relationship with the drainage areas. All the stations become more reliable as lead time increases, but the Yangxian station shows less reliable than others. The forecast uncertainty is effectively estimated by applying the ECMWF System 4 forecast dataset for the ensemble streamflow forecasts. Significant differences in the performance of ECMWF system 4 are found in seasonal predictions. The forecast is more skillful in Post-dry season than otherwise in term of accuracy and reliability. This study will broaden the application field of ECMWF System 4 dataset to long term streamflow forecast for similar climate region. The results would provide effective guidelines for reservoir operation and be helpful for potential users to employ ECMWF System 4 dataset in other basins over China.

  13. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

    Energy Technology Data Exchange (ETDEWEB)

    Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

    2016-04-01

    This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  14. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    Science.gov (United States)

    Candogan Yossef, Naze; van Beek, Rens; Weerts, Albrecht; Winsemius, Hessel; Bierkens, Marc F. P.

    2017-08-01

    In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

  15. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    Directory of Open Access Journals (Sweden)

    N. Candogan Yossef

    2017-08-01

    Full Text Available In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS. FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance. We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

  16. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through be

  17. Performance of the ocean state forecast system at Indian National Centre for Ocean Information Services

    Digital Repository Service at National Institute of Oceanography (India)

    Nair, T.M.B.; Sirisha, P.; Sandhya, K.G.; Srinivas, K.; SanilKumar, V.; Sabique, L.; Nherakkol, A.; KrishnaPrasad, B.; RakhiKumari; Jeyakumar, C.; Kaviyazhahu, K.; RameshKumar, M.; Harikumar, R.; Shenoi, S.S.C.; Nayak, S.

    -dation of the same is done using real-time automated observation systems. The validation results indicate that the forecasted wave parameters agree well with the measurements. The feedback from the user community indicates that the forecast was reliable and highly...

  18. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through be

  19. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through

  20. Seasonal prediction for Southern Africa: Maximising the skill from forecast systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-06-01

    Full Text Available The development of seasonal forecast systems for southern Africa is presented, followed by the justification why certain model configurations have been selected in order to optimise seasonal forecast performance. The skill of the HadGEM3 coupled...

  1. AIRS impact on the analysis and forecast track of tropical cyclone Nargis in a global data assimilation and forecasting system

    Science.gov (United States)

    Reale, O.; Lau, W. K.; Susskind, J.; Brin, E.; Liu, E.; Riishojgaard, L. P.; Fuentes, M.; Rosenberg, R.

    2009-03-01

    Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. Moreover, the automated analyses of cyclones over the northern Indian Ocean, produced by operational global data assimilation systems (DASs), are generally of inferior quality than in other basins, partly because of asymmetric data distribution and the absence of targeted observations inside cyclones. In this work it is shown that the assimilation of Atmospheric Infrared Sounder (AIRS) temperature retrievals under partial cloudy conditions can significantly impact the representation of the cyclone Nargis (which caused devastating loss of life in Myanmar in May 2008) in a global DAS. Forecasts produced from these improved analyses by a global model produce substantially smaller track errors. The impact of the assimilation of clear-sky radiances on the same DAS and forecasting system is positive, but smaller than the one obtained by ingestion of AIRS retrievals, probably due to poorer coverage.

  2. Operational Solar Forecasting System for DNI and GHI for Horizons Covering 5 Minutes to 72 Hours

    Science.gov (United States)

    Coimbra, C. F.

    2014-12-01

    I will describe the methodology used to develop and deploy operationally a comprehensive solar forecasting system for both concentrated and non-concentrated solar technologies. This operational forecasting system ingests data from local telemetry, remote sensing and Numerical Weather Prediction (NWP) models, processes all the diferent types of data (time series, sky images, satellite images, gridded data, etc.) to produce concatenated solar forecasts from 5 minutes out to 72 hours into the future. Each forecast is optimized with stochastic learning techniques that include input selection, model topology optimization, model output statistics, metric fitness optimization and machine learning. These forecasts are used by solar generators (plant managers), utilities and independent system operators for operations, scheduling, dispatching and market participation.

  3. Tropospheric chemistry in the integrated forecasting system of ECMWF

    Directory of Open Access Journals (Sweden)

    J. Flemming

    2014-11-01

    Full Text Available A representation of atmospheric chemistry has been included in the Integrated Forecasting System (IFS of the European Centre for Medium-range Weather Forecasts (ECMWF. The new chemistry modules complement the aerosol modules of the IFS for atmospheric composition, which is named C-IFS. C-IFS for chemistry supersedes a coupled system, in which the Chemical Transport Model (CTM Model for OZone and Related chemical Tracers 3 was two-way coupled to the IFS (IFS-MOZART. This paper contains a description of the new on-line implementation, an evaluation with observations and a comparison of the performance of C-IFS with MOZART and with a re-analysis of atmospheric composition produced by IFS-MOZART within the Monitoring Atmospheric Composition and Climate (MACC project. The chemical mechanism of C-IFS is an extended version of the Carbon Bond 2005 (CB05 chemical mechanism as implemented in the CTM Transport Model 5 (TM5. CB05 describes tropospheric chemistry with 54 species and 126 reactions. Wet deposition and lightning nitrogen monoxide (NO emissions are modelled in C-IFS using the detailed input of the IFS physics package. A one-year simulation by C-IFS, MOZART and the MACC re-analysis is evaluated against ozonesondes, carbon monoxide (CO aircraft profiles, European surface observations of ozone (O3, CO, sulphur dioxide (SO2 and nitrogen dioxide (NO2 as well as satellite retrievals of CO, tropospheric NO2 and formaldehyde. Anthropogenic emissions from the MACC/CityZen (MACCity inventory and biomass burning emissions from the Global Fire Assimilation System (GFAS data set were used in the simulations by both C-IFS and MOZART. C-IFS (CB05 showed an improved performance with respect to MOZART for CO, upper tropospheric O3, winter time SO2 and was of a similar accuracy for other evaluated species. C-IFS (CB05 is about ten times more computationally efficient than IFS-MOZART.

  4. Migraine headaches in Chronic Fatigue Syndrome (CFS: Comparison of two prospective cross-sectional studies

    Directory of Open Access Journals (Sweden)

    Merck Samantha J

    2011-03-01

    Full Text Available Abstract Background Headaches are more frequent in Chronic Fatigue Syndrome (CFS than healthy control (HC subjects. The 2004 International Headache Society (IHS criteria were used to define CFS headache phenotypes. Methods Subjects in Cohort 1 (HC = 368; CFS = 203 completed questionnaires about many diverse symptoms by giving nominal (yes/no answers. Cohort 2 (HC = 21; CFS = 67 had more focused evaluations. They scored symptom severities on 0 to 4 anchored ordinal scales, and had structured headache evaluations. All subjects had history and physical examinations; assessments for exclusion criteria; questionnaires about CFS related symptoms (0 to 4 scale, Multidimensional Fatigue Inventory (MFI and Medical Outcome Survey Short Form 36 (MOS SF-36. Results Demographics, trends for the number of diffuse "functional" symptoms present, and severity of CFS case designation criteria symptoms were equivalent between CFS subjects in Cohorts 1 and 2. HC had significantly fewer symptoms, lower MFI and higher SF-36 domain scores than CFS in both cohorts. Migraine headaches were found in 84%, and tension-type headaches in 81% of Cohort 2 CFS. This compared to 5% and 45%, respectively, in HC. The CFS group had migraine without aura (60%; MO; CFS+MO, with aura (24%; CFS+MA, tension headaches only (12%, or no headaches (4%. Co-morbid tension and migraine headaches were found in 67% of CFS. CFS+MA had higher severity scores than CFS+MO for the sum of scores for poor memory, dizziness, balance, and numbness ("Neuro-construct", p = 0.002 and perceived heart rhythm disturbances, palpitations and noncardiac chest pain ("Cardio-construct"; p = 0.045, t-tests after Bonferroni corrections. CFS+MO subjects had lower pressure-induced pain thresholds (2.36 kg [1.95-2.78; 95% C.I.] n = 40 and a higher prevalence of fibromyalgia (47%; 1990 criteria compared to HC (5.23 kg [3.95-6.52] n = 20; and 0%, respectively. Sumatriptan was beneficial for 13 out of 14 newly diagnosed

  5. Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system

    Indian Academy of Sciences (India)

    V R Durai; Rashmi Bhardwaj

    2014-07-01

    The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May–August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.

  6. System Dynamics Approach to Urban Water Demand Forecasting A Case Study of Tianjin

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongwei; ZHANG Xuehua; ZHANG Baoan

    2009-01-01

    A system dynamics approach to urban water demand forecasting was developed based on the analysis of urban water resources system.which was characterized by multi.feedback and nonlinear interactions among system elements.As an example,Tianjin water resources system dynamic model was set up to forecast water resources demand of the planning years.The practical verification showed that the relative error was lower than 1O%.Furthermore,through the comparison and analysis of the simulation results under different development modes presented in this paper.the forecasting results ofthe water resources demand ofTianiin was achieved based on sustainable utilization strategy of water resources.

  7. A Global Hydrological Ensemble Forecasting System: Uncertainty Quantification and Data Assimilation

    Science.gov (United States)

    Hong, Y.; Zhang, Y.; Xue, X.; Wang, X.; Gourley, J. J.; Kirstetter, P.

    2012-12-01

    A Global Hydrological Ensemble Forecasting System (GHEFS) driven by TRMM Multi-satellite Prediction Analysis (TMPA) precipitation ensembles and Global Ensemble Forecast System (GEFS) Quantitative Precipitation Forecast (QPF) ensembles, via the Coupled Routing and Excess STorage (CREST) distributed hydrological model, provides deterministic and probabilistic (e.g. 95% confidence boundaries) simulations of streamflow. The TMPA inputs enable flood monitoring and short-term forecasts while the GEFS ensembles provide for forecasts up to a seven-day lead time. This talk will focus on a quantification of the system's uncertainty and streamflow ensemble prediction generation using the following three techniques: 1) an error model that first quantifies and then perturbs both temporal and spatial variability of the real-time, TMPA precipitation estimates by considering the version-7 research product as the reference rainfall product; 2) in forecast mode, utilization of the Ensemble Transform method to account for the uncertainty of GEFS forecasts from its initial condition errors; 3) a sequential data assimilation approach - the Ensemble Square Root Kalman Filter (EnSRF) applied to update the CREST model's internal states whenever observations (e.g. streamflow, soil moisture, and actual ET etc.) are available. The GHEFS is validated in several basins in the U.S. and other continents in terms of flood detection capability (e.g. CSI, NSCE, Peak, Timing), showing improved prognostic capability by offering more time for responding agencies and yielding unique uncertainty information about the magnitude of the forecast impacts.

  8. The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

    Science.gov (United States)

    Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

  9. Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

    KAUST Repository

    Xie, Le

    2014-01-01

    We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

  10. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    Energy Technology Data Exchange (ETDEWEB)

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  11. Mediterranean monitoring and forecasting operational system for Copernicus Marine Service

    Science.gov (United States)

    Coppini, Giovanni; Drudi, Massimiliano; Korres, Gerasimos; Fratianni, Claudia; Salon, Stefano; Cossarini, Gianpiero; Clementi, Emanuela; Zacharioudaki, Anna; Grandi, Alessandro; Delrosso, Damiano; Pistoia, Jenny; Solidoro, Cosimo; Pinardi, Nadia; Lecci, Rita; Agostini, Paola; Cretì, Sergio; Turrisi, Giuseppe; Palermo, Francesco; Konstantinidou, Anna; Storto, Andrea; Simoncelli, Simona; Di Pietro, Pier Luigi; Masina, Simona; Ciliberti, Stefania Angela; Ravdas, Michalis; Mancini, Marco; Aloisio, Giovanni; Fiore, Sandro; Buonocore, Mauro

    2016-04-01

    The MEDiterranean Monitoring and Forecasting Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC system provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational system developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days forecasts and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and

  12. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

    Science.gov (United States)

    2016-06-07

    year. Our goal is to develop an ensemble ocean forecast methodology, using Bayesian Hierarchical Modelling (BHM) tools . The ocean ensemble forecast...from above); i.e. we assume Ut ~ Z Λt1/2. WORK COMPLETED The prototype MFS-Wind-BHM was designed and implemented based on stochastic...coding refinements we implemented on the prototype surface wind BHM. A DWF event in February 2005, in the Gulf of Lions, was identified for reforecast

  13. Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

    Science.gov (United States)

    Prive, N. C.; Errico, Ronald M.

    2015-01-01

    The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

  14. Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

    Science.gov (United States)

    Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

    2017-03-15

    As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Impacts of Short-Term Solar Power Forecasts in System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias; Ela, Erik

    2016-05-05

    Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-day operations.

  16. An innovative forecasting and dashboard system for Malaysian dengue trends

    Science.gov (United States)

    Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

    2016-08-01

    Dengue fever has been recognized in over 100 countries and 2.5 billion people live in areas where dengue is endemic. It is currently a serious arthropod-borne disease, affecting around 50-100 million people worldwide every year. Dengue fever is also prevalent in Malaysia with numerous cases including mortality recorded over the past year. In 2012, a total of 21,900 cases of dengue fever were reported with 35 deaths. Dengue, a mosquito-transmitted virus, causes a high fever accompanied by significant pain in afflicted patient and the Aedes Aegypti mosquito is the primary disease carrier. Knowing the dangerous effect of dengue fever, thus one of the solutions is to implement an innovative forecasting and dashboard system of dengue spread in Malaysia, with emphasize on an early prediction of dengue outbreak. Specifically, the model developed will provide with a valuable insight into strategically managing and controlling the future dengue epidemic. Importantly, this research will deliver the message to health policy makers such as The Ministry of Health Malaysia (MOH), practitioners, and researchers of the importance to integrate their collaboration in exploring the potential strategies in order to reduce the future burden of the increase in dengue transmission cases in Malaysia.

  17. Quasi-Operational Coastal Ocean Nowcast/Forecast Systems

    Directory of Open Access Journals (Sweden)

    Christopher N. K. Mooers

    2010-01-01

    Full Text Available For several years, quasi-operational (i.e., real-time, semi-autonomous, research-mode nowcast/forecast systems have been run in two quite different regimes: (1 the Straits of Florida/East Florida Shelf, which includes the Florida Current, and (2 Prince William Sound, Alaska, which is a small, semi-enclosed sea with two major straits. For both regimes, the Princeton Ocean Model (POM has been implemented with mesoscale resolution. Both implementations are forced by mesoscale numerical weather predictions, the US Navy's operational global ocean model (NCOM, which assimilates satellite altimetric sea surface height anomalies, MCSST, ARGO float temperature and salinity profiles, etc. for open boundary conditions, and four diurnal and four semi-diurnal tides, also imposed on the open boundaries. Real-time observations are mainly used for model skill assessment, as a prelude to data assimilation. One of the benefits of this activity has been new understanding derived from diagnostics studies made possible by these numerical simulations. For example, the Florida Current Frontal (cyclonic Eddies, which form weekly in the cyclonic shear zone along the shelfbreak, have been more fully characterized than had been possible by observations alone, and the prevalence of three-layered monthly mean flow in the straits of Prince William Sound has been determined in a highly variable regime that is difficult to observe comprehensively.

  18. Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System

    Institute of Scientific and Technical Information of China (English)

    WANG Wei-gong; LI Zheng; CHENG Mei-ling

    2005-01-01

    The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.

  19. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  20. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

    Science.gov (United States)

    Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-02-24

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

  1. Using ensemble weather forecast in a risk based real time optimization of urban drainage systems

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen

    2015-01-01

    on DORA's approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...

  2. Long forecast horizon to improve Real Time Control of urban drainage systems

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen

    2014-01-01

    on DORA’s approach, this study investigated the implementation of long forecast horizon using an ensemble forecast from a Numerical Weather Prediction (NWP) model. The uncertainty of the prediction is characterized by an ensemble of 25 forecast scenarios. According to the status of the UDS......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...

  3. Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

    2017-05-17

    A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.

  4. Demand forecasting

    OpenAIRE

    Gregor, Belčec

    2011-01-01

    Companies operate in an increasingly challenging environment that requires them to continuously improve all areas of the business process. Demand forecasting is one area in manufacturing companies where we can hope to gain great advantages. Improvements in forecasting can result in cost savings throughout the supply chain, improve the reliability of information and the quality of the service for our customers. In the company Danfoss Trata, d. o. o. we did not have a system for demand forecast...

  5. 1. The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System

    Science.gov (United States)

    Pappenberger, Florian; Thielen, Jutta; Del Medico, Mauro

    2010-05-01

    The European Flood Alert System (EFAS) provides early flood alerts on a pre-operational basis to National hydrological services. EFAS river discharge forecasts are based on probabilistic techniques, using ensemble system and deterministic numerical weather prediction data. The performance of EFAS is regularly analysed with regard to individual flood events and case studies. Although this analysis provides important insight into the strengths and weaknesses of the forecast system, it lacks statistical and independent measures of its long-term performance. In this paper an assessment of EFAS results based on ECMWF weather forecasts over a period of 10 years is presented. EFAS river discharge forecasts have been rerun every week for a period of 10 years using the weather forecast available at the time. These are evaluated for a total of 500 river gauging stations distributed across Europe.. The selected stations are sufficiently separated in space to avoid autocorrelation of station time series. Also, analysis is performed with a gap of 3 days between each forecast which reduces the temporal correlation of the time series of the same station. The data are analysed with regard to skill, bias and quality of river discharge forecast. The 10 year simulations clearly show that the skill of the river discharge forecasts have undergone an evolution linked to the quality of the operational meteorological forecast. Overall, over the period of 10 years, the skill of the EFAS forecasts has steadily increased. Important hydrological extreme events cannot be clearly identified with the skill score analysis, highlighting the necessity for event based analysis in addition to statistical long-term assessments for a better understanding of the EFAS system and large scale river discharge predictions in general. he predictability is shown to depend on catchment size and geographical location.

  6. Comparison of the economic impact of different wind power forecast systems for producers

    Science.gov (United States)

    Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

    2014-05-01

    Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a

  7. A high-resolution operational forecast system for oil spill response in Belfast Lough.

    Science.gov (United States)

    Abascal, Ana J; Castanedo, Sonia; Núñez, Paula; Mellor, Adam; Clements, Annika; Pérez, Beatriz; Cárdenas, Mar; Chiri, Helios; Medina, Raúl

    2017-01-15

    This paper presents a high-resolution operational forecast system for providing support to oil spill response in Belfast Lough. The system comprises an operational oceanographic module coupled to an oil spill forecast module that is integrated in a user-friendly web application. The oceanographic module is based on Delft3D model which uses daily boundary conditions and meteorological forcing obtained from COPERNICUS and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions at local scales. Both components of the system are calibrated and validated with observational data, including ADCP data, sea level, temperature and salinity measurements and drifting buoys released in the study area. The transport model is calibrated using a novel methodology to obtain the model coefficients that optimize the numerical simulations. The results obtained show the good performance of the system and its capability for oil spill forecast.

  8. Design of forecast system of mining damage in mining areas and its realization

    Institute of Scientific and Technical Information of China (English)

    WANG Dong-mei

    2005-01-01

    Exploited the forecast system of mining damages, which uses the theory of clean-production in coal-areas as guide. Its aims is developing and using coal resource reasonably and reducing the damage to the environment.

  9. COAWST Forecast System : USGS : US East Coast and Gulf of Mexico (Experimental)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Experimental forecast model product from the USGS Coupled Ocean Atmosphere Wave Sediment-Transport (COAWST) modeling system. Data required to drive the modeling...

  10. COAWST Forecast System : USGS : US East Coast and Gulf of Mexico (Experimental)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Experimental forecast model product from the USGS Coupled Ocean Atmosphere Wave Sediment-Transport (COAWST) modeling system. Data required to drive the modeling...

  11. COAWST Forecast System : USGS : US East Coast and Gulf of Mexico (Experimental)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Experimental forecast model product from the USGS Coupled Ocean Atmosphere Wave Sediment-Transport (COAWST) modeling system. Data required to drive the modeling...

  12. Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

    Directory of Open Access Journals (Sweden)

    Tien Du Duc

    2016-01-01

    Full Text Available The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl. For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

  13. An experimental system for flood risk forecasting and monitoring at global scale

    Science.gov (United States)

    Dottori, Francesco; Alfieri, Lorenzo; Kalas, Milan; Lorini, Valerio; Salamon, Peter

    2017-04-01

    Global flood forecasting and monitoring systems are nowadays a reality and are being applied by a wide range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasting, combining streamflow estimations with expected inundated areas and flood impacts. Finally, emerging technologies such as crowdsourcing and social media monitoring can play a crucial role in flood disaster management and preparedness. Here, we present some recent advances of an experimental procedure for near-real time flood mapping and impact assessment. The procedure translates in near real-time the daily streamflow forecasts issued by the Global Flood Awareness System (GloFAS) into event-based flood hazard maps, which are then combined with exposure and vulnerability information at global scale to derive risk forecast. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To increase the reliability of our forecasts we propose the integration of model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification and correction of impact forecasts. Finally, we present the results of preliminary tests which show the potential of the proposed procedure in supporting emergency response and management.

  14. The impact of implementing a demand forecasting system into a low-income country's supply chain.

    Science.gov (United States)

    Mueller, Leslie E; Haidari, Leila A; Wateska, Angela R; Phillips, Roslyn J; Schmitz, Michelle M; Connor, Diana L; Norman, Bryan A; Brown, Shawn T; Welling, Joel S; Lee, Bruce Y

    2016-07-12

    To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements. Copyright

  15. Verification of a probabilistic flood forecasting system for an Alpine Region of northern Italy

    Science.gov (United States)

    Laiolo, P.; Gabellani, S.; Rebora, N.; Rudari, R.; Ferraris, L.; Ratto, S.; Stevenin, H.

    2012-04-01

    Probabilistic hydrometeorological forecasting chains are increasingly becoming an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is to have reliable systems, for this reason an accurate verification of their predictive performances become essential. The aim of this work is to validate a probabilistic flood forecasting system: Flood-PROOFS. The system works in real time, since 2008, in an alpine Region of northern Italy, Valle d'Aosta. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. Flood-PROOFS uses as input Quantitative Precipitation Forecast (QPF) derived from the Italian limited area model meteorological forecast (COSMO-I7) and forecasts issued by regional expert meteorologists. Furthermore the system manages and uses both real time meteorological and satellite data and real time data on the maneuvers performed by the water company on dams and river devices. The main outputs produced by the computational chain are deterministic and probabilistic discharge forecasts in different cross sections of the considered river network. The validation of the flood prediction system has been conducted on a 25 months period considering different statistical methods such as Brier score, Rank histograms and verification scores. The results highlight good performances of the system as support system for emitting warnings but there is a lack of statistics especially for huge discharge events.

  16. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  17. Space-time wind speed forecasting for improved power system dispatch

    KAUST Repository

    Zhu, Xinxin

    2014-02-27

    To support large-scale integration of wind power into electric energy systems, state-of-the-art wind speed forecasting methods should be able to provide accurate and adequate information to enable efficient, reliable, and cost-effective scheduling of wind power. Here, we incorporate space-time wind forecasts into electric power system scheduling. First, we propose a modified regime-switching, space-time wind speed forecasting model that allows the forecast regimes to vary with the dominant wind direction and with the seasons, hence avoiding a subjective choice of regimes. Then, results from the wind forecasts are incorporated into a power system economic dispatch model, the cost of which is used as a loss measure of the quality of the forecast models. This, in turn, leads to cost-effective scheduling of system-wide wind generation. Potential economic benefits arise from the system-wide generation of cost savings and from the ancillary service cost savings. We illustrate the economic benefits using a test system in the northwest region of the United States. Compared with persistence and autoregressive models, our model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars annually in regions with high wind penetration, such as Texas and the Pacific northwest. © 2014 Sociedad de Estadística e Investigación Operativa.

  18. NOAA’s Nested Northern Gulf of Mexico Operational Forecast Systems Development

    Directory of Open Access Journals (Sweden)

    Eugene Wei

    2014-01-01

    Full Text Available The NOAA National Ocean Service’s (NOS Northern Gulf of Mexico Operational Forecast System (NGOFS became operational in March 2012. Implemented with the Finite Volume Coastal Ocean Model (FVCOM as its core three-dimensional oceanographic circulation model, NGOFS produces a real-time nowcast (−6 h to zero and six-hourly, two-day forecast guidance for water levels and three-dimensional currents, water temperature and salinity over the northern Gulf of Mexico continental shelf. Designed as a regional scale prediction system, NGOFS lacks sufficient spatial coverage and/or resolution to fully resolve hydrodynamic features in critical seaports and estuaries. To overcome this shortcoming and better support the needs of marine navigation, emergency response, and environmental management, two FVCOM-based, high-resolution, estuary-scale nested forecast modeling systems, namely the Northwest and Northeast Gulf of Mexico Operational Forecast Systems (NWGOFS and NEGOFS, have been developed through one-way nesting in NGOFS. Using the atmospheric forecast guidance from the NOAA (National Oceanic and Atmospheric Administration/NWS (National Weather Services’ North American Mesoscale (NAM Forecast System, US Geological Survey (USGS river discharge observations, and the NGOFS water level, current, water temperature and salinity as the surface, river, and open ocean boundary forcing, respectively, a six-month model hindcast for the period October 2010–March 2011 has been conducted. Modeled water levels, currents, salinity and water temperature are compared with observations using the NOS standard skill assessment software. Skill assessment scores indicated that NWGOFS and NEGOFS demonstrate improvement over NGOFS. The NWGOFS and NEGOFS are under real-time nowcast/forecast test and evaluation by NOS’s Center for Operational Oceanographic Products and Services (CO-OPS. The forecast systems are scheduled to be implemented operational on NOAA Weather

  19. Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

    Science.gov (United States)

    Kucera, P. A.; Burek, T.; Halley-Gotway, J.

    2015-12-01

    NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

  20. Integration of a relocatable ocean model in the Mediterranean Forecasting System

    Directory of Open Access Journals (Sweden)

    A. Russo

    2006-10-01

    Full Text Available The MFS (Mediterranean Forecasting System project and its follower MFSTEP (Mediterranean ocean Forecasting System–Towards Environmental Prediction are being covering the Mediterranean Sea with operational Ocean General Circulation Models (OGCMs at horizontal resolution varying from about 12 km till 2005 to 6.5 km in 2006 (reaching 3 km with some regional models and 1.5 km for few shelf models. Heat, water and momentum fluxes through the air-sea interface are derived from the European Center for Medium-range Weather Forecast (ECMWF output at 0.5° horizontal resolution. Such horizontal resolutions could be not able to provide the needed forecast accuracy in some cases (localized emergencies at sea, e.g. oil spill; need for high resolution current forecasts, e.g. offshore works. A solution to this problem is represented by relocatable models able to be rapidly deployed and to produce forecasts starting from the MFS products. The Harvard Ocean Prediction System (HOPS has been chosen as base of the relocatable model and it has been interfaced with the MFSTEP OGCM and one regional model. The relocatable model has demonstrated capability to produce forecasts within 2-3 days in many cases, and more rapid implementation may be obtained.

  1. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  2. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  3. Comparison of short-term rainfall forecasts for modelbased flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Ahm, Malte; Nielsen, Jesper Ellerbek;

    2013-01-01

    Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best...... performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times....

  4. Study on Battery Capacity for Grid-connection Power Planning with Forecasts in Clustered Photovoltaic Systems

    Science.gov (United States)

    Shimada, Takae; Kawasaki, Norihiro; Ueda, Yuzuru; Sugihara, Hiroyuki; Kurokawa, Kosuke

    This paper aims to clarify the battery capacity required by a residential area with densely grid-connected photovoltaic (PV) systems. This paper proposes a planning method of tomorrow's grid-connection power from/to the external electric power system by using demand power forecasting and insolation forecasting for PV power predictions, and defines a operation method of the electricity storage device to control the grid-connection power as planned. A residential area consisting of 389 houses consuming 2390 MWh/year of electricity with 2390kW PV systems is simulated based on measured data and actual forecasts. The simulation results show that 8.3MWh of battery capacity is required in the conditions of half-hour planning and 1% or less of planning error ratio and PV output limiting loss ratio. The results also show that existing technologies of forecasting reduce required battery capacity to 49%, and increase the allowable installing PV amount to 210%.

  5. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  6. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 hours. The best...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  7. Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

    Directory of Open Access Journals (Sweden)

    Radziukynas V.

    2016-04-01

    Full Text Available The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011 and planned wind power capacities (the year 2023.

  8. Azithromycin in Chronic Fatigue Syndrome (CFS, an analysis of clinical data

    Directory of Open Access Journals (Sweden)

    Scholte Hans R

    2006-08-01

    Full Text Available Abstract Background CFS is a clinical state with defined symptoms, but undefined cause. The patients may show a chronic state of immune activation and treatment with an antibiotic in this subgroup has been suggested. Methods In a retrospective study, the response of CFS patients to azithromycin, an antibiotic and immunomodulating drug, has been scored from the patients records and compared with clinical and laboratory data. Azithromycin was not the first choice therapy, but offered when the effect of counseling and L-carnitine was considered insufficient by the patient and the clinician. Results Of the 99 patients investigated, 58 reported a decrease in the symptoms by the use of azithromycin. These responding patients had lower levels of plasma acetylcarnitine. Conclusion The efficacy of azithromycin in the responsive patients could be explained by the modulating effect on a chronic primed state of the immune cells of the brain, or the activated peripheral immune system. Their lower acetylcarnitine levels may reflect a decreased antioxidant defense and/or an increased consumption of acetylcarnitine caused by oxidative stress.

  9. Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

    Energy Technology Data Exchange (ETDEWEB)

    Das, S.

    1991-12-01

    The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

  10. Evaluation of forecasts by accuracy and spread in the MiKlip decadal climate prediction system

    Directory of Open Access Journals (Sweden)

    Christopher Kadow

    2016-12-01

    Full Text Available We present the evaluation of temperature and precipitation forecasts obtained with the MiKlip decadal climate prediction system. These decadal hindcast experiments are verified with respect to the accuracy of the ensemble mean and the ensemble spread as a representative for the forecast uncertainty. The skill assessment follows the verification framework already used by the decadal prediction community, but enhanced with additional evaluation techniques like the logarithmic ensemble spread score. The core of the MiKlip system is the coupled Max Planck Institute Earth System Model. An ensemble of 10 members is initialized annually with ocean and atmosphere reanalyses of the European Centre for Medium-Range Weather Forecasts. For assessing the effect of the initialization, we compare these predictions to uninitialized climate projections with the same model system. Initialization improves the accuracy of temperature and precipitation forecasts in year 1, particularly in the Pacific region. The ensemble spread well represents the forecast uncertainty in lead year 1, except in the tropics. This estimate of prediction skill creates confidence in the respective 2014 forecasts, which depict less precipitation in the tropics and a warming almost everywhere. However, large cooling patterns appear in the Northern Hemisphere, the Pacific South America and the Southern Ocean. Forecasts for 2015 to 2022 show even warmer temperatures than for 2014, especially over the continents. The evaluation of lead years 2 to 9 for temperature shows skill globally with the exception of the eastern Pacific. The ensemble spread can again be used as an estimate of the forecast uncertainty in many regions: It improves over the tropics compared to lead year 1. Due to a reduction of the conditional bias, the decadal predictions of the initialized system gain skill in the accuracy compared to the uninitialized simulations in the lead years 2 to 9. Furthermore, we show that

  11. Semi-distributed flood forecasting system for the Middle Vistula reach

    Science.gov (United States)

    Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena

    2014-05-01

    The aim of this study is the development of an integrated forecasting system for the middle reach of the River Vistula. The system consists of combined in series lumped parameter Stochastic Transfer Function models. In order to prolong the forecast lead-time, the system was extended to include gauging stations situated upstream of Zawichost. There is a number of tributaries located along the studied reach. The largest are Kamienna, Pilica and Wieprz. Therefore apart from Single- Input -Single-Output models (SISO), multiple input models were also developed (MISO). The system is based on water levels instead of flows, in order to avoid errors related to rating curve transformation. The problem of the nonlinear transformation of system inputs in order to separate the nonlinearity of the flow process to obtain the linear model dynamics is equally important for the accuracy of forecasts. The possibility of linearizing the flow routing process was investigated using a State Dependent Parameter approach. The nonparametric relationship was parameterised using a power function. This procedure allowed the application of a model with a nonlinear transformation of input in the forecasting mode. It is important to note that the applied methods are stochastic in nature and the structure of the models and their parameters are estimated from available observations, taking into account inherent observation and model approximation errors. As a result, forecasts are estimated together with uncertainty bands. We apply a Kalman filter updating of model predictions as a data assimilation procedure. The procedure involves formulating the forecasting problem in a state space form. Validation of the developed forecasting system shows that the quality of forecasts obtained using a semi-distributed lumped parameter model is comparable with the forecasts obtained using a distributed model with the advantage of obtaining forecast uncertainty by the former. This work was supported by the

  12. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    Science.gov (United States)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2016-09-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  13. Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System

    Science.gov (United States)

    Dumbović, Mateja; Srivastava, Nandita; Rao, Yamini K.; Vršnak, Bojan; Devos, Andy; Rodriguez, Luciano

    2017-08-01

    Under the European Union 7th Framework Programme (EU FP7) project Coronal Mass Ejections and Solar Energetic Particles (COMESEP, http://comesep.aeronomy.be), an automated space weather alert system has been developed to forecast solar energetic particles (SEP) and coronal mass ejection (CME) risk levels at Earth. The COMESEP alert system uses the automated detection tool called Computer Aided CME Tracking (CACTus) to detect potentially threatening CMEs, a drag-based model (DBM) to predict their arrival, and a CME geoeffectiveness tool (CGFT) to predict their geomagnetic impact. Whenever CACTus detects a halo or partial halo CME and issues an alert, the DBM calculates its arrival time at Earth and the CGFT calculates its geomagnetic risk level. The geomagnetic risk level is calculated based on an estimation of the CME arrival probability and its likely geoeffectiveness, as well as an estimate of the geomagnetic storm duration. We present the evaluation of the CME risk level forecast with the COMESEP alert system based on a study of geoeffective CMEs observed during 2014. The validation of the forecast tool is made by comparing the forecasts with observations. In addition, we test the success rate of the automatic forecasts (without human intervention) against the forecasts with human intervention using advanced versions of the DBM and CGFT (independent tools available at the Hvar Observatory website, http://oh.geof.unizg.hr). The results indicate that the success rate of the forecast in its current form is unacceptably low for a realistic operation system. Human intervention improves the forecast, but the false-alarm rate remains unacceptably high. We discuss these results and their implications for possible improvement of the COMESEP alert system.

  14. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    Science.gov (United States)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  15. A Novel Hydro-information System for Improving National Weather Service River Forecast System

    Science.gov (United States)

    Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.

    2009-12-01

    A novel hydro-information system has been developed to improve the forecast accuracy of the NOAA National Weather Service River Forecast System (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our system to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its forecasting skills, especially for droughts, but also for disaster management in general. Our system supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and the forcing information of the North American Land Data Assimilation System (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our system, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our system employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our system. Extendibility and flexibility of our system are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our system can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current system into the NWSRFS is

  16. Comparing One-way and Two-way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    Science.gov (United States)

    Givati, Amir; Gochis, David; Rummler, Thomas; Kunstmann, Harald; Yu, Wei

    2016-04-01

    A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) model and the Weather Research and Forecasting (WRF) Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from hydrometric stations at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of precipitation data, and less so, to hydrologic model formulation. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However more research needed in order to better understand the land-atmosphere coupling mechanisms driving hydrometeorological processes on a wider variety precipitation and terrestrial hydrologic systems.

  17. Comparing One-Way and Two-Way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    Directory of Open Access Journals (Sweden)

    Amir Givati

    2016-05-01

    Full Text Available A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS model and the Weather Research and Forecasting (WRF Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then, both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from the hydrometric station at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE and the Nash–Sutcliffe (NS efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of simulated and observed precipitation data, and demonstrate improvement, although not significant, at the Hydrological response, like simulated hydrographs. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study, as well as previous studies, suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However, more research needed in order to better understand the land-atmosphere coupling mechanisms

  18. Anvil Forecast Tool in the Advanced Weather Interactive Processing System, Phase II

    Science.gov (United States)

    Barrett, Joe H., III

    2008-01-01

    Meteorologists from the 45th Weather Squadron (45 WS) and Spaceflight Meteorology Group have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Light Rules. As a result, the Applied Meteorology Unit (AMU) created a graphical overlay tool for the Meteorological Interactive Data Display Systems (MIDDS) to indicate the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input.

  19. Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

    OpenAIRE

    Kurbatsky, Victor G.; Spiryaev, Vadim A.; Tomin, Nikita V.; Leahy, Paul G.; Sidorov, Denis N.; Zhukov, Alexei V.

    2014-01-01

    A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to ra...

  20. Pathways to designing and running an operational flood forecasting system: an adventure game!

    Science.gov (United States)

    Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma

    2017-04-01

    In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.

  1. Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting

    Science.gov (United States)

    Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa

    Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV system fluctuates depending on weather conditions. In order to control the fluctuating power output for PV system, it requires control method of energy storage system. This paper proposes an optimization approach to determine the operational planning of power output for PV system with battery energy storage system (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV system. The optimization method applies genetic algorithm (GA) considering PV power output forecast error. The forecast error is based on our previous works with the insolation forecasting at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.

  2. Development of an aerosol assimilation/forecasting system with Himawari-8 aerosol products

    Science.gov (United States)

    Maki, T.; Yumimoto, K.; Tanaka, T. Y.; Yoshida, M.; Kikuchi, M.; Nagao, T. M.; Murakami, H.; Ogi, A.; Sekiyama, T. T.

    2016-12-01

    A new generation geostationary meteorological satellite (GMS), Himawari-8, was launched on 7 October 2014 and became operational on 7 July 2015. Himawari-8 is equipped with more advanced multispectral imager (Advanced Himawari Imager; AHI) ahead of other planned GMSs (e.g., GEOS-R). The AHI has 16 observational bands including three visible lights (i.e. RGB) with high spatial (0.5-2 km) and temporal (every 10 minutes full-disk images) resolutions, and provides about 50 times more data than previous GMSs. It is attractive characteristics for aerosol study that the visible and near-infrared observational bands allow us to obtain full-disk maps of aerosol optical properties (i.e., aerosol optical thickness (AOT) and ångström exponent) with unprecedented temporal resolution. Meteorological Research Institute (MRI)/JMA and Japan Aerospace Exploration Agency (JAXA) have been developing an aerosol assimilation/forecasting system with a global aerosol transport model (MASINGAR mk-2), 2 dimensional variational (2D-Var) method, and the Himawari-8 AOTs. Forecasting results are quantitatively validated by AOTs measured by AERONET and PM2.5 concentrations obtained by in-situ stations. Figure 1 shows model-predicted and satellite-observed AOTs during the 2016 Siberian wildfire. Upper and lower panels exhibit maps of AOT at analysis time (0000 UTC on May 18, 2016) and 27-hour forecast time (03 UTC on May 19, 2016), respectively. The 27-hour forecasted AOT starting with the analyzed initial condition (Figure 1f) successfully predicts heavy smokes covering the northern part of Japan, which forecast without assimilation (Figure 1e) failed to reproduces. Figure 1: Horizontal distribution of observed and forecasted AOTs at 0000 UTC 18 May, 2016 (analysis time; upper panels) and 0300 UTC 19 May, 2016 (18-h forecast from the analysis time; lower panel). (a, d) observed AOT from Himawari-8, (b, e) forecasted AOT without assimilation, and (c, f) forecast AOT with assimilation.

  3. An Intelligent Decision Support System for Workforce Forecast

    Science.gov (United States)

    2011-01-01

    a block recursive BVAR model and a naive BVAR model based on the Minnesota random walk prior. Duffield and Coltrane (1992) test a model of the...Wirth and Catlett, 1988; John , 1995) and alternative data structures (Oliver, 1993; Oliveira and Sangiovanni-Vincentelli, 1995; Kohavi and Li, 1995...80. Armstrong, J.S. (1985). Long-term forecasting: From crystal ball to computer (2nd ed.). New York: John Wiley. Armstrong, J.S. (2001). Judgmental

  4. A Dynamic Forecasting System with Applications in Production Logistics

    Institute of Scientific and Technical Information of China (English)

    CHEUNG; Chi-fai; LEE; Wing-bun; LO; Victor

    2002-01-01

    Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change ...

  5. Decadal prediction skill in the GEOS-5 forecast system

    Science.gov (United States)

    Ham, Yoo-Geun; Rienecker, Michele M.; Suarez, Max J.; Vikhliaev, Yury; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

    2014-01-01

    A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 % improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the subpolar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

  6. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

    2014-01-01

     The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall

  7. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Science.gov (United States)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-03-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993-2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is

  8. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Directory of Open Access Journals (Sweden)

    S. Shukla

    2014-03-01

    Full Text Available The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM scenarios using the Variable Infiltration Capacity (VIC hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's Water Requirement Satisfaction Index (WRSI, an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012. We found that initializing SM forecasts with start-of-season (5 March SM conditions resulted in useful SM forecast skill (> 0.5 correlation at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April SM conditions the skill until the end-of-season improved. This shows that early

  9. Variational assimilation of Lagrangian trajectories in the Mediterranean ocean Forecasting System

    Directory of Open Access Journals (Sweden)

    J. A. U. Nilsson

    2011-12-01

    Full Text Available A novel method for three-dimensional variational assimilation of Lagrangian data with a primitive-equation ocean model is proposed. The assimilation scheme was implemented in the Mediterranean ocean Forecasting System and evaluated for a 4-month period. Four experiments were designed to assess the impact of trajectory assimilation on the model output, i.e. the sea-surface height, velocity, temperature and salinity fields. It was found from the drifter and Argo trajectory assimilation experiment that the forecast skill of surface-drifter trajectories improved by 15 %, that of intermediate-depth float trajectories by 20 %, and moreover, the forecasted sea-surface height fields improved locally by 5 % compared to satellite data, while the quality of the temperature and salinity fields remained at previous levels. In conclusion, the addition of Lagrangian trajectory assimilation proved to reduce the uncertainties in the model fields, thus yielding a higher accuracy of the ocean forecasts.

  10. Hybrid intelligent system for Sale Forecasting using Delphi and adaptive Fuzzy Back-Propagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Attariuas Hicham

    2012-12-01

    Full Text Available ales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP Neural Networks with adaptive learning rate. The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. Winter’s Exponential Smoothing method will be utilized to take the trend effect into consideration. The data for this search come from an industrial company that manufactures packaging. Analyze of results show that the proposed model outperforms other three different forecasting models in MAPE and RMSE measures.

  11. Looking toward to the next-generation space weather forecast system. Comments former a former space weather forecaster

    Energy Technology Data Exchange (ETDEWEB)

    Tomita, Fumihiko [Communications Research Laboratory, Koganei, Tokyo (Japan)

    1999-03-01

    In the 21st century, man's space-based activities will increase significantly and many kinds of space utilization technologies will assume a vital role in the infrastructure, creating new businesses, securing the global environment, contributing much to human welfare in the world. Communications Research Laboratory (CRL) has been contributing to the safety of human activity in space and to the further understanding of the solar terrestrial environment through the study of space weather, including the upper atmosphere, magnetosphere, interplanetary space, and the sun. The next-generation Space Weather Integrated Monitoring System (SWIMS) for future space activities based on the present international space weather forecasting system is introduced in this paper. (author)

  12. Comparison of Observation Impacts in Two Forecast Systems using Adjoint Methods

    Science.gov (United States)

    Gelaro, Ronald; Langland, Rolf; Todling, Ricardo

    2009-01-01

    An experiment is being conducted to compare directly the impact of all assimilated observations on short-range forecast errors in different operational forecast systems. We use the adjoint-based method developed by Langland and Baker (2004), which allows these impacts to be efficiently calculated. This presentation describes preliminary results for a "baseline" set of observations, including both satellite radiances and conventional observations, used by the Navy/NOGAPS and NASA/GEOS-5 forecast systems for the month of January 2007. In each system, about 65% of the total reduction in 24-h forecast error is provided by satellite observations, although the impact of rawinsonde, aircraft, land, and ship-based observations remains significant. Only a small majority (50- 55%) of all observations assimilated improves the forecast, while the rest degrade it. It is found that most of the total forecast error reduction comes from observations with moderate-size innovations providing small to moderate impacts, not from outliers with very large positive or negative innovations. In a global context, the relative impacts of the major observation types are fairly similar in each system, although regional differences in observation impact can be significant. Of particular interest is the fact that while satellite radiances have a large positive impact overall, they degrade the forecast in certain locations common to both systems, especially over land and ice surfaces. Ongoing comparisons of this type, with results expected from other operational centers, should lead to more robust conclusions about the impacts of the various components of the observing system as well as about the strengths and weaknesses of the methodologies used to assimilate them.

  13. A meteo-hydrological prediction system based on a multi-model approach for precipitation forecasting

    Directory of Open Access Journals (Sweden)

    S. Davolio

    2008-02-01

    Full Text Available The precipitation forecasted by a numerical weather prediction model, even at high resolution, suffers from errors which can be considerable at the scales of interest for hydrological purposes. In the present study, a fraction of the uncertainty related to meteorological prediction is taken into account by implementing a multi-model forecasting approach, aimed at providing multiple precipitation scenarios driving the same hydrological model. Therefore, the estimation of that uncertainty associated with the quantitative precipitation forecast (QPF, conveyed by the multi-model ensemble, can be exploited by the hydrological model, propagating the error into the hydrological forecast.

    The proposed meteo-hydrological forecasting system is implemented and tested in a real-time configuration for several episodes of intense precipitation affecting the Reno river basin, a medium-sized basin located in northern Italy (Apennines. These episodes are associated with flood events of different intensity and are representative of different meteorological configurations responsible for severe weather affecting northern Apennines.

    The simulation results show that the coupled system is promising in the prediction of discharge peaks (both in terms of amount and timing for warning purposes. The ensemble hydrological forecasts provide a range of possible flood scenarios that proved to be useful for the support of civil protection authorities in their decision.

  14. Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

    Science.gov (United States)

    Manukalo, V.

    2012-12-01

    Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of

  15. Using Self-Organizing Maps in Creation of an Ocean Forecasting System

    Science.gov (United States)

    Vilibic, I.; Zagar, N.; Cosoli, S.; Dadic, V.; Ivankovic, D.; Jesenko, B.; Kalinic, H.; Mihanovic, H.; Sepic, J.; Tudor, M.

    2014-12-01

    We present the first results of the NEURAL project (www.izor.hr/neural), which is dedicated to creation of an efficient and reliable ocean surface current forecasting system. This system is based on high-frequency (HF) radar measurements, numerical weather prediction (NWP) models and neural network algorithms (Self-Organizing Maps, SOM). Joint mapping of mesoscale ground winds and HF radars in a coastal area points to a high correlation between two sets, indicating that wind forecast may be used as a basis for forecasting ocean surface currents. NEURAL project consists of three modules: (i) the technological module which covers installation of new HF radars in the coastal area of the middle Adriatic, and implementation of data management procedures; (ii) the research module which deals with an assessment of different combinations of input variables (radial vs. Cartesian vectors, original vs. detided vs. filtered series, WRF-ARW vs. Aladin meteorological model), all in order to get the best hindcasted surface currents; and finally (iii) the operational module in which NWP operational products will be used for short-term forecasting of ocean surface currents. Both historical and newly observed HF radar data, as well as reanalysis and operational NWP model runs will be used within the (ii) and (iii) modules of the project. Finally, the observed, hindcasted and forecasted ocean current will be compared to the operational ROMS model outputs to compare skill reliability of the forecasting system based on neural network approach to the skill and reliability of numerical ocean models. We expect the forecasting system based on neural network approach to be more reliable than the one based on numerical ocean model as it is more exclusively based on measurements. Disadvantages of such a system are that it can be applied only in areas where long series surface currents measurements exist and where the recognized patterns can be properly ascribed to a forcing field.

  16. An integrated user-oriented weather forecast system for air traffic using real-time observations and model data

    OpenAIRE

    Forster, Caroline; Tafferner, Arnold

    2009-01-01

    This paper presents the Weather Forecast User-oriented System Including Object Nowcasting (WxFUSION), an integrated weather forecast system for air traffic. The system is currently under development within a new project named “Weather and Flying” under the leadership of the Institute of Atmospheric Physics (IPA) at the German Aerospace Center (DLR). WxFUSION aims at combining data from various sources, as there are weather observations, remote sensing, nowcasting and numerical model forecast ...

  17. Operational perspective of remote sensing-based forest fire danger forecasting systems

    Science.gov (United States)

    Chowdhury, Ehsan H.; Hassan, Quazi K.

    2015-06-01

    Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is the forecasting of fire danger conditions. Here, our aim was to critically analyse the following issues, (i) current operational forest fire danger forecasting systems and their limitations; (ii) remote sensing-based fire danger monitoring systems and usefulness in operational perspective; (iii) remote sensing-based fire danger forecasting systems and their functional implications; and (iv) synergy between operational forecasting systems and remote sensing-based methods. In general, the operational systems use point-based measurements of meteorological variables (e.g., temperature, wind speed and direction, relative humidity, precipitations, cloudiness, solar radiation, etc.) and generate danger maps upon employing interpolation techniques. Theoretically, it is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. During the last several decades, efforts were given to develop fire danger condition systems, which could be broadly classified into two major groups: fire danger monitoring and forecasting systems. Most of the monitoring systems focused on determining the danger during and/or after the period of image acquisition. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. Synergy between the operational systems and remote sensing-based methods were investigated in the past but too much complex in nature. Thus, the elaborated understanding about these developments would be worthwhile to advance research in the area of fire danger in the context of making them operational.

  18. The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts

    Directory of Open Access Journals (Sweden)

    J. C. Bartholmes

    2009-02-01

    Full Text Available Since 2005 the European Flood Alert System (EFAS has been producing probabilistic hydrological forecasts in pre-operational mode at the Joint Research Centre (JRC of the European Commission. EFAS aims at increasing preparedness for floods in trans-national European river basins by providing medium-range deterministic and probabilistic flood forecasting information, from 3 to 10 days in advance, to national hydro-meteorological services.

    This paper is Part 2 of a study presenting the development and skill assessment of EFAS. In Part 1, the scientific approach adopted in the development of the system has been presented, as well as its basic principles and forecast products. In the present article, two years of existing operational EFAS forecasts are statistically assessed and the skill of EFAS forecasts is analysed with several skill scores. The analysis is based on the comparison of threshold exceedances between proxy-observed and forecasted discharges. Skill is assessed both with and without taking into account the persistence of the forecasted signal during consecutive forecasts.

    Skill assessment approaches are mostly adopted from meteorology and the analysis also compares probabilistic and deterministic aspects of EFAS. Furthermore, the utility of different skill scores is discussed and their strengths and shortcomings illustrated. The analysis shows the benefit of incorporating past forecasts in the probability analysis, for medium-range forecasts, which effectively increases the skill of the forecasts.

  19. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  20. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  1. Cascading model uncertainty from medium range weather forecasts (10 days through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS

    Directory of Open Access Journals (Sweden)

    F. Pappenberger

    2005-01-01

    Full Text Available The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfall-runoff model (LisFlood for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

  2. A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

    Science.gov (United States)

    Cong, Z.

    2015-12-01

    In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting

  3. Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

    Directory of Open Access Journals (Sweden)

    T. Niu

    2008-07-01

    Full Text Available A data assimilation system (DAS was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust forecast system and applied in the operational forecasts of sand and dust storm (SDS in spring 2006. The system is based on a three dimensional variational method (3D-Var and uses extensively the measurements of surface visibility (phenomena and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

  4. Wavelet time series MPARIMA modeling for power system short term load forecasting

    Institute of Scientific and Technical Information of China (English)

    冉启文; 单永正; 王建赜; 王骐

    2003-01-01

    The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near-periodicity, nonstationarity and nonlinearity existed in power system short term quarter-hour load time series, and can therefore accurately forecast the quarter-hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.

  5. Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

    Science.gov (United States)

    Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

    2017-04-01

    Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53

  6. A simple method of observation impact analysis for operational storm surge forecasting systems

    Science.gov (United States)

    Sumihar, Julius; Verlaan, Martin

    2016-04-01

    In this work, a simple method is developed for analyzing the impact of assimilating observations in improving forecast accuracy of a model. The method simply makes use of observation time series and the corresponding model output that are generated without data assimilation. These two time series are usually available in an operational database. The method is therefore easy to implement. Moreover, it can be used before actually implementing any data assimilation to the forecasting system. In this respect, it can be used as a tool for designing a data assimilation system, namely for searching for an optimal observing network. The method can also be used as a diagnostic tool, for example, for evaluating an existing operational data assimilation system to check if all observations are contributing positively to the forecast accuracy. The method has been validated with some twin experiments using a simple one-dimensional advection model as well as with an operational storm surge forecasting system based on the Dutch Continental Shelf model version 5 (DCSMv5). It has been applied for evaluating the impact of observations in the operational data assimilation system with DCSMv5 and for designing a data assimilation system for the new model DCSMv6. References: Verlaan, M. and J. Sumihar (2016), Observation impact analysis methods for storm surge forecasting systems, Ocean Dynamics, ODYN-D-15-00061R1 (in press) Zijl, F., J. Sumihar, and M. Verlaan (2015), Application of data assimilation for improved operational water level forecasting of the northwest European shelf and North Sea, Ocean Dynamics, 65, Issue 12, pp 1699-1716.

  7. Multi-platform operational validation of the Western Mediterranean SOCIB forecasting system

    Science.gov (United States)

    Juza, Mélanie; Mourre, Baptiste; Renault, Lionel; Tintoré, Joaquin

    2014-05-01

    The development of science-based ocean forecasting systems at global, regional, and local scales can support a better management of the marine environment (maritime security, environmental and resources protection, maritime and commercial operations, tourism, ...). In this context, SOCIB (the Balearic Islands Coastal Observing and Forecasting System, www.socib.es) has developed an operational ocean forecasting system in the Western Mediterranean Sea (WMOP). WMOP uses a regional configuration of the Regional Ocean Modelling System (ROMS, Shchepetkin and McWilliams, 2005) nested in the larger scale Mediterranean Forecasting System (MFS) with a spatial resolution of 1.5-2km. WMOP aims at reproducing both the basin-scale ocean circulation and the mesoscale variability which is known to play a crucial role due to its strong interaction with the large scale circulation in this region. An operational validation system has been developed to systematically assess the model outputs at daily, monthly and seasonal time scales. Multi-platform observations are used for this validation, including satellite products (Sea Surface Temperature, Sea Level Anomaly), in situ measurements (from gliders, Argo floats, drifters and fixed moorings) and High-Frequency radar data. The validation procedures allow to monitor and certify the general realism of the daily production of the ocean forecasting system before its distribution to users. Additionally, different indicators (Sea Surface Temperature and Salinity, Eddy Kinetic Energy, Mixed Layer Depth, Heat Content, transports in key sections) are computed every day both at the basin-scale and in several sub-regions (Alboran Sea, Balearic Sea, Gulf of Lion). The daily forecasts, validation diagnostics and indicators from the operational model over the last months are available at www.socib.es.

  8. An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

    Science.gov (United States)

    Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

    2014-01-01

    Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

  9. VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

    Science.gov (United States)

    An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...

  10. Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system.

    Science.gov (United States)

    Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

    2014-06-28

    The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid

  11. Spatial-temporal reproducibility assessment of global seasonal forecasting system version 5 model for Dam Inflow forecasting

    Science.gov (United States)

    Moon, S.; Suh, A. S.; Soohee, H.

    2016-12-01

    The GloSea5(Global Seasonal forecasting system version 5) is provided and operated by the KMA(Korea Meteorological Administration). GloSea5 provides Forecast(FCST) and Hindcast(HCST) data and its horizontal resolution is about 60km (0.83° x 0.56°) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation(R2 = 0.60, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5(600.1mm) showed the greatest difference(-26.5%) compared to observations(816.1mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a ?3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

  12. COST ES0602: towards a European network on chemical weather forecasting and information systems

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2009-04-01

    Full Text Available The COST ES0602 action provides a forum for benchmarking approaches and practices in data exchange and multi-model capabilities for chemical weather forecasting and near real-time information services in Europe. The action includes approximately 30 participants from 19 countries, and its duration is from 2007 to 2011 (http://www.chemicalweather.eu/. Major efforts have been dedicated in other actions and projects to the development of infrastructures for data flow. We have therefore aimed for collaboration with ongoing actions towards developing near real-time exchange of input data for air quality forecasting. We have collected information on the operational air quality forecasting models on a regional and continental scale in a structured form, and inter-compared and evaluated the physical and chemical structure of these models. We have also constructed a European chemical weather forecasting portal that includes links to most of the available chemical weather forecasting systems in Europe. The collaboration also includes the examination of the case studies that have been organized within COST-728, in order to inter-compare and evaluate the models against experimental data. We have also constructed an operational model forecasting ensemble. Data from a representative set of regional background stations have been selected, and the operational forecasts for this set of sites will be inter-compared and evaluated. The Action has investigated, analysed and reviewed existing chemical weather information systems and services, and will provide recommendations on best practices concerning the presentation and dissemination of chemical weather information towards the public and decision makers.

  13. Innovation Forecasting

    Science.gov (United States)

    1997-11-01

    relating to “ injectors ”) to develop a map of the related technologies [33.] Another approach is to develop a “tree” showing a system branching into its...additional terms such as “trend,” “forecast,” “ delphi ,” “assessment,” and so forth may call up other forecasts and assessments relating to the topic...present and future engine technologies. A preliminary search (Step 1, Table 5) located prior forecasts, in particular, a Delphi study [36]. The Delphi

  14. Establishing an Environmental Scanning/Forecasting System to Augment College and University Planning.

    Science.gov (United States)

    Morrison, James L.

    1987-01-01

    The major benefit of an environmental scanning/forecasting system is in providing critical information for strategic planning. Such a system allows the institution to detect social, technological, economic, and political trends and potential events. The environmental scanning database developed by United Way of America is described. (MLW)

  15. CUACE/Dust ─ an integrated system of observation and modeling systems for operational dust forecasting in Asia

    Directory of Open Access Journals (Sweden)

    X. Y. Zhang

    2008-05-01

    Full Text Available An integrated sand and dust storm (SDS forecasting system – CUACE/Dust (Chinese Unified Atmospheric Chemistry Environment for Dust has been developed, which consists of a comprehensive dust aerosol module with emission, dry/wet depositions and other atmospheric dynamic processes, and a data assimilation system (DAS using observational data from the CMA (China Meteorological Administration ground dust monitoring network and retrieved dust information from a Chinese geostationary satellite – FY-2C. This is the first time that a combination of surface network observations and satellite retrievals of the dust aerosol has been successfully used in the real time operational forecasts in East Asia through a DAS. During its application for the operational SDS forecasts in East Asia for spring 2006, this system captured the major 31 SDS episodes observed by both surface and satellite observations. Analysis shows that the seasonal mean threat score (TS for 0–24 h forecast over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the DAS, a 41% enhancement. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against the surface PM10 monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. This is a summary paper for a special issue of ACP featuring the development and results of the forecasting system.

  16. New tool for integration of wind power forecasting into power system operation

    DEFF Research Database (Denmark)

    Gubina, Andrej F.; Keane, Andrew; Meibom, Peter

    2009-01-01

    for evaluation of the impacts that different types of wind energy forecasts (stochastic vs. deterministic vs. perfect) have on the schedules, and how the new incoming information via in-day scheduling impacts the quality of the schedules. Within the methodology, metrics to assess the quality of the schedules......The paper describes the methodology that has been developed for transmission system operators (TSOs) of Republic of Ireland, Eirgrid, and Northern Ireland, SONI the TSO in Northern Ireland, to study the effects of advanced wind power forecasting on optimal short-term power system scheduling...

  17. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    Energy Technology Data Exchange (ETDEWEB)

    Zulkepli, Jafri, E-mail: zhjafri@uum.edu.my; Abidin, Norhaslinda Zainal, E-mail: nhaslinda@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah (Malaysia); Fong, Chan Hwa, E-mail: hfchan7623@yahoo.com [SWM Environment Sdn. Bhd.Level 17, Menara LGB, Taman Tun Dr. Ismail Kuala Lumpur (Malaysia)

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  18. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    Science.gov (United States)

    Zulkepli, Jafri; Fong, Chan Hwa; Abidin, Norhaslinda Zainal

    2015-12-01

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  19. The effects of land surface process perturbations in a global ensemble forecast system

    Science.gov (United States)

    Deng, Guo; Zhu, Yuejian; Gong, Jiandong; Chen, Dehui; Wobus, Richard; Zhang, Zhe

    2016-10-01

    Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

  20. Hybrid artificial neural network system for short-term load forecasting

    Directory of Open Access Journals (Sweden)

    Ilić Slobodan A.

    2012-01-01

    Full Text Available This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF. The system comprises of two Artificial Neural Networks (ANN, assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP which functions as integrated load predictor (ILP for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP, additional information is presented to the actual forecasting ANN (HLP, while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE.

  1. Forecasting drought risks for a water supply storage system using bootstrap position analysis

    Science.gov (United States)

    Tasker, Gary; Dunne, Paul

    1997-01-01

    Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

  2. Study on the forecasting and maintenance system of special railway subsidence in mine area

    Institute of Scientific and Technical Information of China (English)

    王喜富; 朱德明; 任占营

    2003-01-01

    The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence forecasting is incorrect and maintenance measure is not suitable in the preceding and the process of mining, the normal operation of the railway in mine area will not be ensured and perhaps the safety accident will be resulted. The railway subsidence forecasting and maintenance system for fully mechanized caving coal face are studied and developed in this connection. Based on the accurate subsidence forecasting of the terrene and railway, the maintenance measure for track and switch turnout in railway is put forward in this system.

  3. Towards a Multi-Model Subseasonal Excessive Heat Outlook System

    Science.gov (United States)

    Vintzileos, A.

    2015-12-01

    We developed an experimental realtime subseasonal excessive heat outlook and monitoring system (SEHOMS) based on the detection of heat events in dynamical forecasts and reanalyses. Our definition of a heat event takes into account both the challenges of subseasonal forecasting and the effects of heat stress on human physiology e.g., the dependence of heat impacts on duration, geographical location and timing of the heat event. The prototype outlook system focuses on forecast lead time week-2 and uses the Global Ensemble Forecast System (GEFS) reforecast conducted at ESRL and the NCEP-GEFS operational realtime ensemble forecasts. The prototype monitoring system, on which we base forecast verification, provides a dual output. The first product uses the NCAR/NCEP reanalysis; the second monitoring product is based on the day-1 forecast from the GEFS reforecast and from the operational GEFS realtime forecast. In this presentation we first show results from the prototype forecasting and monitoring system. We then compare these results with forecasts from the SEHOMS in which we gradually add reforecasts obtained from the S2S database (NCEP - Climate forecast System and ECMWF models). Finally we discuss the possibility of expanding the SEHOMS to week-3 and week-4 based on results from the CFS, ECMWF model, and the North American Multi-Model Ensemble system (NMME).

  4. Ocean Model, Analysis and Prediction System version 3: operational global ocean forecasting

    Science.gov (United States)

    Brassington, Gary; Sandery, Paul; Sakov, Pavel; Freeman, Justin; Divakaran, Prasanth; Beckett, Duan

    2017-04-01

    The Ocean Model, Analysis and Prediction System version 3 (OceanMAPSv3) is a near-global (75S-75N; no sea-ice), uniform horizontal resolution (0.1°x0.1°), 51 vertical level ocean forecast system producing daily analyses and 7 day forecasts. This system was declared operational at the Bureau of Meteorology in April 2016 and subsequently upgraded to include ACCESS-G APS2 in June 2016 and finally ported to the Bureau's new supercomputer in Sep 2016. This system realises the original vision of the BLUElink projects (2003-2015) to provide global forecasts of the ocean geostrophic turbulence (eddies and fronts) in support of Naval operations as well as other national services. The analysis system has retained an ensemble-based optimal interpolation method with 144 stationary ensemble members derived from a multi-year hindcast. However, the BODAS code has been upgraded to a new code base ENKF-C. A new strategy for initialisation has been introduced leading to greater retention of analysis increments and reduced shock. The analysis cycle has been optimised for a 3-cycle system with 3 day observation windows retaining an advantage as a multi-cycle time-lagged ensemble. The sea surface temperature and sea surface height anomaly analysis errors in the Australian region are 0.34 degC and 6.2 cm respectively an improvement of 10% and 20% respectively over version 2. In addition, the RMSE of the 7 day forecast has lower error than the 1 day forecast from the previous system (version 2). International intercomparisons have shown that this system is comparable in performance with the two leading systems and is often the leading performer for surface temperature and upper ocean temperature. We present an overview of the system, the data assimilation and initialisation, demonstrate the performance and outline future directions.

  5. Web-based hydrological modeling system for flood forecasting and risk mapping

    Science.gov (United States)

    Wang, Lei; Cheng, Qiuming

    2008-10-01

    Mechanism of flood forecasting is a complex system, which involves precipitation, drainage characterizes, land use/cover types, ground water and runoff discharge. The application of flood forecasting model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no single organization can collect and maintain all the multidisciplinary data. The possible usage of the available datasets remains limited primarily because of the difficulty associated with combining data from diverse and distributed data sources. Difficulty in linking data, analysis tools and model is one of the barriers to be overcome in developing real-time flood forecasting and risk prediction system. The current revolution in technology and online availability of spatial data, particularly, with the construction of Canadian Geospatial Data Infrastructure (CGDI), a lot of spatial data and information can be accessed in real-time from distributed sources over the Internet to facilitate Canadians' need for information sharing in support of decision-making. This has resulted in research studies demonstrating the suitability of the web as a medium for implementation of flood forecasting and flood risk prediction. Web-based hydrological modeling system can provide the framework within which spatially distributed real-time data accessed remotely to prepare model input files, model calculation and evaluate model results for flood forecasting and flood risk prediction. This paper will develop a prototype web-base hydrological modeling system for on-line flood forecasting and risk mapping in the Oak Ridges Moraine (ORM) area, southern Ontario, Canada, integrating information retrieval, analysis and model analysis for near real time river runoff prediction, flood frequency prediction, flood risk and flood inundation

  6. Error discrimination of an operational hydrological forecasting system at a national scale

    Science.gov (United States)

    Jordan, F.; Brauchli, T.

    2010-09-01

    The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation

  7. A stochastic space-time rainfall forecasting system for real time flow forecasting II: Application of SHETRAN and ARNO rainfall runoff models to the Brue catchment

    Directory of Open Access Journals (Sweden)

    D. Mellor

    2000-01-01

    Full Text Available Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes. Keywords: SHETRAN, ARNO, HYREX, rainfall-runoff model, Brue, real-time flow forecasting

  8. Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

    Science.gov (United States)

    Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

    2016-01-01

    Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

  9. Performance comparison of meso-scale ensemble wave forecasting systems for Mediterranean sea states

    Science.gov (United States)

    Pezzutto, Paolo; Saulter, Andrew; Cavaleri, Luigi; Bunney, Christopher; Marcucci, Francesca; Torrisi, Lucio; Sebastianelli, Stefano

    2016-08-01

    This paper compares the performance of two wind and wave short range ensemble forecast systems for the Mediterranean Sea. In particular, it describes a six month verification experiment carried out by the U.K. Met Office and Italian Air Force Meteorological Service, based on their respective systems: the Met Office Global-Regional Ensemble Prediction System and the Nettuno Ensemble Prediction System. The latter is the ensemble version of the operational Nettuno forecast system. Attention is focused on the differences between the two implementations (e.g. grid resolution and initial ensemble members sampling) and their effects on the prediction skill. The cross-verification of the two ensemble systems shows that from a macroscopic point of view the differences cancel out, suggesting similar skill. More in-depth analysis indicates that the Nettuno wave forecast is better resolved but, on average, slightly less reliable than the Met Office product. Assessment of the added value of the ensemble techniques at short range in comparison with the deterministic forecast from Nettuno, reveals that adopting the ensemble approach has small, but substantive, advantages.

  10. A space weather forecasting system with multiple satellites based on a self-recognizing network.

    Science.gov (United States)

    Tokumitsu, Masahiro; Ishida, Yoshiteru

    2014-05-05

    This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  11. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

    Science.gov (United States)

    Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-01-01

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

  12. A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

    Directory of Open Access Journals (Sweden)

    Masahiro Tokumitsu

    2014-05-01

    Full Text Available This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV. The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

  13. Analysis of data systems requirements for global crop production forecasting in the 1985 time frame

    Science.gov (United States)

    Downs, S. W.; Larsen, P. A.; Gerstner, D. A.

    1978-01-01

    Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process.

  14. Current status and challenges of typhoon forecasting and warning systems in China

    Institute of Scientific and Technical Information of China (English)

    Duan Yihong

    2014-01-01

    China is one of the countries most severely suffering from tropical cyclones. The exact and timely forecasting and warning is of significant importance in fighting against tropical cyclones and mitigating their im pacts on China. The numerical weather prediction(NWP)system for tropical cyclone rainfall and strong wind is going to play a more and more important role. There is also a need for timely and user friendly modern war ning services in order to provide the governments and relevant authorities at all levels and general public with ty phoon forecasts and information about the associated disasters and response strategy services.

  15. Flow Forecasting in Urban Drainage Systems using Deterministic Updating of Water Levels in Distributed Hydraulic Models

    DEFF Research Database (Denmark)

    Hansen, Lisbeth S.; Borup, Morten; Møller, A.;

    2011-01-01

    the performance of the updating procedure for flow forecasting. Measured water levels in combination with rain gauge input are used as basis for the evaluation. When compared to simulations without updating, the results show that it is possible to obtain an improvement in the 20 minute forecast of the water level...... to eliminate some of the unavoidable discrepancies between model and reality. The latter can partly be achieved by using the commercial tool MOUSE UPDATE, which is capable of inserting measured water levels from the system into the distributed, physically based MOUSE model. This study evaluates and documents...

  16. High-quality Wind Power Scenario Forecasts for Decision-making Under Uncertainty in Power Systems

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Pinson, Pierre

    2014-01-01

    The large scale integration of wind generation in existing power systems requires novel operational strategies and market clearing mechanisms to account for the variable nature of this energy source. An efficient method to cope with this uncertainty is stochastic optimization which however requires...... high-quality forecasts in the form of scenarios. The main goal of this work is to release a public dataset of wind power forecasts to be used as a reference for future research. To that extent, we provide a complete framework to describe wind power uncertainty in terms of single...

  17. Technical Note: The normal quantile transformation and its application in a flood forecasting system

    Directory of Open Access Journals (Sweden)

    K. Bogner

    2012-04-01

    Full Text Available The Normal Quantile Transform (NQT has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

  18. LICORS: Light Cone Reconstruction of States for Non-parametric Forecasting of Spatio-Temporal Systems

    CERN Document Server

    Goerg, Georg M

    2012-01-01

    We present a new, non-parametric forecasting method for data where continuous values are observed discretely in space and time. Our method, "light-cone reconstruction of states" (LICORS), uses physical principles to identify predictive states which are local properties of the system, both in space and time. LICORS discovers the number of predictive states and their predictive distributions automatically, and consistently, under mild assumptions on the data source. We provide an algorithm to implement our method, along with a cross-validation scheme to pick control settings. Simulations show that CV-tuned LICORS outperforms standard methods in forecasting challenging spatio-temporal dynamics. Our work provides applied researchers with a new, highly automatic method to analyze and forecast spatio-temporal data.

  19. Global crop production forecasting - A simulation analysis of the data system problems and their solutions

    Science.gov (United States)

    Golden, H.; Neiers, J. W.

    1978-01-01

    Alternative data systems for a global crop production forecasting system were studied with the aid of a unique simulation facility called the Data System Dynamic Simulator (DSDS). Information system requirements were determined and compared with existing and planned data systems, and deficiencies were identified and analyzed. A first step was to determine the data load for an operational global crop production forecasting system as a function of data frequency, crop types, biophases, cloud coverage, and number of satellites. The DSDS was used to correlate the interrelated influence of orbital parameters, crop calendars, and cloud conditions to generate global data loading profiles. Some of the more important conclusions and the main features of the simulation system are presented.

  20. nowCOAST's Map Service for NOAA NOS Lake Erie Operational Forecast System (LEOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map displays the latest nowcasts and forecast guidance of water temperature, water currents, and water level guidance...

  1. nowCOAST's Map Service for NOAA NOS Northern Gulf of Mexico Operational Forecast System (NGOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  2. nowCOAST's Map Service for NOAA NOS Tampa Bay Operational Forecast System (TBOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  3. nowCOAST's Map Service for NOAA NOS Northwest Gulf of Mexico Operational Forecast System (NWGOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  4. nowCOAST's Map Service for NOAA NOS Extratropical Surge and Tide Operational Forecast System (ESTOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of combined (tides + wind driven) water level and...

  5. nowCOAST's Map Service for NOAA NOS Lake Huron Operational Forecast System (LHOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  6. nowCOAST's Map Service for NOAA NOS Lake Ontario Operational Forecast System (LOOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  7. nowCOAST's Map Service for NOAA NOS Northern Gulf of Mexico Operational Forecast System (NGOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  8. nowCOAST's Map Service for NOAA NOS Lake Superior Operational Forecast System (LSOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  9. nowCOAST's Map Service for NOAA NOS Lake Superior Operational Forecast System (LSOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  10. nowCOAST's Map Service for NOAA NOS St. Johns River Operational Forecast System (SJROFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of surface water temperature, salinity, and water...

  11. nowCOAST's Map Service for NOAA NOS Delaware Bay Operational Forecast System (DBOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of surface water temperature, salinity, water...

  12. nowCOAST's Map Service for NOAA NOS Chesapeake Bay Operational Forecast System (CBOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  13. nowCOAST's Map Service for NOAA NOS Lake Huron Operational Forecast System (LHOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  14. nowCOAST's Map Service for NOAA NOS Lake Michigan Operational Forecast System (LMOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map services provides map of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  15. nowCOAST's Map Service for NOAA NOS San Francisco Bay Operational Forecast System (SFBOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  16. nowCOAST's Map Service for NOAA NOS Columbia River Estuary Operational Forecast System (CREOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offets map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents, and...

  17. nowCOAST's Map Service for NOAA NOS Lake Ontario Operational Forecast System (LOOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  18. nowCOAST's Map Service for NOAA NOS Northeast Gulf of Mexico Operational Forecast System (NEGOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  19. nowCOAST's Map Service for NOAA NOS Columbia River Estuary Operational Forecast System (CREOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  20. nowCOAST's Map Service for NOAA NOS Delaware Bay Operational Forecast System (DBOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of surface water temperature, salinity, and water...

  1. nowCOAST's Map Service for NOAA NOS Lake Michigan Operational Forecast System (LMOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map services provides map of the latest nowcasts and forecast guidance of water temperature, water currents, and water...

  2. nowCOAST's Map Service for NOAA NOS Tampa Bay Operational Forecast System (TBOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  3. nowCOAST's Map Service for NOAA NOS Lake Erie Operational Forecast System (LEOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map displays the latest nowcasts and forecast guidance of water temperature, water currents, and water level guidance...

  4. nowCOAST's Map Service for NOAA NOS St. Johns River Operational Forecast System (SJROFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of surface water temperature, salinity, and water...

  5. nowCOAST's Map Service for NOAA NOS San Francisco Bay Operational Forecast System (SFBOFS) Forecast Guidance (Time Offsets)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-offsets map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  6. nowCOAST's Map Service for NOAA NOS Chesapeake Bay Operational Forecast System (CBOFS) Forecast Guidance (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps of the latest nowcasts and forecast guidance of water temperature, salinity, water currents,...

  7. SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

    Science.gov (United States)

    Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

    2014-12-01

    Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

  8. UQ -- Fast Surrogates Key to New Methodologies in an Operational and Research Volcanic Hazard Forecasting System

    Science.gov (United States)

    Hughes, C. G.; Stefanescu, R. E. R.; Patra, A. K.; Bursik, M. I.; Madankan, R.; Pouget, S.; Jones, M.; Singla, P.; Singh, T.; Pitman, E. B.; Morton, D.; Webley, P.

    2014-12-01

    As the decision to construct a hazard map is frequently precipitated by the sudden initiation of activity at a volcano that was previously considered dormant, timely completion of the map is imperative. This prohibits the calculation of probabilities through direct sampling of a numerical ash-transport and dispersion model. In developing a probabilistic forecast for ash cloud locations following an explosive volcanic eruption, we construct a number of possible meta-models (a model of the simulator) to act as fast surrogates for the time-expensive model. We will illustrate the new fast surrogates based on both polynomial chaos and multilevel sparse representations that have allowed us to conduct the Uncertainty Quantification (UQ) in a timely fashion. These surrogates allow orders of magnitude improvement in cost associated with UQ, and are likely to have a major impact in many related domains.This work will be part of an operational and research volcanic forecasting system (see the Webley et al companion presentation) moving towards using ensembles of eruption source parameters and Numerical Weather Predictions (NWPs), rather than single deterministic forecasts, to drive the ash cloud forecasting systems. This involves using an Ensemble Prediction System (EPS) as input to an ash transport and dispersion model, such as PUFF, to produce ash cloud predictions, which will be supported by a Decision Support System. Simulation ensembles with different input volcanic source parameters are intelligently chosen to predict the average and higher-order moments of the output correctly.

  9. Verification of Ensemble Forecasts for the New York City Operations Support Tool

    Science.gov (United States)

    Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

    2012-12-01

    forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

  10. Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2012-01-01

    We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain...... satisfactory predictions for the smaller catchment but rather large uncertainties for the bigger catchment where the applied storage cascade seems too simple. Radar rainfall introduces more uncertainty into the flow forecast model estimation. However, the radar rainfall forecasts also result in a slightly...

  11. Forecasting the absolute and relative shortage of physicians in Japan using a system dynamics model approach.

    Science.gov (United States)

    Ishikawa, Tomoki; Ohba, Hisateru; Yokooka, Yuki; Nakamura, Kozo; Ogasawara, Katsuhiko

    2013-08-27

    In Japan, a shortage of physicians, who serve a key role in healthcare provision, has been pointed out as a major medical issue. The healthcare workforce policy planner should consider future dynamic changes in physician numbers. The purpose of this study was to propose a physician supply forecasting methodology by applying system dynamics modeling to estimate future absolute and relative numbers of physicians. We constructed a forecasting model using a system dynamics approach. Forecasting the number of physician was performed for all clinical physician and OB/GYN specialists. Moreover, we conducted evaluation of sufficiency for the number of physicians and sensitivity analysis. As a result, it was forecast that the number of physicians would increase during 2008-2030 and the shortage would resolve at 2026 for all clinical physicians. However, the shortage would not resolve for the period covered. This suggests a need for measures for reconsidering the allocation system of new entry physicians to resolve maldistribution between medical departments, in addition, for increasing the overall number of clinical physicians.

  12. Implementation of RAP/HRRR operational system for precipitation forecast over Southeast China

    Science.gov (United States)

    Pan, Ning; Liu, Ming; Zhang, Changan; Zhang, Xin; Du, Yu; Zhang, Meng

    2017-04-01

    The hourly-updated hybrid data assimilation system (RAP/HRRR) is developed at Fujian Meteorological Center of China for operational precipitation forecast. The 9km resolution RAP (Rapid Refresh) covering Southeast China is coupled with the higher-resolution 3km HRRR (High-Resolution Rapid Refresh) model that covers Fujian province. Long-term sensitivity experiments for the rainy season of Southeast China (i.e., from May to August) are conducted to investigate the best combination of data assimilation schemes and physical parameterizations in RAP/HRRR. Observations from various sources, including surface measurements, sounding, Doppler radar, satellites and aircraft, are assimilated in this system through Hybrid EnKF-3DVar method. High-frequency analysis noise is reduced by digital filtering. Using the dynamic filtering blending method, the large scale information from global model is updated in the RAP through partial cycle. Both the retrospective and the real-time forecasting performance of the RAP and HRRR for those rainfall events are evaluated. The evaluation results demonstrate that the RAP/HRRR system has good capability of forecasting 12-hour precipitation. Especially, for the 3-hour precipitation forecasts, the thread scores of light, moderate and heavy rainfall show better performance than the control experiments without hybrid data assimilation during the experimental months over Southeast China.

  13. FORECASTING OF A REMAINING LIFE OF ENGINEERING SYSTEMS BY USING THE PARAMETRIC MODELS OF RELIABILITY VARIATION

    Directory of Open Access Journals (Sweden)

    Gennadiy A. Berketov

    2013-01-01

    Full Text Available The article is devoted to the problem of forecasting of a remaining life of complex engineering systems at the operational stage. The example of solving this problem is replacement of devices if their specified life has been completed (preventive replacements or if we extend the lifetime a system in addition to guarantee. This method allows to organize a preventive maintenance of a system taking into account its technical position at this moment all that lead to reducing the maintenance costs.

  14. Valuing year-to-go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

    Science.gov (United States)

    Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

    2016-05-01

    We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

  15. A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China.Part Ⅰ: A Case Study

    Institute of Scientific and Technical Information of China (English)

    ZHU Jiangshan; Fanyou KONG; LEI Hengchi

    2012-01-01

    A single-model,short-range,ensemble forecasting system (Institute of Atmospheric Physics,Regional Ensemble Forecast System,IAP REFS) with 15-km grid spacing,configured with multiple initial conditions,multiple lateral boundary conditions,and multiple physics parameterizations with 11 ensemble members,was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China.This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework.The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System (NCEP GEFS) forecasts,and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region.Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system.The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system.The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts.Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts (PQPF).However,the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables,and its influence on PQPF resolution was limited as well.

  16. CFORS - Regional Chemical and Weather Forecast System in Support of Field Experiments

    Science.gov (United States)

    Yienger, J. J.; Uno, I.; Guttikunda, S. K.; Carmichael, G. R.; Tang, Y.; Thongboonchoo, N.; Woo, J.; Dorwart, J.; Streets, D.

    2001-12-01

    In this paper we will present the development, evaluation, and use of improved modeling techniques and methodologies for the integration of meteorological forecasts with air pollution forecasts in support of field operations during the TRACE-P and Ace-Asia experiments in East Asia. During the campaign period we provided a variety of forecast products using our regional modeling system built upon the dynamic meteorological model RAMS and the 3-D regional chemical transport models STEM-III. These models were run in both on-line and off-line modes, and the results integrated into an interactive web-based data mining and analysis framework. This resulting Chemical Weather Forecasting System CFORS, was run operationally for the period February through May 2001, and provided 72-hr forecasts of a variety of aerosol, chemical and air mass and emission marker quantities. These included aerosol mass distribution and optical depth by major component (e.g., dust, sea salt, black carbon, organic carbon, and sulfate), photochemical quantities including ozone and OH/HO2, and air mass & emissions markers including lightning, volcanic, mega-cities, and biomass burning. These model products were presented along with meteorological forecasts and satellite products, and used to help determine the flight plans, the positioning of the ship, and to alert surface stations of upcoming events (such as dust storms). The use of CFORS forecasts (along with other model results) models were shown to provide important new information and level of detail into mission planning. For example many of the mission objectives required designing flight paths that sampled across gradients of optical depth, or flew above, below and through vertical layers of aerosol, intercepted biomass emission plumes, or sampled dust storms. CFORS, forecasts of dust outbreaks and plume locations, etc., proved to be very useful in designing missions that meet these objective. In this paper we will present an overview of

  17. a system approach to the long term forecasting of the climat data in baikal region

    Science.gov (United States)

    Abasov, N.; Berezhnykh, T.

    2003-04-01

    optimal vectors of parameters obtained are tested on the examination (verifying) subsample. If the procedure is successful, the forecast is immediately made by integration of several best solutions. Peculiarities of forecasting extreme processes. Methods of long-term forecasting allow the sufficiently reliable forecasts to be made within the interval of xmin+Δ_1, xmax - Δ_2 (i.e. in the interval of medium values of indices). Meanwhile, in the intervals close to extreme ones, reliability of forecasts is substantially lower. While for medium values the statistics of the100-year sequence gives acceptable results owing to a sufficiently large number of revealed analogs that correspond to prognostic samples, for extreme values the situation is quite different, first of all by virtue of poverty of statistical data. Decreasing the values of Δ_1,Δ_2: Δ_1,Δ_2 rightarrow 0 (by including them into optimization parameters of the considered forecasting methods) could be one of the ways to improve reliability of forecasts. Partially, such an approach has been realized in the method of analog-similarity relations, giving the possibility to form a range of possible forecasted trajectories in two variants - from the minimum possible trajectory to the maximum possible one. Reliability of long-term forecasts. Both the methodology and the methods considered above have been realized as the information-forecasting system "GIPSAR". The system includes some tools implementing several methods of forecasting, analysis of initial and forecasted information, a developed database, a set of tools for verification of algorithms, additional information on the algorithms of statistical processing of sequences (sliding averaging, integral-difference curves, etc.), aids to organize input of initial information (in its various forms) as well as aids to draw up output prognostic documents. Risk management. The normal functioning of the Angara cascade is periodically interrupted by risks of two types

  18. Prediction of Wintertime Northern Hemisphere Blocking by the NCEP Climate Forecast System

    Institute of Scientific and Technical Information of China (English)

    JIA Xiaolong; YANG Song; SONG Wenling; HE Bin

    2014-01-01

    Daily output from the hindcasts by the NCEP Climate Forecast System version 2 (CFSv2) is analyzed to understand CFSv2’s skill in forecasting wintertime atmospheric blocking in the Northern Hemisphere. Prediction skills of sector blocking, sector-blocking episodes, and blocking onset/decay are assessed with a focus on the Euro-Atlantic sector (20◦W-45◦E) and the Pacifi c sector (160◦E-135◦W). Features of associated circulation and climate patterns are also examined. The CFSv2 well captures the observed features of longitudinal distribution of blocking activity, but underestimates blocking frequency and intensity and shows a decreasing trend in blocking frequency with increasing forecast lead time. Within 14-day lead time, the Euro-Atlantic sector blocking receives a higher skill than the Pacifi c sector blocking. Skillful forecast (taking the hit rate of 50% as a criterion) can be obtained up to 9 days in the Euro-Atlantic sector, which is slightly longer than that in the Pacifi c sector (7 days). The forecast skill of sector-blocking episodes is slightly lower than that of sector blocking in both sectors, and it is slightly higher in the Euro-Atlantic sector than in the Pacifi c sector. Compared to block onset, the skill for block decay is lower in the Euro-Atlantic sector, slightly higher in the Pacifi c sector during the early three days but lower after three days in lead time. In both the Euro-Atlantic and the Pacifi c sectors, a local dipole pattern in 500-hPa geopotential height associated with blocking is well presented in the CFSv2 prediction, but the wave-train like pattern that is far away from the blocking sector can only maintain in the forecast of relative short lead time. The CFSv2 well reproduces the observed characteristics of local temperature and precipitation anomalies associated with blocking.

  19. Impact of various observing systems on weather analysis and forecast over the Indian region

    Science.gov (United States)

    Singh, Randhir; Ojha, Satya P.; Kishtawal, C. M.; Pal, P. K.

    2014-09-01

    To investigate the potential impact of various types of data on weather forecast over the Indian region, a set of data-denial experiments spanning the entire month of July 2012 is executed using the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. The experiments are designed to allow the assessment of mass versus wind observations and terrestrial versus space-based instruments, to evaluate the relative importance of the classes of conventional instrument such as radiosonde, and finally to investigate the role of individual spaceborne instruments. The moist total energy norm is used for validation and forecast skill assessment. The results show that the contribution of wind observations toward error reduction is larger than mass observations in the short range (48 h) forecast. Terrestrial-based observations generally contribute more than space-based observations except for the moisture fields, where the role of the space-based instruments becomes more prevalent. Only about 50% of individual instruments are found to be beneficial in this experiment configuration, with the most important role played by radiosondes. Thereafter, Meteosat Atmospheric Motion Vectors (AMVs) (only for short range forecast) and Special Sensor Microwave Imager (SSM/I) are second and third, followed by surface observations, Sounder for Probing Vertical Profiles of Humidity (SAPHIR) radiances and pilot observations. Results of the additional experiments of comparative performance of SSM/I total precipitable water (TPW), Microwave Humidity Sounder (MHS), and SAPHIR radiances indicate that SSM/I is the most important instrument followed by SAPHIR and MHS for improving the quality of the forecast over the Indian region. Further, the impact of single SAPHIR instrument (onboard Megha-Tropiques) is significantly larger compared to three MHS instruments (onboard NOAA-18/19 and MetOp-A).

  20. MOSE: A Demonstrator for an Automatic Operational System for the Optical Turbulence Forecast for ESO Sites

    Science.gov (United States)

    Masciadri, Elena; Lascaux, F.; Turchi, A.; Fini, L.

    2017-09-01

    "Most of the observations performed with new-generation ground-based telescopes are employing the Service Mode. To optimize the flexible-scheduling of scientific programs and instruments, the optical turbulence (OT) forecast is a must, particularly when observations are supported by adaptive optics (AO) and Interferometry. Reliable OT forecast are crucial to optimize the usage of AO and interferometric facilities which is not possible when using only optical measurements. Numerical techniques are the best placed to achieve such a goal. The MOSE project (MOdeling ESO Sites), co-funded by ESO, aimed at proving the feasibility of the forecast of (1) all the classical atmospheric parameters (such as temperature, wind speed and direction, relative humidity) and (2) the optical turbulence i.e. the CN 2 profiles and all the main integrated astro-climatic parameters derived from the CN 2 (the seeing, the isoplanatic angle, the wavefront coherence time) above the two ESO sites of Cerro Paranal and Cerro Armazones. The proposed technique is based on the use of a non-hydrostatic atmospheric meso-scale model and a dedicated code for the optical turbulence. The final goal of the project aimed at implementing an automatic system for the operational forecasts of the aforementioned parameters to support the astronomical observations above the two sites. MOSE Phase A and B have been completed and a set of dedicated papers have been published on the topic. Model performances have been extensively quantified with several dedicated figures of merit and we proved that our tool is able to provide reliable forecasts of optical turbulence and atmospheric parameters with very satisfactory score of success. This should guarantee us to make a step ahead in the framework of the Service Mode of new generation telescopes. A conceptual design as well as an operational plan of the automatic system has been submitted to ESO as integral part of the feasibility study. We completed a negotiation with

  1. Forecasting of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction System

    Science.gov (United States)

    Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely

    2015-04-01

    The study presents and compares several approaches in EPS (ensemble prediction system) forecasting based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS forecasts (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The forecast length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in forecasting intense multicell thunderstorms or mesoscale convective systems of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to forecast the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the system in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble systems. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.

  2. A model output statistics system to forecast the 2 metre temperature at the "Wettermast Hamburg" site

    Science.gov (United States)

    Finn, Tobias Sebastian; Ament, Felix

    2016-04-01

    The model output statistics (MOS) method is frequently used to downscale and improve numerical weather models for specific measurement sites. One of these is the "Wettermast Hamburg" (http://wettermast-hamburg.zmaw.de/) in the south-east of Hamburg. It is operated by the Meteorological Institute of the University of Hamburg. The MOS approach was used to develop a not yet existing 2 metre temperature forecasting system for this site. The forecast system is based on the 0 UTC control run of the legacy "global ensemble forecast system". The multiple linear equations were calculated using a training period of 2 years (01.03.2012-28.02.2014), while the developed models were evaluated using the following year (01.03.2014-28.02.2015). During the development process it was found that a combination of forward and backward selection together with the "Bayesian information criterion", a warm-cold splitting and a five-fold cross-validation was the best automated method to minimize the risk of overfitting. To further reduce the risk, the number of predictors were limited to 6. Also the first 3 possible predictors were selected by hand. In comparison to the fully automated method, the error was not changed significantly through this restrictions for the evaluation period. The analysis of the importance of selected predictors shows that the global weather model has problems characterizing specific weather phenomena. Large model errors by misrepresenting the boundary layer were highlighted through the 10 metre wind speed, the surface temperature and the 1000 hPa temperature as frequently selected predictors. The final forecast system has a root-mean-square error minimum of 1.15 K for the initialization and a maximum 2.2 K at the 84 hour lead time. Compared to the direct model output this is a mean improvement of ˜ 22%. The main error reduction is achieved in the first 24 hours of the forecast, especially at the initialization (up to 45% error reduction).

  3. A Brief Historic Overview of Clinical Disorders Associated with Tryptophan: The Relevance to Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM).

    Science.gov (United States)

    Blankfield, Adele

    2012-01-01

    Last century there was a short burst of interest in the tryptophan related disorders of pellagra and related abnormalities that are usually presented in infancy.1,2 Nutritional physiologists recognized that a severe human dietary deficiency of either tryptophan or the B group vitamins could result in central nervous system (CNS) sequelae such as ataxia, cognitive dysfunction and dysphoria, accompanied by skin hyperpigmentation.3,4 The current paper will focus on the emerging role of tryptophan in chronic fatigue syndrome (CFS) and fibromyalgia (FM).

  4. Retrospective forecasting of the 2010-2014 Melbourne influenza seasons using multiple surveillance systems.

    Science.gov (United States)

    Moss, R; Zarebski, A; Dawson, P; McCAW, J M

    2017-01-01

    Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4-6 weeks in advance. We also show that making simultaneous use of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a 'ground truth' for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.

  5. Predicting the Heat Consumption in District Heating Systems using Meteorological Forecasts

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg, orlov 31.07.2008; Madsen, Henrik

    Methods for on-line prediction of heat consumption in district heating systems hour by hour for horizons up to 72 hours are considered in this report. Data from the district heating system Vestegnens Kraftvarmeselskab I/S is used in the investigation. During the development it has been assumed...... that meteorological forecasts are available on-line. Such a service has recently been introduced by the Danish Meteorological Institute. However, actual meteorological forecasts has not been available for the work described here. Assuming the climate to be known the mean absolute relative prediction error for 72 hour......, this is somewhat contrary to practice. The work presented is a demonstration of the value of the so called gray box approach where theoretical knowledge about the system under consideration is combined with information from measurements performed on the system in order to obtain a mathematical description...

  6. Forecasting the future: is it possible for adiabatically time-varying nonlinear dynamical systems?

    Science.gov (United States)

    Yang, Rui; Lai, Ying-Cheng; Grebogi, Celso

    2012-09-01

    Nonlinear dynamical systems in reality are often under environmental influences that are time-dependent. To assess whether such a system can perform as desired or as designed and is sustainable requires forecasting its future states and attractors based solely on time series. We propose a viable solution to this challenging problem by resorting to the compressive-sensing paradigm. In particular, we demonstrate that, for a dynamical system whose equations are unknown, a series expansion in both dynamical and time variables allows the forecasting problem to be formulated and solved in the framework of compressive sensing using only a few measurements. We expect our method to be useful in addressing issues of significant current concern such as the sustainability of various natural and man-made systems.

  7. Two examples of expert knowledge based system for avalanche forecasting and protection

    Science.gov (United States)

    Buisson, Laurent; Giraud, Gérald

    1995-11-01

    In avalanche modelling and control and in avalanche forecasting, most of the knowledge is based on scientific theory but the experience of specialists (field practitioners, forecasters...) plays a large role. This paper presents two French computer-based systems dedicated to avalanche modelling and control and to avalanche forecasting. They are both based on expert knowledge. ELSA (Etude et Limites de Sites d'Avalanches), is a computer system dedicated to the modelling of the knowledge of avalanche experts and to the integration of new symbolic computer models with classical numerical models. The basic aim of integration is to build a unique computer system incorporating all these models. After a description of the terrain representation, we present the different scenarios that ELSA takes into account. Then, the methods which deal with some phenomena occurring in avalanches are described. The problems involved in the integration of these methods close this first part. MEPRA is an expert system built to create an objective tool in avalanche risk forecasting. This development allowed us to imagine a processing system for 2 of the most important problems in avalanche risk forecasting: representation of the present snow cover characteristics and evaluation of avalanche instability and risk. In this way, mechanics and thermodynamics play a major role in the system. After a punctual validation at the location of a snow weather station and in order to describe the great variability of the snow pack and the avalanche risk in a massif, the MEPRA expert system was connected with a meteorological analysis system, SAFRAN and a numerical model to simulate the snow cover CROCUS. Then, every day, a MEPRA expert analysis is carried out in different locations with different orientations, slopes and altitudes. Its results were used successfully during the Winter Olympic Games of Albertville and by avalanche forecasters during the 92/93 winter season. The daily avalanche risks

  8. Maintaining a Local Data Integration System in Support of Weather Forecast Operations

    Science.gov (United States)

    Watson, Leela R.; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian

    2010-01-01

    Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division

  9. Towards the Development of an Operational Forecast System for the Florida Coast

    Directory of Open Access Journals (Sweden)

    Vladimir A. Paramygin

    2017-01-01

    Full Text Available A nowcasting and forecasting system for storm surge, inundation, waves, and baroclinic flow for the Florida coast has been developed. The system is based on dynamically coupled CH3D and SWAN models and can use a variety of modules to provide different input forcing, boundary and initial conditions. The system is completely automated and operates unattended at pre-scheduled intervals as well as in event-triggered mode in response to Atlantic-basin tropical cyclone advisories issued by the National Hurricane Center. The system provides up to 72-h forecasts forward depending on the input dataset duration. Spatially, the system spans the entire Florida coastline by employing four high-resolution domains with resolutions as fine as 10–30 m in the near-shore and overland to allow the system to resolve fine estuarine details such as in the Intracoastal Waterway and minor tributaries. The system has been validated in both hindcast and nowcast/forecast modes using water level and salinity data from a variety of sources and has been found to run robustly during the test periods. Low level products (e.g., raw output datasets are disseminated using THREDDS while a custom defined web-based graphical user interface (GUI was developed for high level access.

  10. FORECASTING AND ANALYSIS OF TRENDS IN AREA OF QUALITY MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Aleksandar Vujović

    2009-12-01

    Full Text Available This research presents chronology and trends in area of quality management system through nonconformities. The aim of the work is to forecast possible scenario to foresee activities for future period and time what will point out on critical indicators and on possible measures for improvement. Furthermore, research identifies advantages, disadvantages and possibilities, especially for production and service sectors. The work presents long-term research on quality management system and experience and knowledge that are obtained based on real indicators.

  11. Managing Forecast Uncertainty and Risk in Multireservoir, Multiobjective, and Multistakeholder Systems

    Science.gov (United States)

    Kistenmacher, M.; Georgakakos, A. P.

    2012-12-01

    Hydroclimatic forecasts are often issued in probabilistic form to capture the uncertainties that remain in forecasting inflow magnitude and timing. These forecasts can then be used as input to stochastic management models to help operate reservoir systems and provide stakeholders with probabilistic forecasts of system variables, such as reservoir storages, releases, hydropower production, water withdrawals, and water quality parameters. However, due to high computational requirements, many stochastic management models only evaluate one management policy and are therefore not capable of exploring risk management options. Furthermore, such models usually neglect elements of the real-life water resources systems that they are supposed to represent. This presentation focuses on several advances designed to address these shortcoming and shows their application to a real-life reservoir system in California's Central Valley. This first part of the study focuses on exploring and managing the risks and uncertainties in multi-objective and multi-stakeholder systems. Traditional management models only find management policies that optimize the expected values of system benefits or costs, thereby not allowing operators and stakeholders to explicitly consider issues related to uncertainty and risk management. A technique that can be used to explore different risk management options was developed. The technique allows users to derive policies that produce desired probabilistic distributions of reservoir system outputs reflecting stakeholder preferences by imposing variance constraints on relevant system variables. The second part of this study highlights the expansion of an existing management model, previously only considering monthly water quantity fluxes, to incorporate water quality and flood control objectives. An existing water temperature model is analyzed and used to produce a reduced order model while the flood control objectives are considered through the development

  12. Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

    Science.gov (United States)

    Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph; hide

    2017-01-01

    GEOS-5 is the Goddard Earth Observing System model. GEOS-5 is maintained by the NASA Global Modeling and Assimilation Office. Core development is within GMAO,Goddard Atmospheric Chemistry and Dynamics Laboratory, and with external partners. Primary GEOS-5 functions: Earth system model for studying climate variability and change, provide research quality reanalyses for supporting NASA instrument teams and scientific community, provide near-real time forecasts of meteorology,aerosols, and other atmospheric constituents to support NASA airborne campaigns.

  13. Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

    Directory of Open Access Journals (Sweden)

    Dirk Cannon

    2017-06-01

    Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

  14. Anvil Forecast Tool in the Advanced Weather Interactive Processing System (AWIPS)

    Science.gov (United States)

    Barrett, Joe H., III; Hood, Doris

    2009-01-01

    Launch Weather Officers (LWOs) from the 45th Weather Squadron (45 WS) and forecasters from the National Weather Service (NWS) Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violating the Lightning Launch Commit Criteria (LLCC) (Krider et al. 2006; Space Shuttle Flight Rules (FR), NASA/JSC 2004)). As a result, the Applied Meteorology Unit (AMU) developed a tool that creates an anvil threat corridor graphic that can be overlaid on satellite imagery using the Meteorological Interactive Data Display System (MIDDS, Short and Wheeler, 2002). The tool helps forecasters estimate the locations of thunderstorm anvils at one, two, and three hours into the future. It has been used extensively in launch and landing operations by both the 45 WS and SMG. The Advanced Weather Interactive Processing System (AWIPS) is now used along with MIDDS for weather analysis and display at SMG. In Phase I of this task, SMG tasked the AMU to transition the tool from MIDDS to AWIPS (Barrett et aI., 2007). For Phase II, SMG requested the AMU make the Anvil Forecast Tool in AWIPS more configurable by creating the capability to read model gridded data from user-defined model files instead of hard-coded files. An NWS local AWIPS application called AGRID was used to accomplish this. In addition, SMG needed to be able to define the pressure levels for the model data, instead of hard-coding the bottom level as 300 mb and the top level as 150 mb. This paper describes the initial development of the Anvil Forecast Tool for MIDDS, followed by the migration of the tool to AWIPS in Phase I. It then gives a detailed presentation of the Phase II improvements to the AWIPS tool.

  15. Wet snow hazard for power lines: a forecast and alert system applied in Italy

    Directory of Open Access Journals (Sweden)

    P. Bonelli

    2011-09-01

    Full Text Available Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly.

    The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast, developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.

  16. Electricity demand load forecasting of the Hellenic power system using an ARMA model

    Energy Technology Data Exchange (ETDEWEB)

    Pappas, S.Sp. [ASPETE - School of Pedagogical and Technological Education Department of Electrical Engineering Educators N. Heraklion, 141 21 Athens (Greece); Ekonomou, L.; Chatzarakis, G.E.; Skafidas, P.D. [ASPETE-School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece); Karampelas, P. [Hellenic American University, IT Department, 12 Kaplanon Str., 106 80 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24 100 Kalamata (Greece); Katsikas, S.K. [University of Piraeus, Department of Technology Education and Digital Systems, 150 Androutsou St., 18 532 Piraeus (Greece)

    2010-03-15

    Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts. (author)

  17. Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter

    Science.gov (United States)

    Kudo, R.; Chikamori, H.; Nagai, A.

    2008-12-01

    A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow forecast of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian system, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This system was applied to the Gono-kawa River Basin in Japan, and flood forecasting accuracy of the system with both Particle filtering and extended Kalman filtering and that of the system with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of forecasting accuracy. The result confirms that Particle filtering of the downstream part model improves forecasting accuracy of runoff at

  18. The Use of Fuzzy Systems for Forecasting the Hardenability of Steel

    Directory of Open Access Journals (Sweden)

    Sitek W.

    2016-06-01

    Full Text Available The goal of the research carried out was to develop the fuzzy systems, allowing the determination of the Jominy hardenability curve based on the chemical composition of structural steels for quenching and tempering. Fuzzy system was created to calculate hardness of the steel, based on the alloying elements concentrations, and to forecast the hardenability curves. This was done based on information from the PN-EN 10083-3: 2008. Examples of hardenability curves calculated for exemplar steels were presented. Results of the research confirmed that fuzzy systems are a useful tool in evaluation the effect of alloying elements on the properties of materials compared to conventional methods. It has been demonstrated the practical usefulness of the developed models which allows forecasting the steels’ Jominy hardenability curve.

  19. Model Development and Hindcast Simulations of NOAA’s Gulf of Maine Operational Forecast System

    Directory of Open Access Journals (Sweden)

    Zizang Yang

    2016-11-01

    Full Text Available The National Ocean Service (NOS of National Oceanic and Atmospheric Administration is developing an operational nowcast/forecast system for the Gulf of Maine (GoMOFS. The system aims to produce real-time nowcasts and short-range forecast guidance for water levels, 3-dimensional currents, water temperature, and salinity over the broad GoM region. GoMOFS will be implemented using the Regional Ocean Model System (ROMS. This paper describes the system setup and results from a one-year (2012 hindcast simulation. The hindcast performance was evaluated using the NOS standard skill assessment software. The results indicate favorable agreement between observations and model forecasts. The root-mean-squared errors are about 0.12 m for water level, less than 1.5 °C for temperature, less than 1.5 psu for salinity, and less than 0.2 m/s for currents. It is anticipated to complete the system development and the transition into operations in fiscal year 2017.

  20. Consultation system of forecasting the mine floor flood

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wen-quan; ZHANG Hong-ri; LIU Wei-tao

    2001-01-01

    In this paper, on the basis of the analysis of the current situation of the mine floor flood and the cnsultation system study, the overall design and structure, specially, the mechanism of many kinds of method for making a strategic decision and reasoning of the system of prevention and control of mine floor flood are introduced. The applied examples are given at last.

  1. Information system of forecasting infrastructure development in tourism

    Directory of Open Access Journals (Sweden)

    Gats Bogdan

    2013-01-01

    Full Text Available Manuscript is devoted to the development of information system for tourist objects infrastructure growth and its practical implementation in form of information system using methods of fuzzy logic, theory of fractals and diffusion. Developed technology allows compute attractiveness of Carpathian region, structure, dynamics of the main tourist settlements Vorochta and Slavske, prospective territories for tourist business, growing strategies for region.

  2. Development of a Regional Coastal and Open Ocean Forecast System

    Science.gov (United States)

    1999-09-30

    Stream, — Special Issue — in Memory of Dr. Antonio Michelato. Journal of Marine Systems , 20, 129-156, 1999. [9] Warn-Varnas, A., J. Sellschopp, P.J. Haley...Reviews, 1999. Submitted [12] Lermusiaux, P.F.J. Evolving the sub-space of the three-dimensional ocean variability, Journal of Marine Systems , 1999

  3. Forecasting of cyclone Viyaru and Phailin by NWP-based cyclone prediction system (CPS) of IMD – an evaluation

    Indian Academy of Sciences (India)

    S D Kotal; S K Bhattacharya; S K Roy Bhowmik; P K Kundu

    2014-10-01

    An objective NWP-based cyclone prediction system (CPS) was implemented for the operational cyclone forecasting work over the Indian seas. The method comprises of five forecast components, namely (a) Cyclone Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for cyclone track prediction, (c) cyclone intensity prediction, (d) rapid intensification, and (e) predicting decaying intensity after the landfall. GPP is derived based on dynamical and thermodynamical parameters from the model output of IMD operational Global Forecast System. The MME technique for the cyclone track prediction is based on multiple linear regression technique. The predictor selected for the MME are forecast latitude and longitude positions of cyclone at 12-hr intervals up to 120 hours forecasts from five NWP models namely, IMD-GFS, IMD-WRF, NCEP-GFS, UKMO, and JMA. A statistical cyclone intensity prediction (SCIP) model for predicting 12 hourly cyclone intensity (up to 72 hours) is developed applying multiple linear regression technique. Various dynamical and thermodynamical parameters as predictors are derived from the model outputs of IMD operational Global Forecast System and these parameters are also used for the prediction of rapid intensification. For forecast of inland wind after the landfall of a cyclone, an empirical technique is developed. This paper briefly describes the forecast system CPS and evaluates the performance skill for two recent cyclones Viyaru (non-intensifying) and Phailin (rapid intensifying), converse in nature in terms of track and intensity formed over Bay of Bengal in 2013. The evaluation of performance shows that the GPP analysis at early stages of development of a low pressure system indicated the potential of the system for further intensification. The 12-hourly track forecast by MME, intensity forecast by SCIP model, and rapid intensification forecasts are found to be consistent and very useful to the operational forecasters. The error

  4. An Electrical Energy Consumption Monitoring and Forecasting System

    National Research Council Canada - National Science Library

    J. L. Rojas-Renteria; T. D. Espinoza-Huerta; F. S. Tovar-Pacheco; J. L. Gonzalez-Perez; R. Lozano-Dorantes

    2016-01-01

    Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations...

  5. A wave prediction system for real time sea state forecasting in Black Sea

    CERN Document Server

    Kortcheva, Anna; Galabov, Vasko

    2012-01-01

    This paper briefly describes the existing operational system for wind waves forecasting in the Black Sea. It is a system of coupled atmospheric and wave numerical models aiming at a detailed and accurate sea state forecast on an operational level. The system was created at the National Institute of Meteorology and Hydrology Bulgarian Academy of Sciences (NIMH-BAS) in collaboration with the Meteorological Office of France - Meteo-France. The present work introduces the use of wave models at NIMH-BAS and shows the model results, as well as an intercomparison. The numerical wave models VAG, WAVEWATCH III and WAM, developed by the research groups of Meteo-France, NCEP and WAMDI, have been adopted for the Black Sea area and implemented at the NIMH-BAS to allow real-time forecasts and hindcasts of the waves in the Black Sea. The coupling of two atmospherics models ARPEGE and ALADIN has been used to force the wave models. The operational use has indicated that the system is suitable for general purposes and the resu...

  6. Impact of forecast errors on expansion planning of power systems with a renewables target

    DEFF Research Database (Denmark)

    Pineda, Salvador; Morales González, Juan Miguel; Boomsma, Trine Krogh

    2015-01-01

    This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a programming modeling framework to determine the generation...... and transmission expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts...... and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using...

  7. A Free-Rider Forecasting Model Based on Gray System Theory in P2P Networks

    Directory of Open Access Journals (Sweden)

    He Xu

    2012-11-01

    Full Text Available The aim of this study is to forecast the number of free-riders in P2P networks which can help network managers to know the status of the networks in advance and take appropriate measures to cope with free-riding behavior. Free-riding behavior is common in P2P networks, which has a negative impact on the robustness, availability and stability of the networks. Severe free-riding behavior may lead to the crash of the whole P2P application system. Based on the research of free-riding behavior in P2P networks, this paper constructs a free-rider forecasting model (GST model using Gray System Theory. Simulation experiments show that this model has high feasibility, and can carry out reasonable predictions on the number of free-riders in P2P networks.

  8. Forecast-Based Operations Support Tool for the New York City Water Supply System

    Science.gov (United States)

    Pyke, G.; Porter, J.

    2012-12-01

    The NYC water supply system serves 9 million people with over 1 BGD of water drawn from 19 reservoirs. To support operation of the system to meet multiple objectives (e.g. supply reliability, water quality, environmental releases, hydropower, peak flow mitigation), the New York City Department of Environmental Protection (DEP) is developing an Operations Support Tool (OST), a forecast-based decision support system that provides a probabilistic foundation for water supply operations and planning. Key features of OST include: the ability to run both long-term simulations and short-term probabilistic simulations on the same model platform; automated processing of near-real-time (NRT) data sources; use of inflow forecasts to support look-ahead operational simulations; and water supply-water quality model linkage to account for feedback and tradeoffs between supply and quality objectives. OST supports two types of simulations. Long-term runs execute the system model over an extended historical record and are used to evaluate reservoir operating rules, infrastructure modifications, and climate change scenarios (with inflows derived from downscaled GCM data). Short-term runs for operational guidance consist of multiple (e.g. 80+) short (e.g. one year) runs, all starting from the same initial conditions (typically those of the current day). Ensemble reservoir inflow forecast traces are used to drive the model for the duration of the simulation period. The result of these runs is a distribution of potential future system states. DEP managers analyze the distributions for alternate scenarios and make operations decisions using risk-based metrics such as probability of refill or the likelihood of a water quality event. For operational simulations, the OST data system acquires NRT data from DEP internal sources (SCADA operations data, keypoint water quality, in-stream/in-reservoir water quality, meteorological and snowpack monitoring sites). OST acquires streamflow data from

  9. Definition of Pluviometric Thresholds For A Real Time Flood Forecasting System In The Arno Watershed

    Science.gov (United States)

    Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.

    The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.

  10. The Impact of TRMM Data on Numerical Forecast of Mesoscale Systems

    Science.gov (United States)

    Pu, Zhao-Xia; Tao, Wei-Kuo

    2002-01-01

    The impact of surface rainfall data derived from the TRMM Microwave Image (TMI) on the numerical forecast of mesoscale systems is evaluated. A series of numerical experiments are performed that assimilate TMI rainfall data into the Penn State University/National Centers for Atmospheric Research (PSU/NCAR) Mesoscale Model version 5 (MM5) using a four-dimensional variational data assimilation (4DVAR) technique. Experiments are conducted incorporating TMI rainfall data into the mesoscale model to improve hurricane initialization. It is found that assimilation of rainfall data into the model is beneficial in producing a more realistic eye and rain bands and also helps to improve the intensity forecast for the hurricane. Further 4DVAR experiments are performed on mesoscale convective systems (MCSs). Detailed results and related issues will be presented during the conference.

  11. Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

    Science.gov (United States)

    Khajehei, Sepideh; Moradkhani, Hamid

    2017-03-01

    Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

  12. Stock market modeling and forecasting a system adaptation approach

    CERN Document Server

    Zheng, Xiaolian

    2013-01-01

    Stock Market Modeling translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics. The modeling process is considered as identifying a dynamic system in which a real stock market is treated as an unknown plant and the identification model proposed is tuned by feedback of the matching error. Like a physical system, a stock market exhibits fast and slow dynamics corresponding to internal (such as company value and profitability) and external forces (such as investor sentiment and commodity prices) respectively. The framework presented here, consisting of an internal model and an adaptive filter, is successful at considering both fast and slow market dynamics. A double selection method is efficacious in identifying input factors influential in market movements, revealing them to be both frequency- and market-dependent.   The authors present work on both developed and developing markets ...

  13. Ideal versus reality: physicians perspectives on patients with chronic fatigue syndrome (CFS) and fibromyalgia.

    Science.gov (United States)

    Asbring, Pia; Närvänen, Anna-Liisa

    2003-08-01

    Encountering patients with chronic fatigue syndrome (CFS) or fibromyalgia can cause dilemmas for physicians due to the uncertainty inherent in these illnesses. The aim of this study was to investigate: (1). How physicians in a Swedish sample describe and categorize patients with CFS and fibromyalgia; (2). What the character of CFS and fibromyalgia, with regard to diagnosing, treatment and medical knowledge/aetiology, mean to the physicians in encounters with patients; and (3). Which strategies physicians describe that they use in the encounter with these patients. Semi-structured interviews were carried out with 26 physicians, specialists in various fields who all had some experience of either CFS or fibromyalgia. The results suggest that there is a discrepancy between the ideal role of the physician and reality in the everyday work in interaction with these patients. This may lead to the professional role being questioned. Different strategies are developed to handle the encounters with these patients. The results also illuminate the physician's interpretations of patients in moralising terms. Conditions given the status of illness were regarded, for example, as less serious by the physicians than those with disease status. Scepticism was expressed regarding especially CFS, but also fibromyalgia. Moreover, it is shown how the patients are characterised by the physicians as ambitious, active, illness focused, demanding and medicalising. The patient groups in question do not always gain full access to the sick-role, in part as a consequence of the conditions not being defined as diseases.

  14. On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

    Science.gov (United States)

    Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

    2016-12-01

    Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours

  15. C-IFS-CB05-BASCOE: stratospheric chemistry in the Integrated Forecasting System of ECMWF

    Science.gov (United States)

    Huijnen, Vincent; Flemming, Johannes; Chabrillat, Simon; Errera, Quentin; Christophe, Yves; Blechschmidt, Anne-Marlene; Richter, Andreas; Eskes, Henk

    2016-09-01

    We present a model description and benchmark evaluation of an extension of the tropospheric chemistry module in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) with stratospheric chemistry, referred to as C-IFS-CB05-BASCOE (for brevity here referred to as C-IFS-TS). The stratospheric chemistry originates from the one used in the Belgian Assimilation System for Chemical ObsErvations (BASCOE), and is here combined with the modified CB05 chemistry module for the troposphere as currently used operationally in the Copernicus Atmosphere Monitoring Service (CAMS). In our approach either the tropospheric or stratospheric chemistry module is applied, depending on the altitude of each individual grid box with respect to the tropopause. An evaluation of a 2.5-year long C-IFS-TS simulation with respect to various satellite retrieval products and in situ observations indicates good performance of the system in terms of stratospheric ozone, and a general improvement in terms of stratospheric composition compared to the C-IFS predecessor model version. Possible issues with transport processes in the stratosphere are identified. This marks a key step towards a chemistry module within IFS that encompasses both tropospheric and stratospheric composition, and could expand the CAMS analysis and forecast capabilities in the near future.

  16. Integrated systems for forecasting urban meteorology, air pollution and population exposure

    Directory of Open Access Journals (Sweden)

    A. Baklanov

    2007-01-01

    Full Text Available Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

  17. Enhancements to the Economic Impact Forecast System (EIFS).

    Science.gov (United States)

    1984-04-01

    Output study cost about $250,000 and took 5 ’ years to complete. Walter Isard and T. Langford indicate that the 1958 Philadephia input-output table was...J. Glickman, Econometric Analysis of Regional Systems (Academic Press, 1977), p 35. 37W. Isard , T. W. Longford, and E. Romanoff, The Philadelphia...can be found in Hoover 4 (1948), Isard (1956), Nourse (1968), and Smith (1981). "," - -% -- . .. L .--- -. The extent and magnitude of the industrial

  18. An operational coupled wave-current forecasting system for the northern Adriatic Sea

    Science.gov (United States)

    Russo, A.; Coluccelli, A.; Deserti, M.; Valentini, A.; Benetazzo, A.; Carniel, S.

    2012-04-01

    Since 2005 an Adriatic implementation of the Regional Ocean Modeling System (AdriaROMS) is being producing operational short-term forecasts (72 hours) of some hydrodynamic properties (currents, sea level, temperature, salinity) of the Adriatic Sea at 2 km horizontal resolution and 20 vertical s-levels, on a daily basis. The main objective of AdriaROMS, which is managed by the Hydro-Meteo-Clima Service (SIMC) of ARPA Emilia Romagna, is to provide useful products for civil protection purposes (sea level forecasts, outputs to run other forecasting models as for saline wedge, oil spills and coastal erosion). In order to improve the forecasts in the coastal area, where most of the attention is focused, a higher resolution model (0.5 km, again with 20 vertical s-levels) has been implemented for the northern Adriatic domain. The new implementation is based on the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST)and adopts ROMS for the hydrodynamic and Simulating WAve Nearshore (SWAN) for the wave module, respectively. Air-sea fluxes are computed using forecasts produced by the COSMO-I7 operational atmospheric model. At the open boundary of the high resolution model, temperature, salinity and velocity fields are provided by AdriaROMS while the wave characteristics are provided by an operational SWAN implementation (also managed by SIMC). Main tidal components are imposed as well, derived from a tidal model. Work in progress is oriented now on the validation of model results by means of extensive comparisons with acquired hydrographic measurements (such as CTDs or XBTs from sea-truth campaigns), currents and waves acquired at observational sites (including those of SIMC, CNR-ISMAR network and its oceanographic tower, located off the Venice littoral) and satellite-derived wave-heights data. Preliminary results on the forecast waves denote how, especially during intense storms, the effect of coupling can lead to significant variations in the wave

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-03 (NODC Accession 0001531)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-12 (NODC Accession 0002659)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-06 (NODC Accession 0002406)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-07 (NODC Accession 0001523)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-09 (NODC Accession 0001525)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-04 (NODC Accession 0001520)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-06 (NODC Accession 0001522)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-05 (NODC Accession 0001533)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-03 (NODC Accession 0001519)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-05 (NODC Accession 0001545)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-10 (NODC Accession 0043271)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-03 (NODC Accession 0002742)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-04 (NODC Accession 0001532)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-11 (NODC Accession 0001527)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-09 (NODC Accession 0043270)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-10 (NODC Accession 0001550)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-12 (NODC Accession 0043273)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-02 (NODC Accession 0001554)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-09 (NODC Accession 0001549)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-02 (NODC Accession 0001542)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-02 (NODC Accession 0001530)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-11 (NODC Accession 0001587)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-05 (NODC Accession 0001569)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-08 (NODC Accession 0001524)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1998-01 (NODC Accession 0001529)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-03 (NODC Accession 0001603)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-05 (NODC Accession 0043281)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-07 (NODC Accession 0001571)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-05 (NODC Accession 0001521)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-04 (NODC Accession 0001568)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-07 (NODC Accession 0001559)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-03 (NODC Accession 0001591)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-08 (NODC Accession 0043284)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-10 (NODC Accession 0001562)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01 (NODC Accession 0001517)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-03 (NODC Accession 0002162)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-04 (NODC Accession 0043262)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-01 (NODC Accession 0001577)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-12 (NODC Accession 0001588)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-09 (NODC Accession 0001573)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-01 (NODC Accession 0001565)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-08 (NODC Accession 0002504)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-02 (NODC Accession 0001590)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-06 (NODC Accession 0043282)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-07 (NODC Accession 0001583)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-08 (NODC Accession 0001560)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-08 (NODC Accession 0043268)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-09 (NODC Accession 0001585)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-06 (NODC Accession 0001558)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-07 (NODC Accession 0043267)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-03 (NODC Accession 0001579)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-06 (NODC Accession 0001582)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-03 (NODC Accession 0001567)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1999-11 (NODC Accession 0001551)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-01 (NODC Accession 0002660)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-02 (NODC Accession 0001518)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-12 (NODC Accession 0001576)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-08 (NODC Accession 0001596)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-12 (NODC Accession 0001564)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-09 (NODC Accession 0001597)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-04 (NODC Accession 0001556)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-04 (NODC Accession 0001604)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  1. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-02 (NODC Accession 0001566)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  2. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2007-07 (NODC Accession 0043283)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  3. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-05 (NODC Accession 0001593)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  4. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-11 (NODC Accession 0001599)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  5. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-06 (NODC Accession 0001594)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  6. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-12 (NODC Accession 0001600)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  7. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2002-08 (NODC Accession 0001584)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  8. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-04 (NODC Accession 0002340)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  9. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-09 (NODC Accession 0001561)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  10. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-04 (NODC Accession 0001592)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  11. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-02 (NODC Accession 0002160)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  12. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-08 (NODC Accession 0001572)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  13. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2005-01 (NODC Accession 0002159)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  14. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2004-01 (NODC Accession 0001601)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  15. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-11 (NODC Accession 0001575)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  16. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2000-11 (NODC Accession 0001563)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  17. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-10 (NODC Accession 0001598)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  18. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2006-05 (NODC Accession 0043265)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2001-06 (NODC Accession 0001570)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...

  20. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 2003-07 (NODC Accession 0001595)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) has been developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center,...