On the assimilation of satellite derived soil moisture in numerical weather prediction models

Science.gov (United States)

Drusch, M.

2006-12-01

Satellite derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analysed from the modelled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. Three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF) have been performed for the two months period of June and July 2002: A control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating bias corrected TMI (TRMM Microwave Imager) derived soil moisture over the southern United States through a nudging scheme using 6-hourly departures. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analysed in the nudging experiment is the most accurate estimate when compared against in-situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage. The transferability of the results to other satellite derived soil moisture data sets will be discussed.

Sub-kilometer Numerical Weather Prediction in complex urban areas

Science.gov (United States)

Leroyer, S.; Bélair, S.; Husain, S.; Vionnet, V.

2013-12-01

A Sub-kilometer atmospheric modeling system with grid-spacings of 2.5 km, 1 km and 250 m and including urban processes is currently being developed at the Meteorological Service of Canada (MSC) in order to provide more accurate weather forecasts at the city scale. Atmospheric lateral boundary conditions are provided with the 15-km Canadian Regional Deterministic Prediction System (RDPS). Surface physical processes are represented with the Town Energy Balance (TEB) model for the built-up covers and with the Interactions between the Surface, Biosphere, and Atmosphere (ISBA) land surface model for the natural covers. In this study, several research experiments over large metropolitan areas and using observational networks at the urban scale are presented, with a special emphasis on the representation of local atmospheric circulations and their impact on extreme weather forecasting. First, numerical simulations are performed over the Vancouver metropolitan area during a summertime Intense Observing Period (IOP of 14-15 August 2008) of the Environmental Prediction in Canadian Cities (EPiCC) observational network. The influence of the horizontal resolution on the fine-scale representation of the sea-breeze development over the city is highlighted (Leroyer et al., 2013). Then severe storms cases occurring in summertime within the Greater Toronto Area (GTA) are simulated. In view of supporting the 2015 PanAmerican and Para-Pan games to be hold in GTA, a dense observational network has been recently deployed over this region to support model evaluations at the urban and meso scales. In particular, simulations are conducted for the case of 8 July 2013 when exceptional rainfalls were recorded. Leroyer, S., S. Bélair, J. Mailhot, S.Z. Husain, 2013: Sub-kilometer Numerical Weather Prediction in an Urban Coastal Area: A case study over the Vancouver Metropolitan Area, submitted to Journal of Applied Meteorology and Climatology.

Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

Tailored high-resolution numerical weather forecasts for energy efficient predictive building control

Science.gov (United States)

Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.

2010-09-01

The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the

Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

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National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

Preliminary results of an attempt to provide soil moisture datasets in order to verify numerical weather prediction models

International Nuclear Information System (INIS)

Cassardo, C.; Loglisci, N.

2005-01-01

In the recent years, there has been a significant growth in the recognition of the soil moisture importance in large-scale hydrology and climate modelling. Soil moisture is a lower boundary condition, which rules the partitioning of energy in terms of sensible and latent heat flux. Wrong estimations of soil moisture lead to wrong simulation of the surface layer evolution and hence precipitations and cloud cover forecasts could be consequently affected. This is true for large scale medium-range weather forecasts as well as for local-scale short range weather forecasts, particularly in those situations in which local convection is well developed. Unfortunately; despite the importance of this physical parameter there are only few soil moisture data sets sparse in time and in space around in the world. Due to this scarcity of soil moisture observations, we developed an alternative method to provide soil moisture datasets in order to verify numerical weather prediction models. In this paper are presented the preliminary results of an attempt to verify soil moisture fields predicted by a mesoscale model. The data for the comparison were provided by the simulations of the diagnostic land surface scheme LSPM (Land Surface Process Model), widely used at the Piedmont Regional Weather Service for agro-meteorological purposes. To this end, LSPM was initialized and driven by Synop observations, while the surface (vegetation and soil) parameter values were initialized by ECOCLIMAP global dataset at 1km 2 resolution

Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

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National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

Operational Numerical Weather Prediction systems based on Linux cluster architectures

International Nuclear Information System (INIS)

Pasqui, M.; Baldi, M.; Gozzini, B.; Maracchi, G.; Giuliani, G.; Montagnani, S.

2005-01-01

The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

A review of operational, regional-scale, chemical weather forecasting models in Europe

Directory of Open Access Journals (Sweden)

J. Kukkonen

2012-01-01

Full Text Available Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

Assessment of the Suitability of High Resolution Numerical Weather Model Outputs for Hydrological Modelling in Mountainous Cold Regions

Science.gov (United States)

Rasouli, K.; Pomeroy, J. W.; Hayashi, M.; Fang, X.; Gutmann, E. D.; Li, Y.

2017-12-01

The hydrology of mountainous cold regions has a large spatial variability that is driven both by climate variability and near-surface process variability associated with complex terrain and patterns of vegetation, soils, and hydrogeology. There is a need to downscale large-scale atmospheric circulations towards the fine scales that cold regions hydrological processes operate at to assess their spatial variability in complex terrain and quantify uncertainties by comparison to field observations. In this research, three high resolution numerical weather prediction models, namely, the Intermediate Complexity Atmosphere Research (ICAR), Weather Research and Forecasting (WRF), and Global Environmental Multiscale (GEM) models are used to represent spatial and temporal patterns of atmospheric conditions appropriate for hydrological modelling. An area covering high mountains and foothills of the Canadian Rockies was selected to assess and compare high resolution ICAR (1 km × 1 km), WRF (4 km × 4 km), and GEM (2.5 km × 2.5 km) model outputs with station-based meteorological measurements. ICAR with very low computational cost was run with different initial and boundary conditions and with finer spatial resolution, which allowed an assessment of modelling uncertainty and scaling that was difficult with WRF. Results show that ICAR, when compared with WRF and GEM, performs very well in precipitation and air temperature modelling in the Canadian Rockies, while all three models show a fair performance in simulating wind and humidity fields. Representation of local-scale atmospheric dynamics leading to realistic fields of temperature and precipitation by ICAR, WRF, and GEM makes these models suitable for high resolution cold regions hydrological predictions in complex terrain, which is a key factor in estimating water security in western Canada.

Distributed Sensor Network for meteorological observations and numerical weather Prediction Calculations

Directory of Open Access Journals (Sweden)

Á. Vas

2013-06-01

Full Text Available The prediction of weather generally means the solution of differential equations on the base of the measured initial conditions where the data of close and distant neighboring points are used for the calculations. It requires the maintenance of expensive weather stations and supercomputers. However, if weather stations are not only capable of measuring but can also communicate with each other, then these smart sensors can also be applied to run forecasting calculations. This applies the highest possible level of parallelization without the collection of measured data into one place. Furthermore, if more nodes are involved, the result becomes more accurate, but the computing power required from one node does not increase. Our Distributed Sensor Network for meteorological sensing and numerical weather Prediction Calculations (DSN-PC can be applied in several different areas where sensing and numerical calculations, even the solution of differential equations, are needed.

Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

Science.gov (United States)

Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

2013-04-01

Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

Science.gov (United States)

Bell, Jordan R.; Case, Jonathan L.; Molthan, Andrew L.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true

Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

Science.gov (United States)

Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A.

2018-01-01

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007-2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from "observational data." The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

Science.gov (United States)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

A hybrid convection scheme for use in non-hydrostatic numerical weather prediction models

Directory of Open Access Journals (Sweden)

Volker Kuell

2008-12-01

Full Text Available The correct representation of convection in numerical weather prediction (NWP models is essential for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized, e.g. by mass flux convection schemes. Classical schemes originally developed for use in coarse grid NWP models assume zero net convective mass flux, because the whole circulation of a convective cell is confined to the local grid column and all convective mass fluxes cancel out. However, in contemporary NWP models with grid sizes of a few kilometers this assumption becomes questionable, because here convection is partially resolved on the grid. To overcome this conceptual problem we propose a hybrid mass flux convection scheme (HYMACS in which only the convective updrafts and downdrafts are parameterized. The generation of the larger scale environmental subsidence, which may cover several grid columns, is transferred to the grid scale equations. This means that the convection scheme now has to generate a net convective mass flux exerting a direct dynamical forcing to the grid scale model via pressure gradient forces. The hybrid convection scheme implemented into the COSMO model of Deutscher Wetterdienst (DWD is tested in an idealized simulation of a sea breeze circulation initiating convection in a realistic manner. The results are compared with analogous simulations with the classical Tiedtke and Kain-Fritsch convection schemes.

Comparison of radar and numerical weather model rainfall forecasts in the perspective of urban flood prediction

DEFF Research Database (Denmark)

Lovring, Maite Monica; Löwe, Roland; Courdent, Vianney Augustin Thomas

An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...

Creating Weather System Ensembles Through Synergistic Process Modeling and Machine Learning

Science.gov (United States)

Chen, B.; Posselt, D. J.; Nguyen, H.; Wu, L.; Su, H.; Braverman, A. J.

2017-12-01

Earth's weather and climate are sensitive to a variety of control factors (e.g., initial state, forcing functions, etc). Characterizing the response of the atmosphere to a change in initial conditions or model forcing is critical for weather forecasting (ensemble prediction) and climate change assessment. Input - response relationships can be quantified by generating an ensemble of multiple (100s to 1000s) realistic realizations of weather and climate states. Atmospheric numerical models generate simulated data through discretized numerical approximation of the partial differential equations (PDEs) governing the underlying physics. However, the computational expense of running high resolution atmospheric state models makes generation of more than a few simulations infeasible. Here, we discuss an experiment wherein we approximate the numerical PDE solver within the Weather Research and Forecasting (WRF) Model using neural networks trained on a subset of model run outputs. Once trained, these neural nets can produce large number of realization of weather states from a small number of deterministic simulations with speeds that are orders of magnitude faster than the underlying PDE solver. Our neural network architecture is inspired by the governing partial differential equations. These equations are location-invariant, and consist of first and second derivations. As such, we use a 3x3 lon-lat grid of atmospheric profiles as the predictor in the neural net to provide the network the information necessary to compute the first and second moments. Results indicate that the neural network algorithm can approximate the PDE outputs with high degree of accuracy (less than 1% error), and that this error increases as a function of the prediction time lag.

Parameterisation of sea and lake ice in numerical weather prediction models of the German Weather Service

Directory of Open Access Journals (Sweden)

Dmitrii Mironov

2012-04-01

Full Text Available A bulk thermodynamic (no rheology sea-ice parameterisation scheme for use in numerical weather prediction (NWP is presented. The scheme is based on a self-similar parametric representation (assumed shape of the evolving temperature profile within the ice and on the integral heat budget of the ice slab. The scheme carries ordinary differential equations (in time for the ice surface temperature and the ice thickness. The proposed sea-ice scheme is implemented into the NWP models GME (global and COSMO (limited-area of the German Weather Service. In the present operational configuration, the horizontal distribution of the sea ice is governed by the data assimilation scheme, no fractional ice cover within the GME/COSMO grid box is considered, and the effect of snow above the ice is accounted for through an empirical temperature dependence of the ice surface albedo with respect to solar radiation. The lake ice is treated similarly to the sea ice, except that freeze-up and break-up of lakes occurs freely, independent of the data assimilation. The sea and lake ice schemes (the latter is a part of the fresh-water lake parameterisation scheme FLake show a satisfactory performance in GME and COSMO. The ice characteristics are not overly sensitive to the details of the treatment of heat transfer through the ice layer. This justifies the use of a simplified but computationally efficient bulk approach to model the ice thermodynamics in NWP, where the ice surface temperature is a major concern whereas details of the temperature distribution within the ice are of secondary importance. In contrast to the details of the heat transfer through the ice, the cloud cover is of decisive importance for the ice temperature as it controls the radiation energy budget at the ice surface. This is particularly true for winter, when the long-wave radiation dominates the surface energy budget. During summer, the surface energy budget is also sensitive to the grid-box mean ice

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

Science.gov (United States)

Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

2012-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

Wind field near complex terrain using numerical weather prediction model

Science.gov (United States)

Chim, Kin-Sang

The PennState/NCAR MM5 model was modified to simulate an idealized flow pass through a 3D obstacle in the Micro- Alpha Scale domain. The obstacle used were the idealized Gaussian obstacle and the real topography of Lantau Island of Hong Kong. The Froude number under study is ranged from 0.22 to 1.5. Regime diagrams for both the idealized Gaussian obstacle and Lantau island were constructed. This work is divided into five parts. The first part is the problem definition and the literature review of the related publications. The second part briefly discuss as the PennState/NCAR MM5 model and a case study of long- range transport is included. The third part is devoted to the modification and the verification of the PennState/NCAR MM5 model on the Micro-Alpha Scale domain. The implementation of the Orlanski (1976) open boundary condition is included with the method of single sounding initialization of the model. Moreover, an upper dissipative layer, Klemp and Lilly (1978), is implemented on the model. The simulated result is verified by the Automatic Weather Station (AWS) data and the Wind Profiler data. Four different types of Planetary Boundary Layer (PBL) parameterization schemes have been investigated in order to find out the most suitable one for Micro-Alpha Scale domain in terms of both accuracy and efficiency. Bulk Aerodynamic type of PBL parameterization scheme is found to be the most suitable PBL parameterization scheme. Investigation of the free- slip lower boundary condition is performed and the simulated result is compared with that with friction. The fourth part is the use of the modified PennState/NCAR MM5 model for an idealized flow simulation. The idealized uniform flow used is nonhydrostatic and has constant Froude number. Sensitivity test is performed by varying the Froude number and the regime diagram is constructed. Moreover, nondimensional drag is found to be useful for regime identification. The model result is also compared with the analytic

Predictability of extreme weather events for NE U.S.: improvement of the numerical prediction using a Bayesian regression approach

Science.gov (United States)

Yang, J.; Astitha, M.; Anagnostou, E. N.; Hartman, B.; Kallos, G. B.

2015-12-01

Weather prediction accuracy has become very important for the Northeast U.S. given the devastating effects of extreme weather events in the recent years. Weather forecasting systems are used towards building strategies to prevent catastrophic losses for human lives and the environment. Concurrently, weather forecast tools and techniques have evolved with improved forecast skill as numerical prediction techniques are strengthened by increased super-computing resources. In this study, we examine the combination of two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) by utilizing a Bayesian regression approach to improve the prediction of extreme weather events for NE U.S. The basic concept behind the Bayesian regression approach is to take advantage of the strengths of two atmospheric modeling systems and, similar to the multi-model ensemble approach, limit their weaknesses which are related to systematic and random errors in the numerical prediction of physical processes. The first part of this study is focused on retrospective simulations of seventeen storms that affected the region in the period 2004-2013. Optimal variances are estimated by minimizing the root mean square error and are applied to out-of-sample weather events. The applicability and usefulness of this approach are demonstrated by conducting an error analysis based on in-situ observations from meteorological stations of the National Weather Service (NWS) for wind speed and wind direction, and NCEP Stage IV radar data, mosaicked from the regional multi-sensor for precipitation. The preliminary results indicate a significant improvement in the statistical metrics of the modeled-observed pairs for meteorological variables using various combinations of the sixteen events as predictors of the seventeenth. This presentation will illustrate the implemented methodology and the obtained results for wind speed, wind direction and precipitation, as well as set the research steps that will be

Adaptation of Mesoscale Weather Models to Local Forecasting

Science.gov (United States)

Manobianco, John T.; Taylor, Gregory E.; Case, Jonathan L.; Dianic, Allan V.; Wheeler, Mark W.; Zack, John W.; Nutter, Paul A.

2003-01-01

Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes

Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

Science.gov (United States)

Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

2018-07-01

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.

Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

Science.gov (United States)

Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi

2010-01-01

The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.

The origins of computer weather prediction and climate modeling

International Nuclear Information System (INIS)

Lynch, Peter

2008-01-01

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed

Towards assimilation of InSAR data in operational weather models

Science.gov (United States)

Mulder, Gert; van Leijen, Freek; Barkmeijer, Jan; de Haan, Siebren; Hanssen, Ramon

2017-04-01

InSAR signal delays due to the varying atmospheric refractivity are a potential data source to improve weather models [1]. Especially with the launch of the new Sentinel-1 satellites, which increases data coverage, latency and accessibility, it may become possible to operationalize the assimilation of differential integrated refractivity (DIR) values in numerical weather models. Although studies exist on comparison between InSAR data and weather models [2], the impact of assimilation of DIR values in an operational weather model has never been assessed. In this study we present different ways to assimilate DIR values in an operational weather model and show the first forecast results. There are different possibilities to assimilate InSAR-data in a weather model. For example, (i) absolute DIR values can be derived using additional GNSS zenith or slant delay values, (ii) DIR values can be converted to water vapor pressures, or (iii) water vapor pressures can be derived for different heights by combining GNSS and InSAR data. However, an increasing number of assumptions in these processing steps will increase the uncertainty in the final results. Therefore, we chose to insert the InSAR derived DIR values after minimal additional processing. In this study we use the HARMONIE model [3], which is a spectral, non-hydrostatic model with a resolution of about 2.5 km. Currently, this is the operational model in 11 European countries and based on the AROME model [4]. To assimilate the DIR values in the weather model we use a simple adjustment of the weather parameters over the full slant column to match the DIR values. This is a first step towards a more sophisticated approach based on the 3D-VAR or 4D-VAR schemes [5]. Where both assimilation schemes can correct for different weather parameters simultaneously, and 4D-VAR allow us to assimilate DIR values at the exact moment of satellite overpass instead of the start of the forecast window. The approach will be demonstrated

On The Development of One-way Nesting of Air-pollution Model Smog Into Numerical Weather Prediction Model Eta

Science.gov (United States)

Halenka, T.; Bednar, J.; Brechler, J.

The spatial distribution of air pollution on the regional scale (Bohemian region) is simulated by means of Charles University puff model SMOG. The results are used for the assessment of the concentration fields of ozone, nitrogen oxides and other ozone precursors. Current improved version of the model covers up to 16 groups of basic compounds and it is based on trajectory computation and puff interaction both by means of Gaussian diffusion mixing and chemical reactions of basic species. Gener- ally, the method used for trajectory computation is valuable mainly for episodes sim- ulation, nevertheless, climatological study can be solved as well by means of average wind rose. For the study being presented huge database of real emission sources was incorporated with all kind of sources included. Some problem with the background values of concentrations was removed. The model SMOG has been nested into the forecast model ETA to obtain appropriate meteorological data input. We can estimate air pollution characteristics both for episodes analysis and the prediction of future air quality conditions. Necessary prognostic variables from the numerical weather pre- diction model are taken for the region of the central Bohemia, where the original puff model was tested. We used mainly 850 hPa wind field for computation of prognos- tic trajectories, the influence of surface temperature as a parameter of photochemistry reactions as well as the effect of cloudness has been tested.

The HIRLAM fast radiation scheme for mesoscale numerical weather prediction models

Science.gov (United States)

Rontu, Laura; Gleeson, Emily; Räisänen, Petri; Pagh Nielsen, Kristian; Savijärvi, Hannu; Hansen Sass, Bent

2017-07-01

This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system

Numerical Modeling of the Severe Cold Weather Event over Central Europe (January 2006

Directory of Open Access Journals (Sweden)

D. Hari Prasad

2010-01-01

Full Text Available Cold waves commonly occur in higher latitudes under prevailing high pressure systems especially during winter season which cause serious economical loss and cold related death. Accurate prediction of such severe weather events is important for decision making by administrators and for mitigation planning. An Advanced high resolution Weather Research and Forecasting mesoscale model is used to simulate a severe cold wave event occurred during January 2006 over Europe. The model is integrated for 31 days starting from 00UTC of 1 January 2006 with 30 km horizontal resolution. Comparison of the model derived area averaged daily mean temperatures at 2m height from different zones over the central Europe with observations indicates that the model is able to simulate the occurrence of the cold wave with the observed time lag of 1 to 3days but with lesser intensity. The temperature, winds, surface pressure and the geopential heights at 500 hPa reveal that the cold wave development associates with the southward progression of a high pressure system and cold air advection. The results have good agreement with the analysis fields indicates that the model has the ability to reproduce the time evolution of the cold wave event.

Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling Numerical Weather prediction model

Directory of Open Access Journals (Sweden)

Yassine Charabi

2016-11-01

Full Text Available A bankable solar radiation database is required for the financial viability of solar energy project. Accurate estimation of solar energy resources in a country is very important for proper siting, sizing and life cycle cost analysis of solar energy systems. During the last decade an important progress has been made to develop multiple solar irradiance database (Global Horizontal Irradiance (GHI and Direct Normal Irradiance (DNI, using satellite of different resolution and sophisticated models. This paper assesses the performance of High-resolution solar irradiance derived with dynamical downscaling Numerical Weather Prediction model with, GIS topographical solar radiation model, satellite data and ground measurements, for the production of bankable solar radiation datasets. For this investigation, NWP model namely Consortium for Small-scale Modeling (COSMO is used for the dynamical downscaling of solar radiation. The obtained results increase confidence in solar radiation data base obtained from dynamical downscaled NWP model. The mean bias of dynamical downscaled NWP model is small, on the order of a few percents for GHI, and it could be ranked as a bankable datasets. Fortunately, these data are usually archived in the meteorological department and gives a good idea of the hourly, monthly, and annual incident energy. Such short time-interval data are valuable in designing and operating the solar energy facility. The advantage of the NWP model is that it can be used for solar radiation forecast since it can estimate the weather condition within the next 72–120 hours. This gives a reasonable estimation of the solar radiation that in turns can be used to forecast the electric power generation by the solar power plant.

A time-spectral approach to numerical weather prediction

Science.gov (United States)

Scheffel, Jan; Lindvall, Kristoffer; Yik, Hiu Fai

2018-05-01

Finite difference methods are traditionally used for modelling the time domain in numerical weather prediction (NWP). Time-spectral solution is an attractive alternative for reasons of accuracy and efficiency and because time step limitations associated with causal CFL-like criteria, typical for explicit finite difference methods, are avoided. In this work, the Lorenz 1984 chaotic equations are solved using the time-spectral algorithm GWRM (Generalized Weighted Residual Method). Comparisons of accuracy and efficiency are carried out for both explicit and implicit time-stepping algorithms. It is found that the efficiency of the GWRM compares well with these methods, in particular at high accuracy. For perturbative scenarios, the GWRM was found to be as much as four times faster than the finite difference methods. A primary reason is that the GWRM time intervals typically are two orders of magnitude larger than those of the finite difference methods. The GWRM has the additional advantage to produce analytical solutions in the form of Chebyshev series expansions. The results are encouraging for pursuing further studies, including spatial dependence, of the relevance of time-spectral methods for NWP modelling.

Applications of Kalman filters based on non-linear functions to numerical weather predictions

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G. Galanis

2006-10-01

Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

Applications of Kalman filters based on non-linear functions to numerical weather predictions

Directory of Open Access Journals (Sweden)

G. Galanis

2006-10-01

Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

Training the next generation of scientists in Weather Forecasting: new approaches with real models

Science.gov (United States)

Carver, Glenn; Váňa, Filip; Siemen, Stephan; Kertesz, Sandor; Keeley, Sarah

2014-05-01

The European Centre for Medium Range Weather Forecasts operationally produce medium range forecasts using what is internationally acknowledged as the world leading global weather forecast model. Future development of this scientifically advanced model relies on a continued availability of experts in the field of meteorological science and with high-level software skills. ECMWF therefore has a vested interest in young scientists and University graduates developing the necessary skills in numerical weather prediction including both scientific and technical aspects. The OpenIFS project at ECMWF maintains a portable version of the ECMWF forecast model (known as IFS) for use in education and research at Universities, National Meteorological Services and other research and education organisations. OpenIFS models can be run on desktop or high performance computers to produce weather forecasts in a similar way to the operational forecasts at ECMWF. ECMWF also provide the Metview desktop application, a modern, graphical, and easy to use tool for analysing and visualising forecasts that is routinely used by scientists and forecasters at ECMWF and other institutions. The combination of Metview with the OpenIFS models has the potential to deliver classroom-friendly tools allowing students to apply their theoretical knowledge to real-world examples using a world-leading weather forecasting model. In this paper we will describe how the OpenIFS model has been used for teaching. We describe the use of Linux based 'virtual machines' pre-packaged on USB sticks that support a technically easy and safe way of providing 'classroom-on-a-stick' learning environments for advanced training in numerical weather prediction. We welcome discussions with interested parties.

Wind gust estimation by combining numerical weather prediction model and statistical post-processing

Science.gov (United States)

Patlakas, Platon; Drakaki, Eleni; Galanis, George; Spyrou, Christos; Kallos, George

2017-04-01

The continuous rise of off-shore and near-shore activities as well as the development of structures, such as wind farms and various offshore platforms, requires the employment of state-of-the-art risk assessment techniques. Such analysis is used to set the safety standards and can be characterized as a climatologically oriented approach. Nevertheless, a reliable operational support is also needed in order to minimize cost drawbacks and human danger during the construction and the functioning stage as well as during maintenance activities. One of the most important parameters for this kind of analysis is the wind speed intensity and variability. A critical measure associated with this variability is the presence and magnitude of wind gusts as estimated in the reference level of 10m. The latter can be attributed to different processes that vary among boundary-layer turbulence, convection activities, mountain waves and wake phenomena. The purpose of this work is the development of a wind gust forecasting methodology combining a Numerical Weather Prediction model and a dynamical statistical tool based on Kalman filtering. To this end, the parameterization of Wind Gust Estimate method was implemented to function within the framework of the atmospheric model SKIRON/Dust. The new modeling tool combines the atmospheric model with a statistical local adaptation methodology based on Kalman filters. This has been tested over the offshore west coastline of the United States. The main purpose is to provide a useful tool for wind analysis and prediction and applications related to offshore wind energy (power prediction, operation and maintenance). The results have been evaluated by using observational data from the NOAA's buoy network. As it was found, the predicted output shows a good behavior that is further improved after the local adjustment post-process.

Weather forecast

CERN Document Server

Courtier, P

1994-02-07

Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

Graphical tools for TV weather presentation

Science.gov (United States)

Najman, M.

2010-09-01

Contemporary meteorology and its media presentation faces in my opinion following key tasks: - Delivering the meteorological information to the end user/spectator in understandable and modern fashion, which follows industry standard of video output (HD, 16:9) - Besides weather icons show also the outputs of numerical weather prediction models, climatological data, satellite and radar images, observed weather as actual as possible. - Does not compromise the accuracy of presented data. - Ability to prepare and adjust the weather show according to actual synoptic situtation. - Ability to refocus and completely adjust the weather show to actual extreme weather events. - Ground map resolution weather data presentation need to be at least 20 m/pixel to be able to follow the numerical weather prediction model resolution. - Ability to switch between different numerical weather prediction models each day, each show or even in the middle of one weather show. - The graphical weather software need to be flexible and fast. The graphical changes nee to be implementable and airable within minutes before the show or even live. These tasks are so demanding and the usual original approach of custom graphics could not deal with it. It was not able to change the show every day, the shows were static and identical day after day. To change the content of the weather show daily was costly and most of the time impossible with the usual approach. The development in this area is fast though and there are several different options for weather predicting organisations such as national meteorological offices and private meteorological companies to solve this problem. What are the ways to solve it? What are the limitations and advantages of contemporary graphical tools for meteorologists? All these questions will be answered.

A review of operational, regional-scale, chemical weather forecasting models in Europe

Czech Academy of Sciences Publication Activity Database

Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; PoupKou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, Kryštof

2012-01-01

Roč. 12, - (2012), s. 1-87 ISSN 1680-7316 Institutional research plan: CEZ:AV0Z10300504 Keywords : chemical weather * numerical models * operational forecasting * air Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 5.510, year: 2012

Evaluation of numerical weather predictions performed in the context of the project DAPHNE

Science.gov (United States)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Bampzelis, Dimitris; Karacostas, Theodore

2014-05-01

The region of Thessaly in central Greece is one of the main areas of agricultural production in Greece. Severe weather phenomena affect the agricultural production in this region with adverse effects for farmers and the national economy. For this reason the project DAPHNE aims at tackling the problem of drought by means of weather modification through the development of the necessary tools to support the application of a rainfall enhancement program. In the present study the numerical weather prediction system WRF-ARW is used, in order to assess its ability to represent extreme weather phenomena in the region of Thessaly. WRF is integrated in three domains covering Europe, Eastern Mediterranean and Central-Northern Greece (Thessaly and a large part of Macedonia) using telescoping nesting with grid spacing of 15km, 5km and 1.667km, respectively. The cases examined span throughout the transitional and warm period (April to September) of the years 2008 to 2013, including days with thunderstorm activity. Model results are evaluated against all available surface observations and radar products, taking into account the spatial characteristics and intensity of the storms. Preliminary results indicate a good level of agreement between the simulated and observed fields as far as the standard parameters (such as temperature, humidity and precipitation) are concerned. Moreover, the model generally exhibits a potential to represent the occurrence of the convective activity, but not its exact spatiotemporal characteristics. Acknowledgements This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013)

Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction

Directory of Open Access Journals (Sweden)

Takemasa Miyoshi

2012-09-01

Full Text Available In addition to conventional observations, atmospheric temperature and humidity profile data from the Atmospheric Infrared Sounder (AIRS Version 5 retrieval products are assimilated into the Weather Research and Forecasting (WRF model, using the local ensemble transform Kalman filter (LETKF. Although a naive assimilation of all available quality-controlled AIRS retrieval data yields an inferior analysis, the additional enhancements of adaptive inflation and horizontal data thinning result in a general improvement of numerical weather prediction skill due to AIRS data. In particular, the adaptive inflation method is enhanced so that it no longer assumes temporal homogeneity of the observing network and allows for a better treatment of the temporally inhomogeneous AIRS data. Results indicate that the improvements due to AIRS data are more significant in longer-lead forecasts. Forecasts of Typhoons Sinlaku and Jangmi in September 2008 show improvements due to AIRS data.

Sensitivity of Numerical Weather Prediction to the Choice of Variable for Atmospheric Moisture Analysis into the Brazilian Global Model Data Assimilation System

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Thamiris B. Campos

2018-03-01

Full Text Available Due to the high spatial and temporal variability of atmospheric water vapor associated with the deficient methodologies used in its quantification and the imperfect physics parameterizations incorporated in the models, there are significant uncertainties in characterizing the moisture field. The process responsible for incorporating the information provided by observation into the numerical weather prediction is denominated data assimilation. The best result in atmospheric moisture depend on the correct choice of the moisture control variable. Normalized relative humidity and pseudo-relative humidity are the variables usually used by the main weather prediction centers. The objective of this study is to assess the sensibility of the Center for Weather Forecast and Climate Studies to choose moisture control variable in the data assimilation scheme. Experiments using these variables are carried out. The results show that the pseudo-relative humidity improves the variables that depend on temperature values but damage the moisture field. The opposite results show when the simulation used the normalized relative humidity. These experiments suggest that the pseudo-relative humidity should be used in the cyclical process of data assimilation and the normalized relative humidity should be used in non-cyclic process (e.g., nowcasting application in high resolution.

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

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Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

Effectiveness of short-term numerical weather prediction in predicting growing degree days and meteorological conditions for apple scab appearance

Czech Academy of Sciences Publication Activity Database

Lalic, B.; Francia, M.; Eitzinger, Josef; Podrascanin, Z.; Arsenic, I.

2016-01-01

Roč. 23, č. 1 (2016), s. 50-56 ISSN 1350-4827 Institutional support: RVO:86652079 Keywords : venturia-inaequalis * temperature * equation * schemes * model * numerical weather prediction * disease prediction * verification * apple scab * growing degree days Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 1.411, year: 2016

Numerical Methods in Atmospheric and Oceanic Modelling: The Andre J. Robert Memorial Volume

Science.gov (United States)

Rosmond, Tom

Most people, even including some in the scientific community, do not realize how much the weather forecasts they use to guide the activities of their daily lives depend on very complex mathematics and numerical methods that are the basis of modern numerical weather prediction (NWP). André Robert (1929-1993), to whom Numerical Methods in Atmospheric and Oceanic Modelling is dedicated, had a career that contributed greatly to the growth of NWP and the role that the atmospheric computer models of NWP play in our society. There are probably no NWP models running anywhere in the world today that do not use numerical methods introduced by Robert, and those of us who work with and use these models everyday are indebted to him.The first two chapters of the volume are chronicles of Robert's life and career. The first is a 1987 interview by Harold Ritchie, one of Robert's many proteges and colleagues at the Canadian Atmospheric Environment Service. The interview traces Robert's life from his birth in New York to French Canadian parents, to his emigration to Quebec at an early age, his education and early employment, and his rise in stature as one of the preeminent research meteorologists of our time. An amusing anecdote he relates is his impression of weather forecasts while he was considering his first job as a meteorologist in the early 1950s. A newspaper of the time placed the weather forecast and daily horoscope side by side, and Robert regarded each to have a similar scientific basis. Thankfully he soon realized there was a difference between the two, and his subsequent career certainly confirmed the distinction.

Weather forecasting for Eastern Amazon with OLAM model

Directory of Open Access Journals (Sweden)

Renato Ramos da Silva

2014-12-01

Full Text Available The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.

Wind laws for shockless initialization. [numerical forecasting model

Science.gov (United States)

Ghil, M.; Shkoller, B.

1976-01-01

A system of diagnostic equations for the velocity field, or wind laws, was derived for each of a number of models of large-scale atmospheric flow. The derivation in each case is mathematically exact and does not involve any physical assumptions not already present in the prognostic equations, such as nondivergence or vanishing of derivatives of the divergence. Therefore, initial states computed by solving these diagnostic equations should be compatible with the type of motion described by the prognostic equations of the model and should not generate initialization shocks when inserted into the model. Numerical solutions of the diagnostic system corresponding to a barotropic model are exhibited. Some problems concerning the possibility of implementing such a system in operational numerical weather prediction are discussed.

Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

Science.gov (United States)

Zavodsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

2014-01-01

Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use

Turbulence Dissipation Rates in the Planetary Boundary Layer from Wind Profiling Radars and Mesoscale Numerical Weather Prediction Models during WFIP2

Science.gov (United States)

Bianco, L.; McCaffrey, K.; Wilczak, J. M.; Olson, J. B.; Kenyon, J.

2016-12-01

When forecasting winds at a wind plant for energy production, the turbulence parameterizations in the forecast models are crucial for understanding wind plant performance. Recent research shows that the turbulence (eddy) dissipation rate in planetary boundary layer (PBL) parameterization schemes introduces significant uncertainty in the Weather Research and Forecasting (WRF) model. Thus, developing the capability to measure dissipation rates in the PBL will allow for identification of weaknesses in, and improvements to the parameterizations. During a preliminary field study at the Boulder Atmospheric Observatory in spring 2015, a 915-MHz wind profiling radar (WPR) measured dissipation rates concurrently with sonic anemometers mounted on a 300-meter tower. WPR set-up parameters (e.g., spectral resolution), post-processing techniques (e.g., filtering for non-atmospheric signals), and spectral averaging were optimized to capture the most accurate Doppler spectra for measuring spectral widths for use in the computation of the eddy dissipation rates. These encouraging results lead to the implementation of the observing strategy on a 915-MHz WPR in Wasco, OR, operating as part of the Wind Forecasting Improvement Project 2 (WFIP2). These observations are compared to dissipation rates calculated from the High-Resolution Rapid Refresh model, a WRF-based mesoscale numerical weather prediction model run for WFIP2 at 3000 m horizontal grid spacing and with a nest, which has 750-meter horizontal grid spacing, in the complex terrain region of the Columbia River Gorge. The observed profiles of dissipation rates are used to evaluate the PBL parameterization schemes used in the HRRR model, which are based on the modeled turbulent kinetic energy and a tunable length scale.

A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model

Science.gov (United States)

Riette, Sébastien; Lac, Christine

2016-08-01

In the Application of Research to Operations at Mesoscale (AROME) numerical weather forecast model used in operations at Météo-France, five mass-flux schemes are available to parametrize shallow convection at kilometre resolution. All but one are based on the eddy-diffusivity-mass-flux approach, and differ in entrainment/detrainment, the updraft vertical velocity equation and the closure assumption. The fifth is based on a more classical mass-flux approach. Screen-level scores obtained with these schemes show few discrepancies and are not sufficient to highlight behaviour differences. Here, we describe and use a new experimental framework, able to compare and discriminate among different schemes. For a year, daily forecast experiments were conducted over small domains centred on the five French metropolitan radio-sounding locations. Cloud base, planetary boundary-layer height and normalized vertical profiles of specific humidity, potential temperature, wind speed and cloud condensate were compared with observations, and with each other. The framework allowed the behaviour of the different schemes in and above the boundary layer to be characterized. In particular, the impact of the entrainment/detrainment formulation, closure assumption and cloud scheme were clearly visible. Differences mainly concerned the transport intensity thus allowing schemes to be separated into two groups, with stronger or weaker updrafts. In the AROME model (with all interactions and the possible existence of compensating errors), evaluation diagnostics gave the advantage to the first group.

Numerical simulation of rainfall and temperature over Kenya using weather research and forecasting-environmental modelling system (WRF-EMS

Directory of Open Access Journals (Sweden)

Sagero Obaigwa Philip

2016-01-01

Full Text Available This paper focuses on one of the high resolution models used for weather forecasting at Kenya Meteorological Department (KMD. It reviews the skill and accuracy of the Weather Research and Forecasting (WRF - Environmental Modeling System (EMS model, in simulating weather over Kenya. The study period was March to May 2011, during the rainy season over Kenya. The model output was compared with the observed data from 27 synoptic stations spread over the study area, to determine the performance of the model in terms of its skill and accuracy in forecasting. The spatial distribution of rainfall and temperature showed that the WRF model was capable of reproducing the observed general pattern especially for temperature. The model has skill in forecasting both rainfall and temperature over the study area. However, the model may underestimate rainfall of more than 10 mm/day and displace its location and overestimate rainfall of less than 1 mm/day. Therefore, during the period of enhanced rainfall especially in the month of April and part of May the model forecast needs to be complemented by other models or forecasting methods before giving a forecast. There is need to improve its performance over the domain through review of the parameterization of small scale physical processes and more observed data need to be simulated into the model.

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

Science.gov (United States)

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases

Review and Extension of Suitability Assessment Indicators of Weather Model Output for Analyzing Decentralized Energy Systems

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Hans Schermeyer

2015-12-01

Full Text Available Electricity from renewable energy sources (RES-E is gaining more and more influence in traditional energy and electricity markets in Europe and around the world. When modeling RES-E feed-in on a high temporal and spatial resolution, energy systems analysts frequently use data generated by numerical weather models as input since there is no spatial inclusive and comprehensive measurement data available. However, the suitability of such model data depends on the research questions at hand and should be inspected individually. This paper focuses on new methodologies to carry out a performance evaluation of solar irradiation data provided by a numerical weather model when investigating photovoltaic feed-in and effects on the electricity grid. Suitable approaches of time series analysis are researched from literature and applied to both model and measurement data. The findings and limits of these approaches are illustrated and a new set of validation indicators is presented. These novel indicators complement the assessment by measuring relevant key figures in energy systems analysis: e.g., gradients in energy supply, maximum values and volatility. Thus, the results of this paper contribute to the scientific community of energy systems analysts and researchers who aim at modeling RES-E feed-in on a high temporal and spatial resolution using weather model data.

Effects of forest cover changes in European Russia on regional weather conditions: results of numerical experiments with the COSMO-CLM model

Science.gov (United States)

Olchev, Alexander; Kuzmina, Ekaterina; Rozinkina, Inna; Nikitin, Mikhail; Rivin, Gdaly S.

2017-04-01

The forests have a significant effect on the climatic system. They capture CO2 from the atmosphere, regulate the surface evaporation and runoff, and influence the radiation and thermal conditions of the land surface. It is obvious, that their influence depends on many different factors including regional climate conditions, land use and vegetation structure, surface topography, etc. The main goal of the study is to assess the possible influence of forest cover changes (under deforestation and/or afforestation) on regional weather conditions in the central part of European Russia using the results of modeling experiments provided by the meso-scale COSMO-CLM model. The need of the study lies in a lack of the experimental and modeling data characterizing the influence of the forest and land-use changes on regional weather conditions in European part of Russia. The forest ecosystems in the study region play a very important biosphere role that is significantly increased in the last decades due to considerable strengthening of anthropogenic activity in the area of European Russia. The area selected for the study is located in the central part of European Russia between 55 and 59N and 28 and 37E. It comprises several geographical zones including dark-coniferous forests of the South-European taiga in the north, the mixed forests in the central part and the broad-leaved forests in the south. The forests within the study area are very heterogeneous. The total area covered by forests according to recent remote sensing data is about 50%. The numerical experiments were provided using the COSMO-CLM model with the spatial resolution 13.2 km. As initial and boundary conditions for the numerical experiments the global reanalysis ERA Interim (with the 6-hour resolution in time and 0.75° × 0.75° in space) were used. The weather conditions were simulated in a continuous cycle for several months for the entire area of European Russia using the results of global reanalysis on

The representation of low-level clouds during the West African monsoon in weather and climate models

Science.gov (United States)

Kniffka, Anke; Hannak, Lisa; Knippertz, Peter; Fink, Andreas

2016-04-01

The West African monsoon is one of the most important large-scale circulation features in the tropics and the associated seasonal rainfalls are crucial to rain-fed agriculture and water resources for hundreds of millions of people. However, numerical weather and climate models still struggle to realistically represent salient features of the monsoon across a wide range of scales. Recently it has been shown that substantial errors in radiation and clouds exist in the southern parts of West Africa (8°W-8°E, 5-10°N) during summer. This area is characterised by strong low-level jets associated with the formation of extensive ultra-low stratus clouds. Often persisting long after sunrise, these clouds have a substantial impact on the radiation budget at the surface and thus the diurnal evolution of the planetary boundary layer (PBL). Here we present some first results from a detailed analysis of the representation of these clouds and the associated PBL features across a range of weather and climate models. Recent climate model simulations for the period 1991-2010 run in the framework of the Year of Tropical Convection (YOTC) offer a great opportunity for this analysis. The models are those used for the latest Assessment Report of the Intergovernmental Panel on Climate Change, but for YOTC the model output has a much better temporal resolution, allowing to resolve the diurnal cycle, and includes diabatic terms, allowing to much better assess physical reasons for errors in low-level temperature, moisture and thus cloudiness. These more statistical climate model analyses are complemented by experiments using ICON (Icosahedral non-hydrostatic general circulation model), the new numerical weather prediction model of the German Weather Service and the Max Planck Institute for Meteorology. ICON allows testing sensitivities to model resolution and numerical schemes. These model simulations are validated against (re-)analysis data, satellite observations (e.g. CM SAF cloud and

An introduction to the application of relaxation method in numerical weather prediction

International Nuclear Information System (INIS)

Aquino, E.M.

1984-11-01

This paper is intended for workers in the field of numerical weather prediction to acquire experience and gain insight on the use of the relaxation method. Two approaches were carried out, one by explaining the method using hand calculations as applied to a given problem and the second one was the discussion of how the calculations could be carried out on a digital computer. (author)

Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

Directory of Open Access Journals (Sweden)

B. Calpini

2011-08-01

Full Text Available The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers. This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS. The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours. This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an

Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

Science.gov (United States)

Wu, Yanling

2018-05-01

In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

An introduction to Space Weather Integrated Modeling

Science.gov (United States)

Zhong, D.; Feng, X.

2012-12-01

The need for a software toolkit that integrates space weather models and data is one of many challenges we are facing with when applying the models to space weather forecasting. To meet this challenge, we have developed Space Weather Integrated Modeling (SWIM) that is capable of analysis and visualizations of the results from a diverse set of space weather models. SWIM has a modular design and is written in Python, by using NumPy, matplotlib, and the Visualization ToolKit (VTK). SWIM provides data management module to read a variety of spacecraft data products and a specific data format of Solar-Interplanetary Conservation Element/Solution Element MHD model (SIP-CESE MHD model) for the study of solar-terrestrial phenomena. Data analysis, visualization and graphic user interface modules are also presented in a user-friendly way to run the integrated models and visualize the 2-D and 3-D data sets interactively. With these tools we can locally or remotely analysis the model result rapidly, such as extraction of data on specific location in time-sequence data sets, plotting interplanetary magnetic field lines, multi-slicing of solar wind speed, volume rendering of solar wind density, animation of time-sequence data sets, comparing between model result and observational data. To speed-up the analysis, an in-situ visualization interface is used to support visualizing the data 'on-the-fly'. We also modified some critical time-consuming analysis and visualization methods with the aid of GPU and multi-core CPU. We have used this tool to visualize the data of SIP-CESE MHD model in real time, and integrated the Database Model of shock arrival, Shock Propagation Model, Dst forecasting model and SIP-CESE MHD model developed by SIGMA Weather Group at State Key Laboratory of Space Weather/CAS.

Space Weather Models at the CCMC And Their Capabilities

Science.gov (United States)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2007-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. In this presentation, we will provide an overview of the community-provided, space weather-relevant, model suite, which resides at CCMC. We will discuss current capabilities, and analyze expected future developments of space weather related modeling.

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

Directory of Open Access Journals (Sweden)

Y. Cao

2017-09-01

Full Text Available Most atmospheric models, including the Weather Research and Forecasting (WRF model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania.

A Two-Dimensional Gridded Solar Forecasting System using Situation-Dependent Blending of Multiple Weather Models

Science.gov (United States)

Lu, S.; Hwang, Y.; Shao, X.; Hamann, H.

2015-12-01

Previously, we reported the application of a "weather situation" dependent multi-model blending approach to improve the forecast accuracy of solar irradiance and other atmospheric parameters. The approach uses machine-learning techniques to classify "weather situations" by a set of atmospheric parameters. The "weather situation" classification is location-dependent and each "weather situation" has characteristic forecast errors from a set of individual input numerical weather prediction (NWP) models. The input models are thus corrected or combined differently for different "weather situations" to minimize the overall forecast error. While the original implementation of the model-blending is applicable to only point-like locations having historical data of both measurements and forecasts, here we extend the approach to provide two-dimensional (2D) gridded forecasts. An experimental 2D forecasting system has been set up to provide gridded forecasts of solar irradiance (global horizontal irradiance), temperature, wind speed, and humidity for the contiguous United States (CONUS). Validation results show around 30% enhancement of 0 to 48 hour ahead solar irradiance forecast accuracy compared to the best input NWP model. The forecasting system may be leveraged by other site- or region-specific solar energy forecast products. To enable the 2D forecasting system, historical solar irradiance measurements from around 1,600 selected sites of the remote automated weather stations (RAWS) network have been employed. The CONUS was divided into smaller sub-regions, each containing a group of 10 to 20 RAWS sites. A group of sites, as classified by statistical analysis, have similar "weather patterns", i.e. the NWPs have similar "weather situation" dependent forecast errors for all sites in a group. The model-blending trained by the historical data from a group of sites is then applied for all locations in the corresponding sub-region. We discuss some key techniques developed for

Simulation of a severe convective storm using a numerical model with explicitly incorporated aerosols

Science.gov (United States)

Lompar, Miloš; Ćurić, Mladjen; Romanic, Djordje

2017-09-01

Despite an important role the aerosols play in all stages of cloud lifecycle, their representation in numerical weather prediction models is often rather crude. This paper investigates the effects the explicit versus implicit inclusion of aerosols in a microphysics parameterization scheme in Weather Research and Forecasting (WRF) - Advanced Research WRF (WRF-ARW) model has on cloud dynamics and microphysics. The testbed selected for this study is a severe mesoscale convective system with supercells that struck west and central parts of Serbia in the afternoon of July 21, 2014. Numerical products of two model runs, i.e. one with aerosols explicitly (WRF-AE) included and another with aerosols implicitly (WRF-AI) assumed, are compared against precipitation measurements from surface network of rain gauges, as well as against radar and satellite observations. The WRF-AE model accurately captured the transportation of dust from the north Africa over the Mediterranean and to the Balkan region. On smaller scales, both models displaced the locations of clouds situated above west and central Serbia towards southeast and under-predicted the maximum values of composite radar reflectivity. Similar to satellite images, WRF-AE shows the mesoscale convective system as a merged cluster of cumulonimbus clouds. Both models over-predicted the precipitation amounts; WRF-AE over-predictions are particularly pronounced in the zones of light rain, while WRF-AI gave larger outliers. Unlike WRF-AI, the WRF-AE approach enables the modelling of time evolution and influx of aerosols into the cloud which could be of practical importance in weather forecasting and weather modification. Several likely causes for discrepancies between models and observations are discussed and prospects for further research in this field are outlined.

Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10): a World Weather Research Programme Project

Science.gov (United States)

Isaac, G. A.; Joe, P. I.; Mailhot, J.; Bailey, M.; Bélair, S.; Boudala, F. S.; Brugman, M.; Campos, E.; Carpenter, R. L.; Crawford, R. W.; Cober, S. G.; Denis, B.; Doyle, C.; Reeves, H. D.; Gultepe, I.; Haiden, T.; Heckman, I.; Huang, L. X.; Milbrandt, J. A.; Mo, R.; Rasmussen, R. M.; Smith, T.; Stewart, R. E.; Wang, D.; Wilson, L. J.

2014-01-01

A World Weather Research Programme (WWRP) project entitled the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) was developed to be associated with the Vancouver 2010 Olympic and Paralympic Winter Games conducted between 12 February and 21 March 2010. The SNOW-V10 international team augmented the instrumentation associated with the Winter Games and several new numerical weather forecasting and nowcasting models were added. Both the additional observational and model data were available to the forecasters in real time. This was an excellent opportunity to demonstrate existing capability in nowcasting and to develop better techniques for short term (0-6 h) nowcasts of winter weather in complex terrain. Better techniques to forecast visibility, low cloud, wind gusts, precipitation rate and type were evaluated. The weather during the games was exceptionally variable with many periods of low visibility, low ceilings and precipitation in the form of both snow and rain. The data collected should improve our understanding of many physical phenomena such as the diabatic effects due to melting snow, wind flow around and over terrain, diurnal flow reversal in valleys associated with daytime heating, and precipitation reductions and increases due to local terrain. Many studies related to these phenomena are described in the Special Issue on SNOW-V10 for which this paper was written. Numerical weather prediction and nowcast models have been evaluated against the unique observational data set now available. It is anticipated that the data set and the knowledge learned as a result of SNOW-V10 will become a resource for other World Meteorological Organization member states who are interested in improving forecasts of winter weather.

Models of Weather Impact on Air Traffic

Science.gov (United States)

Kulkarni, Deepak; Wang, Yao

2017-01-01

Flight delays have been a serious problem in the national airspace system costing about $30B per year. About 70 of the delays are attributed to weather and upto two thirds of these are avoidable. Better decision support tools would reduce these delays and improve air traffic management tools. Such tools would benefit from models of weather impacts on the airspace operations. This presentation discusses use of machine learning methods to mine various types of weather and traffic data to develop such models.

Numerical Modeling of Ocean Circulation

Science.gov (United States)

Miller, Robert N.

2007-01-01

The modelling of ocean circulation is important not only for its own sake, but also in terms of the prediction of weather patterns and the effects of climate change. This book introduces the basic computational techniques necessary for all models of the ocean and atmosphere, and the conditions they must satisfy. It describes the workings of ocean models, the problems that must be solved in their construction, and how to evaluate computational results. Major emphasis is placed on examining ocean models critically, and determining what they do well and what they do poorly. Numerical analysis is introduced as needed, and exercises are included to illustrate major points. Developed from notes for a course taught in physical oceanography at the College of Oceanic and Atmospheric Sciences at Oregon State University, this book is ideal for graduate students of oceanography, geophysics, climatology and atmospheric science, and researchers in oceanography and atmospheric science. Features examples and critical examination of ocean modelling and results Demonstrates the strengths and weaknesses of different approaches Includes exercises to illustrate major points and supplement mathematical and physical details

Numerical model for the thermal behavior of thermocline storage tanks

Science.gov (United States)

Ehtiwesh, Ismael A. S.; Sousa, Antonio C. M.

2018-03-01

Energy storage is a critical factor in the advancement of solar thermal power systems for the sustained delivery of electricity. In addition, the incorporation of thermal energy storage into the operation of concentrated solar power systems (CSPs) offers the potential of delivering electricity without fossil-fuel backup even during peak demand, independent of weather conditions and daylight. Despite this potential, some areas of the design and performance of thermocline systems still require further attention for future incorporation in commercial CSPs, particularly, their operation and control. Therefore, the present study aims to develop a simple but efficient numerical model to allow the comprehensive analysis of thermocline storage systems aiming better understanding of their dynamic temperature response. The validation results, despite the simplifying assumptions of the numerical model, agree well with the experiments for the time evolution of the thermocline region. Three different cases are considered to test the versatility of the numerical model; for the particular type of a storage tank with top round impingement inlet, a simple analytical model was developed to take into consideration the increased turbulence level in the mixing region. The numerical predictions for the three cases are in general good agreement against the experimental results.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

Science.gov (United States)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

Application of a COSMO Mesoscale Model to Assess the Influence of Forest Cover Changes on Regional Weather Conditions

Science.gov (United States)

Olchev, A.; Rozinkina, I.; Kuzmina, E.; Nikitin, M.; Rivin, G. S.

2017-12-01

Modern changes in land use and forest cover have a significant influence on local, regional, and global weather and climate conditions. In this study, the mesoscale model COSMO is used to estimate the possible influence of forest cover change in the central part of the East European Plain on regional weather conditions. The "model region" of the study is surrounded by geographical coordinates 55° and 59°N and 28° and 37°E and situated in the central part of a large modeling domain (50° - 70° N and 15° 55° E), covering almost the entire East European Plain in Northern Eurasia. The forests cover about 50% of the area of the "model region". The modeling study includes 3 main numerical experiments. The first assumes total deforestation of the "model region" and replacement of forests by grasslands. The second is represented by afforestation of the "model region." In the third, weather conditions are simulated with present land use and vegetation structures of the "model region." Output of numerical experiments is at 13.2 km grid resolution, and the ERA-Interim global atmospheric reanalysis (with 6-h resolution in time and 0.75°×0.75° in space) is used to quantify initial and boundary conditions. Numerical experiments for the warm period of 2010 taken as an example show that deforestation and afforestation processes in the selected region can lead to significant changes in weather conditions. Deforestation processes in summer conditions can result in increased air temperature and wind speed, reduction of precipitation, lower clouds, and relative humidity. The afforestation process can result in opposite effects (decreased air temperature, increased precipitation, higher air humidity and fog frequency, and strengthened storm winds). Maximum meteorological changes under forest cover changes are projected for the summer months (July and August). It was also shown that changes of some meteorological characteristics (e.g., air temperature) is observed in the

Weather and forecasting at Wilkins ice runway, Antarctica

International Nuclear Information System (INIS)

Carpentier, Scott

2010-01-01

Aviation forecasts for Wilkins ice runway in East Antarctica are developed within the conceptual framework of flow against a single dome shaped hill. Forecast challenges include the sudden onset of blizzards associated with the formation of an internal gravity wave; frontal weather; transient wake vortices and mesoscale lows; temperature limitations on runway use; and snow and fog events. These key weather aspects are presented within the context of synoptic to local scale climatologies and numerical weather prediction models.

Space Weather Forecasting and Supporting Research in the USA

Science.gov (United States)

Pevtsov, A. A.

2017-12-01

In the United State, scientific research in space weather is funded by several Government Agencies including the National Science Foundation (NSF) and the National Aeronautics and Space Agency (NASA). For civilian and commercial purposes, space weather forecast is done by the Space Weather Prediction Center (SWPC) of the National Oceanic and Atmospheric Administration (NOAA). Observational data for modeling come from the network of groundbased observatories funded via various sources, as well as from the instruments on spacecraft. Numerical models used in forecast are developed in framework of individual research projects. The article provides a brief review of current state of space weather-related research and forecasting in the USA.

Real-time dynamic control of the Three Gorges Reservoir by coupling numerical weather rainfall prediction and flood forecasting

DEFF Research Database (Denmark)

Wang, Y.; Chen, H.; Rosbjerg, Dan

2013-01-01

In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which...... is developed by the Japan Meteorological Agency, was used to forecast rainfall with 5 days lead-time in the upper region of the Three Gorges Reservoir (TGR). A conceptual hydrological model, the Xinanjiang Model, has been set up to forecast the inflow flood of TGR by the Ministry of Water Resources Information...... season 2012 as example, real-time dynamic control of the FLWL was implemented by using the forecasted reservoir flood inflow as input. The forecasted inflow with 5 days lead-time rainfall forecast was evaluated by several performance indices, including the mean relative error of the volumetric reservoir...

Operational Numerical Weather Prediction at the Met Office and potential ways forward for operational space weather prediction systems

Science.gov (United States)

Jackson, David

NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar

Forecasting severe ice storms using numerical weather prediction: the March 2010 Newfoundland event

Directory of Open Access Journals (Sweden)

J. Hosek

2011-02-01

Full Text Available The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event.

The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

Hydrological modeling using a multi-site stochastic weather generator

Science.gov (United States)

Weather data is usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multi-site stochastic weather generator which considers the spatial correl...

Preparing for Operational Use of High Priority Products from the Joint Polar Satellite System (JPSS) in Numerical Weather Prediction

Science.gov (United States)

Nandi, S.; Layns, A. L.; Goldberg, M.; Gambacorta, A.; Ling, Y.; Collard, A.; Grumbine, R. W.; Sapper, J.; Ignatov, A.; Yoe, J. G.

2017-12-01

This work describes end to end operational implementation of high priority products from National Oceanic and Atmospheric Administration's (NOAA) operational polar-orbiting satellite constellation, to include Suomi National Polar-orbiting Partnership (S-NPP) and the Joint Polar Satellite System series initial satellite (JPSS-1), into numerical weather prediction and earth systems models. Development and evaluation needed for the initial implementations of VIIRS Environmental Data Records (EDR) for Sea Surface Temperature ingestion in the Real-Time Global Sea Surface Temperature Analysis (RTG) and Polar Winds assimilated in the National Weather Service (NWS) Global Forecast System (GFS) is presented. These implementations ensure continuity of data in these models in the event of loss of legacy sensor data. Also discussed is accelerated operational implementation of Advanced Technology Microwave Sounder (ATMS) Temperature Data Records (TDR) and Cross-track Infrared Sounder (CrIS) Sensor Data Records, identified as Key Performance Parameters by the National Weather Service. Operational use of SNPP after 28 October, 2011 launch took more than one year due to the learning curve and development needed for full exploitation of new remote sensing capabilities. Today, ATMS and CrIS data positively impact weather forecast accuracy. For NOAA's JPSS initial satellite (JPSS-1), scheduled for launch in late 2017, we identify scope and timelines for pre-launch and post-launch activities needed to efficiently transition these capabilities into operations. As part of these alignment efforts, operational readiness for KPPs will be possible as soon as 90 days after launch. The schedule acceleration is possible because of the experience with S-NPP. NOAA operational polar-orbiting satellite constellation provides continuity and enhancement of earth systems observations out to 2036. Program best practices and lessons learned will inform future implementation for follow-on JPSS-3 and -4

The effect of high-resolution orography on numerical modelling of atmospheric flow: a preliminary experiment

International Nuclear Information System (INIS)

Scarani, C.; Tampieri, F.; Tibaldi, S.

1983-01-01

The effect of increasing the resolution of the topography in models of numerical weather prediction is assessed. Different numerical experiments have been performed, referring to a case of cyclogenesis in the lee of the Alps. From the comparison, it appears that the lower atmospheric levels are better described by the model with higherresolution topography; comparable horizontal resolution runs with smoother topography appear to be less satisfactory in this respect. It turns out also that the vertical propagation of the signal due to the front-mountain interaction is faster in the high-resolution experiment

Space Weather Forecasting and Research at the Community Coordinated Modeling Center

Science.gov (United States)

Aronne, M.

2015-12-01

The Space Weather Research Center (SWRC), within the Community Coordinated Modeling Center (CCMC), provides experimental research forecasts and analysis for NASA's robotic mission operators. Space weather conditions are monitored to provide advance warning and forecasts based on observations and modeling using the integrated Space Weather Analysis Network (iSWA). Space weather forecasters come from a variety of backgrounds, ranging from modelers to astrophysicists to undergraduate students. This presentation will discuss space weather operations and research from an undergraduate perspective. The Space Weather Research, Education, and Development Initiative (SW REDI) is the starting point for many undergraduate opportunities in space weather forecasting and research. Space weather analyst interns play an active role year-round as entry-level space weather analysts. Students develop the technical and professional skills to forecast space weather through a summer internship that includes a two week long space weather boot camp, mentorship, poster session, and research opportunities. My unique development of research projects includes studying high speed stream events as well as a study of 20 historic, high-impact solar energetic particle events. This unique opportunity to combine daily real-time analysis with related research prepares students for future careers in Heliophysics.

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

Science.gov (United States)

Zhu, Jiang; Stevens, E.; Zavodsky, B. T.; Zhang, X.; Heinrichs, T.; Broderson, D.

2014-01-01

Data assimilation has been demonstrated very useful in improving both global and regional numerical weather prediction. Alaska has very coarser surface observation sites. On the other hand, it gets much more satellite overpass than lower 48 states. How to utilize satellite data to improve numerical prediction is one of hot topics among weather forecast community in Alaska. The Geographic Information Network of Alaska (GINA) at University of Alaska is conducting study on satellite data assimilation for WRF model. AIRS/CRIS sounder profile data are used to assimilate the initial condition for the customized regional WRF model (GINA-WRF model). Normalized standard deviation, RMSE, and correlation statistic analysis methods are applied to analyze one case of 48 hours forecasts and one month of 24-hour forecasts in order to evaluate the improvement of regional numerical model from Data assimilation. The final goal of the research is to provide improved real-time short-time forecast for Alaska regions.

A Data Model for Determining Weather's Impact on Travel Time

DEFF Research Database (Denmark)

Andersen, Ove; Torp, Kristian

2016-01-01

Accurate estimating travel times in road networks is a complex task because travel times depends on factors such as the weather. In this paper, we present a generic model for integrating weather data with GPS data to improve the accuracy of the estimated travel times. First, we present a data model...... for storing and map-matching GPS data, and integrating this data with detailed weather data. The model is generic in the sense that it can be used anywhere GPS data and weather data is available. Next, we analyze the correlation between travel time and the weather classes dry, fog, rain, and snow along...... with winds impact on travel time. Using a data set of 1.6 billion GPS records collected from 10,560 vehicles, over a 5 year period from all of Denmark, we show that snow can increase the travel time up to 27% and strong headwind can increase the travel time with up to 19% (compared to dry calm weather...

Weather radar rainfall data in urban hydrology

NARCIS (Netherlands)

Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick; Ellerbæk Nielsen, Jesper; ten Veldhuis, J.A.E.; Arnbjerg-Nielsen, Karsten; Rasmussen, Michael R.; Molnar, Peter

2017-01-01

Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology

Weathering of oils at sea: model/field data comparisons

International Nuclear Information System (INIS)

Daling, Per S.; Stroem, Tove

1999-01-01

The SINTEF Oil Weathering Model (OWM) has been extensively tested with results from full-scale field trials with experimental oil slicks in the Norwegian NOFO Sea trials in 1994 and 1995 and the AEA 1997 trials in UK. The comparisons between oil weathering values predicted by the model and ground-truth obtained from the field trials are presented and discussed. Good laboratory weathering data of the specific oil as input to the model is essential for obtaining reliable weathering predictions. Predications provided by the SINTEF-OWM enable oil spill personnel to estimate the most appropriate 'window of opportunity' for use of chemical dispersants under various spill situations. Pre-spill scenario analysis with the SINTEF Oil Spill Contingency and Response (OSCAR) model system, in which the SINTEF-OWM is one of several components, has become an important part of contingency plans as well as contingency training of oil spill personnel at refineries, oil terminals and offshore installations in Norway. (Author)

Numerical Weather Prediction and Relative Economic Value framework to improve Integrated Urban Drainage- Wastewater management

DEFF Research Database (Denmark)

Courdent, Vianney Augustin Thomas

domains during which the IUDWS can be coupled with the electrical smart grid to optimise its energy consumption. The REV framework was used to determine which decision threshold of the EPS (i.e. number of ensemble members predicting an event) provides the highest benefit for a given situation...... in cities where space is scarce and large-scale construction work a nuisance. This the-sis focuses on flow domain predictions of IUDWS from numerical weather prediction (NWP) to select relevant control objectives for the IUDWS and develops a framework based on the relative economic value (REV) approach...... to evaluate when acting on the forecast is beneficial or not. Rainfall forecasts are extremely valuable for estimating near future storm-water-related impacts on the IUDWS. Therefore, weather radar extrapolation “nowcasts” provide valuable predictions for RTC. However, radar nowcasts are limited...

Improved Local Weather Forecasts Using Artificial Neural Networks

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

2015-01-01

Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

Science.gov (United States)

Mukkavilli, S. K.; Kay, M. J.; Taylor, R.; Prasad, A. A.; Troccoli, A.

2014-12-01

The Australian Solar Energy Forecasting System (ASEFS) project requires forecasting timeframes which range from nowcasting to long-term forecasts (minutes to two years). As concentrating solar power (CSP) plant operators are one of the key stakeholders in the national energy market, research and development enhancements for direct normal irradiance (DNI) forecasts is a major subtask. This project involves comparing different radiative scheme codes to improve day ahead DNI forecasts on the national supercomputing infrastructure running mesoscale simulations on NOAA's Weather Research & Forecast (WRF) model. ASEFS also requires aerosol data fusion for improving accurate representation of spatio-temporally variable atmospheric aerosols to reduce DNI bias error in clear sky conditions over southern Queensland & New South Wales where solar power is vulnerable to uncertainities from frequent aerosol radiative events such as bush fires and desert dust. Initial results from thirteen years of Bureau of Meteorology's (BOM) deseasonalised DNI and MODIS NASA-Terra aerosol optical depth (AOD) anomalies demonstrated strong negative correlations in north and southeast Australia along with strong variability in AOD (~0.03-0.05). Radiative transfer schemes, DNI and AOD anomaly correlations will be discussed for the population and transmission grid centric regions where current and planned CSP plants dispatch electricity to capture peak prices in the market. Aerosol and solar irradiance datasets include satellite and ground based assimilations from the national BOM, regional aerosol researchers and agencies. The presentation will provide an overview of this ASEFS project task on WRF and results to date. The overall goal of this ASEFS subtask is to develop a hybrid numerical weather prediction (NWP) and statistical/machine learning multi-model ensemble strategy that meets future operational requirements of CSP plant operators.

Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

Directory of Open Access Journals (Sweden)

Ośródka Katarzyna

2014-09-01

Full Text Available The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

Very short-term rainfall forecasting by effectively using the ensemble outputs of numerical weather prediction models

Science.gov (United States)

Wu, Ming-Chang; Lin, Gwo-Fong; Feng, Lei; Hwang, Gong-Do

2017-04-01

In Taiwan, heavy rainfall brought by typhoons often causes serious disasters and leads to loss of life and property. In order to reduce the impact of these disasters, accurate rainfall forecasts are always important for civil protection authorities to prepare proper measures in advance. In this study, a methodology is proposed for providing very short-term (1- to 6-h ahead) rainfall forecasts in a basin-scale area. The proposed methodology is developed based on the use of analogy reasoning approach to effectively integrate the ensemble precipitation forecasts from a numerical weather prediction system in Taiwan. To demonstrate the potential of the proposed methodology, an application to a basin-scale area (the Choshui River basin located in west-central Taiwan) during five typhoons is conducted. The results indicate that the proposed methodology yields more accurate hourly rainfall forecasts, especially the forecasts with a lead time of 1 to 3 hours. On average, improvement of the Nash-Sutcliffe efficiency coefficient is about 14% due to the effective use of the ensemble forecasts through the proposed methodology. The proposed methodology is expected to be useful for providing accurate very short-term rainfall forecasts during typhoons.

Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications

Energy Technology Data Exchange (ETDEWEB)

Mirocha, J. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kosovic, B. [National Center for Atmospheric Research, Boulder, CO (United States); Aitken, M. L. [Univ. of Colorado, Boulder, CO (United States); Lundquist, J. K. [Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab., Golden, CO (United States)

2014-01-10

A generalized actuator disk (GAD) wind turbine parameterization designed for large-eddy simulation (LES) applications was implemented into the Weather Research and Forecasting (WRF) model. WRF-LES with the GAD model enables numerical investigation of the effects of an operating wind turbine on and interactions with a broad range of atmospheric boundary layer phenomena. Numerical simulations using WRF-LES with the GAD model were compared with measurements obtained from the Turbine Wake and Inflow Characterization Study (TWICS-2011), the goal of which was to measure both the inflow to and wake from a 2.3-MW wind turbine. Data from a meteorological tower and two light-detection and ranging (lidar) systems, one vertically profiling and another operated over a variety of scanning modes, were utilized to obtain forcing for the simulations, and to evaluate characteristics of the simulated wakes. Simulations produced wakes with physically consistent rotation and velocity deficits. Two surface heat flux values of 20 W m^–2 and 100 W m^–2 were used to examine the sensitivity of the simulated wakes to convective instability. Simulations using the smaller heat flux values showed good agreement with wake deficits observed during TWICS-2011, whereas those using the larger value showed enhanced spreading and more-rapid attenuation. This study demonstrates the utility of actuator models implemented within atmospheric LES to address a range of atmospheric science and engineering applications. In conclusion, validated implementation of the GAD in a numerical weather prediction code such as WRF will enable a wide range of studies related to the interaction of wind turbines with the atmosphere and surface.

How accurate are the weather forecasts for Bierun (southern Poland)?

Science.gov (United States)

Gawor, J.

2012-04-01

Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why

Numerical modeling of a downwind-developing mesoscale convective system over the Masurian Lake District

Directory of Open Access Journals (Sweden)

Wójcik Damian K.

2017-01-01

Full Text Available Meteorological data concerning the severe convective system from the 21 August 2007 are analyzed in this study. Compiled information allows to understand the reason for the storm development and to identify its fundamental convective mode. Next, the EULAG model is utilized to perform an idealized test that shows a downwind–developing storm growth in an environment comparable to the one that was observed on the 21 August 2007 in the Masurian Lake District. Finally, the COSMO numerical weather prediction model is applied to reconstruct the storm development. The experiment is carried out for various computational grids having the horizontal grid length between 7.0 and 0.55 km. It turns out that the COSMO model is capable in simulating storms of that type. Since the model is used for operational weather forecasting in Poland the evaluation of this skill contributes to the increase of public safety.

Modeling the weather impact on aviation in a global air traffic model

Science.gov (United States)

Himmelsbach, S.; Hauf, T.; Rokitansky, C. H.

2009-09-01

Weather has a strong impact on aviation safety and efficiency. For a better understanding of that impact, especially of thunderstorms and similar other severe hazards, we pursued a modeling approach. We used the detailed simulation software (NAVSIM) of worldwide air traffic, developed by Rokitansky [Eurocontrol, 2005] and implemented a specific weather module. NAVSIM models each aircraft with its specific performance characteristics separately along preplanned and prescribed routes. The specific weather module in its current version simulates a thunderstorm as an impenetrable 3D object, which forces an aircraft to circumvent the latter. We refer to that object in general terms as a weather object. The Cb-weather object, as a specific weather object, is a heuristic model of a real thunderstorm, with its characteristics based on actually observed satellite and precipitation radar data. It is comprised of an upper volume, mostly the anvil, and a bottom volume, the up- and downdrafts and the lower outflow area [Tafferner and Forster, 2009; Kober and Tafferner 2009; Zinner et al, 2008]. The Cb-weather object is already implemented in NAVSIM, other weather objects like icing and turbulence will follow. This combination of NAVSIM with a weather object allows a detailed investigation of situations where conflicts exist between planned flight routes and adverse weather. The first objective is to simulate the observed circum-navigation in NAVSIM. Real occurring routes will be compared with simulated ones. Once this has successfully completed, NAVSIM offers a platform to assess existing rules and develop more efficient strategies to cope with adverse weather. An overview will be given over the implementation status of weather objects within NAVSIM and first results will be presented. Cb-object data provision by A. Tafferner, C. Forster, T. Zinner, K. Kober, M. Hagen (DLR Oberpfaffenhofen) is greatly acknowledged. References: Eurocontrol, VDL Mode 2 Capacity Analysis through

Modeling Silicate Weathering for Elevated CO2 and Temperature

Science.gov (United States)

Bolton, E. W.

2016-12-01

A reactive transport model (RTM) is used to assess CO2 drawdown by silicate weathering over a wide range of temperature, pCO2, and infiltration rates for basalts and granites. Although RTM's have been used extensively to model weathering of basalts and granites for present-day conditions, we extend such modeling to higher CO2 that could have existed during the Archean and Proterozoic. We also consider a wide range of surface temperatures and infiltration rates. We consider several model basalt and granite compositions. We normally impose CO2 in equilibrium with the various atmospheric ranges modeled and CO2 is delivered to the weathering zone by aqueous transport. We also consider models with fixed CO2 (aq) throughout the weathering zone as could occur in soils with partial water saturation or with plant respiration, which can strongly influence pH and mineral dissolution rates. For the modeling, we use Kinflow: a model developed at Yale that includes mineral dissolution and precipitation under kinetic control, aqueous speciation, surface erosion, dynamic porosity, permeability, and mineral surface areas via sub-grid-scale grain models, and exchange of volatiles at the surface. Most of the modeling is done in 1D, but some comparisons to 2D domains with heterogeneous permeability are made. We find that when CO2 is fixed only at the surface, the pH tends toward higher values for basalts than granites, in large part due to the presence of more divalent than monovalent cations in the primary minerals, tending to decrease rates of mineral dissolution. Weathering rates increase (as expected) with increasing CO2 and temperature. This modeling is done with the support of the Virtual Planetary Laboratory.

Operational forecasting based on a modified Weather Research and Forecasting model

Energy Technology Data Exchange (ETDEWEB)

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

Quality assurance of weather data for agricultural system model input

Science.gov (United States)

It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

Performance of the operational high-resolution numerical weather predictions of the Daphne project

Science.gov (United States)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Karacostas, Theodore; Kartsios, Stergios; Kotsopoulos, Stelios; Bampzelis, Dimitrios

2015-04-01

In the framework of the DAPHNE project, the Department of Meteorology and Climatology (http://meteo.geo.auth.gr) of the Aristotle University of Thessaloniki, Greece, utilizes the nonhydrostatic Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF-ARW) in order to produce high-resolution weather forecasts over Thessaly in central Greece. The aim of the DAPHNE project is to tackle the problem of drought in this area by means of Weather Modification. Cloud seeding assists the convective clouds to produce rain more efficiently or reduce hailstone size in favour of raindrops. The most favourable conditions for such a weather modification program in Thessaly occur in the period from March to October when convective clouds are triggered more frequently. Three model domains, using 2-way telescoping nesting, cover: i) Europe, the Mediterranean sea and northern Africa (D01), ii) Greece (D02) and iii) the wider region of Thessaly (D03; at selected periods) at horizontal grid-spacings of 15km, 5km and 1km, respectively. This research work intents to describe the atmospheric model setup and analyse its performance during a selected period of the operational phase of the project. The statistical evaluation of the high-resolution operational forecasts is performed using surface observations, gridded fields and radar data. Well established point verification methods combined with novel object based upon these methods, provide in depth analysis of the model skill. Spatial characteristics are adequately captured but a variable time lag between forecast and observation is noted. Acknowledgments: This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness

Transition of R&D into Operations at Fleet Numerical Meteorology and Oceanography Center

Science.gov (United States)

Clancy, R. M.

2006-12-01

The U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC) plays a significant role in the National capability for operational weather and ocean prediction through its operation of sophisticated global and regional meteorological and oceanographic models, extending from the top of the atmosphere to the bottom of the ocean. FNMOC uniquely satisfies the military's requirement for a global operational weather prediction capability based on software certified to DoD Information Assurance standards and operated in a secure classified computer environment protected from outside intrusion by DoD certified firewalls. FNMOC operates around-the-clock, 365 days per year and distributes products to military and civilian users around the world, both ashore and afloat, through a variety of means. FNMOC's customers include all branches of the Department of Defense, other government organizations such as the National Weather Service, private companies, a number of colleges and universities, and the general public. FNMOC employs three primary models, the Navy Operational Global Atmospheric Prediction System (NOGAPS), the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), and the WaveWatch III model (WW3), along with a number of specialized models and related applications. NOGAPS is a global weather model, driving nearly all other FNMOC models and applications in some fashion. COAMPS is a high- resolution regional model that has proved to be particularly valuable for forecasting weather and ocean conditions in highly complex coastal areas. WW3 is a state-of-the-art ocean wave model that is employed both globally and regionally in support of a wide variety of naval operations. Other models support and supplement the main models with predictions of ocean thermal structure, ocean currents, sea-ice characteristics, and other data. Fleet Numerical operates at the leading edge of science and technology, and benefits greatly from collocation with its supporting

Simple model for crop photosynthesis in terms of weather variables ...

African Journals Online (AJOL)

A theoretical mathematical model for describing crop photosynthetic rate in terms of the weather variables and crop characteristics is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of possible photosynthetic rate permitted by the different weather elements or crop architecture.

Numerical simulation for regional ozone concentrations: A case study by weather research and forecasting/chemistry (WRF/Chem) model

Energy Technology Data Exchange (ETDEWEB)

Habib Al Razi, Khandakar Md; Hiroshi, Moritomi [Environmental and Renewable Energy System, Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu City, 501-1193 (Japan)

2013-07-01

The objective of this research is to better understand and predict the atmospheric concentration distribution of ozone and its precursor (in particular, within the Planetary Boundary Layer (Within 110 km to 12 km) over Kasaki City and the Greater Tokyo Area using fully coupled online WRF/Chem (Weather Research and Forecasting/Chemistry) model. In this research, a serious and continuous high ozone episode in the Greater Tokyo Area (GTA) during the summer of 14–18 August 2010 was investigated using the observation data. We analyzed the ozone and other trace gas concentrations, as well as the corresponding weather conditions in this high ozone episode by WRF/Chem model. The simulation results revealed that the analyzed episode was mainly caused by the impact of accumulation of pollution rich in ozone over the Greater Tokyo Area. WRF/Chem has shown relatively good performance in modeling of this continuous high ozone episode, the simulated and the observed concentrations of ozone, NOx and NO2 are basically in agreement at Kawasaki City, with best correlation coefficients of 0.87, 0.70 and 0.72 respectively. Moreover, the simulations of WRF/Chem with WRF preprocessing software (WPS) show a better agreement with meteorological observations such as surface winds and temperature profiles in the ground level of this area. As a result the surface ozone simulation performances have been enhanced in terms of the peak ozone and spatial patterns, whereas WRF/Chem has been succeeded to generate meteorological fields as well as ozone, NOx, NO2 and NO.

A reactive transport model for Marcellus shale weathering

Science.gov (United States)

Heidari, Peyman; Li, Li; Jin, Lixin; Williams, Jennifer Z.; Brantley, Susan L.

2017-11-01

Shale formations account for 25% of the land surface globally and contribute a large proportion of the natural gas used in the United States. One of the most productive shale-gas formations is the Marcellus, a black shale that is rich in organic matter and pyrite. As a first step toward understanding how Marcellus shale interacts with water in the surface or deep subsurface, we developed a reactive transport model to simulate shale weathering under ambient temperature and pressure conditions, constrained by soil and water chemistry data. The simulation was carried out for 10,000 years since deglaciation, assuming bedrock weathering and soil genesis began after the last glacial maximum. Results indicate weathering was initiated by pyrite dissolution for the first 1000 years, leading to low pH and enhanced dissolution of chlorite and precipitation of iron hydroxides. After pyrite depletion, chlorite dissolved slowly, primarily facilitated by the presence of CO2 and organic acids, forming vermiculite as a secondary mineral. A sensitivity analysis indicated that the most important controls on weathering include the presence of reactive gases (CO2 and O2), specific surface area, and flow velocity of infiltrating meteoric water. The soil chemistry and mineralogy data could not be reproduced without including the reactive gases. For example, pyrite remained in the soil even after 10,000 years if O2 was not continuously present in the soil column; likewise, chlorite remained abundant and porosity remained small if CO2 was not present in the soil gas. The field observations were only simulated successfully when the modeled specific surface areas of the reactive minerals were 1-3 orders of magnitude smaller than surface area values measured for powdered minerals. Small surface areas could be consistent with the lack of accessibility of some fluids to mineral surfaces due to surface coatings. In addition, some mineral surface is likely interacting only with equilibrated pore

Modeling the influence of organic acids on soil weathering

Science.gov (United States)

Lawrence, Corey R.; Harden, Jennifer W.; Maher, Kate

2014-01-01

Biological inputs and organic matter cycling have long been regarded as important factors in the physical and chemical development of soils. In particular, the extent to which low molecular weight organic acids, such as oxalate, influence geochemical reactions has been widely studied. Although the effects of organic acids are diverse, there is strong evidence that organic acids accelerate the dissolution of some minerals. However, the influence of organic acids at the field-scale and over the timescales of soil development has not been evaluated in detail. In this study, a reactive-transport model of soil chemical weathering and pedogenic development was used to quantify the extent to which organic acid cycling controls mineral dissolution rates and long-term patterns of chemical weathering. Specifically, oxalic acid was added to simulations of soil development to investigate a well-studied chronosequence of soils near Santa Cruz, CA. The model formulation includes organic acid input, transport, decomposition, organic-metal aqueous complexation and mineral surface complexation in various combinations. Results suggest that although organic acid reactions accelerate mineral dissolution rates near the soil surface, the net response is an overall decrease in chemical weathering. Model results demonstrate the importance of organic acid input concentrations, fluid flow, decomposition and secondary mineral precipitation rates on the evolution of mineral weathering fronts. In particular, model soil profile evolution is sensitive to kaolinite precipitation and oxalate decomposition rates. The soil profile-scale modeling presented here provides insights into the influence of organic carbon cycling on soil weathering and pedogenesis and supports the need for further field-scale measurements of the flux and speciation of reactive organic compounds.

A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction

Science.gov (United States)

Pergaud, Julien; Masson, Valéry; Malardel, Sylvie; Couvreux, Fleur

2009-07-01

For numerical weather prediction models and models resolving deep convection, shallow convective ascents are subgrid processes that are not parameterized by classical local turbulent schemes. The mass flux formulation of convective mixing is now largely accepted as an efficient approach for parameterizing the contribution of larger plumes in convective dry and cloudy boundary layers. We propose a new formulation of the EDMF scheme (for Eddy DiffusivityMass Flux) based on a single updraft that improves the representation of dry thermals and shallow convective clouds and conserves a correct representation of stratocumulus in mesoscale models. The definition of entrainment and detrainment in the dry part of the updraft is original, and is specified as proportional to the ratio of buoyancy to vertical velocity. In the cloudy part of the updraft, the classical buoyancy sorting approach is chosen. The main closure of the scheme is based on the mass flux near the surface, which is proportional to the sub-cloud layer convective velocity scale w *. The link with the prognostic grid-scale cloud content and cloud cover and the projection on the non- conservative variables is processed by the cloud scheme. The validation of this new formulation using large-eddy simulations focused on showing the robustness of the scheme to represent three different boundary layer regimes. For dry convective cases, this parameterization enables a correct representation of the countergradient zone where the mass flux part represents the top entrainment (IHOP case). It can also handle the diurnal cycle of boundary-layer cumulus clouds (EUROCSARM) and conserve a realistic evolution of stratocumulus (EUROCSFIRE).

An equilibrium pricing model for weather derivatives in a multi-commodity setting

International Nuclear Information System (INIS)

Lee, Yongheon; Oren, Shmuel S.

2009-01-01

Many industries are exposed to weather risk. Weather derivatives can play a key role in hedging and diversifying such risk because the uncertainty in a company's profit function can be correlated to weather condition which affects diverse industry sectors differently. Unfortunately the weather derivatives market is a classical example of an incomplete market that is not amenable to standard methodologies used for derivative pricing in complete markets. In this paper, we develop an equilibrium pricing model for weather derivatives in a multi-commodity setting. The model is constructed in the context of a stylized economy where agents optimize their hedging portfolios which include weather derivatives that are issued in a fixed quantity by a financial underwriter. The supply and demand resulting from hedging activities and the supply by the underwriter are combined in an equilibrium pricing model under the assumption that all agents maximize some risk averse utility function. We analyze the gains due to the inclusion of weather derivatives in hedging portfolios and examine the components of that gain attributable to hedging and to risk sharing. (author)

Strategies for Effective Implementation of Science Models into 6-9 Grade Classrooms on Climate, Weather, and Energy Topics

Science.gov (United States)

Yarker, M. B.; Stanier, C. O.; Forbes, C.; Park, S.

2011-12-01

As atmospheric scientists, we depend on Numerical Weather Prediction (NWP) models. We use them to predict weather patterns, to understand external forcing on the atmosphere, and as evidence to make claims about atmospheric phenomenon. Therefore, it is important that we adequately prepare atmospheric science students to use computer models. However, the public should also be aware of what models are in order to understand scientific claims about atmospheric issues, such as climate change. Although familiar with weather forecasts on television and the Internet, the general public does not understand the process of using computer models to generate a weather and climate forecasts. As a result, the public often misunderstands claims scientists make about their daily weather as well as the state of climate change. Since computer models are the best method we have to forecast the future of our climate, scientific models and modeling should be a topic covered in K-12 classrooms as part of a comprehensive science curriculum. According to the National Science Education Standards, teachers are encouraged to science models into the classroom as a way to aid in the understanding of the nature of science. However, there is very little description of what constitutes a science model, so the term is often associated with scale models. Therefore, teachers often use drawings or scale representations of physical entities, such as DNA, the solar system, or bacteria. In other words, models used in classrooms are often used as visual representations, but the purpose of science models is often overlooked. The implementation of a model-based curriculum in the science classroom can be an effective way to prepare students to think critically, problem solve, and make informed decisions as a contributing member of society. However, there are few resources available to help teachers implement science models into the science curriculum effectively. Therefore, this research project looks at

Numerical study of Asian dust transport during the springtime of 2001 simulated with the Chemical Weather Forecasting System (CFORS) model

Science.gov (United States)

Uno, Itsushi; Satake, Shinsuke; Carmichael, Gregory R.; Tang, Youhua; Wang, Zifa; Takemura, Toshihiko; Sugimoto, Nobuo; Shimizu, Atsushi; Murayama, Toshiyuki; Cahill, Thomas A.; Cliff, Steven; Uematsu, Mitsuo; Ohta, Sachio; Quinn, Patricia K.; Bates, Timothy S.

2004-10-01

The regional-scale aerosol transport model Chemical Weather Forecasting System (CFORS) is used for analysis of large-scale dust phenomena during the Asian Pacific Regional Characterization Experiment (ACE-Asia) intensive observation. Dust modeling results are examined with the surface weather reports, satellite-derived dust index (Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI)), Mie-scattering lidar observation, and surface aerosol observations. The CFORS dust results are shown to accurately reproduce many of the important observed features. Model analysis shows that the simulated dust vertical loading correlates well with TOMS AI and that the dust loading is transported with the meandering of the synoptic-scale temperature field at the 500-hPa level. Quantitative examination of aerosol optical depth shows that model predictions are within 20% difference of the lidar observations for the major dust episodes. The structure of the ACE-Asia Perfect Dust Storm, which occurred in early April, is clarified with the help of the CFORS model analysis. This storm consisted of two boundary layer components and one elevated dust (>6-km height) feature (resulting from the movement of two large low-pressure systems). Time variation of the CFORS dust fields shows the correct onset timing of the elevated dust for each observation site, but the model results tend to overpredict dust concentrations at lower latitude sites. The horizontal transport flux at 130°E longitude is examined, and the overall dust transport flux at 130°E during March-April is evaluated to be 55 Tg.

Modeling and Forecasting Average Temperature for Weather Derivative Pricing

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2015-01-01

Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

DEM investigation of weathered rocks using a novel bond contact model

Directory of Open Access Journals (Sweden)

Zhenming Shi

2015-06-01

Full Text Available The distinct element method (DEM incorporated with a novel bond contact model was applied in this paper to shed light on the microscopic physical origin of macroscopic behaviors of weathered rock, and to achieve the changing laws of microscopic parameters from observed decaying properties of rocks during weathering. The changing laws of macroscopic mechanical properties of typical rocks were summarized based on the existing research achievements. Parametric simulations were then conducted to analyze the relationships between macroscopic and microscopic parameters, and to derive the changing laws of microscopic parameters for the DEM model. Equipped with the microscopic weathering laws, a series of DEM simulations of basic laboratory tests on weathered rock samples was performed in comparison with analytical solutions. The results reveal that the relationships between macroscopic and microscopic parameters of rocks against the weathering period can be successfully attained by parametric simulations. In addition, weathering has a significant impact on both stress–strain relationship and failure pattern of rocks.

Modeling extreme (Carrington-type) space weather events using three-dimensional MHD code simulations

Science.gov (United States)

Ngwira, C. M.; Pulkkinen, A. A.; Kuznetsova, M. M.; Glocer, A.

2013-12-01

There is growing concern over possible severe societal consequences related to adverse space weather impacts on man-made technological infrastructure and systems. In the last two decades, significant progress has been made towards the modeling of space weather events. Three-dimensional (3-D) global magnetohydrodynamics (MHD) models have been at the forefront of this transition, and have played a critical role in advancing our understanding of space weather. However, the modeling of extreme space weather events is still a major challenge even for existing global MHD models. In this study, we introduce a specially adapted University of Michigan 3-D global MHD model for simulating extreme space weather events that have a ground footprint comparable (or larger) to the Carrington superstorm. Results are presented for an initial simulation run with ``very extreme'' constructed/idealized solar wind boundary conditions driving the magnetosphere. In particular, we describe the reaction of the magnetosphere-ionosphere system and the associated ground induced geoelectric field to such extreme driving conditions. We also discuss the results and what they might mean for the accuracy of the simulations. The model is further tested using input data for an observed space weather event to verify the MHD model consistence and to draw guidance for future work. This extreme space weather MHD model is designed specifically for practical application to the modeling of extreme geomagnetically induced electric fields, which can drive large currents in earth conductors such as power transmission grids.

Using Virtualization to Integrate Weather, Climate, and Coastal Science Education

Science.gov (United States)

Davis, J. R.; Paramygin, V. A.; Figueiredo, R.; Sheng, Y.

2012-12-01

To better understand and communicate the important roles of weather and climate on the coastal environment, a unique publically available tool is being developed to support research, education, and outreach activities. This tool uses virtualization technologies to facilitate an interactive, hands-on environment in which students, researchers, and general public can perform their own numerical modeling experiments. While prior efforts have focused solely on the study of the coastal and estuary environments, this effort incorporates the community supported weather and climate model (WRF-ARW) into the Coastal Science Educational Virtual Appliance (CSEVA), an education tool used to assist in the learning of coastal transport processes; storm surge and inundation; and evacuation modeling. The Weather Research and Forecasting (WRF) Model is a next-generation, community developed and supported, mesoscale numerical weather prediction system designed to be used internationally for research, operations, and teaching. It includes two dynamical solvers (ARW - Advanced Research WRF and NMM - Nonhydrostatic Mesoscale Model) as well as a data assimilation system. WRF-ARW is the ARW dynamics solver combined with other components of the WRF system which was developed primarily at NCAR, community support provided by the Mesoscale and Microscale Meteorology (MMM) division of National Center for Atmospheric Research (NCAR). Included with WRF is the WRF Pre-processing System (WPS) which is a set of programs to prepare input for real-data simulations. The CSEVA is based on the Grid Appliance (GA) framework and is built using virtual machine (VM) and virtual networking technologies. Virtualization supports integration of an operating system, libraries (e.g. Fortran, C, Perl, NetCDF, etc. necessary to build WRF), web server, numerical models/grids/inputs, pre-/post-processing tools (e.g. WPS / RIP4 or UPS), graphical user interfaces, "Cloud"-computing infrastructure and other tools into a

Orogen and long-term carbon cycle, what numerical modelling can tell us about their interactions.

Science.gov (United States)

Maffre, P.; Godderis, Y.; Carretier, S.; Ladant, J. B.; Moquet, J. S.; Donnadieu, Y.

2017-12-01

If the uplift of current mountain ranges is often cited as a possible cause for Cenozoic cooling and the onset of the quaternary glaciation, this hypothesis is highly discussed. The main reason is that mountain uplift has a wide range of consequences, turning on or of sources or sinks of CO2. Most of these CO2 fluxes are still poorly constrained. Indeed, high erosion rates of mountain ranges increase silicate weathering by increasing fresh material supply (Goddéris et al. 2017) and enhance organic matter burial throughout intense sediment discharge by rivers (Galy et al. 2007). Yet, the effect of fresh matter supply by erosion is different if it happens on a weathering-limited or a supply-limited place (West 2012), and as eroded clasts are often weathered in pediments or floodplains (Moquet et al 2011, Lupker et al. 2012), it makes the issue more complex. Moreover, mountain ranges dramatically alter local and global climatic pattern by affecting atmospheric and oceanic circulation (Maffre et al. 2017), which must have consequences on weathering efficiency. Finally, it has been shown that the CO2 source due to sulphur oxidation can locally exceed the CO2 sink associated to silicate weathering (Torres et al. 2016) and may be relevant at geological timescale (Torres et al. 2014). Our aim here is to investigate theses processes in a global model in order to quantify their relative importance. We used the spatially resolved numerical model GEOCLIM (geoclimmodel.worpress.com) to test the effect of orography on CO2 fluxes with present-day continent configuration. We designed for that purpose two experiments, with and without orography, everything else kept as present-day state. Preliminary results show antagonist effects of mountain ranges. While erosion acts to enhance weathering efficiency when mountains are built, dryer and cooler conditions triggered by reorganization of ocean-atmosphere circulation act to reduce it. A first quantification using weathering data to

Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

Science.gov (United States)

Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

2014-01-01

Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

Spatial bias and uncertainty in numerical weather predictions for urban runoff forecasts with long time horizons

DEFF Research Database (Denmark)

Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

2017-01-01

Numerical Weather Predictions (NWP) can be used to forecast urban runoff with long lead times. However, NWP exhibit large spatial uncertainties and using forecasted precipitation directly above the catchment might therefore not be an ideal approach in an online setup. We use the Danish...... Meteorological Institute’s NWP ensemble and investigate a large spatial neighborhood around the catchment over a two-year period. When compared against in-sewer observations, runoff forecasts forced with precipitation from north-east of the catchment are most skillful. This highlights spatial biases...

Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

Directory of Open Access Journals (Sweden)

Sergei Soldatenko

2018-01-01

Full Text Available The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator global numerical weather prediction (NWP system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%, radiosondes (10.5%, wind profilers (3.9%, pilot balloons (2.8%, buoys (1.7% and ships (1.2%. In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network.

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

Science.gov (United States)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

DEFF Research Database (Denmark)

Courdent, Vianney Augustin Thomas; Grum, Morten; Mikkelsen, Peter Steen

2018-01-01

Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods...... to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select...... itself from earlier research in being the first application to urban hydrology, with fast runoff and small catchments that are highly sensitive to local extremes. Furthermore, no earlier reference has been found on the highly efficient third approach using only neighbouring cells with the highest threat...

Space Weather Models and Their Validation and Verification at the CCMC

Science.gov (United States)

Hesse, Michael

2010-01-01

The Community Coordinated l\\lodeling Center (CCMC) is a US multi-agency activity with a dual mission. With equal emphasis, CCMC strives to provide science support to the international space research community through the execution of advanced space plasma simulations, and it endeavors to support the space weather needs of the CS and partners. Space weather support involves a broad spectrum, from designing robust forecasting systems and transitioning them to forecasters, to providing space weather updates and forecasts to NASA's robotic mission operators. All of these activities have to rely on validation and verification of models and their products, so users and forecasters have the means to assign confidence levels to the space weather information. In this presentation, we provide an overview of space weather models resident at CCMC, as well as of validation and verification activities undertaken at CCMC or through the use of CCMC services.

A Reactive Transport Model for Marcellus Shale Weathering

Science.gov (United States)

Li, L.; Heidari, P.; Jin, L.; Williams, J.; Brantley, S.

2017-12-01

Shale formations account for 25% of the land surface globally. One of the most productive shale-gas formations is the Marcellus, a black shale that is rich in organic matter and pyrite. As a first step toward understanding how Marcellus shale interacts with water, we developed a reactive transport model to simulate shale weathering under ambient temperature and pressure conditions, constrained by soil chemistry and water data. The simulation was carried out for 10,000 years, assuming bedrock weathering and soil genesis began right after the last glacial maximum. Results indicate weathering was initiated by pyrite dissolution for the first 1,000 years, leading to low pH and enhanced dissolution of chlorite and precipitation of iron hydroxides. After pyrite depletion, chlorite dissolved slowly, primarily facilitated by the presence of CO2 and organic acids, forming vermiculite as a secondary mineral. A sensitivity analysis indicated that the most important controls on weathering include the presence of reactive gases (CO2 and O2), specific surface area, and flow velocity of infiltrating meteoric water. The soil chemistry and mineralogy data could not be reproduced without including the reactive gases. For example, pyrite remained in the soil even after 10,000 years if O2 was not continuously present in the soil column; likewise, chlorite remained abundant and porosity remained small with the presence of soil CO2. The field observations were only simulated successfully when the specific surface areas of the reactive minerals were 1-3 orders of magnitude smaller than surface area values measured for powdered minerals, reflecting the lack of accessibility of fluids to mineral surfaces and potential surface coating. An increase in the water infiltration rate enhanced weathering by removing dissolution products and maintaining far-from-equilibrium conditions. We conclude that availability of reactive surface area and transport of H2O and gases are the most important

Atmospheric Diabatic Heating in Different Weather States and the General Circulation

Science.gov (United States)

Rossow, William B.; Zhang, Yuanchong; Tselioudis, George

2016-01-01

Analysis of multiple global satellite products identifies distinctive weather states of the atmosphere from the mesoscale pattern of cloud properties and quantifies the associated diabatic heating/cooling by radiative flux divergence, precipitation, and surface sensible heat flux. The results show that the forcing for the atmospheric general circulation is a very dynamic process, varying strongly at weather space-time scales, comprising relatively infrequent, strong heating events by ''stormy'' weather and more nearly continuous, weak cooling by ''fair'' weather. Such behavior undercuts the value of analyses of time-averaged energy exchanges in observations or numerical models. It is proposed that an analysis of the joint time-related variations of the global weather states and the general circulation on weather space-time scales might be used to establish useful ''feedback like'' relationships between cloud processes and the large-scale circulation.

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Numerical analysis of the construction of Odelouca Dam using a Subloading Surface Soil Model

OpenAIRE

Brito, A.; Maranha, J. R.; Caldeira, L.

2014-01-01

Odelouca dam is an embankment dam, with 76 m height, recently constructed in the south of Portugal. It is zoned with a core consisting of colluvial and residual schist soil, and with soil-rockfill mixtures making up the shells (weathered schist with a significant fraction of coarse sized particles). This paper presents a numerical analysis of Odelouca dam construction. In this analysis the explicit finite difference program FLAC is used. An unconventional elastoplastic soil model, a Subloadin...

Modeling extreme "Carrington-type" space weather events using three-dimensional global MHD simulations

Science.gov (United States)

Ngwira, Chigomezyo M.; Pulkkinen, Antti; Kuznetsova, Maria M.; Glocer, Alex

2014-06-01

There is a growing concern over possible severe societal consequences related to adverse space weather impacts on man-made technological infrastructure. In the last two decades, significant progress has been made toward the first-principles modeling of space weather events, and three-dimensional (3-D) global magnetohydrodynamics (MHD) models have been at the forefront of this transition, thereby playing a critical role in advancing our understanding of space weather. However, the modeling of extreme space weather events is still a major challenge even for the modern global MHD models. In this study, we introduce a specially adapted University of Michigan 3-D global MHD model for simulating extreme space weather events with a Dst footprint comparable to the Carrington superstorm of September 1859 based on the estimate by Tsurutani et. al. (2003). Results are presented for a simulation run with "very extreme" constructed/idealized solar wind boundary conditions driving the magnetosphere. In particular, we describe the reaction of the magnetosphere-ionosphere system and the associated induced geoelectric field on the ground to such extreme driving conditions. The model setup is further tested using input data for an observed space weather event of Halloween storm October 2003 to verify the MHD model consistence and to draw additional guidance for future work. This extreme space weather MHD model setup is designed specifically for practical application to the modeling of extreme geomagnetically induced electric fields, which can drive large currents in ground-based conductor systems such as power transmission grids. Therefore, our ultimate goal is to explore the level of geoelectric fields that can be induced from an assumed storm of the reported magnitude, i.e., Dst˜=-1600 nT.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

Science.gov (United States)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

Science.gov (United States)

Courdent, Vianney; Grum, Morten; Mikkelsen, Peter Steen

2018-01-01

Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods. However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong. In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select the weight of evidence that matches the desired trade-off between POD and POFD. In the first approach, the rainfall input to the model is calculated for each of 25 ensemble members as a weighted average of rainfall from the NWP cells over the catchment where the weights are proportional to the areal intersection between the catchment and the NWP cells. In the second approach, a total of 2825 flow ensembles are generated using rainfall input from the neighbouring NWP cells up to approximately 6 cells in all directions from the catchment. In the third approach, the first approach is extended spatially by successively increasing the area covered and for each spatial increase and each time step selecting only the cell with the highest intensity resulting in a total of 175 ensemble

Implementation of bayesian model averaging on the weather data forecasting applications utilizing open weather map

Science.gov (United States)

Rahmat, R. F.; Nasution, F. R.; Seniman; Syahputra, M. F.; Sitompul, O. S.

2018-02-01

Weather is condition of air in a certain region at a relatively short period of time, measured with various parameters such as; temperature, air preasure, wind velocity, humidity and another phenomenons in the atmosphere. In fact, extreme weather due to global warming would lead to drought, flood, hurricane and other forms of weather occasion, which directly affects social andeconomic activities. Hence, a forecasting technique is to predict weather with distinctive output, particullary mapping process based on GIS with information about current weather status in certain cordinates of each region with capability to forecast for seven days afterward. Data used in this research are retrieved in real time from the server openweathermap and BMKG. In order to obtain a low error rate and high accuracy of forecasting, the authors use Bayesian Model Averaging (BMA) method. The result shows that the BMA method has good accuracy. Forecasting error value is calculated by mean square error shows (MSE). The error value emerges at minumum temperature rated at 0.28 and maximum temperature rated at 0.15. Meanwhile, the error value of minimum humidity rates at 0.38 and the error value of maximum humidity rates at 0.04. Afterall, the forecasting error rate of wind speed is at 0.076. The lower the forecasting error rate, the more optimized the accuracy is.

Evolution of porosity and diffusivity associated with chemical weathering of a basalt clast

Energy Technology Data Exchange (ETDEWEB)

Navarre-Sitchler, A.; Steefel, C.I.; Yang, L.; Tomutsa, L.; Brantley, S.L.

2009-02-15

Weathering of rocks as a result of exposure to water and the atmosphere can cause significant changes in their chemistry and porosity. In low-porosity rocks, such as basalts, changes in porosity, resulting from chemical weathering, are likely to modify the rock's effective diffusivity and permeability, affecting the rate of solute transport and thus potentially the rate of overall weathering to the extent that transport is the rate limiting step. Changes in total porosity as a result of mineral dissolution and precipitation have typically been used to calculate effective diffusion coefficients through Archie's law for reactive transport simulations of chemical weathering, but this approach fails to account for unconnected porosity that does not contribute to transport. In this study, we combine synchrotron X-ray microcomputed tomography ({mu}CT) and laboratory and numerical diffusion experiments to examine changes in both total and effective porosity and effective diffusion coefficients across a weathering interface in a weathered basalt clast from Costa Rica. The {mu}CT data indicate that below a critical value of {approx}9%, the porosity is largely unconnected in the basalt clast. The {mu}CT data were further used to construct a numerical pore network model to determine upscaled, effective diffusivities as a function of total porosity (ranging from 3 to 30%) for comparison with diffusivities determined in laboratory tracer experiments. By using effective porosity as the scaling parameter and accounting for critical porosity, a model is developed that accurately predicts continuum-scale effective diffusivities across the weathering interface of the basalt clast.

Computer Modeling of Hydrology, Weathering, and Isotopic Fractionation in Andrews Creek, Rocky Mountain National Park, Colorado for Water Years 1992 through 2012

Science.gov (United States)

Webb, R. M. T.; Parkhurst, D. L.; Mast, A.; Clow, D. W.

2014-12-01

The U.S. Geological Survey's (USGS) Water, Energy, and Biogeochemical Model (WEBMOD) was used to simulate hydrology, weathering, and isotopic fractionation in the 1.7 square kilometer Andrews Creek alpine watershed. WEBMOD includes hydrologic modules derived from the USGS Precipitation Runoff Modeling System, the National Weather Service Hydro-17 snow model, and TOPMODEL. PHREEQC, a geochemical reaction model, is coupled with the hydrologic model to simulate the geochemical evolution of waters as they evaporate, mix, and react within the landscape. Major solute concentrations and δ18O were modeled over the period 1992-2012. Variations of chloride and inorganic nitrogen respond almost entirely to variations in atmospheric deposition and preferential elution of snowpack. Both evaporation and melting result in isotopic enrichment of heavy isotopes in the residual snowpack throughout the summer. Magnesium and potassium, derived mostly from weathering with some atmospheric inputs, vary seasonally with uptake during the growing season and release during the fall and winter. The weathering of granitic minerals—oligoclase, biotite, chlorite, pyrite, calcite, and formation of secondary minerals—kaolinite, goethite, gibbsite, and smectite-illite—were selected as primary reactions based on mole-balance modeling of basin outflows. The rates of these reactions were quantified by calibrating WEBMOD to match observed concentrations and loads. Exported annual loads of most weathering products are highly correlated with discharge, whereas silica loads are less correlated with discharge, suggesting a source that is more active during dry years and less active during wet years. Potential sources include net dissolution of kaolinite and smectite-illite or mineralization of colloids with high silica content. WEBMOD is a valuable tool for simulating water quality variations in response to climate change, acid mine drainage, acid rain, biological transformations, and other

Approach to Integrate Global-Sun Models of Magnetic Flux Emergence and Transport for Space Weather Studies

Science.gov (United States)

Mansour, Nagi N.; Wray, Alan A.; Mehrotra, Piyush; Henney, Carl; Arge, Nick; Godinez, H.; Manchester, Ward; Koller, J.; Kosovichev, A.; Scherrer, P.;

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

Energy Technology Data Exchange (ETDEWEB)

Auffhammer, M.; Hsiang, S. M.; Schlenker, W.; Sobel, A.

2013-06-28

Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

NOAA's Strategy to Improve Operational Weather Prediction Outlooks at Subseasonal Time Range

Science.gov (United States)

Schneider, T.; Toepfer, F.; Stajner, I.; DeWitt, D.

2017-12-01

NOAA is planning to extend operational global numerical weather prediction to sub-seasonal time range under the auspices of its Next Generation Global Prediction System (NGGPS) and Extended Range Outlook Programs. A unification of numerical prediction capabilities for weather and subseasonal to seasonal (S2S) timescales is underway at NOAA using the Finite Volume Cubed Sphere (FV3) dynamical core as the basis for the emerging unified system. This presentation will overview NOAA's strategic planning and current activities to improve prediction at S2S time-scales that are ongoing in response to the Weather Research and Forecasting Innovation Act of 2017, Section 201. Over the short-term, NOAA seeks to improve the operational capability through improvements to its ensemble forecast system to extend its range to 30 days using the new FV3 Global Forecast System model, and by using this system to provide reforecast and re-analyses. In parallel, work is ongoing to improve NOAA's operational product suite for 30 day outlooks for temperature, precipitation and extreme weather phenomena.

Geodetic Space Weather Monitoring by means of Ionosphere Modelling

Science.gov (United States)

Schmidt, Michael

2017-04-01

The term space weather indicates physical processes and phenomena in space caused by radiation of energy mainly from the Sun. Manifestations of space weather are (1) variations of the Earth's magnetic field, (2) the polar lights in the northern and southern hemisphere, (3) variations within the ionosphere as part of the upper atmosphere characterized by the existence of free electrons and ions, (4) the solar wind, i.e. the permanent emission of electrons and photons, (5) the interplanetary magnetic field, and (6) electric currents, e.g. the van Allen radiation belt. It can be stated that ionosphere disturbances are often caused by so-called solar storms. A solar storm comprises solar events such as solar flares and coronal mass ejections (CMEs) which have different effects on the Earth. Solar flares may cause disturbances in positioning, navigation and communication. CMEs can effect severe disturbances and in extreme cases damages or even destructions of modern infrastructure. Examples are interruptions to satellite services including the global navigation satellite systems (GNSS), communication systems, Earth observation and imaging systems or a potential failure of power networks. Currently the measurements of solar satellite missions such as STEREO and SOHO are used to forecast solar events. Besides these measurements the Earth's ionosphere plays another key role in monitoring the space weather, because it responses to solar storms with an increase of the electron density. Space-geodetic observation techniques, such as terrestrial GNSS, satellite altimetry, space-borne GPS (radio occultation), DORIS and VLBI provide valuable global information about the state of the ionosphere. Additionally geodesy has a long history and large experience in developing and using sophisticated analysis and combination techniques as well as empirical and physical modelling approaches. Consequently, geodesy is predestinated for strongly supporting space weather monitoring via

Linking the M&Rfi Weather Generator with Agrometeorological Models

Science.gov (United States)

Dubrovsky, Martin; Trnka, Miroslav

2015-04-01

Realistic meteorological inputs (representing the present and/or future climates) for the agrometeorological model simulations are often produced by stochastic weather generators (WGs). This contribution presents some methodological issues and results obtained in our recent experiments. We also address selected questions raised in the synopsis of this session. The input meteorological time series for our experiments are produced by the parametric single site weather generator (WG) Marfi, which is calibrated from the available observational data (or interpolated from surrounding stations). To produce meteorological series representing the future climate, the WG parameters are modified by climate change scenarios, which are prepared by the pattern scaling method: the standardised scenarios derived from Global or Regional Climate Models are multiplied by the change in global mean temperature (ΔTG) determined by the simple climate model MAGICC. The presentation will address following questions: (i) The dependence of the quality of the synthetic weather series and impact results on the WG settings. An emphasis will be put on an effect of conditioning the daily WG on monthly WG (presently being one of our hot topics), which aims at improvement of the reproduction of the low-frequency weather variability. Comparison of results obtained with various WG settings is made in terms of climatic and agroclimatic indices (including extreme temperature and precipitation characteristics and drought indices). (ii) Our methodology accounts for the uncertainties coming from various sources. We will show how the climate change impact results are affected by 1. uncertainty in climate modelling, 2. uncertainty in ΔTG, and 3. uncertainty related to the complexity of the climate change scenario (focusing on an effect of inclusion of changes in variability into the climate change scenarios). Acknowledgements: This study was funded by project "Building up a multidisciplinary scientific

Space Weather Forecasting Operational Needs: A view from NOAA/SWPC

Science.gov (United States)

Biesecker, D. A.; Onsager, T. G.; Rutledge, R.

2017-12-01

The gaps in space weather forecasting are many. From long lead time forecasts, to accurate warnings with lead time to take action, there is plenty of room for improvement. Significant numbers of new observations would improve this picture, but it's also important to recognize the value of numerical modeling. The obvious interplanetary mission concepts that would be ideal would be 1) to measure the in-situ solar wind along the entire Sun-Earth line from as near to the Sun as possible all the way to Earth 2) a string of spacecraft in 1 AU heliocentric orbits making in-situ measurements as well as remote-sensing observations of the Sun, corona, and heliosphere. Even partially achieving these ideals would benefit space weather services, improving lead time and providing greater accuracy further into the future. The observations alone would improve forecasting. However, integrating these data into numerical models, as boundary conditions or via data assimilation, would provide the greatest improvements.

The benefit of high-resolution operational weather forecasts for flash flood warning

Directory of Open Access Journals (Sweden)

J. Younis

2008-07-01

Full Text Available In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods.

One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts.

This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen.

Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to

Recent Progress of Solar Weather Forecasting at Naoc

Science.gov (United States)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

Supercomputing for weather and climate modelling: convenience or necessity

CSIR Research Space (South Africa)

Landman, WA

2009-12-01

Full Text Available Weather and climate modelling require dedicated computer infrastructure in order to generate high-resolution, large ensemble, various models with different configurations, etc. in order to optimise operational forecasts and climate projections. High...

Analyzing numerics of bulk microphysics schemes in community models: warm rain processes

Directory of Open Access Journals (Sweden)

I. Sednev

2012-08-01

Full Text Available Implementation of bulk cloud microphysics (BLK parameterizations in atmospheric models of different scales has gained momentum in the last two decades. Utilization of these parameterizations in cloud-resolving models when timesteps used for the host model integration are a few seconds or less is justified from the point of view of cloud physics. However, mechanistic extrapolation of the applicability of BLK schemes to the regional or global scales and the utilization of timesteps of hundreds up to thousands of seconds affect both physics and numerics.

We focus on the mathematical aspects of BLK schemes, such as stability and positive-definiteness. We provide a strict mathematical definition for the problem of warm rain formation. We also derive a general analytical condition (SM-criterion that remains valid regardless of parameterizations for warm rain processes in an explicit Eulerian time integration framework used to advanced finite-difference equations, which govern warm rain formation processes in microphysics packages in the Community Atmosphere Model and the Weather Research and Forecasting model. The SM-criterion allows for the existence of a unique positive-definite stable mass-conserving numerical solution, imposes an additional constraint on the timestep permitted due to the microphysics (like the Courant-Friedrichs-Lewy condition for the advection equation, and prohibits use of any additional assumptions not included in the strict mathematical definition of the problem under consideration.

By analyzing the numerics of warm rain processes in source codes of BLK schemes implemented in community models we provide general guidelines regarding the appropriate choice of time steps in these models.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

Science.gov (United States)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

Numerical Transducer Modeling

DEFF Research Database (Denmark)

Henriquez, Vicente Cutanda

This thesis describes the development of a numerical model of the propagation of sound waves in fluids with viscous and thermal losses, with application to the simulation of acoustic transducers, in particular condenser microphones for measurement. The theoretical basis is presented, numerical...... manipulations are developed to satisfy the more complicated boundary conditions, and a model of a condenser microphone with a coupled membrane is developed. The model is tested against measurements of ¼ inch condenser microphones and analytical calculations. A detailed discussion of the results is given....

Psychological mechanisms in outdoor place and weather assessment: towards a conceptual model

Science.gov (United States)

Knez, Igor; Thorsson, Sofia; Eliasson, Ingegärd; Lindberg, Fredrik

2009-01-01

The general aim has been to illuminate the psychological mechanisms involved in outdoor place and weather assessment. This reasoning was conceptualized in a model, tentatively proposing direct and indirect links of influence in an outdoor place-human relationship. The model was subsequently tested by an empirical study, performed in a Nordic city, on the impact of weather and personal factors on participants’ perceptual and emotional estimations of outdoor urban places. In line with our predictions, we report significant influences of weather parameters (air temperature, wind, and cloudlessness) and personal factors (environmental attitude and age) on participants’ perceptual and emotional estimations of outdoor urban places. All this is a modest, yet significant, step towards an understanding of the psychology of outdoor place and weather assessment.

Weathering model for the quantification of atmospheric oxygen evolution during the Paleoproterozoic

Science.gov (United States)

Yokota, Kohei; Kanzaki, Yoshiki; Murakami, Takashi

2013-09-01

A weathering model has been developed to quantify atmospheric oxygen evolution during the Paleoproterozoic. The weathering model calculates the concentrations of Fe2+ dissolved from Fe2+-bearing primary minerals and oxidized Fe3+ out of the dissolved Fe2+ at a given partial pressure of atmospheric oxygen (PO2) during weathering and establishes the relationships between PO2 and ϕ, where ϕ is the ratio of oxidized and then precipitated Fe3+ out of the Fe2+ dissolved from primary minerals to the dissolved Fe2+ in a whole weathering profile. The weathering model considers controlling factors of the redistribution of Fe during weathering, that is, the dissolution rate of Fe2+-bearing primary minerals, the oxidation rate of Fe2+, and the groundwater flow rate. The validity of the model was confirmed by applying the model to the experimental data of olivine dissolution carried out under low O2 conditions. The sensitivity analysis of the model has revealed that the formation time of weathering, the mineral dissolution rate and the diffusion of O2 into a weathering profile have no or slight influence on ϕ, resulting in ˜0, 0 and 0.3 changes in log(PO2) caused by four orders of magnitude change of the formation time, more than 10 orders change of the mineral dissolution rate, and assumed change of the O2 diffusion, respectively. On the other hand, the temperature, the pH and the groundwater flow rate have moderate to large effects on ϕ: 0.6, 1.4 and 1.5 changes in log(PO2) for changes of 5 °C in temperature, 0.5 in pH, and one order of magnitude in groundwater flow rate, respectively. Using possible surface temperature, pH and groundwater flow rate estimated from the literature, we calculated the ϕ-PO2 relationships which were then applied to the ϕ values of paleosols (fossil weathering profiles) formed between 2.5 and 1.8 Ga. Taking account of the constraints given by the records of mass independent fractionation in sulfur isotopes and other geological proxies (i

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

Science.gov (United States)

Daniels, Taumi S.; Schaffner, Philip R.; Evans, Emory T.; Neece, Robert T.; Young, Steve D.

2012-01-01

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements

Simulation and Prediction of Weather Radar Clutter Using a Wave Propagator on High Resolution NWP Data

DEFF Research Database (Denmark)

Benzon, Hans-Henrik; Bovith, Thomas

2008-01-01

for prediction of this type of weather radar clutter is presented. The method uses a wave propagator to identify areas of potential non-standard propagation. The wave propagator uses a three dimensional refractivity field derived from the geophysical parameters: temperature, humidity, and pressure obtained from......Weather radars are essential sensors for observation of precipitation in the troposphere and play a major part in weather forecasting and hydrological modelling. Clutter caused by non-standard wave propagation is a common problem in weather radar applications, and in this paper a method...... a high-resolution Numerical Weather Prediction (NWP) model. The wave propagator is based on the parabolic equation approximation to the electromagnetic wave equation. The parabolic equation is solved using the well-known Fourier split-step method. Finally, the radar clutter prediction technique is used...

Utilization of Live Localized Weather Information for Sustainable Agriculture

Science.gov (United States)

Anderson, J.; Usher, J.

2010-09-01

Authors: Jim Anderson VP, Global Network and Business Development WeatherBug® Professional Jeremy Usher Managing Director, Europe WeatherBug® Professional Localized, real-time weather information is vital for day-to-day agronomic management of all crops. The challenge for agriculture is twofold in that local and timely weather data is not often available for producers and farmers, and it is not integrated into decision-support tools they require. Many of the traditional sources of weather information are not sufficient for agricultural applications because of the long distances between weather stations, meaning the data is not always applicable for on-farm decision making processes. The second constraint with traditional weather information is the timeliness of the data. Most delivery systems are designed on a one-hour time step, whereas many decisions in agriculture are based on minute-by-minute weather conditions. This is especially true for decisions surrounding chemical and fertilizer application and frost events. This presentation will outline how the creation of an agricultural mesonet (weather network) can enable producers and farmers with live, local weather information from weather stations installed in farm/field locations. The live weather information collected from each weather station is integrated into a web-enabled decision support tool, supporting numerous on-farm agronomic activities such as pest management, or dealing with heavy rainfall and frost events. Agronomic models can be used to assess the potential of disease pressure, enhance the farmer's abilities to time pesticide applications, or assess conditions contributing to yield and quality fluctuations. Farmers and industry stakeholders may also view quality-assured historical weather variables at any location. This serves as a record-management tool for viewing previously uncharted agronomic weather events in graph or table form. This set of weather tools is unique and provides a

Process-based modeling of silicate mineral weathering responses to increasing atmospheric CO2 and climate change

Science.gov (United States)

Banwart, Steven A.; Berg, Astrid; Beerling, David J.

2009-12-01

A mathematical model describes silicate mineral weathering processes in modern soils located in the boreal coniferous region of northern Europe. The process model results demonstrate a stabilizing biological feedback mechanism between atmospheric CO2 levels and silicate weathering rates as is generally postulated for atmospheric evolution. The process model feedback response agrees within a factor of 2 of that calculated by a weathering feedback function of the type generally employed in global geochemical carbon cycle models of the Earth's Phanerozoic CO2 history. Sensitivity analysis of parameter values in the process model provides insight into the key mechanisms that influence the strength of the biological feedback to weathering. First, the process model accounts for the alkalinity released by weathering, whereby its acceleration stabilizes pH at values that are higher than expected. Although the process model yields faster weathering with increasing temperature, because of activation energy effects on mineral dissolution kinetics at warmer temperature, the mineral dissolution rate laws utilized in the process model also result in lower dissolution rates at higher pH values. Hence, as dissolution rates increase under warmer conditions, more alkalinity is released by the weathering reaction, helping maintain higher pH values thus stabilizing the weathering rate. Second, the process model yields a relatively low sensitivity of soil pH to increasing plant productivity. This is due to more rapid decomposition of dissolved organic carbon (DOC) under warmer conditions. Because DOC fluxes strongly influence the soil water proton balance and pH, this increased decomposition rate dampens the feedback between productivity and weathering. The process model is most sensitive to parameters reflecting soil structure; depth, porosity, and water content. This suggests that the role of biota to influence these characteristics of the weathering profile is as important, if not

Evaluation of operational numerical weather predictions in relation to the prevailing synoptic conditions

Science.gov (United States)

Pytharoulis, Ioannis; Tegoulias, Ioannis; Karacostas, Theodore; Kotsopoulos, Stylianos; Kartsios, Stergios; Bampzelis, Dimitrios

2015-04-01

The Thessaly plain, which is located in central Greece, has a vital role in the financial life of the country, because of its significant agricultural production. The aim of DAPHNE project (http://www.daphne-meteo.gr) is to tackle the problem of drought in this area by means of Weather Modification in convective clouds. This problem is reinforced by the increase of population and the water demand for irrigation, especially during the warm period of the year. The nonhydrostatic Weather Research and Forecasting model (WRF), is utilized for research and operational purposes of DAPHNE project. The WRF output fields are employed by the partners in order to provide high-resolution meteorological guidance and plan the project's operations. The model domains cover: i) Europe, the Mediterranean sea and northern Africa, ii) Greece and iii) the wider region of Thessaly (at selected periods), at horizontal grid-spacings of 15km, 5km and 1km, respectively, using 2-way telescoping nesting. The aim of this research work is to investigate the model performance in relation to the prevailing upper-air synoptic circulation. The statistical evaluation of the high-resolution operational forecasts of near-surface and upper air fields is performed at a selected period of the operational phase of the project using surface observations, gridded fields and weather radar data. The verification is based on gridded, point and object oriented techniques. The 10 upper-air circulation types, which describe the prevailing conditions over Greece, are employed in the synoptic classification. This methodology allows the identification of model errors that occur and/or are maximized at specific synoptic conditions and may otherwise be obscured in aggregate statistics. Preliminary analysis indicates that the largest errors are associated with cyclonic conditions. Acknowledgments This research work of Daphne project (11SYN_8_1088) is co-funded by the European Union (European Regional Development Fund

Traffic analysis toolbox volume XI : weather and traffic analysis, modeling and simulation.

Science.gov (United States)

2010-12-01

This document presents a weather module for the traffic analysis tools program. It provides traffic engineers, transportation modelers and decisions makers with a guide that can incorporate weather impacts into transportation system analysis and mode...

CDIAC catalog of numeric data packages and computer model packages

International Nuclear Information System (INIS)

Boden, T.A.; Stoss, F.W.

1993-05-01

The Carbon Dioxide Information Analysis Center acquires, quality-assures, and distributes to the scientific community numeric data packages (NDPs) and computer model packages (CMPs) dealing with topics related to atmospheric trace-gas concentrations and global climate change. These packages include data on historic and present atmospheric CO 2 and CH 4 concentrations, historic and present oceanic CO 2 concentrations, historic weather and climate around the world, sea-level rise, storm occurrences, volcanic dust in the atmosphere, sources of atmospheric CO 2 , plants' response to elevated CO 2 levels, sunspot occurrences, and many other indicators of, contributors to, or components of climate change. This catalog describes the packages presently offered by CDIAC, reviews the processes used by CDIAC to assure the quality of the data contained in these packages, notes the media on which each package is available, describes the documentation that accompanies each package, and provides ordering information. Numeric data are available in the printed NDPs and CMPs, in CD-ROM format, and from an anonymous FTP area via Internet. All CDIAC information products are available at no cost

The influence of weather on Golden Eagle migration in northwestern Montana

Science.gov (United States)

Yates, R.E.; McClelland, B.R.; Mcclelland, P.T.; Key, C.H.; Bennetts, R.E.

2001-01-01

We analyzed the influence of 17 weather factors on migrating Golden Eagles (Aquila chrysaetos) near the Continental Divide in Glacier National Park, Montana, U.S.A. Local weather measurements were recorded at automated stations on the flanks of two peaks within the migration path. During a total of 506 hr of observation, the yearly number of Golden Eagles in autumn counts (1994-96) averaged 1973; spring counts (1995 and 1996) averaged 605 eagles. Mean passage rates (eagles/hr) were 16.5 in autumn and 8.2 in spring. Maximum rates were 137 in autumn and 67 in spring. Using generalized linear modeling, we tested for the effects of weather factors on the number of eagles counted. In the autumn model, the number of eagles increased with increasing air temperature, rising barometric pressure, decreasing relative humidity, and interactions among those factors. In the spring model, the number of eagles increased with increasing wind speed, barometric pressure, and the interaction between these factors. Our data suggest that a complex interaction among weather factors influenced the number of eagles passing on a given day. We hypothesize that in complex landscapes with high topographic relief, such as Glacier National Park, numerous weather factors produce different daily combinations to which migrating eagles respond opportunistically. ?? 2001 The Raptor Research Foundation, Inc.

The CCPP-ARM Parameterization Testbed (CAPT): Where Climate Simulation Meets Weather Prediction

Energy Technology Data Exchange (ETDEWEB)

Phillips, T J; Potter, G L; Williamson, D L; Cederwall, R T; Boyle, J S; Fiorino, M; Hnilo, J J; Olson, J G; Xie, S; Yio, J J

2003-11-21

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands, in particular, that the GCM parameterizations of unresolved processes should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provied that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by realistically initialized climate GCM, and the application of six-hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be similarly tested. In order to further this method for evaluating and analyzing parameterizations in climate GCMs, the USDOE is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM. Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.

Analysis and Modeling of Influenza Outbreaks as Driven by Weather

Science.gov (United States)

Thrastarson, H. T.; Teixeira, J.; Serman, E. A.; Parekh, A.; Yeo, E.

2017-12-01

Seasonal influenza outbreaks are a major source of illness, mortality and economic burden worldwide. Attributing what drives the seasonality of the outbreaks is still an unsettled problem. But in temperate regions absolute humidity conditions are a strong candidate (Shaman et al., 2010) and some studies have associated temperature conditions with influenza outbreaks. We use humidity and temperature data from NASA's AIRS (Atmospheric Infra-Red Sounder) instrument as well as data for influenza incidence in the US and South Africa to explore the connection between weather and influenza seasonality at different spatial scales. We also incorporate influenza surveillance data, satellite data and humidity forecasts into a numerical epidemiological prediction system. Our results give support for the role of local weather conditions as drivers of the seasonality of influenza in temperate regions. This can have implications for public health efforts where forecasting of the timing and intensity of influenza outbreaks has a great potential role (e.g., aiding management and organization of vaccines, drugs and other resources).

Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model.

Science.gov (United States)

Krissansen-Totton, Joshua; Catling, David C

2017-05-22

The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO 2 and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an accurate representation of the carbon cycle, to fit proxies the temperature dependence of continental weathering must be weaker than commonly assumed. We find that 15-31 °C (1σ) surface warming is required to double the continental weathering flux, versus 3-10 °C in previous work. In addition, continental weatherability has increased 1.7-3.3 times since 100 Myr ago, demanding explanation by uplift and sea-level changes. The average Earth system climate sensitivity is  K (1σ) per CO 2 doubling, which is notably higher than fast-feedback estimates. These conclusions are robust to assumptions about outgassing, modern fluxes and seafloor weathering kinetics.

Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

International Nuclear Information System (INIS)

Ekman, Annica

2000-02-01

Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO 2 (a)), sulfate in air (SO 4 (a)), sulfate in cloud water (SO 4 (aq)) and hydrogen peroxide (H 2 O 2 ). Modeled concentrations of SO 2 (a), SO 4 (a) and SO 4 (aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO 2 (a) concentration and underpredicts the SO 4 (a) concentration. The agreement between modeled and observed SO 4 (aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO 2 (a) and 2-2.5 days for SO 4 (a). For SO 2 (a) this is in accordance with earlier simulated global turnover times, but for SO 4 (a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO 4 (a) and SO 4 (aq), if an additional oxidant for converting SO 2 (a) to SO 4 (a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations

Impact of grain size and rock composition on simulated rock weathering

Science.gov (United States)

Israeli, Yoni; Emmanuel, Simon

2018-05-01

Both chemical and mechanical processes act together to control the weathering rate of rocks. In rocks with micrometer size grains, enhanced dissolution at grain boundaries has been observed to cause the mechanical detachment of particles. However, it remains unclear how important this effect is in rocks with larger grains, and how the overall weathering rate is influenced by the proportion of high- and low-reactivity mineral phases. Here, we use a numerical model to assess the effect of grain size on chemical weathering and chemo-mechanical grain detachment. Our model shows that as grain size increases, the weathering rate initially decreases; however, beyond a critical size no significant decrease in the rate is observed. This transition occurs when the density of reactive boundaries is less than ˜ 20 % of the entire domain. In addition, we examined the weathering rates of rocks containing different proportions of high- and low-reactivity minerals. We found that as the proportion of low-reactivity minerals increases, the weathering rate decreases nonlinearly. These simulations indicate that for all compositions, grain detachment contributes more than 36 % to the overall weathering rate, with a maximum of ˜ 50 % when high- and low-reactivity minerals are equally abundant in the rock. This occurs because selective dissolution of the high-reactivity minerals creates large clusters of low-reactivity minerals, which then become detached. Our results demonstrate that the balance between chemical and mechanical processes can create complex and nonlinear relationships between the weathering rate and lithology.

Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

Directory of Open Access Journals (Sweden)

Isaac Mugume

2016-01-01

Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

A statistical model to estimate the local vulnerability to severe weather

Science.gov (United States)

Pardowitz, Tobias

2018-06-01

We present a spatial analysis of weather-related fire brigade operations in Berlin. By comparing operation occurrences to insured losses for a set of severe weather events we demonstrate the representativeness and usefulness of such data in the analysis of weather impacts on local scales. We investigate factors influencing the local rate of operation occurrence. While depending on multiple factors - which are often not available - we focus on publicly available quantities. These include topographic features, land use information based on satellite data and information on urban structure based on data from the OpenStreetMap project. After identifying suitable predictors such as housing coverage or local density of the road network we set up a statistical model to be able to predict the average occurrence frequency of local fire brigade operations. Such model can be used to determine potential hotspots for weather impacts even in areas or cities where no systematic records are available and can thus serve as a basis for a broad range of tools or applications in emergency management and planning.

Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

Science.gov (United States)

DY, C. Y.; Fung, J. C. H.

2016-08-01

A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

Weather Radar Estimations Feeding an Artificial Neural Network Model Weather Radar Estimations Feeding an Artificial Neural Network Model

Directory of Open Access Journals (Sweden)

Dawei Han

2012-02-01

Full Text Available The application of ANNs (Artifi cial Neural Networks has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.

Solar weather monitoring

Directory of Open Access Journals (Sweden)

J.-F. Hochedez

2005-11-01

Full Text Available Space Weather nowcasting and forecasting require solar observations because geoeffective disturbances can arise from three types of solar phenomena: coronal mass ejections (CMEs, flares and coronal holes. For each, we discuss their definition and review their precursors in terms of remote sensing and in-situ observations. The objectives of Space Weather require some specific instrumental features, which we list using the experience gained from the daily operations of the Solar Influences Data analysis Centre (SIDC at the Royal Observatory of Belgium. Nowcasting requires real-time monitoring to assess quickly and reliably the severity of any potentially geoeffective solar event. Both research and forecasting could incorporate more observations in order to feed case studies and data assimilation respectively. Numerical models will result in better predictions of geomagnetic storms and solar energetic particle (SEP events. We review the data types available to monitor solar activity and interplanetary conditions. They come from space missions and ground observatories and range from sequences of dopplergrams, magnetograms, white-light, chromospheric, coronal, coronagraphic and radio images, to irradiance and in-situ time-series. Their role is summarized together with indications about current and future solar monitoring instruments.

Candidate Prediction Models and Methods

DEFF Research Database (Denmark)

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

2005-01-01

This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

Don Quixote Pond: A Small Scale Model of Weathering and Salt Accumulation

Science.gov (United States)

Englert, P.; Bishop, J. L.; Patel, S. N.; Gibson, E. K.; Koeberl, C.

2015-01-01

The formation of Don Quixote Pond in the North Fork of Wright Valley, Antarctica, is a model for unique terrestrial calcium, chlorine, and sulfate weathering, accumulation, and distribution processes. The formation of Don Quixote Pond by simple shallow and deep groundwater contrasts more complex models for Don Juan Pond in the South Fork of Wright Valley. Our study intends to understand the formation of Don Quixote Pond as unique terrestrial processes and as a model for Ca, C1, and S weathering and distribution on Mars.

rpe v5: an emulator for reduced floating-point precision in large numerical simulations

Science.gov (United States)

Dawson, Andrew; Düben, Peter D.

2017-06-01

This paper describes the rpe (reduced-precision emulator) library which has the capability to emulate the use of arbitrary reduced floating-point precision within large numerical models written in Fortran. The rpe software allows model developers to test how reduced floating-point precision affects the result of their simulations without having to make extensive code changes or port the model onto specialized hardware. The software can be used to identify parts of a program that are problematic for numerical precision and to guide changes to the program to allow a stronger reduction in precision.The development of rpe was motivated by the strong demand for more computing power. If numerical precision can be reduced for an application under consideration while still achieving results of acceptable quality, computational cost can be reduced, since a reduction in numerical precision may allow an increase in performance or a reduction in power consumption. For simulations with weather and climate models, savings due to a reduction in precision could be reinvested to allow model simulations at higher spatial resolution or complexity, or to increase the number of ensemble members to improve predictions. rpe was developed with a particular focus on the community of weather and climate modelling, but the software could be used with numerical simulations from other domains.

Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures

KAUST Repository

Akbudak, Kadir; Ltaief, Hatem; Mikhalev, Aleksandr; Keyes, David E.

2017-01-01

Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.

Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures

KAUST Repository

Akbudak, Kadir

2017-05-11

Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.

A delta-rule model of numerical and non-numerical order processing.

Science.gov (United States)

Verguts, Tom; Van Opstal, Filip

2014-06-01

Numerical and non-numerical order processing share empirical characteristics (distance effect and semantic congruity), but there are also important differences (in size effect and end effect). At the same time, models and theories of numerical and non-numerical order processing developed largely separately. Currently, we combine insights from 2 earlier models to integrate them in a common framework. We argue that the same learning principle underlies numerical and non-numerical orders, but that environmental features determine the empirical differences. Implications for current theories on order processing are pointed out. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Community Coordinated Modeling Center: A Powerful Resource in Space Science and Space Weather Education

Science.gov (United States)

Chulaki, A.; Kuznetsova, M. M.; Rastaetter, L.; MacNeice, P. J.; Shim, J. S.; Pulkkinen, A. A.; Taktakishvili, A.; Mays, M. L.; Mendoza, A. M. M.; Zheng, Y.; Mullinix, R.; Collado-Vega, Y. M.; Maddox, M. M.; Pembroke, A. D.; Wiegand, C.

2015-12-01

Community Coordinated Modeling Center (CCMC) is a NASA affiliated interagency partnership with the primary goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research. Additionally, over the past ten years it has established itself as a global space science education resource supporting undergraduate and graduate education and research, and spreading space weather awareness worldwide. A unique combination of assets, capabilities and close ties to the scientific and educational communities enable this small group to serve as a hub for raising generations of young space scientists and engineers. CCMC resources are publicly available online, providing unprecedented global access to the largest collection of modern space science models (developed by the international research community). CCMC has revolutionized the way simulations are utilized in classrooms settings, student projects, and scientific labs and serves hundreds of educators, students and researchers every year. Another major CCMC asset is an expert space weather prototyping team primarily serving NASA's interplanetary space weather needs. Capitalizing on its unrivaled capabilities and experiences, the team provides in-depth space weather training to students and professionals worldwide, and offers an amazing opportunity for undergraduates to engage in real-time space weather monitoring, analysis, forecasting and research. In-house development of state-of-the-art space weather tools and applications provides exciting opportunities to students majoring in computer science and computer engineering fields to intern with the software engineers at the CCMC while also learning about the space weather from the NASA scientists.

Applied environmental fluid mechanics: what's the weather in your backyard?

Science.gov (United States)

Chow, F. K.

2011-12-01

The microclimates of the San Francisco Bay Area can lead to 30-40F differences in temperature from the coast to just 30 miles inland. The reasons for this include local topography which affects development of the atmospheric boundary layer. A Bay Area resident's experience of fog, air pollution, and weather events therefore differs greatly depending on exactly where they live. Such local weather phenomena provide a natural topic for introduction to boundary layer processes and are the basis of a new course developed at the University of California, Berkeley. This course complements the PI's research focus on numerical methods applied to atmospheric boundary layer flow over complex terrain. This new outreach and research-based course was created to teach students about the boundary layer and teach them how to use a community weather prediction model, WRF, to simulate conditions in the local area, while at the same time being actively involved in public outreach. The course was offered in the Civil and Environmental Engineering department with the collaboration and support of the Lawrence Hall of Science, Berkeley's public science museum. The students chose topics such as air quality, wind energy, climate change, and plume dispersion, all applied to the local San Francisco Bay Area. The students conducted independent research on their team projects, involving literature reviews, numerical model setup, and analysis of model results through comparison with field observations. The outreach component of the course included website design and culminated in demonstrations at the Lawrence Hall of Science. The seven student teams presented hands-on demos to 300-400 visitors, mostly kids 4-9 years old and their parents. Involving students directly in outreach efforts is hoped to encourage continued integration of research and education in their own careers. Early exposure to numerical modeling also improves student technical skills for future career experiences . Given

Modeling the evolution of natural cliffs subject to weathering. 1, Limit analysis approach

OpenAIRE

Utili, Stefano; Crosta, Giovanni B.

2011-01-01

Retrogressive landsliding evolution of natural slopes subjected to weathering has been modeled by assuming Mohr-Coulomb material behavior and by using an analytical method. The case of weathering-limited slope conditions, with complete erosion of the accumulated debris, has been modeled. The limit analysis upper-bound method is used to study slope instability induced by a homogeneous decrease of material strength in space and time. The only assumption required in the model concerns the degree...

Implementation of an atmospheric sulfur scheme in the HIRLAM regional weather forecast model

Energy Technology Data Exchange (ETDEWEB)

Ekman, Annica [Stockholm Univ. (Sweden). Dept. of Meteorology

2000-02-01

Sulfur chemistry has been implemented into the regional weather forecast model HIRLAM in order to simulate sulfur fields during specific weather situations. The model calculates concentrations of sulfur dioxide in air (SO{sub 2}(a)), sulfate in air (SO{sub 4}(a)), sulfate in cloud water (SO{sub 4}(aq)) and hydrogen peroxide (H{sub 2}O{sub 2}). Modeled concentrations of SO{sub 2}(a), SO{sub 4}(a) and SO{sub 4}(aq) in rain water are compared with observations for two weather situations, one winter case with an extensive stratiform cloud cover and one summer case with mostly convective clouds. A comparison of the weather forecast parameters precipitation, relative humidity, geopotential and temperature with observations is also performed. The results show that the model generally overpredicts the SO{sub 2}(a) concentration and underpredicts the SO{sub 4}(a) concentration. The agreement between modeled and observed SO{sub 4}(aq) in rain water is poor. Calculated turnover times are approximately 1 day for SO{sub 2}(a) and 2-2.5 days for SO{sub 4}(a). For SO{sub 2}(a) this is in accordance with earlier simulated global turnover times, but for SO{sub 4}(a) it is substantially lower. Several sensitivity simulations show that the fractional mean bias and root mean square error decreases, mainly for SO{sub 4}(a) and SO{sub 4}(aq), if an additional oxidant for converting SO{sub 2}(a) to SO{sub 4}(a) is included in the model. All weather forecast parameters, except precipitation, agree better with observations than the sulfur variables do. Wet scavenging is responsible for about half of the deposited sulfur and in addition, a major part of the sulfate production occurs through in-cloud oxidation. Hence, the distribution of clouds and precipitation must be better simulated by the weather forecast model in order to improve the agreement between observed and simulated sulfur concentrations.

Integrated analysis of numerical weather prediction and computational fluid dynamics for estimating cross-ventilation effects on inhaled air quality inside a factory

Science.gov (United States)

Murga, Alicia; Sano, Yusuke; Kawamoto, Yoichi; Ito, Kazuhide

2017-10-01

Mechanical and passive ventilation strategies directly impact indoor air quality. Passive ventilation has recently become widespread owing to its ability to reduce energy demand in buildings, such as the case of natural or cross ventilation. To understand the effect of natural ventilation on indoor environmental quality, outdoor-indoor flow paths need to be analyzed as functions of urban atmospheric conditions, topology of the built environment, and indoor conditions. Wind-driven natural ventilation (e.g., cross ventilation) can be calculated through the wind pressure coefficient distributions of outdoor wall surfaces and openings of a building, allowing the study of indoor air parameters and airborne contaminant concentrations. Variations in outside parameters will directly impact indoor air quality and residents' health. Numerical modeling can contribute to comprehend these various parameters because it allows full control of boundary conditions and sampling points. In this study, numerical weather prediction modeling was used to calculate wind profiles/distributions at the atmospheric scale, and computational fluid dynamics was used to model detailed urban and indoor flows, which were then integrated into a dynamic downscaling analysis to predict specific urban wind parameters from the atmospheric to built-environment scale. Wind velocity and contaminant concentration distributions inside a factory building were analyzed to assess the quality of the human working environment by using a computer simulated person. The impact of cross ventilation flows and its variations on local average contaminant concentration around a factory worker, and inhaled contaminant dose, were then discussed.

Bifurcation of Lane Change and Control on Highway for Tractor-Semitrailer under Rainy Weather

Directory of Open Access Journals (Sweden)

Tao Peng

2017-01-01

Full Text Available A new method is proposed for analyzing the nonlinear dynamics and stability in lane changes on highways for tractor-semitrailer under rainy weather. Unlike most of the literature associated with a simulated linear dynamic model for tractor-semitrailers steady steering on dry road, a verified 5DOF mechanical model with nonlinear tire based on vehicle test was used in the lane change simulation on low adhesion coefficient road. According to Jacobian matrix eigenvalues of the vehicle model, bifurcations of steady steering and sinusoidal steering on highways under rainy weather were investigated using a numerical method. Furthermore, based on feedback linearization theory, taking the tractor yaw rate and joint angle as control objects, a feedback linearization controller combined with AFS and DYC was established. The numerical simulation results reveal that Hopf bifurcations are identified in steady and sinusoidal steering conditions, which translate into an oscillatory behavior leading to instability. And simulations of urgent step and single-lane change in high velocity show that the designed controller has good effects on eliminating bifurcations and improving lateral stability of tractor-semitrailer, during lane changing on highway under rainy weather. It is a valuable reference for safety design of tractor-semitrailers to improve the traffic safety with driver-vehicle-road closed-loop system.

Weather model performance on extreme rainfall events simulation's over Western Iberian Peninsula

Science.gov (United States)

Pereira, S. C.; Carvalho, A. C.; Ferreira, J.; Nunes, J. P.; Kaiser, J. J.; Rocha, A.

2012-08-01

This study evaluates the performance of the WRF-ARW numerical weather model in simulating the spatial and temporal patterns of an extreme rainfall period over a complex orographic region in north-central Portugal. The analysis was performed for the December month of 2009, during the Portugal Mainland rainy season. The heavy rainfall to extreme heavy rainfall periods were due to several low surface pressure's systems associated with frontal surfaces. The total amount of precipitation for December exceeded, in average, the climatological mean for the 1971-2000 time period in +89 mm, varying from 190 mm (south part of the country) to 1175 mm (north part of the country). Three model runs were conducted to assess possible improvements in model performance: (1) the WRF-ARW is forced with the initial fields from a global domain model (RunRef); (2) data assimilation for a specific location (RunObsN) is included; (3) nudging is used to adjust the analysis field (RunGridN). Model performance was evaluated against an observed hourly precipitation dataset of 15 rainfall stations using several statistical parameters. The WRF-ARW model reproduced well the temporal rainfall patterns but tended to overestimate precipitation amounts. The RunGridN simulation provided the best results but model performance of the other two runs was good too, so that the selected extreme rainfall episode was successfully reproduced.

Modeling rock weathering in small watersheds

NARCIS (Netherlands)

Pacheco, F.A.L.; van der Weijden, C.H.

2014-01-01

Many mountainous watersheds are conceived as aquifer media where multiple groundwater flow systems have developed (Tóth, 1963), and as bimodal landscapes where differential weathering of bare and soil-mantled rock has occurred (Wahrhaftig, 1965). The results of a weathering algorithm (Pacheco and

Evaluation of weather-based rice yield models in India

Science.gov (United States)

Sudharsan, D.; Adinarayana, J.; Reddy, D. Raji; Sreenivas, G.; Ninomiya, S.; Hirafuji, M.; Kiura, T.; Tanaka, K.; Desai, U. B.; Merchant, S. N.

2013-01-01

The objective of this study was to compare two different rice simulation models—standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])—with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.

Managing wildland fires: integrating weather models into fire projections

Science.gov (United States)

Anne M. Rosenthal; Francis Fujioka

2004-01-01

Flames from the Old Fire sweep through lands north of San Bernardino during late fall of 2003. Like many Southern California fires, the Old Fire consumed susceptible forests at the urban-wildland interface and spread to nearby city neighborhoods. By incorporating weather models into fire perimeter projections, scientist Francis Fujioka is improving fire modeling as a...

Simulation and Data Analytics for Mobile Road Weather Sensors

Science.gov (United States)

Chettri, S. R.; Evans, J. D.; Tislin, D.

2016-12-01

Numerous algorithmic and theoretical considerations arise in simulating a vehicle-based weather observation network known as the Mobile Platform Environmental Data (MoPED). MoPED integrates sensor data from a fleet of commercial vehicles (about 600 at last count, with thousands more to come) as they travel interstate, state and local routes and metropolitan areas throughout the conterminous United States. The MoPED simulator models a fleet of anywhere between 1000-10,000 vehicles that travel a highway network encoded in a geospatial database, starting and finishing at random times and moving at randomly-varying speeds. Virtual instruments aboard these vehicles interpolate surface weather parameters (such as temperature and pressure) from the High-Resolution Rapid Refresh (HRRR) data series, an hourly, coast-to-coast 3km grid of weather parameters modeled by the National Centers for Environmental Prediction. Whereas real MoPED sensors have noise characteristics that lead to drop-outs, drift, or physically unrealizable values, our simulation introduces a variety of noise distributions into the parameter values inferred from HRRR (Fig. 1). Finally, the simulator collects weather readings from the National Weather Service's Automated Surface Observation System (ASOS, comprised of over 800 airports around the country) for comparison, validation, and analytical experiments. The simulator's MoPED-like weather data stream enables studies like the following: Experimenting with data analysis and calibration methods - e.g., by comparing noisy vehicle data with ASOS "ground truth" in close spatial and temporal proximity (e.g., 10km, 10 min) (Fig. 2). Inter-calibrating different vehicles' sensors when they pass near each other. Detecting spatial structure in the surface weather - such as dry lines, sudden changes in humidity that accompany severe weather - and estimating how many vehicles are needed to reliably map these structures and their motion. Detecting bottlenecks in the

Probability for Weather and Climate

Science.gov (United States)

Smith, L. A.

2013-12-01

Over the last 60 years, the availability of large-scale electronic computers has stimulated rapid and significant advances both in meteorology and in our understanding of the Earth System as a whole. The speed of these advances was due, in large part, to the sudden ability to explore nonlinear systems of equations. The computer allows the meteorologist to carry a physical argument to its conclusion; the time scales of weather phenomena then allow the refinement of physical theory, numerical approximation or both in light of new observations. Prior to this extension, as Charney noted, the practicing meteorologist could ignore the results of theory with good conscience. Today, neither the practicing meteorologist nor the practicing climatologist can do so, but to what extent, and in what contexts, should they place the insights of theory above quantitative simulation? And in what circumstances can one confidently estimate the probability of events in the world from model-based simulations? Despite solid advances of theory and insight made possible by the computer, the fidelity of our models of climate differs in kind from the fidelity of models of weather. While all prediction is extrapolation in time, weather resembles interpolation in state space, while climate change is fundamentally an extrapolation. The trichotomy of simulation, observation and theory which has proven essential in meteorology will remain incomplete in climate science. Operationally, the roles of probability, indeed the kinds of probability one has access too, are different in operational weather forecasting and climate services. Significant barriers to forming probability forecasts (which can be used rationally as probabilities) are identified. Monte Carlo ensembles can explore sensitivity, diversity, and (sometimes) the likely impact of measurement uncertainty and structural model error. The aims of different ensemble strategies, and fundamental differences in ensemble design to support of

The effort to increase the space weather forecasting accuracy in KSWC

Science.gov (United States)

Choi, J. S.

2017-12-01

The Korean Space Weather Center (KSWC) of the National Radio Research Agency (RRA) is a government agency which is the official source of space weather information for Korean Government and the primary action agency of emergency measure to severe space weather condition as the Regional Warning Center of the International Space Environment Service (ISES). KSWC's main role is providing alerts, watches, and forecasts in order to minimize the space weather impacts on both of public and commercial sectors of satellites, aviation, communications, navigations, power grids, and etc. KSWC is also in charge of monitoring the space weather condition and conducting research and development for its main role of space weather operation in Korea. Recently, KSWC are focusing on increasing the accuracy of space weather forecasting results and verifying the model generated results. The forecasting accuracy will be calculated based on the probability statistical estimation so that the results can be compared numerically. Regarding the cosmic radiation does, we are gathering the actual measured data of radiation does using the instrument by cooperation with the domestic airlines. Based on the measurement, we are going to verify the reliability of SAFE system which was developed by KSWC to provide the cosmic radiation does information with the airplane cabin crew and public users.

Validation of crop weather models for crop assessment arid yield ...

African Journals Online (AJOL)

IRSIS and CRPSM models were used in this study to see how closely they could predict grain yields for selected stations in Tanzania. Input for the models comprised of weather, crop and soil data collected from five selected stations. Simulation results show that IRSIS model tends to over predict grain yields of maize, ...

Model for expressing leaf photosynthesis in terms of weather variables

African Journals Online (AJOL)

A theoretical mathematical model for describing photosynthesis in individual leaves in terms of weather variables is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of potential photosynthetic rate permitted by the different environmental elements. These parameters are useful ...

Numerical Modeling of Large-Scale Rocky Coastline Evolution

Science.gov (United States)

Limber, P.; Murray, A. B.; Littlewood, R.; Valvo, L.

2008-12-01

Seventy-five percent of the world's ocean coastline is rocky. On large scales (i.e. greater than a kilometer), many intertwined processes drive rocky coastline evolution, including coastal erosion and sediment transport, tectonics, antecedent topography, and variations in sea cliff lithology. In areas such as California, an additional aspect of rocky coastline evolution involves submarine canyons that cut across the continental shelf and extend into the nearshore zone. These types of canyons intercept alongshore sediment transport and flush sand to abyssal depths during periodic turbidity currents, thereby delineating coastal sediment transport pathways and affecting shoreline evolution over large spatial and time scales. How tectonic, sediment transport, and canyon processes interact with inherited topographic and lithologic settings to shape rocky coastlines remains an unanswered, and largely unexplored, question. We will present numerical model results of rocky coastline evolution that starts with an immature fractal coastline. The initial shape is modified by headland erosion, wave-driven alongshore sediment transport, and submarine canyon placement. Our previous model results have shown that, as expected, an initial sediment-free irregularly shaped rocky coastline with homogeneous lithology will undergo smoothing in response to wave attack; headlands erode and mobile sediment is swept into bays, forming isolated pocket beaches. As this diffusive process continues, pocket beaches coalesce, and a continuous sediment transport pathway results. However, when a randomly placed submarine canyon is introduced to the system as a sediment sink, the end results are wholly different: sediment cover is reduced, which in turn increases weathering and erosion rates and causes the entire shoreline to move landward more rapidly. The canyon's alongshore position also affects coastline morphology. When placed offshore of a headland, the submarine canyon captures local sediment

Dynamic Weather Routes: A Weather Avoidance Concept for Trajectory-Based Operations

Science.gov (United States)

McNally, B. David; Love, John

2011-01-01

The integration of convective weather modeling with trajectory automation for conflict detection, trial planning, direct routing, and auto resolution has uncovered a concept that could help controllers, dispatchers, and pilots identify improved weather routes that result in significant savings in flying time and fuel burn. Trajectory automation continuously and automatically monitors aircraft in flight to find those that could potentially benefit from improved weather reroutes. Controllers, dispatchers, and pilots then evaluate reroute options to assess their suitability given current weather and traffic. In today's operations aircraft fly convective weather avoidance routes that were implemented often hours before aircraft approach the weather and automation does not exist to automatically monitor traffic to find improved weather routes that open up due to changing weather conditions. The automation concept runs in real-time and employs two keysteps. First, a direct routing algorithm automatically identifies flights with large dog legs in their routes and therefore potentially large savings in flying time. These are common - and usually necessary - during convective weather operations and analysis of Fort Worth Center traffic shows many aircraft with short cuts that indicate savings on the order of 10 flying minutes. The second and most critical step is to apply trajectory automation with weather modeling to determine what savings could be achieved by modifying the direct route such that it avoids weather and traffic and is acceptable to controllers and flight crews. Initial analysis of Fort Worth Center traffic suggests a savings of roughly 50% of the direct route savings could be achievable.The core concept is to apply trajectory automation with convective weather modeling in real time to identify a reroute that is free of weather and traffic conflicts and indicates enough time and fuel savings to be considered. The concept is interoperable with today

A critical view on temperature modelling for application in weather derivatives markets

International Nuclear Information System (INIS)

Šaltytė Benth, Jūratė; Benth, Fred Espen

2012-01-01

In this paper we present a stochastic model for daily average temperature. The model contains seasonality, a low-order autoregressive component and a variance describing the heteroskedastic residuals. The model is estimated on daily average temperature records from Stockholm (Sweden). By comparing the proposed model with the popular model of Campbell and Diebold (2005), we point out some important issues to be addressed when modelling the temperature for application in weather derivatives market. - Highlights: ► We present a stochastic model for daily average temperature, containing seasonality, a low-order autoregressive component and a variance describing the heteroskedastic residuals. ► We compare the proposed model with the popular model of Campbell and Diebold (2005). ► Some important issues to be addressed when modelling the temperature for application in weather derivatives market are pointed out.

The effect of different weather data sets and their resolution on climate-based daylight modelling

DEFF Research Database (Denmark)

Iversen, A; Svendsen, Svend; Nielsen, Toke Rammer

2013-01-01

Climate-based daylight modelling is based on the available weather data, which means that the weather data used as input to the daylight simulations are of great importance. In this paper, the effect on the outcome of the daylight simulations of using one weather data file rather than another...

Weather Risk Management in Agriculture

Directory of Open Access Journals (Sweden)

Martina Bobriková

2016-01-01

Full Text Available The paper focuses on valuation of a weather derivative with payoffs depending on temperature. We use historical data from the weather station in the Slovak town Košice to obtain unique prices of option contracts in an incomplete market. Numerical examples of prices of some contracts are presented, using the Burn analysis. We provide an example of how a weather contract can be designed to hedge the financial risk of a suboptimal temperature condition. The comparative comparison of the selected option hedging strategies has shown the best results for the producers in agricultural industries who hedges against an unfavourable weather conditions. The results of analysis proved that by buying put option or call option, the farmer establishes the highest payoff in the case of temperature decrease or increase. The Long Straddle Strategy is the most expensive but is available to the farmer who hedges against a high volatility in temperature movement. We conclude with the findings that weather derivatives could be useful tools to diminish the financial losses for agricultural industries highly dependent for temperature.

Simulated building energy demand biases resulting from the use of representative weather stations

Energy Technology Data Exchange (ETDEWEB)

Burleyson, Casey D.; Voisin, Nathalie; Taylor, Z. Todd; Xie, Yulong; Kraucunas, Ian

2018-01-01

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in bias from using an increasing number of weather stations over the western U.S. The approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to capture weather across different seasons. Using 8 stations, one from each climate zone, across the western U.S. results in an average absolute summertime temperature bias of 7.2°F with respect to a spatially-resolved gridded dataset. The mean absolute bias drops to 2.8°F using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.8%, a significant error for capacity expansion planners who may use these types of simulations. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20-40% overestimation of peak building loads during both summer and winter. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

Science.gov (United States)

Teixeira, J.; Cardoso, S.; Bonazzola, M.; Cole, J.; DeGenio, A.; DeMott, C.; Franklin, C.; Hannay, C.; Jakob, C.; Jiao, Y.;

Realistic natural atmospheric phenomena and weather effects for interactive virtual environments

Science.gov (United States)

McLoughlin, Leigh

Clouds and the weather are important aspects of any natural outdoor scene, but existing dynamic techniques within computer graphics only offer the simplest of cloud representations. The problem that this work looks to address is how to provide a means of simulating clouds and weather features such as precipitation, that are suitable for virtual environments. Techniques for cloud simulation are available within the area of meteorology, but numerical weather prediction systems are computationally expensive, give more numerical accuracy than we require for graphics and are restricted to the laws of physics. Within computer graphics, we often need to direct and adjust physical features or to bend reality to meet artistic goals, which is a key difference between the subjects of computer graphics and physical science. Pure physically-based simulations, however, evolve their solutions according to pre-set rules and are notoriously difficult to control. The challenge then is for the solution to be computationally lightweight and able to be directed in some measure while at the same time producing believable results. This work presents a lightweight physically-based cloud simulation scheme that simulates the dynamic properties of cloud formation and weather effects. The system simulates water vapour, cloud water, cloud ice, rain, snow and hail. The water model incorporates control parameters and the cloud model uses an arbitrary vertical temperature profile, with a tool described to allow the user to define this. The result of this work is that clouds can now be simulated in near real-time complete with precipitation. The temperature profile and tool then provide a means of directing the resulting formation..

Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

Directory of Open Access Journals (Sweden)

Cristina Forbes

2014-05-01

Full Text Available Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012 are carried out using the National Weather Service (NWS Sea Lakes and Overland Surges from Hurricanes (SLOSH storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS, that the SLOSH-simulated water levels at 71% (89% of the data measurement locations have less than 20% (30% relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.

A Real-Time Offshore Weather Risk Advisory System

Science.gov (United States)

Jolivet, Samuel; Zemskyy, Pavlo; Mynampati, Kalyan; Babovic, Vladan

2015-04-01

Offshore oil and gas operations in South East Asia periodically face extended downtime due to unpredictable weather conditions, including squalls that are accompanied by strong winds, thunder, and heavy rains. This downtime results in financial losses. Hence, a real time weather risk advisory system is developed to provide the offshore Oil and Gas (O&G) industry specific weather warnings in support of safety and environment security. This system provides safe operating windows based on sensitivity of offshore operations to sea state. Information products for safety and security include area of squall occurrence for the next 24 hours, time before squall strike, and heavy sea state warning for the next 3, 6, 12 & 24 hours. These are predicted using radar now-cast, high resolution Numerical Weather Prediction (NWP) and Data Assimilation (DA). Radar based now-casting leverages the radar data to produce short term (up to 3 hours) predictions of severe weather events including squalls/thunderstorms. A sea state approximation is provided through developing a translational model based on these predictions to risk rank the sensitivity of operations. A high resolution Weather Research and Forecasting (WRF, an open source NWP model) is developed for offshore Brunei, Malaysia and the Philippines. This high resolution model is optimized and validated against the adaptation of temperate to tropical met-ocean parameterization. This locally specific parameters are calibrated against federated data to achieve a 24 hour forecast of high resolution Convective Available Potential Energy (CAPE). CAPE is being used as a proxy for the risk of squall occurrence. Spectral decomposition is used to blend the outputs of the now-cast and the forecast in order to assimilate near real time weather observations as an implementation of the integration of data sources. This system uses the now-cast for the first 3 hours and then the forecast prediction horizons of 3, 6, 12 & 24 hours. The output is

Trends of air pollution in Denmark - Normalised by a simple weather index model

International Nuclear Information System (INIS)

Kiilsholm, S.; Rasmussen, A.

2000-01-01

station at Kastrup Airport just outside Copenhagen. The mixing height was calculated using a bulk Richardson method on vertical profiles provided by the Numerical Weather Prediction model DMI-HIRLAM (Danish Meteorological Institute - High Resolution Limited Area Model). The model in general gives a good explanation of variations from year to year in the air quality. (au)

User's guide to the weather model: a component of the western spruce budworm modeling system.

Science.gov (United States)

W. P. Kemp; N. L. Crookston; P. W. Thomas

1989-01-01

A stochastic model useful in simulating daily maximum and minimum temperature and precipitation developed by Bruhn and others has been adapted for use in the western spruce budworm modeling system. This document describes how to use the weather model and illustrates some aspects of its behavior.

Numerical modelling of multi-pass solar dryer filled with granite pebbles for thermal storage enhancement

International Nuclear Information System (INIS)

Kareem, M W; Habib, K; Ruslan, M H

2015-01-01

In this paper, a theoretical modelling of a cheap solar thermal dryer for small and medium scale farmers with multi-pass approach has been investigated. Comsol Multiphysics modelling tool was employed using numerical technique. The rock particles were used to enhance the thermal storage of the drying system. The local weather data were used during the simulation while parameters and coefficients were sourced from literature. An improvement on efficiency of up to 7% was recorded with error of 10 -5 when compared with the reported double pass solar collector. A fair distribution of hot air within the cabinets was also achieved. Though the modelling tool used was robust but the characterization of the system materials need to be done to improve the system accuracy and better prediction. (paper)

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

Science.gov (United States)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

Numerical modeling of polar mesocyclones generation mechanisms

Science.gov (United States)

Sergeev, Dennis; Stepanenko, Victor

2013-04-01

parameters, lateral boundary conditions are varied in the typically observed range. The approach is fully nonlinear: we use a three-dimensional non-hydrostatic mesoscale model NH3D_MPI [1] coupled with one-dimensional water body model LAKE. A key method used in the present study is the analysis of eddy kinetic and available potential energy budgets. References 1. Mikushin, D.N., and Stepanenko, V.M., The implementation of regional atmospheric model numerical algorithms for CBEA-based clusters. Lecture Notes in Computer Science, Parallel Processing and Applied Mathematics, 2010, vol. 6067, p. 525-534. 2. Rasmussen, E., and Turner, J. (eds), Polar Lows: Mesoscale Weather Systems in the Polar Regions. Cambridge: Cambridge University Press, 2003, 612 pp. 3. Yanase, W., and Niino, H., Dependence of Polar Low Development on Baroclinicity and Physical Processes: An Idealized High-Resolution Experiment, J. Atmos. Sci., 2006, vol. 64, p. 3044-3067.

Weather Effects on Crop Diseases in Eastern Germany

Science.gov (United States)

Conradt, Tobias

2017-04-01

Since the 1970s there are several long-term monitoring programmes for plant diseases and pests in Germany. Within the framework of a national research project, some otherwise confidential databases comprising 77 111 samples from numerous sites accross Eastern Germany could be accessed and analysed. The pest data covered leaf rust (Puccinia triticina) and powdery mildew (Blumeria graminis) in winter wheat, aphids (Aphididae, four genera) on wheat and other cereal crops, late blight (Phytophthora infestans) in potatoes, and pollen beetles (Brassicogethes aeneus) on rape. These data were complemented by daily weather observations from the German Weather Service (DWD). In a first step, Pearson correlations between weather variables and pest frequencies were calculated for seasonal time periods of different start months and durations and ordered into so-called correlograms. This revealed principal weather effects on disease spread - e. g. that wind is favourable for mildew throughout the year or that rape pollen beetles like it warm, but not during wintertime. Secondly, the pest frequency samples were found to resemble gamma distributions, and a generalised linear model was fitted to describe their parameter shift depending on end-of-winter temperatures for aphids on cereals. The method clearly shows potential for systematic pest risk assessments regarding climate change.

Achievement report for fiscal 1999. Research on mesh-based estimation of natural energy for Southeast Asia as represented by Myanmar (Assessment of wind power and solar energy using numerical weather model); 1999 nendo Myanmar koku wo rei ni shita Tonan Asia ni okeru shizen energy no mesh suitei ni kansuru kenkyu seika hokokusho. Suchi kisho model ni yoru furyoku taiyo energy hyoka

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-03-01

As the first step for the introduction of wind power systems and photovoltaic power systems into developing countries in Southeast Asia etc. and for their diffusion in them for the exploitation of natural energy, a numerical weather model useable in Southeast Asia is developed to make up for the insufficiency of weather data in the region. A technique is developed, to explain which the case of Myanmar is cited, for estimating with accuracy such natural conditions as wind direction, wind velocity, and solar radiation in the past one-year period for the assessment of power to be generated using wind turbines and solar panels. The results of the observation of wind conditions indicate that wind directions are mainly northerly or westerly and that wind speeds are as week as 1-3m/s on the average. As for total solar radiation per diem in December through March, it is found that there is 17-23MJ/m{sup 2}/day, which is twice the level to be measured in Tokyo. A comparison is made between the weather observation results and a model calculation, and it is found that the latter sufficiently reproduces the actual weather conditions. Based on the values of wind conditions and solar radiation estimated in Myanmar, the amount of power to be obtained from an assumed arrangement of wind power systems and solar panels is assessed. (NEDO)

Numerical simulations of island effects on airflow and weather during the summer over the island of Oahu

Science.gov (United States)

Hiep Van Nguyen; Yie-Leng Chen; Francis Fujioka

2010-01-01

The high-resolution (1.5 km) nonhydrostatic fifth-generation Pennsylvania StateUniversity–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and an advanced land surface model (LSM) are used to study the island-induced airflow and weather for the island of Oahu, Hawaii, under summer trade wind conditions. Despite Oahu’s relatively small...

Development of a regional ensemble prediction method for probabilistic weather prediction

International Nuclear Information System (INIS)

Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

2015-01-01

A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

Reconstruction of Historical Weather by Assimilating Old Weather Diary Data

Science.gov (United States)

Neluwala, P.; Yoshimura, K.; Toride, K.; Hirano, J.; Ichino, M.; Okazaki, A.

2017-12-01

Climate can control not only human life style but also other living beings. It is important to investigate historical climate to understand the current and future climates. Information about daily weather can give a better understanding of past life on earth. Long-term weather influences crop calendar as well as the development of civilizations. Unfortunately, existing reconstructed daily weather data are limited to 1850s due to the availability of instrumental data. The climate data prior to that are derived from proxy materials (e.g., tree-ring width, ice core isotopes, etc.) which are either in annual or decadal scale. However, there are many historical documents which contain information about weather such as personal diaries. In Japan, around 20 diaries in average during the 16th - 19th centuries have been collected and converted into a digitized form. As such, diary data exist in many other countries. This study aims to reconstruct historical daily weather during the 18th and 19th centuries using personal daily diaries which have analogue weather descriptions such as `cloudy' or `sunny'. A recent study has shown the possibility of assimilating coarse weather data using idealized experiments. We further extend this study by assimilating modern weather descriptions similar to diary data in recent periods. The Global Spectral model (GSM) of National Centers for Environmental Prediction (NCEP) is used to reconstruct weather with the Local Ensemble Kalman filter (LETKF). Descriptive data are first converted to model variables such as total cloud cover (TCC), solar radiation and precipitation using empirical relationships. Those variables are then assimilated on a daily basis after adding random errors to consider the uncertainty of actual diary data. The assimilation of downward short wave solar radiation using weather descriptions improves RMSE from 64.3 w/m2 to 33.0 w/m2 and correlation coefficient (R) from 0.5 to 0.8 compared with the case without any

The NASA Community Coordinated Modeling Center (CCMC) Next Generation Space Weather Data Warehouse

Science.gov (United States)

Maddox, M. M.; Kuznetsova, M. M.; Pulkkinen, A. A.; Zheng, Y.; Rastaetter, L.; Chulaki, A.; Pembroke, A. D.; Wiegand, C.; Mullinix, R.; Boblitt, J.; Mendoza, A. M. M.; Swindell, M. J., IV; Bakshi, S. S.; Mays, M. L.; Shim, J. S.; Hesse, M.; Collado-Vega, Y. M.; Taktakishvili, A.; MacNeice, P. J.

2014-12-01

The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center enables, supports, and performs research and development for next generation space science and space weather models. The CCMC currently hosts a large and expanding collection of state-or-the-art, physics-based space weather models that have been developed by the international research community. There are many tools and services provided by the CCMC that are currently available world-wide, along with the ongoing development of new innovative systems and software for research, discovery, validation, visualization, and forecasting. Over the history of the CCMC's existence, there has been one constant engineering challenge - describing, managing, and disseminating data. To address the challenges that accompany an ever-expanding number of models to support, along with a growing catalog of simulation output - the CCMC is currently developing a flexible and extensible space weather data warehouse to support both internal and external systems and applications. This paper intends to chronicle the evolution and future of the CCMC's data infrastructure, and the current infrastructure re-engineering activities that seek to leverage existing community data model standards like SPASE and the IMPEx Simulation Data Model.

Planetary Space Weather Services for the Europlanet 2020 Research Infrastructure

Science.gov (United States)

André, Nicolas; Grande, Manuel

2016-04-01

Under Horizon 2020, the Europlanet 2020 Research Infrastructure (EPN2020-RI) will include an entirely new Virtual Access Service, WP5 VA1 "Planetary Space Weather Services" (PSWS) that will extend the concepts of space weather and space situational awareness to other planets in our Solar System and in particular to spacecraft that voyage through it. VA1 will make five entirely new 'toolkits' accessible to the research community and to industrial partners planning for space missions: a general planetary space weather toolkit, as well as three toolkits dedicated to the following key planetary environments: Mars (in support ExoMars), comets (building on the expected success of the ESA Rosetta mission), and outer planets (in preparation for the ESA JUICE mission to be launched in 2022). This will give the European planetary science community new methods, interfaces, functionalities and/or plugins dedicated to planetary space weather in the tools and models available within the partner institutes. It will also create a novel event-diary toolkit aiming at predicting and detecting planetary events like meteor showers and impacts. A variety of tools (in the form of web applications, standalone software, or numerical models in various degrees of implementation) are available for tracing propagation of planetary and/or solar events through the Solar System and modelling the response of the planetary environment (surfaces, atmospheres, ionospheres, and magnetospheres) to those events. But these tools were not originally designed for planetary event prediction and space weather applications. So WP10 JRA4 "Planetary Space Weather Services" (PSWS) will provide the additional research and tailoring required to apply them for these purposes. The overall objectives of this Joint Research Aactivities will be to review, test, improve and adapt methods and tools available within the partner institutes in order to make prototype planetary event and space weather services operational in

Asteroid age distributions determined by space weathering and collisional evolution models

Science.gov (United States)

Willman, Mark; Jedicke, Robert

2011-01-01

We provide evidence of consistency between the dynamical evolution of main belt asteroids and their color evolution due to space weathering. The dynamical age of an asteroid's surface (Bottke, W.F., Durda, D.D., Nesvorný, D., Jedicke, R., Morbidelli, A., Vokrouhlický, D., Levison, H. [2005]. Icarus 175 (1), 111-140; Nesvorný, D., Jedicke, R., Whiteley, R.J., Ivezić, Ž. [2005]. Icarus 173, 132-152) is the time since its last catastrophic disruption event which is a function of the object's diameter. The age of an S-complex asteroid's surface may also be determined from its color using a space weathering model (e.g. Willman, M., Jedicke, R., Moskovitz, N., Nesvorný, D., Vokrouhlický, D., Mothé-Diniz, T. [2010]. Icarus 208, 758-772; Jedicke, R., Nesvorný, D., Whiteley, R.J., Ivezić, Ž., Jurić, M. [2004]. Nature 429, 275-277; Willman, M., Jedicke, R., Nesvorny, D., Moskovitz, N., Ivezić, Ž., Fevig, R. [2008]. Icarus 195, 663-673. We used a sample of 95 S-complex asteroids from SMASS and obtained their absolute magnitudes and u, g, r, i, z filter magnitudes from SDSS. The absolute magnitudes yield a size-derived age distribution. The u, g, r, i, z filter magnitudes lead to the principal component color which yields a color-derived age distribution by inverting our color-age relationship, an enhanced version of the 'dual τ' space weathering model of Willman et al. (2010). We fit the size-age distribution to the enhanced dual τ model and found characteristic weathering and gardening times of τw = 2050 ± 80 Myr and τg=4400-500+700Myr respectively. The fit also suggests an initial principal component color of -0.05 ± 0.01 for fresh asteroid surface with a maximum possible change of the probable color due to weathering of Δ PC = 1.34 ± 0.04. Our predicted color of fresh asteroid surface matches the color of fresh ordinary chondritic surface of PC1 = 0.17 ± 0.39.

Reproducibility of Carbon and Water Cycle by an Ecosystem Process Based Model Using a Weather Generator and Effect of Temporal Concentration of Precipitation on Model Outputs

Science.gov (United States)

Miyauchi, T.; Machimura, T.

2014-12-01

GCM is generally used to produce input weather data for the simulation of carbon and water cycle by ecosystem process based models under climate change however its temporal resolution is sometimes incompatible to requirement. A weather generator (WG) is used for temporal downscaling of input weather data for models, where the effect of WG algorithms on reproducibility of ecosystem model outputs must be assessed. In this study simulated carbon and water cycle by Biome-BGC model using weather data measured and generated by CLIMGEN weather generator were compared. The measured weather data (daily precipitation, maximum, minimum air temperature) at a few sites for 30 years was collected from NNDC Online weather data. The generated weather data was produced by CLIMGEN parameterized using the measured weather data. NPP, heterotrophic respiration (HR), NEE and water outflow were simulated by Biome-BGC using measured and generated weather data. In the case of deciduous broad leaf forest in Lushi, Henan Province, China, 30 years average monthly NPP by WG was 10% larger than that by measured weather in the growing season. HR by WG was larger than that by measured weather in all months by 15% in average. NEE by WG was more negative in winter and was close to that by measured weather in summer. These differences in carbon cycle were because the soil water content by WG was larger than that by measured weather. The difference between monthly water outflow by WG and by measured weather was large and variable, and annual outflow by WG was 50% of that by measured weather. The inconsistency in carbon and water cycle by WG and measured weather was suggested be affected by the difference in temporal concentration of precipitation, which was assessed.

NASA Space Weather Center Services: Potential for Space Weather Research

Science.gov (United States)

Zheng, Yihua; Kuznetsova, Masha; Pulkkinen, Antti; Taktakishvili, A.; Mays, M. L.; Chulaki, A.; Lee, H.; Hesse, M.

2012-01-01

The NASA Space Weather Center's primary objective is to provide the latest space weather information and forecasting for NASA's robotic missions and its partners and to bring space weather knowledge to the public. At the same time, the tools and services it possesses can be invaluable for research purposes. Here we show how our archive and real-time modeling of space weather events can aid research in a variety of ways, with different classification criteria. We will list and discuss major CME events, major geomagnetic storms, and major SEP events that occurred during the years 2010 - 2012. Highlights of major tools/resources will be provided.

Modeling Apple Surface Temperature Dynamics Based on Weather Data

Directory of Open Access Journals (Sweden)

Lei Li

2014-10-01

Full Text Available The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model for simulating fruit surface temperature (FST dynamics based on energy balance and measured weather data. A series of weather data (air temperature, humidity, solar radiation, and wind speed was recorded for seven hours between 11:00–18:00 for two months at fifteen minute intervals. To validate the model, the FSTs of “Fuji” apples were monitored using an infrared camera in a natural orchard environment. The FST dynamics were measured using a series of thermal images. For the apples that were completely exposed to the sun, the RMSE of the model for estimating FST was less than 2.0 °C. A sensitivity analysis of the emissivity of the apple surface and the conductance of the fruit surface to water vapour showed that accurate estimations of the apple surface emissivity were important for the model. The validation results showed that the model was capable of accurately describing the thermal performances of apples under different solar radiation intensities. Thus, this model could be used to more accurately estimate the FST relative to estimates that only consider the air temperature. In addition, this model provides useful information for sunburn protection management.

Modeling apple surface temperature dynamics based on weather data.

Science.gov (United States)

Li, Lei; Peters, Troy; Zhang, Qin; Zhang, Jingjin; Huang, Danfeng

2014-10-27

The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model for simulating fruit surface temperature (FST) dynamics based on energy balance and measured weather data. A series of weather data (air temperature, humidity, solar radiation, and wind speed) was recorded for seven hours between 11:00-18:00 for two months at fifteen minute intervals. To validate the model, the FSTs of "Fuji" apples were monitored using an infrared camera in a natural orchard environment. The FST dynamics were measured using a series of thermal images. For the apples that were completely exposed to the sun, the RMSE of the model for estimating FST was less than 2.0 °C. A sensitivity analysis of the emissivity of the apple surface and the conductance of the fruit surface to water vapour showed that accurate estimations of the apple surface emissivity were important for the model. The validation results showed that the model was capable of accurately describing the thermal performances of apples under different solar radiation intensities. Thus, this model could be used to more accurately estimate the FST relative to estimates that only consider the air temperature. In addition, this model provides useful information for sunburn protection management.

Fair weather atmospheric electricity

International Nuclear Information System (INIS)

Harrison, R G

2011-01-01

Not long after Franklin's iconic studies, an atmospheric electric field was discovered in 'fair weather' regions, well away from thunderstorms. The origin of the fair weather field was sought by Lord Kelvin, through development of electrostatic instrumentation and early data logging techniques, but was ultimately explained through the global circuit model of C.T.R. Wilson. In Wilson's model, charge exchanged by disturbed weather electrifies the ionosphere, and returns via a small vertical current density in fair weather regions. New insights into the relevance of fair weather atmospheric electricity to terrestrial and planetary atmospheres are now emerging. For example, there is a possible role of the global circuit current density in atmospheric processes, such as cloud formation. Beyond natural atmospheric processes, a novel practical application is the use of early atmospheric electrostatic investigations to provide quantitative information on past urban air pollution.

Improvement of a mesoscale atmospheric dynamic model PHYSIC. Utilization of output from synoptic numerical prediction model for initial and boundary condition

International Nuclear Information System (INIS)

Nagai, Haruyasu; Yamazawa, Hiromi

1995-03-01

This report describes the improvement of the mesoscale atmospheric dynamic model which is a part of the atmospheric dispersion calculation model PHYSIC. To introduce large-scale meteorological changes into the mesoscale atmospheric dynamic model, it is necessary to make the initial and boundary conditions of the model by using GPV (Grid Point Value) which is the output of the numerical weather prediction model of JMA (Japan Meteorological Agency). Therefore, the program which preprocesses the GPV data to make a input file to PHYSIC was developed and the input process and the methods of spatial and temporal interpolation were improved to correspond to the file. Moreover, the methods of calculating the cloud amount and ground surface moisture from GPV data were developed and added to the model code. As the example of calculation by the improved model, the wind field simulations of a north-west monsoon in winter and a sea breeze in summer in the Tokai area were also presented. (author)

Integration of Weather Avoidance and Traffic Separation

Science.gov (United States)

Consiglio, Maria C.; Chamberlain, James P.; Wilson, Sara R.

2011-01-01

This paper describes a dynamic convective weather avoidance concept that compensates for weather motion uncertainties; the integration of this weather avoidance concept into a prototype 4-D trajectory-based Airborne Separation Assurance System (ASAS) application; and test results from a batch (non-piloted) simulation of the integrated application with high traffic densities and a dynamic convective weather model. The weather model can simulate a number of pseudo-random hazardous weather patterns, such as slow- or fast-moving cells and opening or closing weather gaps, and also allows for modeling of onboard weather radar limitations in range and azimuth. The weather avoidance concept employs nested "core" and "avoid" polygons around convective weather cells, and the simulations assess the effectiveness of various avoid polygon sizes in the presence of different weather patterns, using traffic scenarios representing approximately two times the current traffic density in en-route airspace. Results from the simulation experiment show that the weather avoidance concept is effective over a wide range of weather patterns and cell speeds. Avoid polygons that are only 2-3 miles larger than their core polygons are sufficient to account for weather uncertainties in almost all cases, and traffic separation performance does not appear to degrade with the addition of weather polygon avoidance. Additional "lessons learned" from the batch simulation study are discussed in the paper, along with insights for improving the weather avoidance concept. Introduction

Latest Community Coordinated Modeling Center (CCMC) services and innovative tools supporting the space weather research and operational communities.

Science.gov (United States)

Mendoza, A. M. M.; Rastaetter, L.; Kuznetsova, M. M.; Mays, M. L.; Chulaki, A.; Shim, J. S.; MacNeice, P. J.; Taktakishvili, A.; Collado-Vega, Y. M.; Weigand, C.; Zheng, Y.; Mullinix, R.; Patel, K.; Pembroke, A. D.; Pulkkinen, A. A.; Boblitt, J. M.; Bakshi, S. S.; Tsui, T.

2017-12-01

The Community Coordinated Modeling Center (CCMC), with the fundamental goal of aiding the transition of modern space science models into space weather forecasting while supporting space science research, has been serving as an integral hub for over 15 years, providing invaluable resources to both space weather scientific and operational communities. CCMC has developed and provided innovative web-based point of access tools varying from: Runs-On-Request System - providing unprecedented global access to the largest collection of state-of-the-art solar and space physics models, Integrated Space Weather Analysis (iSWA) - a powerful dissemination system for space weather information, Advanced Online Visualization and Analysis tools for more accurate interpretation of model results, Standard Data formats for Simulation Data downloads, and Mobile apps to view space weather data anywhere to the scientific community. In addition to supporting research and performing model evaluations, CCMC also supports space science education by hosting summer students through local universities. In this poster, we will showcase CCMC's latest innovative tools and services, and CCMC's tools that revolutionized the way we do research and improve our operational space weather capabilities. CCMC's free tools and resources are all publicly available online (http://ccmc.gsfc.nasa.gov).

Coding a Weather Model: DOE-FIU Science & Technology Workforce Development Program.

Energy Technology Data Exchange (ETDEWEB)

Bradley, Jon David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-12-01

DOE Fellow, Andres Cremisini, completed a 10-week internship with Sandia National Laboratories (SNL) in Albuquerque, New Mexico. Under the management of Kristopher Klingler and the mentorship of Jon Bradley, he was tasked with conceiving and coding a realistic weather model for use in physical security applications. The objective was to make a weather model that could use real data to accurately predict wind and precipitation conditions at any location of interest on the globe at any user-determined time. The intern received guidance on software design, the C++ programming language and clear communication of project goals and ongoing progress. In addition, Mr. Cremisini was given license to structure the program however he best saw fit, an experience that will benefit ongoing research endeavors.

Earth Remote Sensing for Weather Forecasting and Disaster Applications

Science.gov (United States)

Molthan, Andrew; Bell, Jordan; Case, Jonathan; Cole, Tony; Elmer, Nicholas; McGrath, Kevin; Schultz, Lori; Zavodsky, Brad

2016-01-01

NASA's constellation of current missions provide several opportunities to apply satellite remote sensing observations to weather forecasting and disaster response applications. Examples include: Using NASA's Terra and Aqua MODIS, and the NASA/NOAA Suomi-NPP VIIRS missions to prepare weather forecasters for capabilities of GOES-R; Incorporating other NASA remote sensing assets for improving aspects of numerical weather prediction; Using NASA, NOAA, and international partner resources (e.g. ESA/Sentinel Series); and commercial platforms (high-res, or UAV) to support disaster mapping.

Space Weather Services of Korea

Science.gov (United States)

Yoon, K.; Hong, S.; Jangsuk, C.; Dong Kyu, K.; Jinyee, C.; Yeongoh, C.

2016-12-01

The Korean Space Weather Center (KSWC) of the National Radio Research Agency (RRA) is a government agency which is the official source of space weather information for Korean Government and the primary action agency of emergency measure to severe space weather condition. KSWC's main role is providing alerts, watches, and forecasts in order to minimize the space weather impacts on both of public and commercial sectors of satellites, aviation, communications, navigations, power grids, and etc. KSWC is also in charge of monitoring the space weather condition and conducting research and development for its main role of space weather operation in Korea. In this study, we will present KSWC's recent efforts on development of application-oriented space weather research products and services on user needs, and introduce new international collaborative projects, such as IPS-Driven Enlil model, DREAM model estimating electron in satellite orbit, global network of DSCOVR and STEREO satellites tracking, and ARMAS (Automated Radiation Measurement for Aviation Safety).

Odelouca Dam Construction: Numerical Analysis

OpenAIRE

Brito, A.; Maranha, J. R.; Caldeira, L.

2012-01-01

Odelouca dam is an embankment dam, with 76 m height, recently constructed in the south of Portugal. It is zoned with a core consisting of colluvial and residual schist soil and with soil-rockfill mixtures making up the shells (weathered schist with a significant fraction of coarse sized particles). This paper presents a numerical analysis of Odelouca Dam`s construction. The material con-stants of the soil model used are determined from a comprehensive testing programme carried out in the C...

Assessing Individual Weather Risk-Taking and Its Role in Modeling Likelihood of Hurricane Evacuation

Science.gov (United States)

Stewart, A. E.

2017-12-01

This research focuses upon measuring an individual's level of perceived risk of different severe and extreme weather conditions using a new self-report measure, the Weather Risk-Taking Scale (WRTS). For 32 severe and extreme situations in which people could perform an unsafe behavior (e. g., remaining outside with lightning striking close by, driving over roadways covered with water, not evacuating ahead of an approaching hurricane, etc.), people rated: 1.their likelihood of performing the behavior, 2. The perceived risk of performing the behavior, 3. the expected benefits of performing the behavior, and 4. whether the behavior has actually been performed in the past. Initial development research with the measure using 246 undergraduate students examined its psychometric properties and found that it was internally consistent (Cronbach's a ranged from .87 to .93 for the four scales) and that the scales possessed good temporal (test-retest) reliability (r's ranged from .84 to .91). A second regression study involving 86 undergraduate students found that taking weather risks was associated with having taken similar risks in one's past and with the personality trait of sensation-seeking. Being more attentive to the weather and perceiving its risks when it became extreme was associated with lower likelihoods of taking weather risks (overall regression model, R2adj = 0.60). A third study involving 334 people examined the contributions of weather risk perceptions and risk-taking in modeling the self-reported likelihood of complying with a recommended evacuation ahead of a hurricane. Here, higher perceptions of hurricane risks and lower perceived benefits of risk-taking along with fear of severe weather and hurricane personal self-efficacy ratings were all statistically significant contributors to the likelihood of evacuating ahead of a hurricane. Psychological rootedness and attachment to one's home also tend to predict lack of evacuation. This research highlights the

Effects of Weather on Tourism and its Moderation

Science.gov (United States)

Park, J. H.; Kim, S.; Lee, D. K.

2016-12-01

Tourism is weather sensitive industry (Gómez Martín, 2005). As climate change has been intensifying, the concerns about negative effects of weather on tourism also have been increasing. This study attempted to find ways that mitigate the negative effects from weather on tourism, by analyzing a path of the effects of weather on intention to revisit and its moderation. The data of the study were collected by a self-recording online questionnaire survey of South Korean domestic tourists during August 2015, and 2,412 samples were gathered. A path model of effects of weather on intention to revisit that including moderating effects from physical attraction satisfaction and service satisfaction was ran. Season was controlled in the path model. The model fit was adequate (CMIN/DF=2.372(p=.000), CFI=.974, RMSEA=.024, SRMR=0.040), and the Model Comparison, which assumes that the base model to be correct with season constrained model, showed that there was a seasonal differences in the model ( DF=24, CMIN=32.430, P=.117). By the analysis, it was figured out that weather and weather expectation affected weather satisfaction, and the weather satisfaction affected intention to revisit (spring/fall: .167**, summer: .104**, and winter: .114**). Meanwhile physical attraction satisfaction (.200**), and service satisfaction (.210**) of tourism positively moderated weather satisfaction in summer, and weather satisfaction positively moderated physical attraction (.238**) satisfaction and service satisfaction (.339**). In other words, in summer, dissatisfaction from hot weather was moderated by satisfaction from physical attractions and services, and in spring/fall, comfort weather conditions promoted tourists to accept tourism experience and be satisfied from attractions and services positively. Based on the result, it was expected that if industries focus on offering the good attractions and services based on weather conditions, there would be positive effects to alleviate tourists

Compound microgrid installation operation planning of a PEFC and photovoltaics with prediction of electricity production using GA and numerical weather information

Energy Technology Data Exchange (ETDEWEB)

Obara, Shin-ya; El-Sayed, Abeer Galal [Department of Electrical and Electronic Engineering, Power Engineering Lab., Kitami Institute of Technology, 165 Kouen-cho, Kitami, HOKKAIDO 0908507 (Japan)

2009-10-15

A fuel cell microgrid with photovoltaics effectively reduces greenhouse gas emission. A system operation optimization technique with photovoltaics and unstable power is important. In this paper, the optimal operation algorithm of this compound microgrid is developed using numerical weather information (NWI) that is freely available. A GA (genetic algorithm) was developed to minimize system fuel consumption. Furthermore, the relation between the NWI error characteristics and the operation results of the system was clarified. As a result, the optimized operation algorithm using NWI reduced the energy cost of the system. (author)

A numerical model for the whole Wadden Sea: results on the hydrodynamics

Science.gov (United States)

Gräwe, Ulf; Duran-Matute, Matias; Gerkema, Theo; Flöser, Götz; Burchard, Hans

2015-04-01

A high-resolution baroclinic three-dimensional numerical model for the entire Wadden Sea of the German Bight in the southern North Sea is first validated against field data for surface elevation, current velocity, temperature and salinity at selected stations and then used to calculate fluxes of volume, heat and salt inside the Wadden Sea and the exchange between the Wadden Sea and the adjacent North Sea through the major tidal inlets. The General Estuarine Transport Model (GETM) is simulating the reference years 2009-2011. The numerical grid has a resolution of 200x200m and 30 adaptive vertical layers. It is the final stage of a multi-nested setup, starting from the North Atlantic. The atmospheric forcing is taken from the operational forecast of the German Weather Service. Additionally, the freshwater discharge of 23 local rivers and creeks are included. For validation, we use observations from a ship of opportunity measuring sea surface properties, tidal gauge stations, high frequency of salinity and volume transport estimates for the Mardiep and Spiekeroog inlet. Finally, the estuarine overturning circulation in three tidal gulleys is quantified. Regional differences between the gullies are assessed and drivers of the estuarine circulation are identified. Moreover, we will give a consistent estimate of the tidal prisms for all tidal inlets in the entire Wadden Sea.

Plausible Effect of Weather on Atlantic Meridional Overturning Circulation with a Coupled General Circulation Model

Science.gov (United States)

Liu, Zedong; Wan, Xiuquan

2018-04-01

The Atlantic meridional overturning circulation (AMOC) is a vital component of the global ocean circulation and the heat engine of the climate system. Through the use of a coupled general circulation model, this study examines the role of synoptic systems on the AMOC and presents evidence that internally generated high-frequency, synoptic-scale weather variability in the atmosphere could play a significant role in maintaining the overall strength and variability of the AMOC, thereby affecting climate variability and change. Results of a novel coupling technique show that the strength and variability of the AMOC are greatly reduced once the synoptic weather variability is suppressed in the coupled model. The strength and variability of the AMOC are closely linked to deep convection events at high latitudes, which could be strongly affected by the weather variability. Our results imply that synoptic weather systems are important in driving the AMOC and its variability. Thus, interactions between atmospheric weather variability and AMOC may be an important feedback mechanism of the global climate system and need to be taken into consideration in future climate change studies.

Mechanical weathering and rock erosion by climate-dependent subcritical cracking

Science.gov (United States)

Eppes, Martha-Cary; Keanini, Russell

2017-06-01

This work constructs a fracture mechanics framework for conceptualizing mechanical rock breakdown and consequent regolith production and erosion on the surface of Earth and other terrestrial bodies. Here our analysis of fracture mechanics literature explicitly establishes for the first time that all mechanical weathering in most rock types likely progresses by climate-dependent subcritical cracking under virtually all Earth surface and near-surface environmental conditions. We substantiate and quantify this finding through development of physically based subcritical cracking and rock erosion models founded in well-vetted fracture mechanics and mechanical weathering, theory, and observation. The models show that subcritical cracking can culminate in significant rock fracture and erosion under commonly experienced environmental stress magnitudes that are significantly lower than rock critical strength. Our calculations also indicate that climate strongly influences subcritical cracking—and thus rock weathering rates—irrespective of the source of the stress (e.g., freezing, thermal cycling, and unloading). The climate dependence of subcritical cracking rates is due to the chemophysical processes acting to break bonds at crack tips experiencing these low stresses. We find that for any stress or combination of stresses lower than a rock's critical strength, linear increases in humidity lead to exponential acceleration of subcritical cracking and associated rock erosion. Our modeling also shows that these rates are sensitive to numerous other environment, rock, and mineral properties that are currently not well characterized. We propose that confining pressure from overlying soil or rock may serve to suppress subcritical cracking in near-surface environments. These results are applicable to all weathering processes.

GEOSS interoperability for Weather, Ocean and Water

Science.gov (United States)

Richardson, David; Nyenhuis, Michael; Zsoter, Ervin; Pappenberger, Florian

2013-04-01

forecast skill and concluded that the use of a multi-model forecast is beneficial. Long term analysis of individual centres, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), has been conducted in the past. However, no long term and large scale study has been performed so far with inclusion of different global numerical models. Here we present some initial results from such a study.

Multiple Weather Factors Affect Apparent Survival of European Passerine Birds

Science.gov (United States)

Salewski, Volker; Hochachka, Wesley M.; Fiedler, Wolfgang

2013-01-01

Weather affects the demography of animals and thus climate change will cause local changes in demographic rates. In birds numerous studies have correlated demographic factors with weather but few of those examined variation in the impacts of weather in different seasons and, in the case of migrants, in different regions. Using capture-recapture models we correlated weather with apparent survival of seven passerine bird species with different migration strategies to assess the importance of selected facets of weather throughout the year on apparent survival. Contrary to our expectations weather experienced during the breeding season did not affect apparent survival of the target species. However, measures for winter severity were associated with apparent survival of a resident species, two short-distance/partial migrants and a long-distance migrant. Apparent survival of two short distance migrants as well as two long-distance migrants was further correlated with conditions experienced during the non-breeding season in Spain. Conditions in Africa had statistically significant but relatively minor effects on the apparent survival of the two long-distance migrants but also of a presumably short-distance migrant and a short-distance/partial migrant. In general several weather effects independently explained similar amounts of variation in apparent survival for the majority of species and single factors explained only relatively low amounts of temporal variation of apparent survival. Although the directions of the effects on apparent survival mostly met our expectations and there are clear predictions for effects of future climate we caution against simple extrapolations of present conditions to predict future population dynamics. Not only did weather explains limited amounts of variation in apparent survival, but future demographics will likely be affected by changing interspecific interactions, opposing effects of weather in different seasons, and the potential for

Climate Prediction - NOAA's National Weather Service

Science.gov (United States)

Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Climate > Predictions Climate Prediction Long range forecasts across the U.S. Climate Prediction Web Sites Climate Prediction

Parameterizing road construction in route-based road weather models: can ground-penetrating radar provide any answers?

International Nuclear Information System (INIS)

Hammond, D S; Chapman, L; Thornes, J E

2011-01-01

A ground-penetrating radar (GPR) survey of a 32 km mixed urban and rural study route is undertaken to assess the usefulness of GPR as a tool for parameterizing road construction in a route-based road weather forecast model. It is shown that GPR can easily identify even the smallest of bridges along the route, which previous thermal mapping surveys have identified as thermal singularities with implications for winter road maintenance. Using individual GPR traces measured at each forecast point along the route, an inflexion point detection algorithm attempts to identify the depth of the uppermost subsurface layers at each forecast point for use in a road weather model instead of existing ordinal road-type classifications. This approach has the potential to allow high resolution modelling of road construction and bridge decks on a scale previously not possible within a road weather model, but initial results reveal that significant future research will be required to unlock the full potential that this technology can bring to the road weather industry. (technical design note)

Coupled Weather and Wildfire Behavior Modeling at Los Alamos: An Overview

Energy Technology Data Exchange (ETDEWEB)

Bossert, James E.; Harlow, Francis H.; Linn, Rodman R.; Reisner, Jon M.; White, Andrew B.; Winterkamp, Judith L.

1997-12-31

Over the past two years, researchers at Los Alamos National Laboratory (LANL) have been engaged in coupled weather/wildfire modeling as part of a broader initiative to predict the unfolding of crisis events. Wildfire prediction was chosen for the following reasons: (1) few physics-based wild-fire prediction models presently exist; (2) LANL has expertise in the fields required to develop such a capability; and (3) the development of this predictive capability would be enhanced by LANL`s strength in high performance computing. Wildfire behavior models have historically been used to predict fire spread and heat release for a prescribed set of fuel, slope, and wind conditions (Andrews 1986). In the vicinity of a fire, however, atmospheric conditions are constantly changing due to non-local weather influences and the intense heat of the fire itself. This non- linear process underscores the need for physics-based models that treat the atmosphere-fire feedback. Actual wildfire prediction with full-physics models is both time-critical and computationally demanding, since it must include regional- to local-scale weather forecasting together with the capability to accurately simulate both intense gradients across a fireline, and atmosphere/fire/fuel interactions. Los Alamos has recently (January 1997) acquired a number of SGI/Cray Origin 2000 machines, each presently having 32 to 64 processors. These high performance computing systems are part of the Department of Energy`s Accelerated Strategic Computing Initiative (ASCI). While offering impressive performance now, upgrades to the system promise to deliver over 1 Teraflop (10(12) floating point operations per second) at peak performance before the turn of the century.

NASA GSFC Space Weather Center - Innovative Space Weather Dissemination: Web-Interfaces, Mobile Applications, and More

Science.gov (United States)

Maddox, Marlo; Zheng, Yihua; Rastaetter, Lutz; Taktakishvili, A.; Mays, M. L.; Kuznetsova, M.; Lee, Hyesook; Chulaki, Anna; Hesse, Michael; Mullinix, Richard;

Numerical Modeling of Shoreline Undulations

DEFF Research Database (Denmark)

Kærgaard, Kasper Hauberg

model has been developed which describes the longshore sediment transport along arbitrarily shaped shorelines. The numerical model is based on a spectral wave model, a depth integrated flow model, a wave-phase resolving sediment transport description and a one-line shoreline model. First the theoretical...... of the feature and under predicts the migration speeds of the features. On the second shoreline, the shoreline model predicts undulations lengths which are longer than the observed undulations. Lastly the thesis considers field measurements of undulations of the bottom bathymetry along an otherwise straight...... length of the shoreline undulations is determined in the linear regime using a shoreline stability analysis based on the numerical model. The analysis shows that the length of the undulations in the linear regime depends on the incoming wave conditions and on the coastal profile. For larger waves...

Preparing Middle School Teachers to Use Science Models Effectively when Teaching about Weather and Climate Topics

Science.gov (United States)

Yarker, M. B.; Stanier, C. O.; Forbes, C.; Park, S.

2012-12-01

According to the National Science Education Standards (NSES), teachers are encouraged to use science models in the classroom as a way to aid in the understanding of the nature of the scientific process. This is of particular importance to the atmospheric science community because climate and weather models are very important when it comes to understanding current and future behaviors of our atmosphere. Although familiar with weather forecasts on television and the Internet, most people do not understand the process of using computer models to generate weather and climate forecasts. As a result, the public often misunderstands claims scientists make about their daily weather as well as the state of climate change. Therefore, it makes sense that recent research in science education indicates that scientific models and modeling should be a topic covered in K-12 classrooms as part of a comprehensive science curriculum. The purpose of this research study is to describe how three middle school teachers use science models to teach about topics in climate and weather, as well as the challenges they face incorporating models effectively into the classroom. Participants in this study took part in a week long professional development designed to orient them towards appropriate use of science models for a unit on weather, climate, and energy concepts. The course design was based on empirically tested features of effective professional development for science teachers and was aimed at teaching content to the teachers while simultaneously orienting them towards effective use of science models in the classroom in a way that both aids in learning about the content knowledge as well as how models are used in scientific inquiry. Results indicate that teachers perceive models to be physical representations that can be used as evidence to convince students that the teacher's conception of the concept is correct. Additionally, teachers tended to use them as ways to explain an idea to

Training Early Career Space Weather Researchers and other Space Weather Professionals at the CISM Space Weather Summer School

Science.gov (United States)

Gross, N. A.; Hughes, W.

2011-12-01

This talk will outline the organization of a summer school designed to introduce young professions to a sub-discipline of geophysics. Through out the 10 year life time of the Center for Integrated Space Weather Modeling (CISM) the CISM Team has offered a two week summer school that introduces new graduate students and other interested professional to the fundamentals of space weather. The curriculum covers basic concepts in space physics, the hazards of space weather, and the utility of computer models of the space environment. Graduate students attend from both inside and outside CISM, from all the sub-disciplines involved in space weather (solar, heliosphere, geomagnetic, and aeronomy), and from across the nation and around the world. In addition, between 1/4 and 1/3 of the participants each year are professionals involved in space weather in some way, such as: forecasters from NOAA and the Air Force, Air Force satellite program directors, NASA specialists involved in astronaut radiation safety, and representatives from industries affected by space weather. The summer school has adopted modern pedagogy that has been used successfully at the undergraduate level. A typical daily schedule involves three morning lectures followed by an afternoon lab session. During the morning lectures, student interaction is encouraged using "Timeout to Think" questions and peer instruction, along with question cards for students to ask follow up questions. During the afternoon labs students, working in groups of four, answer thought provoking questions using results from simulations and observation data from a variety of source. Through the interactions with each other and the instructors, as well as social interactions during the two weeks, students network and form bonds that will last them through out their careers. We believe that this summer school can be used as a model for summer schools in a wide variety of disciplines.

Numerical simulations of an advection fog event over Shanghai Pudong International Airport with the WRF model

Science.gov (United States)

Lin, Caiyan; Zhang, Zhongfeng; Pu, Zhaoxia; Wang, Fengyun

2017-10-01

A series of numerical simulations is conducted to understand the formation, evolution, and dissipation of an advection fog event over Shanghai Pudong International Airport (ZSPD) with the Weather Research and Forecasting (WRF) model. Using the current operational settings at the Meteorological Center of East China Air Traffic Management Bureau, the WRF model successfully predicts the fog event at ZSPD. Additional numerical experiments are performed to examine the physical processes associated with the fog event. The results indicate that prediction of this particular fog event is sensitive to microphysical schemes for the time of fog dissipation but not for the time of fog onset. The simulated timing of the arrival and dissipation of the fog, as well as the cloud distribution, is substantially sensitive to the planetary boundary layer and radiation (both longwave and shortwave) processes. Moreover, varying forecast lead times also produces different simulation results for the fog event regarding its onset and duration, suggesting a trade-off between more accurate initial conditions and a proper forecast lead time that allows model physical processes to spin up adequately during the fog simulation. The overall outcomes from this study imply that the complexity of physical processes and their interactions within the WRF model during fog evolution and dissipation is a key area of future research.

Numerical modelling of mine workings.

CSIR Research Space (South Africa)

Lightfoot, N

1999-03-01

Full Text Available to cover most of what is required for a practising rock mechanics engineer to be able to use any of these five programs to solve practical mining problems. The chapters on specific programs discuss their individual strengths and weaknesses and highlight... and applications of numerical modelling in the context of the South African gold and platinum mining industries. This includes an example that utilises a number of different numerical 3 modelling programs to solve a single problem. This particular example...

Detecting weather radar clutter using satellite-based nowcasting products

DEFF Research Database (Denmark)

Jensen, Thomas B.S.; Gill, Rashpal S.; Overgaard, Søren

2006-01-01

This contribution presents the initial results from experiments with detection of weather radar clutter by information fusion with satellite based nowcasting products. Previous studies using information fusion of weather radar data and first generation Meteosat imagery have shown promising results...... for the detecting and removal of clutter. Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sensors, coupled with its increased number of spectral bands, is expected to yield even better detection accuracies. Weather radar data from three C-band Doppler weather radars...... Application Facility' of EUMETSAT and is based on multispectral images from the SEVIRI sensor of the Meteosat-8 platform. Of special interest is the 'Precipitating Clouds' product, which uses the spectral information coupled with surface temperatures from Numerical Weather Predictions to assign probabilities...

A short-range multi-model ensemble weather prediction system for South Africa

CSIR Research Space (South Africa)

Landman, S

2010-09-01

Full Text Available prediction system (EPS) at the South African Weather Service (SAWS) are examined. The ensemble consists of different forecasts from the 12-km LAM of the UK Met Office Unified Model (UM) and the Conformal-Cubic Atmospheric Model (CCAM) covering the South...

Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

Science.gov (United States)

Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

2012-04-01

Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

New Approach To Hour-By-Hour Weather Forecast

Science.gov (United States)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The

Mathematical and Numerical Modeling in Maritime Geomechanics

Directory of Open Access Journals (Sweden)

Miguel Martín Stickle

2012-04-01

Full Text Available A theoretical and numerical framework to model the foundation of marine offshore structures is presented. The theoretical model is composed by a system of partial differential equations describing coupling between seabed solid skeleton and pore fluids (water, air, oil,... combined with a system of ordinary differential equations describing the specific constitutive relation of the seabed soil skeleton. Once the theoretical model is described, the finite element numerical procedure to achieve an approximate solution of the overning equations is outlined. In order to validate the proposed theoretical and numerical framework the seaward tilt mechanism induced by the action of breaking waves over a vertical breakwater is numerically reproduced. The results numerically attained are in agreement with the main conclusions drawn from the literature associated with this failure mechanism.

High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

Directory of Open Access Journals (Sweden)

C. G. Nunalee

2015-08-01

Full Text Available Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL, is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30, the Shuttle Radar Topography Mission (SRTM, and the Global Multi-resolution Terrain Elevation Data set (GMTED2010 terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

Understanding, representing and communicating earth system processes in weather and climate within CNRCWP

Science.gov (United States)

Sushama, Laxmi; Arora, Vivek; de Elia, Ramon; Déry, Stephen; Duguay, Claude; Gachon, Philippe; Gyakum, John; Laprise, René; Marshall, Shawn; Monahan, Adam; Scinocca, John; Thériault, Julie; Verseghy, Diana; Zwiers, Francis

2017-04-01

The Canadian Network for Regional Climate and Weather Processes (CNRCWP) provides significant advances and innovative research towards the ultimate goal of reducing uncertainty in numerical weather prediction and climate projections for Canada's Northern and Arctic regions. This talk will provide an overview of the Network and selected results related to the assessment of the added value of high-resolution modelling that has helped fill critical knowledge gaps in understanding the dynamics of extreme temperature and precipitation events and the complex land-atmosphere interactions and feedbacks in Canada's northern and Arctic regions. In addition, targeted developments in the Canadian regional climate model, that facilitate direct application of model outputs in impact and adaptation studies, particularly those related to the water, energy and infrastructure sectors will also be discussed. The close collaboration between the Network and its partners and end users contributed significantly to this effort.

Numerical model CCC

International Nuclear Information System (INIS)

Bodvarsson, G.S.; Lippmann, M.J.

1980-01-01

The computer program CCC (conduction-convection-consolidation), developed at Lawrence Berkeley Laboratory, solves numerically the heat and mass flow equations for a fully saturated medium, and computes one-dimensional consolidation of the simulated systems. The model employs the Integrated Finite Difference Method (IFDM) in discretizing the saturated medium and formulating the governing equations. The sets of equations are solved either by an iterative solution technique (old version) or an efficient sparse solver (new version). The deformation of the medium is calculated using the one-dimensional consolidation theory of Terzaghi. In this paper, the numerical code is described, validation examples given and areas of application discussed. Several example problems involving flow through fractured media are also presented

HELIOSPHERIC PROPAGATION OF CORONAL MASS EJECTIONS: COMPARISON OF NUMERICAL WSA-ENLIL+CONE MODEL AND ANALYTICAL DRAG-BASED MODEL

Energy Technology Data Exchange (ETDEWEB)

Vršnak, B.; Žic, T.; Dumbović, M. [Hvar Observatory, Faculty of Geodesy, University of Zagreb, Kačćeva 26, HR-10000 Zagreb (Croatia); Temmer, M.; Möstl, C.; Veronig, A. M. [Kanzelhöhe Observatory—IGAM, Institute of Physics, University of Graz, Universittsplatz 5, A-8010 Graz (Austria); Taktakishvili, A.; Mays, M. L. [NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Odstrčil, D., E-mail: bvrsnak@geof.hr, E-mail: tzic@geof.hr, E-mail: mdumbovic@geof.hr, E-mail: manuela.temmer@uni-graz.at, E-mail: christian.moestl@uni-graz.at, E-mail: astrid.veronig@uni-graz.at, E-mail: aleksandre.taktakishvili-1@nasa.gov, E-mail: m.leila.mays@nasa.gov, E-mail: dusan.odstrcil@nasa.gov [George Mason University, Fairfax, VA 22030 (United States)

2014-08-01

Real-time forecasting of the arrival of coronal mass ejections (CMEs) at Earth, based on remote solar observations, is one of the central issues of space-weather research. In this paper, we compare arrival-time predictions calculated applying the numerical ''WSA-ENLIL+Cone model'' and the analytical ''drag-based model'' (DBM). Both models use coronagraphic observations of CMEs as input data, thus providing an early space-weather forecast two to four days before the arrival of the disturbance at the Earth, depending on the CME speed. It is shown that both methods give very similar results if the drag parameter Γ = 0.1 is used in DBM in combination with a background solar-wind speed of w = 400 km s{sup –1}. For this combination, the mean value of the difference between arrival times calculated by ENLIL and DBM is Δ-bar =0.09±9.0 hr with an average of the absolute-value differences of |Δ|-bar =7.1 hr. Comparing the observed arrivals (O) with the calculated ones (C) for ENLIL gives O – C = –0.3 ± 16.9 hr and, analogously, O – C = +1.1 ± 19.1 hr for DBM. Applying Γ = 0.2 with w = 450 km s{sup –1} in DBM, one finds O – C = –1.7 ± 18.3 hr, with an average of the absolute-value differences of 14.8 hr, which is similar to that for ENLIL, 14.1 hr. Finally, we demonstrate that the prediction accuracy significantly degrades with increasing solar activity.

Prediction of Aerosol Optical Depth in West Asia: Machine Learning Methods versus Numerical Models

Science.gov (United States)

Omid Nabavi, Seyed; Haimberger, Leopold; Abbasi, Reyhaneh; Samimi, Cyrus

2017-04-01

Dust-prone areas of West Asia are releasing increasingly large amounts of dust particles during warm months. Because of the lack of ground-based observations in the region, this phenomenon is mainly monitored through remotely sensed aerosol products. The recent development of mesoscale Numerical Models (NMs) has offered an unprecedented opportunity to predict dust emission, and, subsequently Aerosol Optical Depth (AOD), at finer spatial and temporal resolutions. Nevertheless, the significant uncertainties in input data and simulations of dust activation and transport limit the performance of numerical models in dust prediction. The presented study aims to evaluate if machine-learning algorithms (MLAs), which require much less computational expense, can yield the same or even better performance than NMs. Deep blue (DB) AOD, which is observed by satellites but also predicted by MLAs and NMs, is used for validation. We concentrate our evaluations on the over dry Iraq plains, known as the main origin of recently intensified dust storms in West Asia. Here we examine the performance of four MLAs including Linear regression Model (LM), Support Vector Machine (SVM), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS). The Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) and the Dust REgional Atmosphere Model (DREAM) are included as NMs. The MACC aerosol re-analysis of European Centre for Medium-range Weather Forecast (ECMWF) is also included, although it has assimilated satellite-based AOD data. Using the Recursive Feature Elimination (RFE) method, nine environmental features including soil moisture and temperature, NDVI, dust source function, albedo, dust uplift potential, vertical velocity, precipitation and 9-month SPEI drought index are selected for dust (AOD) modeling by MLAs. During the feature selection process, we noticed that NDVI and SPEI are of the highest importance in MLAs predictions. The data set was divided

A Product Development Decision Model for Cockpit Weather Information Systems

Science.gov (United States)

Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin

2003-01-01

There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

A Product Development Decision Model for Cockpit Weather Information System

Science.gov (United States)

Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin; Johnson, Edward J., Jr. (Technical Monitor)

2003-01-01

There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

Numerical Modeling of Piezoelectric Transducers Using Physical Parameters

NARCIS (Netherlands)

Cappon, H.; Keesman, K.J.

2012-01-01

Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and

Semi-implicit semi-Lagrangian modelling of the atmosphere: a Met Office perspective

Directory of Open Access Journals (Sweden)

Benacchio Tommaso

2016-09-01

Full Text Available The semi-Lagrangian numerical method, in conjunction with semi-implicit time integration, provides numerical weather prediction models with numerical stability for large time steps, accurate modes of interest, and good representation of hydrostatic and geostrophic balance. Drawing on the legacy of dynamical cores at the Met Office, the use of the semi-implicit semi-Lagrangian method in an operational numerical weather prediction context is surveyed, together with details of the solution approach and associated issues and challenges. The numerical properties and performance of the current operational version of the Met Office’s numerical model are then investigated in a simplified setting along with the impact of different modelling choices.

On the Characterization of Rainfall Associated with U.S. Landfalling North Atlantic Tropical Cyclones Based on Satellite Data and Numerical Weather Prediction Outputs

Science.gov (United States)

Luitel, B. N.; Villarini, G.; Vecchi, G. A.

2014-12-01

When we talk about tropical cyclones (TCs), the first things that come to mind are strong winds and storm surge affecting the coastal areas. However, according to the Federal Emergency Management Agency (FEMA) 59% of the deaths caused by TCs since 1970 is due to fresh water flooding. Heavy rainfall associated with TCs accounts for 13% of heavy rainfall events nationwide for the June-October months, with this percentage being much higher if the focus is on the eastern and southern United States. This study focuses on the evaluation of precipitation associated with the North Atlantic TCs that affected the continental United States over the period 2007 - 2012. We evaluate the rainfall associated with these TCs using four satellite based rainfall products: Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA; both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); Climate Prediction Center (CPC) MORPHing technique (CMORPH). As a reference data we use gridded rainfall provided by CPC (Daily US Unified Gauge-Based Analysis of Precipitation). Rainfall fields from each of these satellite products are compared to the reference data, providing valuable information about the realism of these products in reproducing the rainfall associated with TCs affecting the continental United States. In addition to the satellite products, we evaluate the forecasted rainfall produced by five state-of-the-art numerical weather prediction (NWP) models: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC). The skill of these models in reproducing TC rainfall is quantified for different lead times, and discussed in light of the performance of the satellite products.

How Satellites Have Contributed to Building a Weather Ready Nation

Science.gov (United States)

Lapenta, W.

2017-12-01

NOAA's primary mission since its inception has been to reduce the loss of life and property, as well as disruptions from, high impact weather and water-related events. In recent years, significant societal losses resulting even from well forecast extreme events have shifted attention from the forecast alone toward ensuring societal response is equal to the risks that exist for communities, businesses and the public. The responses relate to decisions ranging from coastal communities planning years in advance to mitigate impacts from rising sea level, to immediate lifesaving decisions such as a family seeking adequate shelter during a tornado warning. NOAA is committed to building a "Weather-Ready Nation" where communities are prepared for and respond appropriately to these events. The Weather-Ready Nation (WRN) strategic priority is building community resilience in the face of increasing vulnerability to extreme weather, water, climate and environmental threats. To build a Weather-Ready Nation, NOAA is enhancing Impact-Based Decision Support Services (IDSS), transitioning science and technology advances into forecast operations, applying social science research to improve the communication and usefulness of information, and expanding its dissemination efforts to achieve far-reaching readiness, responsiveness and resilience. These four components of Weather-Ready Nation are helping ensure NOAA data, products and services are fully utilized to minimize societal impacts from extreme events. Satellite data and satellite products have been important elements of the national Weather Service (NWS) operations for more than 40 years. When one examines the uses of satellite data specific to the internal forecast and warning operations of NWS, two main applications are evident. The first is the use of satellite data in numerical weather prediction models; the second is the use of satellite imagery and derived products for mesoscale and short-range weather warning and

A status report on weather modification research in the Dakotas

Science.gov (United States)

Smith, Paul L.; Orville, Harold D.; Boe, Bruce A.; Stith, Jeffrey L.

An overview of the status of weather modification research in North and South Dakota (USA) is presented. The operational North Dakota Cloud Modification Projects has, since 1976, been seeding summer convective clouds for the dual objectives of hail suppression and rainfall enhancement. Research being carried out as part of a Federal/State cooperative program, in coordination with the operational activities, has included physical and statistical evaluation studies as well as numerical cloud modeling investigations. The statistical analyses provide some indications that the intended seeding effects are being obtained. The physical studies involve aircraft and radar observations and emphasize tracer experiments to study the transport and dispersion of seeding agents and the activation of ice nuclei. The modeling studies simulate the experiments and aid in investigation of the process involved and the effects of seeding. The 1989 North Dakota Thunderstorm Project, a major field study emphasizing physical and numerical modeling studies, is described briefly.

Ferrofluids: Modeling, numerical analysis, and scientific computation

Science.gov (United States)

Tomas, Ignacio

This dissertation presents some developments in the Numerical Analysis of Partial Differential Equations (PDEs) describing the behavior of ferrofluids. The most widely accepted PDE model for ferrofluids is the Micropolar model proposed by R.E. Rosensweig. The Micropolar Navier-Stokes Equations (MNSE) is a subsystem of PDEs within the Rosensweig model. Being a simplified version of the much bigger system of PDEs proposed by Rosensweig, the MNSE are a natural starting point of this thesis. The MNSE couple linear velocity u, angular velocity w, and pressure p. We propose and analyze a first-order semi-implicit fully-discrete scheme for the MNSE, which decouples the computation of the linear and angular velocities, is unconditionally stable and delivers optimal convergence rates under assumptions analogous to those used for the Navier-Stokes equations. Moving onto the much more complex Rosensweig's model, we provide a definition (approximation) for the effective magnetizing field h, and explain the assumptions behind this definition. Unlike previous definitions available in the literature, this new definition is able to accommodate the effect of external magnetic fields. Using this definition we setup the system of PDEs coupling linear velocity u, pressure p, angular velocity w, magnetization m, and magnetic potential ϕ We show that this system is energy-stable and devise a numerical scheme that mimics the same stability property. We prove that solutions of the numerical scheme always exist and, under certain simplifying assumptions, that the discrete solutions converge. A notable outcome of the analysis of the numerical scheme for the Rosensweig's model is the choice of finite element spaces that allow the construction of an energy-stable scheme. Finally, with the lessons learned from Rosensweig's model, we develop a diffuse-interface model describing the behavior of two-phase ferrofluid flows and present an energy-stable numerical scheme for this model. For a

It's the Physics: Organized Complexity in the Arctic/Midlatitude Weather Controversy

Science.gov (United States)

Overland, J. E.; Francis, J. A.; Wang, M.

2017-12-01

There is intense scientific and public interest in whether major Arctic changes can and will impact mid-latitude weather. Despite numerous workshops and a growing literature, convergence of understanding is lacking, with major objections about possible large impacts within the scientific community. Yet research on the Arctic as a new potential driver in improving subseasonal forecasting at midlatitudes remains a priority. A recent review laid part of the controversy on shortcomings in experimental design and ill-suited metrics, such as examining the influence of only sea-ice loss rather than overall Arctic temperature amplification, and/or calculating averages over large regions, long time periods, or many ensemble members that would tend to obscure event-like Arctic connections. The present analysis lays the difficulty at a deeper level owing to the inherently complex physics. Jet-stream dynamics and weather linkages on the scale of a week to months has characteristics of an organized complex system, with large-scale processes that operate in patterned, quasi-geostrophic ways but whose component feedbacks are continually changing. Arctic linkages may be state dependent, i.e., relationships may be more robust in one atmospheric wave pattern than another, generating intermittency. The observational network is insufficient to fully initialize such a system and the inherent noise obscures linkage signals, leading to an underdetermined problem; often more than one explanation can fit the data. Further, the problem may be computationally irreducible; the only way to know the result of these interactions is to trace out their path over time. Modeling is a suggested approach, but at present it is unclear whether previous model studies fully resolve anticipated complexity. The jet stream from autumn to early winter is characterized by non-linear interactions among enhanced atmospheric planetary waves, irregular transitions between the zonal and meridional flows, and the

Weather regimes in past climate atmospheric general circulation model simulations

Energy Technology Data Exchange (ETDEWEB)

Kageyama, M.; Ramstein, G. [CEA Saclay, Gif-sur-Yvette (France). Lab. des Sci. du Climat et de l' Environnement; D' Andrea, F.; Vautard, R. [Laboratoire de Meteorologie Dynamique, Ecole Normale Superieure, Paris (France); Valdes, P.J. [Department of Meteorology, University of Reading (United Kingdom)

1999-10-01

We investigate the climates of the present-day, inception of the last glaciation (115000 y ago) and last glacial maximum (21000 y ago) in the extratropical north Atlantic and Europe, as simulated by the laboratoire de Meteorologie dynamique atmospheric general circulation model. We use these simulations to investigate the low-frequency variability of the model in different climates. The aim is to evaluate whether changes in the intraseasonal variability, which we characterize using weather regimes, can help describe the impact of different boundary conditions on climate and give a better understanding of climate change processes. Weather regimes are defined as the most recurrent patterns in the 500 hPa geopotential height, using a clustering algorithm method. The regimes found in the climate simulations of the present-day and inception of the last glaciation are similar in their number and their structure. It is the regimes' populations which are found to be different for these climates, with an increase of the model's blocked regime and a decrease in the zonal regime at the inception of the last glaciation. This description reinforces the conclusions from a study of the differences between the climatological averages of the different runs and confirms the northeastward shift to the tail of the Atlantic storm-track, which would favour more precipitation over the site of growth of the Fennoscandian ice-sheet. On the other hand, the last glacial maximum results over this sector are not found to be classifiable, showing that the change in boundary conditions can be responsible for severe changes in the weather regime and low-frequency dynamics. The LGM Atlantic low-frequency variability appears to be dominated by a large-scale retrogressing wave with a period 40 to 50 days. (orig.)

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

Science.gov (United States)

Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

2012-01-01

Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

A Numerical Method to Generate High Temporal Resolution Precipitation Time Series by Combining Weather Radar Measurements with a Nowcast Model

DEFF Research Database (Denmark)

Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

2014-01-01

The topic of this paper is temporal interpolation of precipitation observed by weather radars. Precipitation measurements with high spatial and temporal resolution are, in general, desired for urban drainage applications. An advection-based interpolation method is developed which uses methods...

Numerical Modeling of Ablation Heat Transfer

Science.gov (United States)

Ewing, Mark E.; Laker, Travis S.; Walker, David T.

2013-01-01

A unique numerical method has been developed for solving one-dimensional ablation heat transfer problems. This paper provides a comprehensive description of the method, along with detailed derivations of the governing equations. This methodology supports solutions for traditional ablation modeling including such effects as heat transfer, material decomposition, pyrolysis gas permeation and heat exchange, and thermochemical surface erosion. The numerical scheme utilizes a control-volume approach with a variable grid to account for surface movement. This method directly supports implementation of nontraditional models such as material swelling and mechanical erosion, extending capabilities for modeling complex ablation phenomena. Verifications of the numerical implementation are provided using analytical solutions, code comparisons, and the method of manufactured solutions. These verifications are used to demonstrate solution accuracy and proper error convergence rates. A simple demonstration of a mechanical erosion (spallation) model is also provided to illustrate the unique capabilities of the method.

Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire

Science.gov (United States)

Boer, Matthias M.; Nolan, Rachael H.; Resco De Dios, Víctor; Clarke, Hamish; Price, Owen F.; Bradstock, Ross A.

2017-12-01

Changing frequencies of extreme weather events and shifting fire seasons call for enhanced capability to forecast where and when forested landscapes switch from a nonflammable (i.e., wet fuel) state to the highly flammable (i.e., dry fuel) state required for catastrophic forest fires. Current forest fire danger indices used in Europe, North America, and Australia rate potential fire behavior by combining numerical indices of fuel moisture content, potential rate of fire spread, and fire intensity. These numerical rating systems lack the physical basis required to reliably quantify forest flammability outside the environments of their development or under novel climate conditions. Here, we argue that exceedance of critical forest flammability thresholds is a prerequisite for major forest fires and therefore early warning systems should be based on a reliable prediction of fuel moisture content plus a regionally calibrated model of how forest fire activity responds to variation in fuel moisture content. We demonstrate the potential of this approach through a case study in Portugal. We use a physically based fuel moisture model with historical weather and fire records to identify critical fuel moisture thresholds for forest fire activity and then show that the catastrophic June 2017 forest fires in central Portugal erupted shortly after fuels in the region dried out to historically unprecedented levels.

Decision Making and Risk Evaluation Frameworks for Extreme Space Weather Events

Science.gov (United States)

Uritskaya, O.; Robinson, R. M.; Pulkkinen, A. A.

2017-12-01

Extreme Space Weather events (ESWE) are in the spotlight nowadays because they can produce a significant impact not only due to their intensity and broad geographical scope, but also because of the widespread levels and the multiple sectors of the economy that could be involved. In the task of evaluation of the ESWE consequences, the most problematic and vulnerable aspect is the determination and calculation of the probability of statistically infrequent events and the subsequent assessment of the economic risks. In this work, we conduct a detailed analysis of the available frameworks of the general Decision-Making Theory in the presence of uncertainty, in the context of their applicability for the numerical estimation of the risks and losses associated with ESWE. The results of our study demonstrate that, unlike the Multiple-criteria decision analysis or Minimax approach to modeling of the possible scenarios for the ESWE effects, which prevail in the literature, the most suitable concept is the Games Against Nature (GAN). It enables an evaluation of every economically relevant aspect of space weather conditions and obtain more detailed results. Choosing the appropriate methods for solving GAN models, i.e. determining the most optimal strategy with a given level of uncertainty, requires estimating the conditional probabilities of Space Weather events for each outcome of possible scenarios of this natural disaster. Due to the specifics of complex natural and economic systems, with which we are dealing in this case, this problem remains unsolved, mainly because of inevitable loss of information at every stage of the decision-making process. The analysis is illustrated by deregulated electricity markets of the USA and Canada, whose power grid systems are known to be perceptive to ESWE. The GAN model is more appropriate in identifying potential risks in economic systems. The proposed approach, when applied to the existing database of Space Weather observations and

A Meteorological Supersite for Aviation and Cold Weather Applications

Science.gov (United States)

Gultepe, Ismail; Agelin-Chaab, M.; Komar, J.; Elfstrom, G.; Boudala, F.; Zhou, B.

2018-05-01

The goal of this study is to better understand atmospheric boundary layer processes and parameters, and to evaluate physical processes for aviation applications using data from a supersite observing site. Various meteorological sensors, including a weather and environmental unmanned aerial vehicle (WE-UAV), and a fog and snow tower (FSOS) observations are part of the project. The PanAm University of Ontario Institute of Technology (UOIT) Meteorological Supersite (PUMS) observations are being collected from April 2015 to date. The FSOS tower gathers observations related to rain, snow, fog, and visibility, aerosols, solar radiation, and wind and turbulence, as well as surface and sky temperature. The FSOSs are located at three locations at about 450-800 m away from the PUMS supersite. The WE-UAV measurements representing aerosol, wind speed and direction, as well as temperature (T) and relative humidity (RH) are provided during clear weather conditions. Other measurements at the PUMS site include cloud backscattering profiles from CL51 ceilometer, MWR observations of liquid water content (LWC), T, and RH, and Microwave Rain Radar (MRR) reflectivity profile, as well as the present weather type, snow water depth, icing rate, 3D-ultrasonic wind and turbulence, and conventional meteorological observations from compact weather stations, e.g., WXTs. The results based on important weather event studies, representing fog, snow, rain, blowing snow, wind gust, planetary boundary layer (PBL) wind research for UAV, and icing conditions are given. The microphysical parameterizations and analysis processes for each event are provided, but the results should not be generalized for all weather events and be used cautiously. Results suggested that integrated observing systems based on data from a supersite as well as satellite sites can provide better information applicable to aviation meteorology, including PBL weather research, validation of numerical weather model predictions, and

Global distribution of urban parameters derived from high-resolution global datasets for weather modelling

Science.gov (United States)

Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.

2016-12-01

Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a

Planetary Space Weather Service: Part of the the Europlanet 2020 Research Infrastructure

Science.gov (United States)

Grande, Manuel; Andre, Nicolas

2016-07-01

Over the next four years the Europlanet 2020 Research Infrastructure will set up an entirely new European Planetary Space Weather service (PSWS). Europlanet RI is a part of of Horizon 2020 (EPN2020-RI, http://www.europlanet-2020-ri.eu). The Virtual Access Service, WP5 VA1 "Planetary Space Weather Services" will extend the concepts of space weather and space situational awareness to other planets in our Solar System and in particular to spacecraft that voyage through it. VA1 will make five entirely new 'toolkits' accessible to the research community and to industrial partners planning for space missions: a general planetary space weather toolkit, as well as three toolkits dedicated to the following key planetary environments: Mars (in support ExoMars), comets (building on the expected success of the ESA Rosetta mission), and outer planets (in preparation for the ESA JUICE mission to be launched in 2022). This will give the European planetary science community new methods, interfaces, functionalities and/or plugins dedicated to planetary space weather in the tools and models available within the partner institutes. It will also create a novel event-diary toolkit aiming at predicting and detecting planetary events like meteor showers and impacts. A variety of tools (in the form of web applications, standalone software, or numerical models in various degrees of implementation) are available for tracing propagation of planetary and/or solar events through the Solar System and modelling the response of the planetary environment (surfaces, atmospheres, ionospheres, and magnetospheres) to those events. But these tools were not originally designed for planetary event prediction and space weather applications. So WP10 JRA4 "Planetary Space Weather Services" (PSWS) will provide the additional research and tailoring required to apply them for these purposes. The overall objectives of this Joint Research Aactivities will be to review, test, improve and adapt methods and tools

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models.

Science.gov (United States)

El-Basyouny, Karim; Barua, Sudip; Islam, Md Tazul

2014-12-01

Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

Science.gov (United States)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Numerical modelling of elastic space tethers

DEFF Research Database (Denmark)

Kristiansen, Kristian Uldall; Palmer, P. L.; Roberts, R. M.

2012-01-01

In this paper the importance of the ill-posedness of the classical, non-dissipative massive tether model on an orbiting tether system is studied numerically. The computations document that via the regularisation of bending resistance a more reliable numerical integrator can be produced. Furthermo....... It is also shown that on the slow manifold the dynamics of the satellites are well-approximated by the finite dimensional slack-spring model....

Regional-seasonal weather forecasting

Energy Technology Data Exchange (ETDEWEB)

Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

1980-08-01

In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

Directable weathering of concave rock using curvature estimation.

Science.gov (United States)

Jones, Michael D; Farley, McKay; Butler, Joseph; Beardall, Matthew

2010-01-01

We address the problem of directable weathering of exposed concave rock for use in computer-generated animation or games. Previous weathering models that admit concave surfaces are computationally inefficient and difficult to control. In nature, the spheroidal and cavernous weathering rates depend on the surface curvature. Spheroidal weathering is fastest in areas with large positive mean curvature and cavernous weathering is fastest in areas with large negative mean curvature. We simulate both processes using an approximation of mean curvature on a voxel grid. Both weathering rates are also influenced by rock durability. The user controls rock durability by editing a durability graph before and during weathering simulation. Simulations of rockfall and colluvium deposition further improve realism. The profile of the final weathered rock matches the shape of the durability graph up to the effects of weathering and colluvium deposition. We demonstrate the top-down directability and visual plausibility of the resulting model through a series of screenshots and rendered images. The results include the weathering of a cube into a sphere and of a sheltered inside corner into a cavern as predicted by the underlying geomorphological models.

Numerical methods and modelling for engineering

CERN Document Server

Khoury, Richard

2016-01-01

This textbook provides a step-by-step approach to numerical methods in engineering modelling. The authors provide a consistent treatment of the topic, from the ground up, to reinforce for students that numerical methods are a set of mathematical modelling tools which allow engineers to represent real-world systems and compute features of these systems with a predictable error rate. Each method presented addresses a specific type of problem, namely root-finding, optimization, integral, derivative, initial value problem, or boundary value problem, and each one encompasses a set of algorithms to solve the problem given some information and to a known error bound. The authors demonstrate that after developing a proper model and understanding of the engineering situation they are working on, engineers can break down a model into a set of specific mathematical problems, and then implement the appropriate numerical methods to solve these problems. Uses a “building-block” approach, starting with simpler mathemati...

Effects of Climate on Co-evolution of Weathering Profiles and Hillscapes

Science.gov (United States)

Anderson, R. S.; Rajaram, H.; Anderson, S. P.

2017-12-01

Considerable debate revolves around the relative importance of rock type, tectonics, and climate in creating the architecture of the critical zone. It has recently been proposed that differences in the depths and patterns of weathering between landscapes in Colorado's Front Range and South Carolina's piedmont can be attributed to the state of stress in the rock imposed by the magnitude and orientation the regional stresses with respect to the ridgelines (St. Claire et al., 2016). We argue for the importance of the climate, and in particular, in temperate regions, the amount of recharge. We employ numerical models of hillslope evolution between bounding erosional channels, in which the degree of rock weathering governs the rate of transformation of rock to soil. As the water table drapes between the stream channels, fresh rock is brought into the weathering zone at a rate governed by the rate of incision of the channels. We track the chemical weathering of rock, represented by alteration of feldspar to clays, which in turn requires calculation of the concentration of reactive species in the water along hydrologic flow paths. We present results from analytic solutions to the flow field in which travel times can be efficiently assessed. Below the water table, flow paths are hyperbolic, taking on considerable lateral components as they veer toward the bounding channels that serve as drains to the hillslope. We find that if water is far from equilibrium with respect to weatherable minerals at the water table, as occurs in wet, slowly-eroding landscapes, deep weathering can occur well below the water table to levels approximating the base of the bounding channels. In dry climates, on the other hand, the weathering zone is limited to a shallow surface - parallel layer. These models capture the essence of the observed differences in depth to fresh rock in both wet and dry climates without appeal to the state of stress in the rock.

Modelling of cardiovascular system: development of a hybrid (numerical-physical) model.

Science.gov (United States)

Ferrari, G; Kozarski, M; De Lazzari, C; Górczyńska, K; Mimmo, R; Guaragno, M; Tosti, G; Darowski, M

2003-12-01

Physical models of the circulation are used for research, training and for testing of implantable active and passive circulatory prosthetic and assistance devices. However, in comparison with numerical models, they are rigid and expensive. To overcome these limitations, we have developed a model of the circulation based on the merging of a lumped parameter physical model into a numerical one (producing therefore a hybrid). The physical model is limited to the barest essentials and, in this application, developed to test the principle, it is a windkessel representing the systemic arterial tree. The lumped parameters numerical model was developed in LabVIEW environment and represents pulmonary and systemic circulation (except the systemic arterial tree). Based on the equivalence between hydraulic and electrical circuits, this prototype was developed connecting the numerical model to an electrical circuit--the physical model. This specific solution is valid mainly educationally but permits the development of software and the verification of preliminary results without using cumbersome hydraulic circuits. The interfaces between numerical and electrical circuits are set up by a voltage controlled current generator and a voltage controlled voltage generator. The behavior of the model is analyzed based on the ventricular pressure-volume loops and on the time course of arterial and ventricular pressures and flow in different circulatory conditions. The model can represent hemodynamic relationships in different ventricular and circulatory conditions.

Nonspinning numerical relativity waveform surrogates: assessing the model

Science.gov (United States)

Field, Scott; Blackman, Jonathan; Galley, Chad; Scheel, Mark; Szilagyi, Bela; Tiglio, Manuel

2015-04-01

Recently, multi-modal gravitational waveform surrogate models have been built directly from data numerically generated by the Spectral Einstein Code (SpEC). I will describe ways in which the surrogate model error can be quantified. This task, in turn, requires (i) characterizing differences between waveforms computed by SpEC with those predicted by the surrogate model and (ii) estimating errors associated with the SpEC waveforms from which the surrogate is built. Both pieces can have numerous sources of numerical and systematic errors. We make an attempt to study the most dominant error sources and, ultimately, the surrogate model's fidelity. These investigations yield information about the surrogate model's uncertainty as a function of time (or frequency) and parameter, and could be useful in parameter estimation studies which seek to incorporate model error. Finally, I will conclude by comparing the numerical relativity surrogate model to other inspiral-merger-ringdown models. A companion talk will cover the building of multi-modal surrogate models.

Numerical modeling of economic uncertainty

DEFF Research Database (Denmark)

Schjær-Jacobsen, Hans

2007-01-01

Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...... are made between alternative modeling methods, and characteristics of the methods are discussed....

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Directory of Open Access Journals (Sweden)

Hans-Stefan Bauer

2015-04-01

Full Text Available Quantitative precipitation estimation and forecasting (QPE and QPF are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions.

Science.gov (United States)

Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen; Plósz, Benedek Gy

2014-10-15

Current research focuses on predicting and mitigating the impacts of high hydraulic loadings on centralized wastewater treatment plants (WWTPs) under wet-weather conditions. The maximum permissible inflow to WWTPs depends not only on the settleability of activated sludge in secondary settling tanks (SSTs) but also on the hydraulic behaviour of SSTs. The present study investigates the impacts of ideal and non-ideal flow (dry and wet weather) and settling (good settling and bulking) boundary conditions on the sensitivity of WWTP model outputs to uncertainties intrinsic to the one-dimensional (1-D) SST model structures and parameters. We identify the critical sources of uncertainty in WWTP models through global sensitivity analysis (GSA) using the Benchmark simulation model No. 1 in combination with first- and second-order 1-D SST models. The results obtained illustrate that the contribution of settling parameters to the total variance of the key WWTP process outputs significantly depends on the influent flow and settling conditions. The magnitude of the impact is found to vary, depending on which type of 1-D SST model is used. Therefore, we identify and recommend potential parameter subsets for WWTP model calibration, and propose optimal choice of 1-D SST models under different flow and settling boundary conditions. Additionally, the hydraulic parameters in the second-order SST model are found significant under dynamic wet-weather flow conditions. These results highlight the importance of developing a more mechanistic based flow-dependent hydraulic sub-model in second-order 1-D SST models in the future. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Observing System Simulation Experiment (OSSE to Assess the Impact of Doppler Wind Lidar (DWL Measurements on the Numerical Simulation of a Tropical Cyclone

Directory of Open Access Journals (Sweden)

Lei Zhang

2010-01-01

Full Text Available The importance of wind observations has been recognized for many years. However, wind observations—especially three-dimensional global wind measurements—are very limited. A satellite-based Doppler Wind Lidar (DWL is proposed to measure three-dimensional wind profiles using remote sensing techniques. Assimilating these observations into a mesoscale model is expected to improve the performance of the numerical weather prediction (NWP models. In order to examine the potential impact of the DWL three-dimensional wind profile observations on the numerical simulation and prediction of tropical cyclones, a set of observing simulation system experiments (OSSEs is performed using the advanced research version of the Weather Research and Forecasting (WRF model and its three-dimensional variational (3DVAR data assimilation system. Results indicate that assimilating the DWL wind observations into the mesoscale numerical model has significant potential for improving tropical cyclone track and intensity forecasts.

Numerical model SMODERP

Science.gov (United States)

Kavka, P.; Jeřábek, J.; Strouhal, L.

2016-12-01

The contribution presents a numerical model SMODERP that is used for calculation and prediction of surface runoff and soil erosion from agricultural land. The physically based model includes the processes of infiltration (Phillips equation), surface runoff routing (kinematic wave based equation), surface retention, surface roughness and vegetation impact on runoff. The model is being developed at the Department of Irrigation, Drainage and Landscape Engineering, Civil Engineering Faculty, CTU in Prague. 2D version of the model was introduced in last years. The script uses ArcGIS system tools for data preparation. The physical relations are implemented through Python scripts. The main computing part is stand alone in numpy arrays. Flow direction is calculated by Steepest Descent algorithm and in multiple flow algorithm. Sheet flow is described by modified kinematic wave equation. Parameters for five different soil textures were calibrated on the set of hundred measurements performed on the laboratory and filed rainfall simulators. Spatially distributed models enable to estimate not only surface runoff but also flow in the rills. Development of the rills is based on critical shear stress and critical velocity. For modelling of the rills a specific sub model was created. This sub model uses Manning formula for flow estimation. Flow in the ditches and streams are also computed. Numerical stability of the model is controled by Courant criterion. Spatial scale is fixed. Time step is dynamic and depends on the actual discharge. The model is used in the framework of the project "Variability of Short-term Precipitation and Runoff in Small Czech Drainage Basins and its Influence on Water Resources Management". Main goal of the project is to elaborate a methodology and online utility for deriving short-term design precipitation series, which could be utilized by a broad community of scientists, state administration as well as design planners. The methodology will account for

Introducing a rainfall compound distribution model based on weather patterns sub-sampling

Directory of Open Access Journals (Sweden)

F. Garavaglia

2010-06-01

Full Text Available This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis.

First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France.

The distribution of the multi-exponential weather patterns (MEWP is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period.

A Numerical Approach for Hybrid Simulation of Power System Dynamics Considering Extreme Icing Events

DEFF Research Database (Denmark)

Chen, Lizheng; Zhang, Hengxu; Wu, Qiuwei

2017-01-01

numerical simulation scheme integrating icing weather events with power system dynamics is proposed to extend power system numerical simulation. A technique is developed to efficiently simulate the interaction of slow dynamics of weather events and fast dynamics of power systems. An extended package for PSS...

On the sensitivity of numerical weather prediction to remotely sensed marine surface wind data - A simulation study

Science.gov (United States)

Cane, M. A.; Cardone, V. J.; Halem, M.; Halberstam, I.

1981-01-01

The reported investigation has the objective to assess the potential impact on numerical weather prediction (NWP) of remotely sensed surface wind data. Other investigations conducted with similar objectives have not been satisfactory in connection with a use of procedures providing an unrealistic distribution of initial errors. In the current study, care has been taken to duplicate the actual distribution of information in the conventional observing system, thus shifting the emphasis from accuracy of the data to the data coverage. It is pointed out that this is an important consideration in assessing satellite observing systems since experience with sounder data has shown that improvements in forecasts due to satellite-derived information is due less to a general error reduction than to the ability to fill data-sparse regions. The reported study concentrates on the evaluation of the observing system simulation experimental design and on the assessment of the potential of remotely sensed marine surface wind data.

Singular vectors, predictability and ensemble forecasting for weather and climate

International Nuclear Information System (INIS)

Palmer, T N; Zanna, Laure

2013-01-01

The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (review)

How reliable is the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model?

Science.gov (United States)

The aim for this research is to evaluate the ability of the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological, e.g. evaporation (ET), soil moisture (SM), runoff, and baseflow. First, the VIC mo...

Solar EUV irradiance for space weather applications

Science.gov (United States)

Viereck, R. A.

2015-12-01

Solar EUV irradiance is an important driver of space weather models. Large changes in EUV and x-ray irradiances create large variability in the ionosphere and thermosphere. Proxies such as the F10.7 cm radio flux, have provided reasonable estimates of the EUV flux but as the space weather models become more accurate and the demands of the customers become more stringent, proxies are no longer adequate. Furthermore, proxies are often provided only on a daily basis and shorter time scales are becoming important. Also, there is a growing need for multi-day forecasts of solar EUV irradiance to drive space weather forecast models. In this presentation we will describe the needs and requirements for solar EUV irradiance information from the space weather modeler's perspective. We will then translate these requirements into solar observational requirements such as spectral resolution and irradiance accuracy. We will also describe the activities at NOAA to provide long-term solar EUV irradiance observations and derived products that are needed for real-time space weather modeling.

Report 3: Guidance document on practices to model and implement Extreme Weather hazards in extended PSA

International Nuclear Information System (INIS)

Alzbutas, R.; Ostapchuk, S.; Borysiewicz, M.; Decker, K.; Kumar, Manorma; Haeggstroem, A.; Nitoi, M.; Groudev, P.; Parey, S.; Potempski, S.; Raimond, E.; Siklossy, T.

2016-01-01

The goal of this report is to provide guidance on practices to model Extreme Weather hazards and implement them in extended level 1 PSA. This report is a joint deliverable of work package 21 (WP21) and work package 22 (WP22). The general objective of WP21 is to provide guidance on all of the individual hazards selected at the End Users Workshop. This guidance is focusing on extreme weather hazards, namely: extreme wind, extreme temperature and snow pack. Other hazards, however, are considered in cases where they are correlated/ associated with the hazard under discussion. Guidance developed refers to existing guidance whenever possible. As it was recommended by end users this guidance covers questions of developing integrated and/or separated extreme weathers PSA models. (authors)

Numerical model updating technique for structures using firefly algorithm

Science.gov (United States)

Sai Kubair, K.; Mohan, S. C.

2018-03-01

Numerical model updating is a technique used for updating the existing experimental models for any structures related to civil, mechanical, automobiles, marine, aerospace engineering, etc. The basic concept behind this technique is updating the numerical models to closely match with experimental data obtained from real or prototype test structures. The present work involves the development of numerical model using MATLAB as a computational tool and with mathematical equations that define the experimental model. Firefly algorithm is used as an optimization tool in this study. In this updating process a response parameter of the structure has to be chosen, which helps to correlate the numerical model developed with the experimental results obtained. The variables for the updating can be either material or geometrical properties of the model or both. In this study, to verify the proposed technique, a cantilever beam is analyzed for its tip deflection and a space frame has been analyzed for its natural frequencies. Both the models are updated with their respective response values obtained from experimental results. The numerical results after updating show that there is a close relationship that can be brought between the experimental and the numerical models.

DOE Workshop; Pan-Gass Conference on the Representation of Atmospheric Processes in Weather and Climate Models

Energy Technology Data Exchange (ETDEWEB)

Morrison, Hugh [National Center for Atmospheric Research, Boulder, CO (United States)

2012-11-12

This is the first meeting of the whole new GEWEX (Global Energy and Water Cycle Experiment) Atmospheric System Study (GASS) project that has been formed from the merger of the GEWEX Cloud System Study (GCSS) Project and the GEWEX Atmospheric Boundary Layer Studies (GABLS). As such, this meeting will play a major role in energizing GEWEX work in the area of atmospheric parameterizations of clouds, convection, stable boundary layers, and aerosol-cloud interactions for the numerical models used for weather and climate projections at both global and regional scales. The representation of these processes in models is crucial to GEWEX goals of improved prediction of the energy and water cycles at both weather and climate timescales. This proposal seeks funds to be used to cover incidental and travel expenses for U.S.-based graduate students and early career scientists (i.e., within 5 years of receiving their highest degree). We anticipate using DOE funding to support 5-10 people. We will advertise the availability of these funds by providing a box to check for interested participants on the online workshop registration form. We will also send a note to our participants' mailing lists reminding them that the funds are available and asking senior scientists to encourage their more junior colleagues to participate. All meeting participants are encouraged to submit abstracts for oral or poster presentations. The science organizing committee (see below) will base funding decisions on the relevance and quality of these abstracts, with preference given to under-represented populations (especially women and minorities) and to early career scientists being actively mentored at the meeting (e.g. students or postdocs attending the meeting with their adviser).

2-dimensional numerical modeling of active magnetic regeneration

DEFF Research Database (Denmark)

Nielsen, Kaspar Kirstein; Pryds, Nini; Smith, Anders

2009-01-01

Various aspects of numerical modeling of Active Magnetic Regeneration (AMR) are presented. Using a 2-dimensional numerical model for solving the unsteady heat transfer equations for the AMR system, a range of physical effects on both idealized and non-idealized AMR are investigated. The modeled...

Numerical modelling of steel arc welding

International Nuclear Information System (INIS)

Hamide, M.

2008-07-01

Welding is a highly used assembly technique. Welding simulation software would give access to residual stresses and information about the weld's microstructure, in order to evaluate the mechanical resistance of a weld. It would also permit to evaluate the process feasibility when complex geometrical components are to be made, and to optimize the welding sequences in order to minimize defects. This work deals with the numerical modelling of arc welding process of steels. After describing the industrial context and the state of art, the models implemented in TransWeld (software developed at CEMEF) are presented. The set of macroscopic equations is followed by a discussion on their numerical implementation. Then, the theory of re-meshing and our adaptive anisotropic re-meshing strategy are explained. Two welding metal addition techniques are investigated and are compared in terms of the joint size and transient temperature and stresses. The accuracy of the finite element model is evaluated based on experimental results and the results of the analytical solution. Comparative analysis between experimental and numerical results allows the assessment of the ability of the numerical code to predict the thermomechanical and metallurgical response of the welded structure. The models limitations and the phenomena identified during this study are finally discussed and permit to define interesting orientations for future developments. (author)

Modelling weather effects for impact analysis of residential time-of-use electricity pricing

International Nuclear Information System (INIS)

Miller, Reid; Golab, Lukasz; Rosenberg, Catherine

2017-01-01

Analyzing the impact of pricing policies such as time-of-use (TOU) is challenging in the presence of confounding factors such as weather. Motivated by a lack of consensus and model selection details in prior work, we present a methodology for modelling the effect of weather on residential electricity demand. The best model is selected according to explanatory power, out-of-sample prediction accuracy, goodness of fit and interpretability. We then evaluate the effect of mandatory TOU pricing in a local distribution company in southwestern Ontario, Canada. We use a smart meter dataset of over 20,000 households which is particularly suited to our analysis: it contains data from the summer before and after the implementation of TOU pricing in November 2011, and all customers transitioned from tiered rates to TOU rates at the same time. We find that during the summer rate season, TOU pricing results in electricity conservation across all price periods. The average demand change during on-peak and mid-peak periods is −2.6% and −2.4% respectively. Changes during off-peak periods are not statistically significant. These TOU pricing effects are less pronounced compared to previous studies, underscoring the need for clear, reproducible impact analyses which include full details about the model selection process. - Highlights: • We study models for the effect of weather on residential electricity demand. • We evaluate the effect of mandatory TOU pricing in a local distribution company in Ontario, Canada. • We find the effect of TOU pricing to be less pronounced compared to previous studies.

Evaluating Cloud and Precipitation Processes in Numerical Models using Current and Potential Future Satellite Missions

Science.gov (United States)

van den Heever, S. C.; Tao, W. K.; Skofronick Jackson, G.; Tanelli, S.; L'Ecuyer, T. S.; Petersen, W. A.; Kummerow, C. D.

2015-12-01

Cloud, aerosol and precipitation processes play a fundamental role in the water and energy cycle. It is critical to accurately represent these microphysical processes in numerical models if we are to better predict cloud and precipitation properties on weather through climate timescales. Much has been learned about cloud properties and precipitation characteristics from NASA satellite missions such as TRMM, CloudSat, and more recently GPM. Furthermore, data from these missions have been successfully utilized in evaluating the microphysical schemes in cloud-resolving models (CRMs) and global models. However, there are still many uncertainties associated with these microphysics schemes. These uncertainties can be attributed, at least in part, to the fact that microphysical processes cannot be directly observed or measured, but instead have to be inferred from those cloud properties that can be measured. Evaluation of microphysical parameterizations are becoming increasingly important as enhanced computational capabilities are facilitating the use of more sophisticated schemes in CRMs, and as future global models are being run on what has traditionally been regarded as cloud-resolving scales using CRM microphysical schemes. In this talk we will demonstrate how TRMM, CloudSat and GPM data have been used to evaluate different aspects of current CRM microphysical schemes, providing examples of where these approaches have been successful. We will also highlight CRM microphysical processes that have not been well evaluated and suggest approaches for addressing such issues. Finally, we will introduce a potential NASA satellite mission, the Cloud and Precipitation Processes Mission (CAPPM), which would facilitate the development and evaluation of different microphysical-dynamical feedbacks in numerical models.

Numerical simulation of Higgs models

International Nuclear Information System (INIS)

Jaster, A.

1995-10-01

The SU(2) Higgs and the Schwinger model on the lattice were analysed. Numerical simulations of the SU(2) Higgs model were performed to study the finite temperature electroweak phase transition. With the help of the multicanonical method the distribution of an order parameter at the phase transition point was measured. This was used to obtain the order of the phase transition and the value of the interface tension with the histogram method. Numerical simulations were also performed at zero temperature to perform renormalization. The measured values for the Wilson loops were used to determine the static potential and from this the renormalized gauge coupling. The Schwinger model was simulated at different gauge couplings to analyse the properties of the Kaplan-Shamir fermions. The prediction that the mass parameter gets only multiplicative renormalization was tested and verified. (orig.)

Improving weather predictability by including land-surface model parameter uncertainty

Science.gov (United States)

Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

2016-04-01

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

Urban weather data and building models for the inclusion of the urban heat island effect in building performance simulation.

Science.gov (United States)

Palme, M; Inostroza, L; Villacreses, G; Lobato, A; Carrasco, C

2017-10-01

This data article presents files supporting calculation for urban heat island (UHI) inclusion in building performance simulation (BPS). Methodology is used in the research article "From urban climate to energy consumption. Enhancing building performance simulation by including the urban heat island effect" (Palme et al., 2017) [1]. In this research, a Geographical Information System (GIS) study is done in order to statistically represent the most important urban scenarios of four South-American cities (Guayaquil, Lima, Antofagasta and Valparaíso). Then, a Principal Component Analysis (PCA) is done to obtain reference Urban Tissues Categories (UTC) to be used in urban weather simulation. The urban weather files are generated by using the Urban Weather Generator (UWG) software (version 4.1 beta). Finally, BPS is run out with the Transient System Simulation (TRNSYS) software (version 17). In this data paper, four sets of data are presented: 1) PCA data (excel) to explain how to group different urban samples in representative UTC; 2) UWG data (text) to reproduce the Urban Weather Generation for the UTC used in the four cities (4 UTC in Lima, Guayaquil, Antofagasta and 5 UTC in Valparaíso); 3) weather data (text) with the resulting rural and urban weather; 4) BPS models (text) data containing the TRNSYS models (four building models).

The influence of numerical models on determining the drag coefficient

Directory of Open Access Journals (Sweden)

Dobeš Josef

2014-03-01

Full Text Available The paper deals with numerical modelling of body aerodynamic drag coefficient in the transition from laminar to turbulent flow regimes, where the selection of a suitable numerical model is problematic. On the basic problem of flow around a simple body – sphere selected computational models are tested. The values obtained by numerical simulations of drag coefficients of each model are compared with the graph of dependency of the drag coefficient vs. Reynolds number for a sphere. Next the dependency of Strouhal number vs. Reynolds number is evaluated, where the vortex shedding frequency values for given speed are obtained numerically and experimentally and then the values are compared for each numerical model and experiment. The aim is to specify trends for the selection of appropriate numerical model for flow around bodies problem in which the precise description of the flow field around the obstacle is used to define the acoustic noise source. Numerical modelling is performed by finite volume method using CFD code.

From the Sun’s atmosphere to the Earth’s atmosphere: an overview of scientific models available for space weather developments

Directory of Open Access Journals (Sweden)

C. Lathuillère

Full Text Available Space weather aims at setting operational numerical tools in order to nowcast, forecast and quantify the solar activity events, the magnetosphere, ionosphere and thermosphere responses and the consequences on our technological societies. These tools can be divided in two parts. The first has a geophysical base (Sun, interplanetary medium, magnetosphere, atmosphere. The second concerns technological applications (telecommunications, spacecraft orbits, power plants .... In this paper, we aim at giving an overview of the models that belong to the first class (geophysics that might serve in the future as a basis for building global operational codes. For each model, we consider the physics underneath, the input and output parameters, and whether it is already operational, whether it may become operational in the near future, or if it is an academic research tool. Relevant references are given in order to serve as a starting point for further readings.

Key words. Interplanetary physics (general or miscellaneous, Ionosphere (modelling and forecasting, Magnetospheric physics (general or miscellaneous

From the Sun’s atmosphere to the Earth’s atmosphere: an overview of scientific models available for space weather developments

Directory of Open Access Journals (Sweden)

C. Lathuillère

2002-07-01

Full Text Available Space weather aims at setting operational numerical tools in order to nowcast, forecast and quantify the solar activity events, the magnetosphere, ionosphere and thermosphere responses and the consequences on our technological societies. These tools can be divided in two parts. The first has a geophysical base (Sun, interplanetary medium, magnetosphere, atmosphere. The second concerns technological applications (telecommunications, spacecraft orbits, power plants .... In this paper, we aim at giving an overview of the models that belong to the first class (geophysics that might serve in the future as a basis for building global operational codes. For each model, we consider the physics underneath, the input and output parameters, and whether it is already operational, whether it may become operational in the near future, or if it is an academic research tool. Relevant references are given in order to serve as a starting point for further readings.Key words. Interplanetary physics (general or miscellaneous, Ionosphere (modelling and forecasting, Magnetospheric physics (general or miscellaneous

Influence of weathering and pre-existing large scale fractures on gravitational slope failure: insights from 3-D physical modelling

Directory of Open Access Journals (Sweden)

D. Bachmann

2004-01-01

Full Text Available Using a new 3-D physical modelling technique we investigated the initiation and evolution of large scale landslides in presence of pre-existing large scale fractures and taking into account the slope material weakening due to the alteration/weathering. The modelling technique is based on the specially developed properly scaled analogue materials, as well as on the original vertical accelerator device enabling increases in the 'gravity acceleration' up to a factor 50. The weathering primarily affects the uppermost layers through the water circulation. We simulated the effect of this process by making models of two parts. The shallower one represents the zone subject to homogeneous weathering and is made of low strength material of compressive strength σl. The deeper (core part of the model is stronger and simulates intact rocks. Deformation of such a model subjected to the gravity force occurred only in its upper (low strength layer. In another set of experiments, low strength (σw narrow planar zones sub-parallel to the slope surface (σwl were introduced into the model's superficial low strength layer to simulate localized highly weathered zones. In this configuration landslides were initiated much easier (at lower 'gravity force', were shallower and had smaller horizontal size largely defined by the weak zone size. Pre-existing fractures were introduced into the model by cutting it along a given plan. They have proved to be of small influence on the slope stability, except when they were associated to highly weathered zones. In this latter case the fractures laterally limited the slides. Deep seated rockslides initiation is thus directly defined by the mechanical structure of the hillslope's uppermost levels and especially by the presence of the weak zones due to the weathering. The large scale fractures play a more passive role and can only influence the shape and the volume of the sliding units.

Numerical models of groundwater flow and transport

International Nuclear Information System (INIS)

Konikow, L.F.

1996-01-01

This chapter reviews the state-of-the-art in deterministic modeling of groundwater flow and transport processes, which can be used for interpretation of isotope data through groundwater flow analyses. Numerical models which are available for this purpose are described and their applications to complex field problems are discussed. The theoretical bases of deterministic modeling are summarized, and advantages and limitations of numerical models are described. The selection of models for specific applications and their calibration procedures are described, and results of a few illustrative case study type applications are provided. (author). 145 refs, 17 figs, 2 tabs

Numerical models of groundwater flow and transport

Energy Technology Data Exchange (ETDEWEB)

Konikow, L F [Geological Survey, Reston, VA (United States)

1996-10-01

This chapter reviews the state-of-the-art in deterministic modeling of groundwater flow and transport processes, which can be used for interpretation of isotope data through groundwater flow analyses. Numerical models which are available for this purpose are described and their applications to complex field problems are discussed. The theoretical bases of deterministic modeling are summarized, and advantages and limitations of numerical models are described. The selection of models for specific applications and their calibration procedures are described, and results of a few illustrative case study type applications are provided. (author). 145 refs, 17 figs, 2 tabs.

Mountain range specific analog weather forecast model for ...

Indian Academy of Sciences (India)

used to draw weather forecast for that mountain range in operational weather forecasting mode, three days ... various road management activities and for better .... −0.8. 1.5. 0.0. Pir Panjal range (HP). 1989–90 to 2002–03. 14. Snow day. 2.2. −4.1 ..... ed days,. S. = snow day,. N. S. = no-snow day and. P. C. = per cent correct).

Weather derivatives: Business hedge instrument from weather risks

Directory of Open Access Journals (Sweden)

Đorđević Bojan S.

2014-01-01

Full Text Available In the late 1990s, a new financial market was developed - a market for weather derivatives, so that the risk managers could hedge their exposure to weather risk. After a rather slow start, the weather derivatives market had started to grow rapidly. Risk managers could no longer blame poor financial results on the weather. Weather risk could now be removed by hedging procedure. This paper will explain briefly what the weather derivatives are and will point out at some of the motives for use of derivatives. Thereafter we will look at the history of the weather risk market, how the weather derivatives market has developed in recent years and also who are the current and potential players in the weather derivatives market.

Does weather affect daily pain intensity levels in patients with acute low back pain? A prospective cohort study.

Science.gov (United States)

Duong, Vicky; Maher, Chris G; Steffens, Daniel; Li, Qiang; Hancock, Mark J

2016-05-01

The aim of this study was to investigate the influence of various weather parameters on pain intensity levels in patients with acute low back pain (LBP). We performed a secondary analysis using data from the PACE trial that evaluated paracetamol (acetaminophen) in the treatment of acute LBP. Data on 1604 patients with LBP were included in the analysis. Weather parameters (precipitation, temperature, relative humidity, and air pressure) were obtained from the Australian Bureau of Meteorology. Pain intensity was assessed daily on a 0-10 numerical pain rating scale over a 2-week period. A generalised estimating equation analysis was used to examine the relationship between daily pain intensity levels and weather in three different time epochs (current day, previous day, and change between previous and current days). A second model was adjusted for important back pain prognostic factors. The analysis did not show any association between weather and pain intensity levels in patients with acute LBP in each of the time epochs. There was no change in strength of association after the model was adjusted for prognostic factors. Contrary to common belief, the results demonstrated that the weather parameters of precipitation, temperature, relative humidity, and air pressure did not influence the intensity of pain reported by patients during an episode of acute LBP.

Modeling and numerical simulations of the influenced Sznajd model

Science.gov (United States)

Karan, Farshad Salimi Naneh; Srinivasan, Aravinda Ramakrishnan; Chakraborty, Subhadeep

2017-08-01

This paper investigates the effects of independent nonconformists or influencers on the behavioral dynamic of a population of agents interacting with each other based on the Sznajd model. The system is modeled on a complete graph using the master equation. The acquired equation has been numerically solved. Accuracy of the mathematical model and its corresponding assumptions have been validated by numerical simulations. Regions of initial magnetization have been found from where the system converges to one of two unique steady-state PDFs, depending on the distribution of influencers. The scaling property and entropy of the stationary system in presence of varying level of influence have been presented and discussed.

Numerical Modelling Of Pumpkin Balloon Instability

Science.gov (United States)

Wakefield, D.

Tensys have been involved in the numerical formfinding and load analysis of architectural stressed membrane structures for 15 years. They have recently broadened this range of activities into the `lighter than air' field with significant involvement in aerostat and heavy-lift hybrid airship design. Since early 2004 they have been investigating pumpkin balloon instability on behalf of the NASA ULDB programme. These studies are undertaken using inTENS, an in-house finite element program suite based upon the Dynamic Relaxation solution method and developed especially for the non-linear analysis and patterning of membrane structures. The paper describes the current state of an investigation that started with a numerical simulation of the lobed cylinder problem first studied by Calladine. The influence of material properties and local geometric deformation on stability is demonstrated. A number of models of complete pumpkin balloons have then been established, including a 64-gore balloon with geometry based upon Julian Nott's Endeavour. This latter clefted dramatically upon initial inflation, a phenomenon that has been reproduced in the numerical model. Ongoing investigations include the introduction of membrane contact modelling into inTENS and correlation studies with the series of large-scale ULDB models currently in preparation.

Space weather: Modeling and forecasting ionospheric

International Nuclear Information System (INIS)

Calzadilla Mendez, A.

2008-01-01

Full text: Space weather is the set of phenomena and interactions that take place in the interplanetary medium. It is regulated primarily by the activity originating in the Sun and affects both the artificial satellites that are outside of the protective cover of the Earth's atmosphere as the rest of the planets in the solar system. Among the phenomena that are of great relevance and impact on Earth are the auroras and geomagnetic storms , these are a direct result of irregularities in the flow of the solar wind and the interplanetary magnetic field . Given the high complexity of the physical phenomena involved (magnetic reconnection , particle inlet and ionizing radiation to the atmosphere) one of the great scientific challenges today is to forecast the state of plasmatic means either the interplanetary medium , the magnetosphere and ionosphere , for their importance to the development of various human activities such as radio , global positioning , navigation, etc. . It briefly address some of the international ionospheric modeling methods and contributions and participation that currently has the space group of the Institute of Geophysics Geophysics and Astronomy (IGA) in these activities of modeling and forecasting ionospheric. (author)

Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Directory of Open Access Journals (Sweden)

Chaeyeon Yi

2018-04-01

Full Text Available The Korean peninsula has complex and diverse weather phenomena, and the Korea Meteorological Administration has been working on various numerical models to produce better forecasting data. The Unified Model Local Data Assimilation and Prediction System is a limited-area working model with a horizontal resolution of 1.5 km for estimating local-scale weather forecasts on the Korean peninsula. However, in order to numerically predict the detailed temperature characteristics of the urban space, in which surface characteristics change rapidly in a small spatial area, a city temperature prediction model with higher resolution spatial decomposition capabilities is required. As an alternative to this, a building-scale temperature model was developed, and a 25 m air temperature resolution was determined for the Seoul area. The spatial information was processed using statistical methods, such as linear regression models and machine learning. By comparing the accuracy of the estimated air temperatures with observational data during the summer, the machine learning was improved. In addition, horizontal and vertical characteristics of the urban space were better represented, and the air temperature was better resolved spatially. Air temperature information can be used to manage the response to heat-waves and tropical nights in administrative districts of urban areas.

"Big Data Assimilation" for 30-second-update 100-m-mesh Numerical Weather Prediction

Science.gov (United States)

Miyoshi, Takemasa; Lien, Guo-Yuan; Kunii, Masaru; Ruiz, Juan; Maejima, Yasumitsu; Otsuka, Shigenori; Kondo, Keiichi; Seko, Hiromu; Satoh, Shinsuke; Ushio, Tomoo; Bessho, Kotaro; Kamide, Kazumi; Tomita, Hirofumi; Nishizawa, Seiya; Yamaura, Tsuyoshi; Ishikawa, Yutaka

2017-04-01

A typical lifetime of a single cumulonimbus is within an hour, and radar observations often show rapid changes in only a 5-minute period. For precise prediction of such rapidly-changing local severe storms, we have developed what we call a "Big Data Assimilation" (BDA) system that performs 30-second-update data assimilation cycles at 100-m grid spacing. The concept shares that of NOAA's Warn-on-Forecast (WoF), in which rapidly-updated high-resolution NWP will play a central role in issuing severe-storm warnings even only minutes in advance. The 100-m resolution and 30-second update frequency are a leap above typical recent research settings, and it was possible by the fortunate combination of Japan's most advanced supercomputing and sensing technologies: the 10-petaflops K computer and the Phased Array Weather Radar (PAWR). The X-band PAWR is capable of a dense three-dimensional volume scan at 100-m range resolution with 100 elevation angles and 300 azimuth angles, up to 60-km range within 30 seconds. The PAWR data show temporally-smooth evolution of convective rainstorms. This gives us a hope that we may assume the Gaussian error distribution in 30-second forecasts before strong nonlinear dynamics distort the error distribution for rapidly-changing convective storms. With this in mind, we apply the Local Ensemble Transform Kalman Filter (LETKF) that considers flow-dependent error covariance explicitly under the Gaussian-error assumption. The flow-dependence would be particularly important in rapidly-changing convective weather. Using a 100-member ensemble at 100-m resolution, we have tested the Big Data Assimilation system in real-world cases of sudden local rainstorms, and obtained promising results. However, the real-time application is a big challenge, and currently it takes 10 minutes for a cycle. We explore approaches to accelerating the computations, such as using single-precision arrays in the model computation and developing an efficient I/O middleware for

Numerical Modeling and Mechanical Analysis of Flexible Risers

Directory of Open Access Journals (Sweden)

J. Y. Li

2015-01-01

Full Text Available ABAQUS is used to create a detailed finite element model for a 10-layer unbonded flexible riser to simulate the riser’s mechanical behavior under three load conditions: tension force and internal and external pressure. It presents a technique to create detailed finite element model and to analyze flexible risers. In FEM model, all layers are modeled separately with contact interfaces; interaction between steel trips in certain layers has been considered as well. FEM model considering contact interaction, geometric nonlinearity, and friction has been employed to accurately simulate the structural behavior of riser. The model includes the main features of the riser geometry with very little simplifying assumptions. The model was solved using a fully explicit time-integration scheme implemented in a parallel environment on an eight-processor cluster and 24 G memory computer. There is a very good agreement obtained from numerical and analytical comparisons, which validates the use of numerical model here. The results from the numerical simulation show that the numerical model takes into account various details of the riser. It has been shown that the detailed finite element model can be used to predict riser’s mechanics behavior under various load cases and bound conditions.

Numerical Modelling of Sediment Transport in Combined Sewer Systems

DEFF Research Database (Denmark)

Schlütter, Flemming

A conceptual sediment transport model has been developed. Through a case study a comparison with other numerical models is performed.......A conceptual sediment transport model has been developed. Through a case study a comparison with other numerical models is performed....

Numerical Modelling of Flow and Settling in Secondary Settling Tanks

DEFF Research Database (Denmark)

Dahl, Claus Poulsen

This thesis discusses the development of a numerical model for the simulation of secondary settling tanks. In the first part, the status on the development of numerical models for settling tanks and a discussion of the current design practice are presented. A study of the existing numerical models...... and design practice proved a demand for further development to include numerical models in the design of settling tanks, thus improving the future settling tanks....

Numerical 3-D Modelling of Overflows

DEFF Research Database (Denmark)

Larsen, Torben; Nielsen, L.; Jensen, B.

2008-01-01

-dimensional so-called Volume of Fluid Models (VOF-models) based on the full Navier-Stokes equations (named NS3 and developed by DHI Water & Environment) As a general conclusion, the two numerical models show excellent results when compared with measurements. However, considerable errors occur when...

Reducing prediction uncertainty of weather controlled systems

NARCIS (Netherlands)

Doeswijk, T.G.

2007-01-01

In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are

Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

Science.gov (United States)

Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

2015-04-01

Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations

A Mathematical Model of Hourly Solar Radiation in Varying Weather Conditions for a Dynamic Simulation of the Solar Organic Rankine Cycle

Directory of Open Access Journals (Sweden)

Taehong Sung

2015-07-01

Full Text Available A mathematical model of hourly solar radiation with weather variability is proposed based on the simple sky model. The model uses a superposition of trigonometric functions with short and long periods. We investigate the effects of the model variables on the clearness (kD and the probability of persistence (POPD indices and also evaluate the proposed model for all of the kD-POPD weather classes. A simple solar organic Rankine cycle (SORC system with thermal storage is simulated using the actual weather conditions, and then, the results are compared with the simulation results using the proposed model and the simple sky model. The simulation results show that the proposed model provides more accurate system operation characteristics than the simple sky model.

Urban weather data and building models for the inclusion of the urban heat island effect in building performance simulation

Directory of Open Access Journals (Sweden)

M. Palme

2017-10-01

Full Text Available This data article presents files supporting calculation for urban heat island (UHI inclusion in building performance simulation (BPS. Methodology is used in the research article “From urban climate to energy consumption. Enhancing building performance simulation by including the urban heat island effect” (Palme et al., 2017 [1]. In this research, a Geographical Information System (GIS study is done in order to statistically represent the most important urban scenarios of four South-American cities (Guayaquil, Lima, Antofagasta and Valparaíso. Then, a Principal Component Analysis (PCA is done to obtain reference Urban Tissues Categories (UTC to be used in urban weather simulation. The urban weather files are generated by using the Urban Weather Generator (UWG software (version 4.1 beta. Finally, BPS is run out with the Transient System Simulation (TRNSYS software (version 17. In this data paper, four sets of data are presented: 1 PCA data (excel to explain how to group different urban samples in representative UTC; 2 UWG data (text to reproduce the Urban Weather Generation for the UTC used in the four cities (4 UTC in Lima, Guayaquil, Antofagasta and 5 UTC in Valparaíso; 3 weather data (text with the resulting rural and urban weather; 4 BPS models (text data containing the TRNSYS models (four building models.

Numerical simulations of atmospheric dispersion of iodine-131 by different models.

Directory of Open Access Journals (Sweden)

Ádám Leelőssy

Full Text Available Nowadays, several dispersion models are available to simulate the transport processes of air pollutants and toxic substances including radionuclides in the atmosphere. Reliability of atmospheric transport models has been demonstrated in several recent cases from local to global scale; however, very few actual emission data are available to evaluate model results in real-life cases. In this study, the atmospheric dispersion of 131I emitted to the atmosphere during an industrial process was simulated with different models, namely the WRF-Chem Eulerian online coupled model and the HYSPLIT and the RAPTOR Lagrangian models. Although only limited data of 131I detections has been available, the accuracy of modeled plume direction could be evaluated in complex late autumn weather situations. For the studied cases, the general reliability of models has been demonstrated. However, serious uncertainties arise related to low level inversions, above all in case of an emission event on 4 November 2011, when an important wind shear caused a significant difference between simulated and real transport directions. Results underline the importance of prudent interpretation of dispersion model results and the identification of weather conditions with a potential to cause large model errors.

On the Hughes model and numerical aspects

KAUST Repository

Gomes, Diogo A.

2017-01-05

We study a crowd model proposed by R. Hughes in [11] and we describe a numerical approach to solve it. This model comprises a Fokker-Planck equation coupled with an eikonal equation with Dirichlet or Neumann data. First, we establish a priori estimates for the solutions. Second, we study radial solutions and identify a shock formation mechanism. Third, we illustrate the existence of congestion, the breakdown of the model, and the trend to the equilibrium. Finally, we propose a new numerical method and consider two examples.

Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

Science.gov (United States)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.

Implementation of a generalized actuator line model for wind turbine parameterization in the Weather Research and Forecasting model

Energy Technology Data Exchange (ETDEWEB)

Marjanovic, Nikola [Department of Civil and Environmental Engineering, University of California, Berkeley, MC 1710, Berkeley, California 94720-1710, USA; Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, PO Box 808, L-103, Livermore, California 94551, USA; Mirocha, Jeffrey D. [Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, PO Box 808, L-103, Livermore, California 94551, USA; Kosović, Branko [Research Applications Laboratory, Weather Systems and Assessment Program, University Corporation for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307, USA; Lundquist, Julie K. [Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Campus Box 311, Boulder, Colorado 80309, USA; National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, USA; Chow, Fotini Katopodes [Department of Civil and Environmental Engineering, University of California, Berkeley, MC 1710, Berkeley, California 94720-1710, USA

2017-11-01

A generalized actuator line (GAL) wind turbine parameterization is implemented within the Weather Research and Forecasting model to enable high-fidelity large-eddy simulations of wind turbine interactions with boundary layer flows under realistic atmospheric forcing conditions. Numerical simulations using the GAL parameterization are evaluated against both an already implemented generalized actuator disk (GAD) wind turbine parameterization and two field campaigns that measured the inflow and near-wake regions of a single turbine. The representation of wake wind speed, variance, and vorticity distributions is examined by comparing fine-resolution GAL and GAD simulations and GAD simulations at both fine and coarse-resolutions. The higher-resolution simulations show slightly larger and more persistent velocity deficits in the wake and substantially increased variance and vorticity when compared to the coarse-resolution GAD. The GAL generates distinct tip and root vortices that maintain coherence as helical tubes for approximately one rotor diameter downstream. Coarse-resolution simulations using the GAD produce similar aggregated wake characteristics to both fine-scale GAD and GAL simulations at a fraction of the computational cost. The GAL parameterization provides the capability to resolve near wake physics, including vorticity shedding and wake expansion.

Using GPS RO L1 data for calibration of the atmospheric path delay model for data reduction of the satellite altimetery observations.

Science.gov (United States)

Petrov, L.

2017-12-01

Processing satellite altimetry data requires the computation of path delayin the neutral atmosphere that is used for correcting ranges. The path delayis computed using numerical weather models and the accuracy of its computationdepends on the accuracy of numerical weather models. Accuracy of numerical modelsof numerical weather models over Antarctica and Greenland where there is a very sparse network of ground stations, is not well known. I used the dataset of GPS RO L1 data, computed predicted path delay for ROobservations using the numerical whether model GEOS-FPIT, formed the differences with observed path delay and used these differences for computationof the corrections to the a priori refractivity profile. These profiles wereused for computing corrections to the a priori zenith path delay. The systematic patter of these corrections are used for de-biasing of the the satellite altimetry results and for characterization of the systematic errorscaused by mismodeling atmosphere.

Weather, not climate, defines distributions of vagile bird species.

Directory of Open Access Journals (Sweden)

April E Reside

Full Text Available BACKGROUND: Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species. METHODOLOGY: We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed "weather" models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality from the three month, six month and one year period preceding each bird record over a 58 year period (1950-2008. These weather models were compared against models built using long-term (30 year averages of the same climatic variables. CONCLUSIONS: Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

Space Weather Laboratory

Data.gov (United States)

Federal Laboratory Consortium — The Space Weather Computational Laboratory is a Unix and PC based modeling and simulation facility devoted to research analysis of naturally occurring electrically...

Spatially explicit modelling of extreme weather and climate events ...

African Journals Online (AJOL)

The reality of climate change continues to influence the intensity and frequency of extreme weather events such as heat waves, droughts, floods, and landslides. The impacts of the cumulative interplay of these extreme weather and climate events variation continue to perturb governments causing a scramble into formation ...

Verification of Space Weather Forecasts using Terrestrial Weather Approaches

Science.gov (United States)

Henley, E.; Murray, S.; Pope, E.; Stephenson, D.; Sharpe, M.; Bingham, S.; Jackson, D.

2015-12-01

The Met Office Space Weather Operations Centre (MOSWOC) provides a range of 24/7 operational space weather forecasts, alerts, and warnings, which provide valuable information on space weather that can degrade electricity grids, radio communications, and satellite electronics. Forecasts issued include arrival times of coronal mass ejections (CMEs), and probabilistic forecasts for flares, geomagnetic storm indices, and energetic particle fluxes and fluences. These forecasts are produced twice daily using a combination of output from models such as Enlil, near-real-time observations, and forecaster experience. Verification of forecasts is crucial for users, researchers, and forecasters to understand the strengths and limitations of forecasters, and to assess forecaster added value. To this end, the Met Office (in collaboration with Exeter University) has been adapting verification techniques from terrestrial weather, and has been working closely with the International Space Environment Service (ISES) to standardise verification procedures. We will present the results of part of this work, analysing forecast and observed CME arrival times, assessing skill using 2x2 contingency tables. These MOSWOC forecasts can be objectively compared to those produced by the NASA Community Coordinated Modelling Center - a useful benchmark. This approach cannot be taken for the other forecasts, as they are probabilistic and categorical (e.g., geomagnetic storm forecasts give probabilities of exceeding levels from minor to extreme). We will present appropriate verification techniques being developed to address these forecasts, such as rank probability skill score, and comparing forecasts against climatology and persistence benchmarks. As part of this, we will outline the use of discrete time Markov chains to assess and improve the performance of our geomagnetic storm forecasts. We will also discuss work to adapt a terrestrial verification visualisation system to space weather, to help

Combined experimental and numerical evaluation of a prototype nano-PCM enhanced wallboard

International Nuclear Information System (INIS)

Biswas, Kaushik; Lu, Jue; Soroushian, Parviz; Shrestha, Som

2014-01-01

Highlights: • Field-testing of a nano-PCM wallboard under varying weather conditions. • Numerical model validation and annual simulations of PCM wallboard performance. • Reduced cooling electricity consumption results from PCM wallboard. • PCM wallboard reduces peak cooling loads with implications on power plant capacity. • PCM performance was sensitive to building temperature set point for cooling. - Abstract: In the United States, forty-eight (48) percent of the residential end-use energy consumption is spent on space heating and air conditioning. Reducing envelope-generated heating and cooling loads through application of phase change materials (PCMs) in building envelopes can enhance the energy efficiency of buildings and reduce energy consumption. Experimental testing and numerical modeling of PCM-enhanced envelope components are two important aspects of the evaluation of their energy benefits. An innovative phase change material (nano-PCM) was developed with PCM supported by expanded graphite (interconnected) nanosheets, which are highly conductive and allow enhanced thermal storage and energy distribution. The nano-PCM is shape-stable for convenient incorporation into lightweight building components. A wall with cellulose cavity insulation and a prototype PCM-enhanced interior wallboard was built and tested in a natural exposure test (NET) facility in a hot-humid climate location. The test wall contained the PCM wallboard and a regular gypsum wallboard, for a side-by-side annual comparison study. Further, numerical modeling of the wall containing the nano-PCM wallboard was performed to determine its actual impact on wall-generated heating and cooling loads. The model was first validated using experimental data, and then used for annual simulations using typical meteorological year (TMY3) weather data. This article presents the measured performance and numerical analysis evaluating the energy-saving potential of the nano-PCM-enhanced wallboard

Thermal stress analysis of reactor containment building considering severe weather condition

International Nuclear Information System (INIS)

Lee, Yun; Kim, Yun-Yong; Hyun, Jung-Hwan; Kim, Do-Gyeum

2014-01-01

Highlights: • We examine that through-wall crack risk in cold weather is high. • It is predicted that cracking in concrete wall will not happen in hot region. • Cracking due to hydration heat can be controlled by appropriate curing condition. • Temperature differences between inner and outer face is relatively small in hot weather. - Abstract: Prediction of concrete cracking due to hydration heat in mass concrete such as reactor containment building (RCB) in nuclear power plant is a crucial issue in construction site. In this study, the numerical analysis for heat transfer and stress development is performed for the containment wall in RCB by considering the severe weather conditions. Finally, concrete cracking risk in hot and cold weather is discussed based on analysis results. In analyses considering severe weather conditions, it is found that the through-wall cracking risk in cold weather is high due to the abrupt temperature difference between inside concrete and the ambient air in cold region. In hot weather, temperature differences between inner and outer face is relatively small, and accordingly the relevant cracking risk is relatively low in contrast with cold weather

The Effect of Seasonal Weather Variation on the Dynamics of the Plague Disease

Directory of Open Access Journals (Sweden)

Rigobert C. Ngeleja

2017-01-01

Full Text Available Plague is a historic disease which is also known to be the most devastating disease that ever occurred in human history, caused by gram-negative bacteria known as Yersinia pestis. The disease is mostly affected by variations of weather conditions as it disturbs the normal behavior of main plague disease transmission agents, namely, human beings, rodents, fleas, and pathogens, in the environment. This in turn changes the way they interact with each other and ultimately leads to a periodic transmission of plague disease. In this paper, we formulate a periodic epidemic model system by incorporating seasonal transmission rate in order to study the effect of seasonal weather variation on the dynamics of plague disease. We compute the basic reproduction number of a proposed model. We then use numerical simulation to illustrate the effect of different weather dependent parameters on the basic reproduction number. We are able to deduce that infection rate, progression rates from primary forms of plague disease to more severe forms of plague disease, and the infectious flea abundance affect, to a large extent, the number of bubonic, septicemic, and pneumonic plague infective agents. We recommend that it is more reasonable to consider these factors that have been shown to have a significant effect on RT for effective control strategies.

Simulation of Daily Weather Data Using Theoretical Probability Distributions.

Science.gov (United States)

Bruhn, J. A.; Fry, W. E.; Fick, G. W.

1980-09-01

A computer simulation model was constructed to supply daily weather data to a plant disease management model for potato late blight. In the weather model Monte Carlo techniques were employed to generate daily values of precipitation, maximum temperature, minimum temperature, minimum relative humidity and total solar radiation. Each weather variable is described by a known theoretical probability distribution but the values of the parameters describing each distribution are dependent on the occurrence of rainfall. Precipitation occurrence is described by a first-order Markov chain. The amount of rain, given that rain has occurred, is described by a gamma probability distribution. Maximum and minimum temperature are simulated with a trivariate normal probability distribution involving maximum temperature on the previous day, maximum temperature on the current day and minimum temperature on the current day. Parameter values for this distribution are dependent on the occurrence of rain on the previous day. Both minimum relative humidity and total solar radiation are assumed to be normally distributed. The values of the parameters describing the distribution of minimum relative humidity is dependent on rainfall occurrence on the previous day and current day. Parameter values for total solar radiation are dependent on the occurrence of rain on the current day. The assumptions made during model construction were found to be appropriate for actual weather data from Geneva, New York. The performance of the weather model was evaluated by comparing the cumulative frequency distributions of simulated weather data with the distributions of actual weather data from Geneva, New York and Fort Collins, Colorado. For each location, simulated weather data were similar to actual weather data in terms of mean response, variability and autocorrelation. The possible applications of this model when used with models of other components of the agro-ecosystem are discussed.

Integrating topography, hydrology and rock structure in weathering rate models of spring watersheds

NARCIS (Netherlands)

Pacheco, F.A.L.; Weijden, C.H. van der

2012-01-01

Weathering rate models designed for watersheds combine chemical data of discharging waters with morphologic and hydrologic parameters of the catchments. At the spring watershed scale, evaluation of morphologic parameters is subjective due to difficulties in conceiving the catchment geometry.

Colluvial deposits as a possible weathering reservoir in uplifting mountains

Science.gov (United States)

Carretier, Sébastien; Goddéris, Yves; Martinez, Javier; Reich, Martin; Martinod, Pierre

2018-03-01

The role of mountain uplift in the evolution of the global climate over geological times is controversial. At the heart of this debate is the capacity of rapid denudation to drive silicate weathering, which consumes CO2. Here we present the results of a 3-D model that couples erosion and weathering during mountain uplift, in which, for the first time, the weathered material is traced during its stochastic transport from the hillslopes to the mountain outlet. To explore the response of weathering fluxes to progressively cooler and drier climatic conditions, we run model simulations accounting for a decrease in temperature with or without modifications in the rainfall pattern based on a simple orographic model. At this stage, the model does not simulate the deep water circulation, the precipitation of secondary minerals, variations in the pH, below-ground pCO2, and the chemical affinity of the water in contact with minerals. Consequently, the predicted silicate weathering fluxes probably represent a maximum, although the predicted silicate weathering rates are within the range of silicate and total weathering rates estimated from field data. In all cases, the erosion rate increases during mountain uplift, which thins the regolith and produces a hump in the weathering rate evolution. This model thus predicts that the weathering outflux reaches a peak and then falls, consistent with predictions of previous 1-D models. By tracking the pathways of particles, the model can also consider how lateral river erosion drives mass wasting and the temporary storage of colluvial deposits on the valley sides. This reservoir is comprised of fresh material that has a residence time ranging from several years up to several thousand years. During this period, the weathering of colluvium appears to sustain the mountain weathering flux. The relative weathering contribution of colluvium depends on the area covered by regolith on the hillslopes. For mountains sparsely covered by regolith

Synthetic weather generator SYNTOR: Implementing improvements in precipitation generation

Science.gov (United States)

Infrequent high precipitation events produce a disproportionally large amount of the annual surface runoff, soil erosion, nutrient movement, and watershed sediment yield. Numerical simulation of these watershed processes often lack sufficiently long weather data records to adequately capture the sto...

Flight Deck Weather Avoidance Decision Support: Implementation and Evaluation

Science.gov (United States)

Wu, Shu-Chieh; Luna, Rocio; Johnson, Walter W.

2013-01-01

Weather related disruptions account for seventy percent of the delays in the National Airspace System (NAS). A key component in the weather plan of the Next Generation of Air Transportation System (NextGen) is to assimilate observed weather information and probabilistic forecasts into the decision process of flight crews and air traffic controllers. In this research we explore supporting flight crew weather decision making through the development of a flight deck predicted weather display system that utilizes weather predictions generated by ground-based radar. This system integrates and presents this weather information, together with in-flight trajectory modification tools, within a cockpit display of traffic information (CDTI) prototype. that the CDTI features 2D and perspective 3D visualization models of weather. The weather forecast products that we implemented were the Corridor Integrated Weather System (CIWS) and the Convective Weather Avoidance Model (CWAM), both developed by MIT Lincoln Lab. We evaluated the use of CIWS and CWAM for flight deck weather avoidance in two part-task experiments. Experiment 1 compared pilots' en route weather avoidance performance in four weather information conditions that differed in the type and amount of predicted forecast (CIWS current weather only, CIWS current and historical weather, CIWS current and forecast weather, CIWS current and forecast weather and CWAM predictions). Experiment 2 compared the use of perspective 3D and 21/2D presentations of weather for flight deck weather avoidance. Results showed that pilots could take advantage of longer range predicted weather forecasts in performing en route weather avoidance but more research will be needed to determine what combinations of information are optimal and how best to present them.

Advanced Numerical Model for Irradiated Concrete

Energy Technology Data Exchange (ETDEWEB)

Giorla, Alain B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2015-03-01

In this report, we establish a numerical model for concrete exposed to irradiation to address these three critical points. The model accounts for creep in the cement paste and its coupling with damage, temperature and relative humidity. The shift in failure mode with the loading rate is also properly represented. The numerical model for creep has been validated and calibrated against different experiments in the literature [Wittmann, 1970, Le Roy, 1995]. Results from a simplified model are shown to showcase the ability of numerical homogenization to simulate irradiation effects in concrete. In future works, the complete model will be applied to the analysis of the irradiation experiments of Elleuch et al. [1972] and Kelly et al. [1969]. This requires a careful examination of the experimental environmental conditions as in both cases certain critical information are missing, including the relative humidity history. A sensitivity analysis will be conducted to provide lower and upper bounds of the concrete expansion under irradiation, and check if the scatter in the simulated results matches the one found in experiments. The numerical and experimental results will be compared in terms of expansion and loss of mechanical stiffness and strength. Both effects should be captured accordingly by the model to validate it. Once the model has been validated on these two experiments, it can be applied to simulate concrete from nuclear power plants. To do so, the materials used in these concrete must be as well characterized as possible. The main parameters required are the mechanical properties of each constituent in the concrete (aggregates, cement paste), namely the elastic modulus, the creep properties, the tensile and compressive strength, the thermal expansion coefficient, and the drying shrinkage. These can be either measured experimentally, estimated from the initial composition in the case of cement paste, or back-calculated from mechanical tests on concrete. If some

Generating daily weather data for ecosystem modelling in the Congo River Basin

Science.gov (United States)

Petritsch, Richard; Pietsch, Stephan A.

2010-05-01

Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range

Evaluation of wave runup predictions from numerical and parametric models

Science.gov (United States)

Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

2014-01-01

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Improving weather forecasts for wind energy applications

Science.gov (United States)

Kay, Merlinde; MacGill, Iain

2010-08-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms-1 and around 25 ms-1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

Improving weather forecasts for wind energy applications

International Nuclear Information System (INIS)

Kay, Merlinde; MacGill, Iain

2010-01-01

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 - 15 ms -1 and around 25 ms -1 . A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology - a combination of both bias correction and timing correction.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Science.gov (United States)

Minnis, P.; Smith, W., Jr.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Hong, G.; Trepte, Q.; Chee, T.; Scarino, B. R.; Spangenberg, D.; Sun-Mack, S.; Fleeger, C.; Ayers, J. K.; Chang, F. L.; Heck, P. W.

2014-12-01

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Science.gov (United States)

Minnis, Patrick; Smith, William L., Jr.; Bedka, Kristopher M.; Nguyen, Louis; Palikonda, Rabindra; Hong, Gang; Trepte, Qing Z.; Chee, Thad; Scarino, Benjamin; Spangenberg, Douglas A.;

Strategy for a numerical Rock Mechanics Site Descriptive Model. Further development of the theoretical/numerical approach

International Nuclear Information System (INIS)

Olofsson, Isabelle; Fredriksson, Anders

2005-05-01

The Swedish Nuclear and Fuel Management Company (SKB) is conducting Preliminary Site Investigations at two different locations in Sweden in order to study the possibility of a Deep Repository for spent fuel. In the frame of these Site Investigations, Site Descriptive Models are achieved. These products are the result of an interaction of several disciplines such as geology, hydrogeology, and meteorology. The Rock Mechanics Site Descriptive Model constitutes one of these models. Before the start of the Site Investigations a numerical method using Discrete Fracture Network (DFN) models and the 2D numerical software UDEC was developed. Numerical simulations were the tool chosen for applying the theoretical approach for characterising the mechanical rock mass properties. Some shortcomings were identified when developing the methodology. Their impacts on the modelling (in term of time and quality assurance of results) were estimated to be so important that the improvement of the methodology with another numerical tool was investigated. The theoretical approach is still based on DFN models but the numerical software used is 3DEC. The main assets of the programme compared to UDEC are an optimised algorithm for the generation of fractures in the model and for the assignment of mechanical fracture properties. Due to some numerical constraints the test conditions were set-up in order to simulate 2D plane strain tests. Numerical simulations were conducted on the same data set as used previously for the UDEC modelling in order to estimate and validate the results from the new methodology. A real 3D simulation was also conducted in order to assess the effect of the '2D' conditions in the 3DEC model. Based on the quality of the results it was decided to update the theoretical model and introduce the new methodology based on DFN models and 3DEC simulations for the establishment of the Rock Mechanics Site Descriptive Model. By separating the spatial variability into two parts, one

Numerical modelling approach for mine backfill

Indian Academy of Sciences (India)

Muhammad Zaka Emad

2017-07-24

Jul 24, 2017 ... conditions. This paper discusses a numerical modelling strategy for modelling mine backfill material. The .... placed in an ore pass that leads the ore to the ore bin and crusher, from ... 1 year, depending on the mine plan.

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Science.gov (United States)

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Integration of numerical modeling and observations for the Gulf of Naples monitoring network

Science.gov (United States)

Iermano, I.; Uttieri, M.; Zambianchi, E.; Buonocore, B.; Cianelli, D.; Falco, P.; Zambardino, G.

2012-04-01

Lethal effects of mineral oils on fragile marine and coastal ecosystems are now well known. Risks and damages caused by a maritime accident can be reduced with the help of better forecasts and efficient monitoring systems. The MED project TOSCA (Tracking Oil Spills and Coastal Awareness Network), which gathers 13 partners from 4 Mediterranean countries, has been designed to help create a better response system to maritime accidents. Through the construction of an observational network, based on state of the art technology (HF radars and drifters), TOSCA provides real-time observations and forecasts of the Mediterranean coastal marine environmental conditions. The system is installed and assessed in five test sites on the coastal areas of oil spill outlets (Eastern Mediterranean) and on high traffic areas (Western Mediterranean). The Gulf of Naples, a small semi-closed basin opening to the Tyrrhenian Sea is one of the five test-sites. It is of particular interest from both the environmental point of view, due to peculiar ecosystem properties in the area, and because it sustains important touristic and commercial activities. Currently the Gulf of Naples monitoring network is represented by five automatic weather stations distributed along the coasts of the Gulf, one weather radar, two tide gauges, one waverider buoy, and moored physical, chemical and bio-optical instrumentation. In addition, a CODAR-SeaSonde HF coastal radar system composed of three antennas is located in Portici, Massa Lubrense and Castellammare. The system provides hourly data of surface currents over the entire Gulf with a 1km spatial resolution. A numerical modeling implementation based on Regional Ocean Modeling System (ROMS) is actually integrated in the Gulf of Naples monitoring network. ROMS is a 3-D, free-surface, hydrostatic, primitive equation, finite difference ocean model. In our configuration, the model has high horizontal resolution (250m), and 30 sigma levels in the vertical. Thanks

GLUE Based Uncertainty Estimation of Urban Drainage Modeling Using Weather Radar Precipitation Estimates

DEFF Research Database (Denmark)

Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

2011-01-01

Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...

Near Real Time MISR Wind Observations for Numerical Weather Prediction

Science.gov (United States)

Mueller, K. J.; Protack, S.; Rheingans, B. E.; Hansen, E. G.; Jovanovic, V. M.; Baker, N.; Liu, J.; Val, S.

2014-12-01

The Multi-angle Imaging SpectroRadiometer (MISR) project, in association with the NASA Langley Atmospheric Science Data Center (ASDC), has this year adapted its original production software to generate near-real time (NRT) cloud-motion winds as well as radiance imagery from all nine MISR cameras. These products are made publicly available at the ASDC with a latency of less than 3 hours. Launched aboard the sun-synchronous Terra platform in 1999, the MISR instrument continues to acquire near-global, 275 m resolution, multi-angle imagery. During a single 7 minute overpass of any given area, MISR retrieves the stereoscopic height and horizontal motion of clouds from the multi-angle data, yielding meso-scale near-instantaneous wind vectors. The ongoing 15-year record of MISR height-resolved winds at 17.6 km resolution has been validated against independent data sources. Low-level winds dominate the sampling, and agree to within ±3 ms-1 of collocated GOES and other observations. Low-level wind observations are of particular interest to weather forecasting, where there is a dearth of observations suitable for assimilation, in part due to reliability concerns associated with winds whose heights are assigned by the infrared brightness temperature technique. MISR cloud heights, on the other hand, are generated from stereophotogrammetric pattern matching of visible radiances. MISR winds also address data gaps in the latitude bands between geostationary satellite coverage and polar orbiting instruments that obtain winds from multiple overpasses (e.g. MODIS). Observational impact studies conducted by the Naval Research Laboratory (NRL) and by the German Weather Service (Deutscher Wetterdienst) have both demonstrated forecast improvements when assimilating MISR winds. An impact assessment using the GEOS-5 system is currently in progress. To benefit air quality forecasts, the MISR project is currently investigating the feasibility of generating near-real time aerosol products.

Progress in space weather predictions and applications

Science.gov (United States)

Lundstedt, H.

The methods of today's predictions of space weather and effects are so much more advanced and yesterday's statistical methods are now replaced by integrated knowledge-based neuro-computing models and MHD methods. Within the ESA Space Weather Programme Study a real-time forecast service has been developed for space weather and effects. This prototype is now being implemented for specific users. Today's applications are not only so many more but also so much more advanced and user-oriented. A scientist needs real-time predictions of a global index as input for an MHD model calculating the radiation dose for EVAs. A power company system operator needs a prediction of the local value of a geomagnetically induced current. A science tourist needs to know whether or not aurora will occur. Soon we might even be able to predict the tropospheric climate changes and weather caused by the space weather.

Integrating K-means Clustering with Kernel Density Estimation for the Development of a Conditional Weather Generation Downscaling Model

Science.gov (United States)

Chen, Y.; Ho, C.; Chang, L.

2011-12-01

In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the

Conceptual and Numerical Models for UZ Flow and Transport

International Nuclear Information System (INIS)

Liu, H.

2000-01-01

The purpose of this Analysis/Model Report (AMR) is to document the conceptual and numerical models used for modeling of unsaturated zone (UZ) fluid (water and air) flow and solute transport processes. This is in accordance with ''AMR Development Plan for U0030 Conceptual and Numerical Models for Unsaturated Zone (UZ) Flow and Transport Processes, Rev 00''. The conceptual and numerical modeling approaches described in this AMR are used for models of UZ flow and transport in fractured, unsaturated rock under ambient and thermal conditions, which are documented in separate AMRs. This AMR supports the UZ Flow and Transport Process Model Report (PMR), the Near Field Environment PMR, and the following models: Calibrated Properties Model; UZ Flow Models and Submodels; Mountain-Scale Coupled Processes Model; Thermal-Hydrologic-Chemical (THC) Seepage Model; Drift Scale Test (DST) THC Model; Seepage Model for Performance Assessment (PA); and UZ Radionuclide Transport Models

Economics of extreme weather events: Terminology and regional impact models

OpenAIRE

Jahn, Malte

2015-01-01

Impacts of extreme weather events are relevant for regional (in the sense of subnational) economies and in particular cities in many aspects. Cities are the cores of economic activity and the amount of people and assets endangered by extreme weather events is large, even under the current climate. A changing climate with changing extreme weather patterns and the process of urbanization will make the whole issue even more relevant in the future. In this paper, definitions and terminology in th...

Numerical models for differential problems

CERN Document Server

Quarteroni, Alfio

2017-01-01

In this text, we introduce the basic concepts for the numerical modelling of partial differential equations. We consider the classical elliptic, parabolic and hyperbolic linear equations, but also the diffusion, transport, and Navier-Stokes equations, as well as equations representing conservation laws, saddle-point problems and optimal control problems. Furthermore, we provide numerous physical examples which underline such equations. We then analyze numerical solution methods based on finite elements, finite differences, finite volumes, spectral methods and domain decomposition methods, and reduced basis methods. In particular, we discuss the algorithmic and computer implementation aspects and provide a number of easy-to-use programs. The text does not require any previous advanced mathematical knowledge of partial differential equations: the absolutely essential concepts are reported in a preliminary chapter. It is therefore suitable for students of bachelor and master courses in scientific disciplines, an...

Parametrization of the Richardson weather generator within the European Union

NARCIS (Netherlands)

Voet, van der P.; Kramer, K.; Diepen, van C.A.

1996-01-01

The Richardson model for mathematically generating daily weather data was parametrized. Thirty years' time-series of the 355 main meteorological stations in the European Union formed the database. Model parameters were derived from both observed weather station data and interpolated weather data on

SWIFF: Space weather integrated forecasting framework

Directory of Open Access Journals (Sweden)

Frederiksen Jacob Trier

2013-02-01

Full Text Available SWIFF is a project funded by the Seventh Framework Programme of the European Commission to study the mathematical-physics models that form the basis for space weather forecasting. The phenomena of space weather span a tremendous scale of densities and temperature with scales ranging 10 orders of magnitude in space and time. Additionally even in local regions there are concurrent processes developing at the electron, ion and global scales strongly interacting with each other. The fundamental challenge in modelling space weather is the need to address multiple physics and multiple scales. Here we present our approach to take existing expertise in fluid and kinetic models to produce an integrated mathematical approach and software infrastructure that allows fluid and kinetic processes to be modelled together. SWIFF aims also at using this new infrastructure to model specific coupled processes at the Solar Corona, in the interplanetary space and in the interaction at the Earth magnetosphere.

Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data

Science.gov (United States)

Moore, Angelyn W.; Webb, Frank H.; Fishbein, Evan F.; Fielding, Eric J.; Owen, Susan E.; Granger, Stephanie L.; Bjoerndahl, Fredrik; Loefgren, Johan; Fang, Peng; Means, James D.;

What is a Proper Resolution of Weather Radar Precipitation Estimates for Urban Drainage Modelling?

DEFF Research Database (Denmark)

Nielsen, Jesper Ellerbæk; Rasmussen, Michael R.; Thorndahl, Søren Liedtke

2012-01-01

The resolution of distributed rainfall input for drainage models is the topic of this paper. The study is based on data from high resolution X-band weather radar used together with an urban drainage model of a medium size Danish village. The flow, total run-off volume and CSO volume are evaluated...

High-resolution numerical modeling of meteorological and hydrological conditions during May 2014 floods in Serbia

Science.gov (United States)

Vujadinovic, Mirjam; Vukovic, Ana; Cvetkovic, Bojan; Pejanovic, Goran; Nickovic, Slobodan; Djurdjevic, Vladimir; Rajkovic, Borivoj; Djordjevic, Marija

2015-04-01

In May 2014 west Balkan region was affected by catastrophic floods in Serbia, Bosnia and Herzegovina and eastern parts of Croatia. Observed precipitation amount were extremely high, on many stations largest ever recorded. In the period from 12th to 18th of May, most of Serbia received between 50 to 100 mm of rainfall, while western parts of the country, which were influenced the most, had over 200 mm of rainfall, locally even more than 300 mm. This very intense precipitation came when the soil was already saturated after a very wet period during the second half of April and beginning of May, when most of Serbia received between 120 i 170 mm of rainfall. New abundant precipitation on already saturated soil increased surface and underground water flow, caused floods, soil erosion and landslides. High water levels, most of them record breaking, were measured on the Sava, Drina, Dunav, Kolubara, Ljig, Ub, Toplica, Tamnava, Jadar, Zapadna Morava, Velika Morava, Mlava and Pek river. Overall, two cities and 17 municipals were severely affected by the floods, 32000 people were evacuated from their homes, while 51 died. Material damage to the infrastructure, energy power system, crops, livestock funds and houses is estimated to more than 2 billion euro. Although the operational numerical weather forecast gave in generally good precipitation prediction, flood forecasting in this case was mainly done through the expert judgment rather than relying on dynamic hydrological modeling. We applied an integrated atmospheric-hydrologic modelling system to some of the most impacted catchments in order to timely simulate hydrological response, and examine its potentials as a flood warning system. The system is based on the Non-hydrostatic Multiscale Model NMMB, which is a numerical weather prediction model that can be used on a broad range of spatial and temporal scales. Its non-hydrostatic module allows high horizontal resolution and resolving cloud systems as well as large

Next generation of weather generators on web service framework

Science.gov (United States)

Chinnachodteeranun, R.; Hung, N. D.; Honda, K.; Ines, A. V. M.

2016-12-01

Weather generator is a statistical model that synthesizes possible realization of long-term historical weather in future. It generates several tens to hundreds of realizations stochastically based on statistical analysis. Realization is essential information as a crop modeling's input for simulating crop growth and yield. Moreover, they can be contributed to analyzing uncertainty of weather to crop development stage and to decision support system on e.g. water management and fertilizer management. Performing crop modeling requires multidisciplinary skills which limit the usage of weather generator only in a research group who developed it as well as a barrier for newcomers. To improve the procedures of performing weather generators as well as the methodology to acquire the realization in a standard way, we implemented a framework for providing weather generators as web services, which support service interoperability. Legacy weather generator programs were wrapped in the web service framework. The service interfaces were implemented based on an international standard that was Sensor Observation Service (SOS) defined by Open Geospatial Consortium (OGC). Clients can request realizations generated by the model through SOS Web service. Hierarchical data preparation processes required for weather generator are also implemented as web services and seamlessly wired. Analysts and applications can invoke services over a network easily. The services facilitate the development of agricultural applications and also reduce the workload of analysts on iterative data preparation and handle legacy weather generator program. This architectural design and implementation can be a prototype for constructing further services on top of interoperable sensor network system. This framework opens an opportunity for other sectors such as application developers and scientists in other fields to utilize weather generators.