48-Hour Forecast of Air Temperatures from the National Weather Service's National Digital Forecast Database (NDFD)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The National Digital Forecast Database (NDFD) contains a seamless mosaic of the National Weather Service's (NWS) digital forecasts of air temperature. In...

Statistical Correction of Air Temperature Forecasts for City and Road Weather Applications

Science.gov (United States)

Mahura, Alexander; Petersen, Claus; Sass, Bent; Gilet, Nicolas

2014-05-01

The method for statistical correction of air /road surface temperatures forecasts was developed based on analysis of long-term time-series of meteorological observations and forecasts (from HIgh Resolution Limited Area Model & Road Conditions Model; 3 km horizontal resolution). It has been tested for May-Aug 2012 & Oct 2012 - Mar 2013, respectively. The developed method is based mostly on forecasted meteorological parameters with a minimal inclusion of observations (covering only a pre-history period). Although the st iteration correction is based taking into account relevant temperature observations, but the further adjustment of air and road temperature forecasts is based purely on forecasted meteorological parameters. The method is model independent, e.g. it can be applied for temperature correction with other types of models having different horizontal resolutions. It is relatively fast due to application of the singular value decomposition method for matrix solution to find coefficients. Moreover, there is always a possibility for additional improvement due to extra tuning of the temperature forecasts for some locations (stations), and in particular, where for example, the MAEs are generally higher compared with others (see Gilet et al., 2014). For the city weather applications, new operationalized procedure for statistical correction of the air temperature forecasts has been elaborated and implemented for the HIRLAM-SKA model runs at 00, 06, 12, and 18 UTCs covering forecast lengths up to 48 hours. The procedure includes segments for extraction of observations and forecast data, assigning these to forecast lengths, statistical correction of temperature, one-&multi-days statistical evaluation of model performance, decision-making on using corrections by stations, interpolation, visualisation and storage/backup. Pre-operational air temperature correction runs were performed for the mainland Denmark since mid-April 2013 and shown good results. Tests also showed

Statistical physics when the minimum temperature is not absolute zero

Science.gov (United States)

Chung, Won Sang; Hassanabadi, Hassan

2018-04-01

In this paper, the nonzero minimum temperature is considered based on the third law of thermodynamics and existence of the minimal momentum. From the assumption of nonzero positive minimum temperature in nature, we deform the definitions of some thermodynamical quantities and investigate nonzero minimum temperature correction to the well-known thermodynamical problems.

Unsupervised/supervised learning concept for 24-hour load forecasting

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Babic, B [Electrical Power Industry of Serbia, Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science

1993-07-01

An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

Forecast of dengue incidence using temperature and rainfall.

Directory of Open Access Journals (Sweden)

Yien Ling Hii

Full Text Available An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore.We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98% in 2004-2010 and 98% (CI = 95%-100% in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm.We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.

Forecast of dengue incidence using temperature and rainfall.

Science.gov (United States)

Hii, Yien Ling; Zhu, Huaiping; Ng, Nawi; Ng, Lee Ching; Rocklöv, Joacim

2012-01-01

An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.

Forecasting extreme temperature health hazards in Europe

Science.gov (United States)

Di Napoli, Claudia; Pappenberger, Florian; Cloke, Hannah L.

2017-04-01

Extreme hot temperatures, such as those experienced during a heat wave, represent a dangerous meteorological hazard to human health. Heat disorders such as sunstroke are harmful to people of all ages and responsible for excess mortality in the affected areas. In 2003 more than 50,000 people died in western and southern Europe because of a severe and sustained episode of summer heat [1]. Furthermore, according to the Intergovernmental Panel on Climate Change heat waves are expected to get more frequent in the future thus posing an increasing threat to human lives. Developing appropriate tools for extreme hot temperatures prediction is therefore mandatory to increase public preparedness and mitigate heat-induced impacts. A recent study has shown that forecasts of the Universal Thermal Climate Index (UTCI) provide a valid overview of extreme temperature health hazards on a global scale [2]. UTCI is a parameter related to the temperature of the human body and its regulatory responses to the surrounding atmospheric environment. UTCI is calculated using an advanced thermo-physiological model that includes the human heat budget, physiology and clothing. To forecast UTCI the model uses meteorological inputs, such as 2m air temperature, 2m water vapour pressure and wind velocity at body height derived from 10m wind speed, from NWP models. Here we examine the potential of UTCI as an extreme hot temperature prediction tool for the European area. UTCI forecasts calculated using above-mentioned parameters from ECMWF models are presented. The skill in predicting UTCI for medium lead times is also analysed and discussed for implementation to international health-hazard warning systems. This research is supported by the ANYWHERE project (EnhANcing emergencY management and response to extreme WeatHER and climate Events) which is funded by the European Commission's HORIZON2020 programme. [1] Koppe C. et al., Heat waves: risks and responses. World Health Organization. Health and

Benchmark analysis of forecasted seasonal temperature over different climatic areas

Science.gov (United States)

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

2015-12-01

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

Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran

Directory of Open Access Journals (Sweden)

Hojatollah Daneshmand

2015-01-01

Full Text Available Nowadays, a lot of attention is paid to the application of intelligent systems in predicting natural phenomena. Artificial neural network systems, fuzzy logic, and adaptive neuro-fuzzy inference are used in this field. Daily minimum temperature of the meteorology station of the city of Mashhad, in northeast of Iran, in a 42-year statistical period, 1966-2008, has been received from the Iranian meteorological organization. Adaptive neuro-fuzzy inference system is used for modeling and forecasting the monthly minimum temperature. To find appropriate inputs, three approaches, i.e. spectral analysis, correlation coefficient, and the knowledge of experts,are used. By applying fast Fourier transform to the parameter of monthly minimum temperature and climate indices, and by using correlation coefficient and the knowledge of experts, 3 indices, Nino 1 + 2, NP, and PNA, are selected as model inputs. A hybrid training algorithm is used to train the system. According to simulation results, a correlation coefficient of 0.987 between the observed values and the predicted values, as well as amean absolute percentage deviations of 27.6% indicate an acceptable estimation of the model.

Combining 2-m temperature nowcasting and short range ensemble forecasting

Directory of Open Access Journals (Sweden)

A. Kann

2011-12-01

Full Text Available During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG since 2006 (Haiden et al., 2011, provides short range deterministic forecasts at high temporal (15 min–60 min and spatial (1 km resolution. An INCA Ensemble (INCA-EPS of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting which is running operationally at ZAMG (Wang et al., 2011. The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion for Gaussian distributed continuous

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

Science.gov (United States)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best

Improving Seasonal Crop Monitoring and Forecasting for Soybean and Corn in Iowa

Science.gov (United States)

Togliatti, K.; Archontoulis, S.; Dietzel, R.; VanLoocke, A.

2016-12-01

Accurately forecasting crop yield in advance of harvest could greatly benefit farmers, however few evaluations have been conducted to determine the effectiveness of forecasting methods. We tested one such method that used a combination of short-term weather forecasting from the Weather Research and Forecasting Model (WRF) to predict in season weather variables, such as, maximum and minimum temperature, precipitation and radiation at 4 different forecast lengths (2 weeks, 1 week, 3 days, and 0 days). This forecasted weather data along with the current and historic (previous 35 years) data from the Iowa Environmental Mesonet was combined to drive Agricultural Production Systems sIMulator (APSIM) simulations to forecast soybean and corn yields in 2015 and 2016. The goal of this study is to find the forecast length that reduces the variability of simulated yield predictions while also increasing the accuracy of those predictions. APSIM simulations of crop variables were evaluated against bi-weekly field measurements of phenology, biomass, and leaf area index from early and late planted soybean plots located at the Agricultural Engineering and Agronomy Research Farm in central Iowa as well as the Northwest Research Farm in northwestern Iowa. WRF model predictions were evaluated against observed weather data collected at the experimental fields. Maximum temperature was the most accurately predicted variable, followed by minimum temperature and radiation, and precipitation was least accurate according to RMSE values and the number of days that were forecasted within a 20% error of the observed weather. Our analysis indicated that for the majority of months in the growing season the 3 day forecast performed the best. The 1 week forecast came in second and the 2 week forecast was the least accurate for the majority of months. Preliminary results for yield indicate that the 2 week forecast is the least variable of the forecast lengths, however it also is the least accurate

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.

Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

Science.gov (United States)

Susskind, Joel; Rosenberg, Bob

2016-01-01

We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

Application Of Multi-grid Method On China Seas' Temperature Forecast

Science.gov (United States)

Li, W.; Xie, Y.; He, Z.; Liu, K.; Han, G.; Ma, J.; Li, D.

2006-12-01

Correlation scales have been used in traditional scheme of 3-dimensional variational (3D-Var) data assimilation to estimate the background error covariance for the numerical forecast and reanalysis of atmosphere and ocean for decades. However there are still some drawbacks of this scheme. First, the correlation scales are difficult to be determined accurately. Second, the positive definition of the first-guess error covariance matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. (2005) indicated that a traditional 3D-Var only corrects some certain wavelength errors and its accuracy depends on the accuracy of the first-guess covariance. And in general, short wavelength error can not be well corrected until long one is corrected and then inaccurate first-guess covariance may mistakenly take long wave error as short wave ones and result in erroneous analysis. For the purpose of quickly minimizing the errors of long and short waves successively, a new 3D-Var data assimilation scheme, called multi-grid data assimilation scheme, is proposed in this paper. By assimilating the shipboard SST and temperature profiles data into a numerical model of China Seas, we applied this scheme in two-month data assimilation and forecast experiment which ended in a favorable result. Comparing with the traditional scheme of 3D-Var, the new scheme has higher forecast accuracy and a lower forecast Root-Mean-Square (RMS) error. Furthermore, this scheme was applied to assimilate the SST of shipboard, AVHRR Pathfinder Version 5.0 SST and temperature profiles at the same time, and a ten-month forecast experiment on sea temperature of China Seas was carried out, in which a successful forecast result was obtained. Particularly, the new scheme is demonstrated a great numerical efficiency in these analyses.

Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe

Energy Technology Data Exchange (ETDEWEB)

Hurk, Bart van den; Camargo, Helio [Royal Netherlands Meteorological Institute, KNMI, PO Box 201, AE De Bilt (Netherlands); Doblas-Reyes, Francisco [Catalan Institute of Climate Sciences (IC3), Barcelona (Spain); European Centre for Medium-range Weather Forecasts (ECMWF), Reading (United Kingdom); Balsamo, Gianpaolo [European Centre for Medium-range Weather Forecasts (ECMWF), Reading (United Kingdom); Koster, Randal D. [NASA/Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, MD (United States); Seneviratne, Sonia I. [Institute for Atmospheric and Climate Science, Zurich (Switzerland)

2012-01-15

The Second Global Land Atmosphere Coupling Experiment (GLACE2) is designed to explore the improvement of forecast skill of summertime temperature and precipitation up to 8 weeks ahead by using realistic soil moisture initialization. For the European continent, we show in this study that for temperature the skill does indeed increase up to 6 weeks, but areas with (statistically significant) lower skill also exist at longer lead times. The skill improvement is smaller than shown earlier for the US, partly because of a lower potential predictability of the European climate at seasonal time scales. Selection of extreme soil moisture conditions or a subset of models with similar initial soil moisture conditions does improve the forecast skill, and sporadic positive effects are also demonstrated for precipitation. Using realistic initial soil moisture data increases the interannual variability of temperature compared to the control simulations in the South-Central European area at longer lead times. This leads to better temperature forecasts in a remote area in Western Europe. However, the covered range of forecast dates (1986-1995) is too short to isolate a clear physical mechanism for this remote correlation. (orig.)

Ensemble forecasts of road surface temperatures

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr, jr.; Pešice, Petr; Škuthan, M.

2017-01-01

Roč. 187, 1 May (2017), s. 33-41 ISSN 0169-8095 R&D Projects: GA ČR GA13-34856S; GA TA ČR(CZ) TA01031509 Institutional support: RVO:68378289 Keywords : ensemble prediction * road surface temperature * road weather forecast Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 3.778, year: 2016 http://www.sciencedirect.com/science/article/pii/S0169809516307311

A GM (1, 1 Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Directory of Open Access Journals (Sweden)

Ning-bo Zhao

2014-01-01

Full Text Available Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1 Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1 model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1 model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1 model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1 Markov chain model. The results show that the GM (1, 1 Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time.

Experimental investigations of the minimum ignition energy and the minimum ignition temperature of inert and combustible dust cloud mixtures

Energy Technology Data Exchange (ETDEWEB)

Addai, Emmanuel Kwasi, E-mail: emmanueladdai41@yahoo.com; Gabel, Dieter; Krause, Ulrich

2016-04-15

Highlights: • Ignition sensitivity of a highly flammable dust decreases upon addition of inert dust. • Minimum ignition temperature of a highly flammable dust increases when inert concentration increase. • Minimum ignition energy of a highly flammable dust increases when inert concentration increase. • The permissible range for the inert mixture to minimize the ignition risk lies between 60 to 80%. - Abstract: The risks associated with dust explosions still exist in industries that either process or handle combustible dust. This explosion risk could be prevented or mitigated by applying the principle of inherent safety (moderation). This is achieved by adding an inert material to a highly combustible material in order to decrease the ignition sensitivity of the combustible dust. The presented paper deals with the experimental investigation of the influence of adding an inert dust on the minimum ignition energy and the minimum ignition temperature of the combustible/inert dust mixtures. The experimental investigation was done in two laboratory scale equipment: the Hartmann apparatus and the Godbert-Greenwald furnace for the minimum ignition energy and the minimum ignition temperature test respectively. This was achieved by mixing various amounts of three inert materials (magnesium oxide, ammonium sulphate and sand) and six combustible dusts (brown coal, lycopodium, toner, niacin, corn starch and high density polyethylene). Generally, increasing the inert materials concentration increases the minimum ignition energy as well as the minimum ignition temperatures until a threshold is reached where no ignition was obtained. The permissible range for the inert mixture to minimize the ignition risk lies between 60 to 80%.

Experimental investigations of the minimum ignition energy and the minimum ignition temperature of inert and combustible dust cloud mixtures

International Nuclear Information System (INIS)

Addai, Emmanuel Kwasi; Gabel, Dieter; Krause, Ulrich

2016-01-01

Highlights: • Ignition sensitivity of a highly flammable dust decreases upon addition of inert dust. • Minimum ignition temperature of a highly flammable dust increases when inert concentration increase. • Minimum ignition energy of a highly flammable dust increases when inert concentration increase. • The permissible range for the inert mixture to minimize the ignition risk lies between 60 to 80%. - Abstract: The risks associated with dust explosions still exist in industries that either process or handle combustible dust. This explosion risk could be prevented or mitigated by applying the principle of inherent safety (moderation). This is achieved by adding an inert material to a highly combustible material in order to decrease the ignition sensitivity of the combustible dust. The presented paper deals with the experimental investigation of the influence of adding an inert dust on the minimum ignition energy and the minimum ignition temperature of the combustible/inert dust mixtures. The experimental investigation was done in two laboratory scale equipment: the Hartmann apparatus and the Godbert-Greenwald furnace for the minimum ignition energy and the minimum ignition temperature test respectively. This was achieved by mixing various amounts of three inert materials (magnesium oxide, ammonium sulphate and sand) and six combustible dusts (brown coal, lycopodium, toner, niacin, corn starch and high density polyethylene). Generally, increasing the inert materials concentration increases the minimum ignition energy as well as the minimum ignition temperatures until a threshold is reached where no ignition was obtained. The permissible range for the inert mixture to minimize the ignition risk lies between 60 to 80%.

Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

Directory of Open Access Journals (Sweden)

Herui Cui

2015-01-01

Full Text Available Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects.

Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

Science.gov (United States)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

Experimental investigations of the minimum ignition energy and the minimum ignition temperature of inert and combustible dust cloud mixtures.

Science.gov (United States)

Addai, Emmanuel Kwasi; Gabel, Dieter; Krause, Ulrich

2016-04-15

The risks associated with dust explosions still exist in industries that either process or handle combustible dust. This explosion risk could be prevented or mitigated by applying the principle of inherent safety (moderation). This is achieved by adding an inert material to a highly combustible material in order to decrease the ignition sensitivity of the combustible dust. The presented paper deals with the experimental investigation of the influence of adding an inert dust on the minimum ignition energy and the minimum ignition temperature of the combustible/inert dust mixtures. The experimental investigation was done in two laboratory scale equipment: the Hartmann apparatus and the Godbert-Greenwald furnace for the minimum ignition energy and the minimum ignition temperature test respectively. This was achieved by mixing various amounts of three inert materials (magnesium oxide, ammonium sulphate and sand) and six combustible dusts (brown coal, lycopodium, toner, niacin, corn starch and high density polyethylene). Generally, increasing the inert materials concentration increases the minimum ignition energy as well as the minimum ignition temperatures until a threshold is reached where no ignition was obtained. The permissible range for the inert mixture to minimize the ignition risk lies between 60 to 80%. Copyright © 2016 Elsevier B.V. All rights reserved.

A complex autoregressive model and application to monthly temperature forecasts

Directory of Open Access Journals (Sweden)

X. Gu

2005-11-01

Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.

Low Streamflow Forcasting using Minimum Relative Entropy

Science.gov (United States)

Cui, H.; Singh, V. P.

2013-12-01

Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

Science.gov (United States)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2018-02-01

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

FORWINE - Statistical Downscaling of Seasonal forecasts for wine

Science.gov (United States)

Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

2016-04-01

The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical downscaling of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the ensemble forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS

Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

Science.gov (United States)

Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

2016-10-01

In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

Monthly Mean Temperature Observation for Climate Prediction Center (CPC) Forecast Divisions

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This ASCII dataset contains monthly mean temperatures for 102 Forecast Divisions within the conterminous U.S. and is derived from the monthly NCDC climate division...

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

Energy Technology Data Exchange (ETDEWEB)

Soltanzadeh, I. [Tehran Univ. (Iran, Islamic Republic of). Inst. of Geophysics; Azadi, M.; Vakili, G.A. [Atmospheric Science and Meteorological Research Center (ASMERC), Teheran (Iran, Islamic Republic of)

2011-07-01

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast. (orig.)

Using Bayesian Model Averaging (BMA to calibrate probabilistic surface temperature forecasts over Iran

Directory of Open Access Journals (Sweden)

I. Soltanzadeh

2011-07-01

Full Text Available Using Bayesian Model Averaging (BMA, an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM, with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP Global Forecast System (GFS and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009 over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

Directory of Open Access Journals (Sweden)

R. Barbiero

2007-05-01

Full Text Available Model Output Statistics (MOS refers to a method of post-processing the direct outputs of numerical weather prediction (NWP models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511–L60 and LAMI-3. Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself.

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.

ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

Energy Technology Data Exchange (ETDEWEB)

Stockdale, Timothy N.; Anderson, David L.T.; Balmaseda, Magdalena A.; Ferranti, Laura; Mogensen, Kristian; Palmer, Timothy N.; Molteni, Franco; Vitart, Frederic [ECMWF, Reading (United Kingdom); Doblas-Reyes, Francisco [ECMWF, Reading (United Kingdom); Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

2011-08-15

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. (orig.)

Trends in Mean Annual Minimum and Maximum Near Surface Temperature in Nairobi City, Kenya

Directory of Open Access Journals (Sweden)

George Lukoye Makokha

2010-01-01

Full Text Available This paper examines the long-term urban modification of mean annual conditions of near surface temperature in Nairobi City. Data from four weather stations situated in Nairobi were collected from the Kenya Meteorological Department for the period from 1966 to 1999 inclusive. The data included mean annual maximum and minimum temperatures, and was first subjected to homogeneity test before analysis. Both linear regression and Mann-Kendall rank test were used to discern the mean annual trends. Results show that the change of temperature over the thirty-four years study period is higher for minimum temperature than maximum temperature. The warming trends began earlier and are more significant at the urban stations than is the case at the sub-urban stations, an indication of the spread of urbanisation from the built-up Central Business District (CBD to the suburbs. The established significant warming trends in minimum temperature, which are likely to reach higher proportions in future, pose serious challenges on climate and urban planning of the city. In particular the effect of increased minimum temperature on human physiological comfort, building and urban design, wind circulation and air pollution needs to be incorporated in future urban planning programmes of the city.

Analysis model for forecasting extreme temperature using refined rank set pair

Directory of Open Access Journals (Sweden)

Qiao Ling-Xia

2013-01-01

Full Text Available In order to improve the precision of forecasting extreme temperature time series, a refined rank set pair analysis model with a refined rank transformation function is proposed to improve precision of its prediction. The measured values of the annual highest temperature of two China’s cities, Taiyuan and Shijiazhuang, in July are taken to examine the performance of a refined rank set pair model.

Water temperature forecasting and estimation using fourier series and communication theory techniques

International Nuclear Information System (INIS)

Long, L.L.

1976-01-01

Fourier series and statistical communication theory techniques are utilized in the estimation of river water temperature increases caused by external thermal inputs. An example estimate assuming a constant thermal input is demonstrated. A regression fit of the Fourier series approximation of temperature is then used to forecast daily average water temperatures. Also, a 60-day prediction of daily average water temperature is made with the aid of the Fourier regression fit by using significant Fourier components

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

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.

Extended-range forecasting of Chinese summer surface air temperature and heat waves

Science.gov (United States)

Zhu, Zhiwei; Li, Tim

2018-03-01

Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5-30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial-temporal projection models (STPMs). Based on the training data during 1960-1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10-80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000-2013), the STPMs can reproduce EOF-filtered 30-80 day SAT at all lead times of 5-30 days over most part of China, and observed 30-80 and 10-80 day SAT at 25-30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5-30-day lead times against EOF-filtered and observed 30-80 day SAT, and at a 20-day lead time against observed 10-80 day SAT. The STPMs perform poorly in reproducing 10-30 day SAT. Forecasting for the first two modes of 10-30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10-30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10-80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.

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.

A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

OpenAIRE

Zhao, Ning-bo; Yang, Jia-long; Li, Shu-ying; Sun, Yue-wu

2014-01-01

Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this ap...

An impact analysis of forecasting methods and forecasting parameters on bullwhip effect

Science.gov (United States)

Silitonga, R. Y. H.; Jelly, N.

2018-04-01

Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.

Operational forecasting of daily temperatures in the Valencia Region. Part I: maximum temperatures in summer.

Science.gov (United States)

Gómez, I.; Estrela, M.

2009-09-01

Extreme temperature events have a great impact on human society. Knowledge of summer maximum temperatures is very useful for both the general public and organisations whose workers have to operate in the open, e.g. railways, roadways, tourism, etc. Moreover, summer maximum daily temperatures are considered a parameter of interest and concern since persistent heat-waves can affect areas as diverse as public health, energy consumption, etc. Thus, an accurate forecasting of these temperatures could help to predict heat-wave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The aim of this work is to evaluate the skill of the RAMS model in determining daily maximum temperatures during summer over the Valencia Region. For this, we have used the real-time configuration of this model currently running at the CEAM Foundation. To carry out the model verification process, we have analysed not only the global behaviour of the model for the whole Valencia Region, but also its behaviour for the individual stations distributed within this area. The study has been performed for the summer forecast period of 1 June - 30 September, 2007. The results obtained are encouraging and indicate a good agreement between the observed and simulated maximum temperatures. Moreover, the model captures quite well the temperatures in the extreme heat episodes. Acknowledgement. This work was supported by "GRACCIE" (CSD2007-00067, Programa Consolider-Ingenio 2010), by the Spanish Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and by the Regional Government of Valencia Conselleria de Sanitat, contract "Simulación de las olas de calor e invasiones de frío y su regionalización en la Comunidad Valenciana" ("Heat wave and cold invasion simulation and their regionalization at Valencia Region"). The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (Valencia, Spain).

THE CHROMOSPHERIC SOLAR MILLIMETER-WAVE CAVITY ORIGINATES IN THE TEMPERATURE MINIMUM REGION

Energy Technology Data Exchange (ETDEWEB)

De la Luz, Victor [Instituto Nacional de Astrofisica, Optica y Electronica, Tonantzintla, Puebla, Mexico, Apdo. Postal 51 y 216, 72000 (Mexico); Raulin, Jean-Pierre [CRAAM, Universidade Presbiteriana Mackenzie, Sao Paulo, SP 01302-907 (Brazil); Lara, Alejandro [Instituto de Geofisica, Universidad Nacional Autonoma de Mexico, Mexico 04510 (Mexico)

2013-01-10

We present a detailed theoretical analysis of the local radio emission at the lower part of the solar atmosphere. To accomplish this, we have used a numerical code to simulate the emission and transport of high-frequency electromagnetic waves from 2 GHz up to 10 THz. As initial conditions, we used VALC, SEL05, and C7 solar chromospheric models. In this way, the generated synthetic spectra allow us to study the local emission and absorption processes with high resolution in both altitude and frequency. Associated with the temperature minimum predicted by these models, we found that the local optical depth at millimeter wavelengths remains constant, producing an optically thin layer that is surrounded by two layers of high local emission. We call this structure the Chromospheric Solar Millimeter-wave Cavity (CSMC). The temperature profile, which features temperature minimum layers and a subsequent temperature rise, produces the CSMC phenomenon. The CSMC shows the complexity of the relation between the theoretical temperature profile and the observed brightness temperature and may help us to understand the dispersion of the observed brightness temperature in the millimeter wavelength range.

Improving Forecast Skill by Assimilation of Quality Controlled AIRS Version 5 Temperature Soundings

Science.gov (United States)

Susskind, Joel; Reale, Oreste

2009-01-01

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains two significant improvements over Version 4: 1) Improved physics allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval of temperature profile T(p) during both day and night. Tropospheric sounding 15 micron CO2 observations are now used primarily in the generation of cloud cleared radiances R(sub i). This approach allows for the generation of accurate values of R(sub i) and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel error estimates for R(sub i). These error estimates are used for Quality Control of the retrieved products. We have conducted forecast impact experiments assimilating AIRS temperature profiles with different levels of Quality Control using the NASA GEOS-5 data assimilation system. Assimilation of Quality Controlled T(p) resulted in significantly improved forecast skill compared to that obtained from analyses obtained when all data used operationally by NCEP, except for AIRS data, is assimilated. We also conducted an experiment assimilating AIRS radiances uncontaminated by clouds, as done operationally by ECMWF and NCEP. Forecast resulting from assimilated AIRS radiances were of poorer quality than those obtained assimilating AIRS temperatures.

Rocket photographs of fine structure and wave patterns in the solar temperature minimum

Science.gov (United States)

Bonnet, R. M.; Decaudin, M.; Foing, B.; Bruner, M.; Acton, L. W.; Brown, W. A.

1982-01-01

A new series of high resolution pictures of the sun has been obtained during the second flight of the Transition Region Camera which occurred on September 23, 1980. The qualitative analysis of the results indicates that a substantial portion of the solar surface at the temperature minimum radiates in non-magnetic regions and from features below 1 arcsec in size. Wave patterns are observed on the 160 nm temperature minimum pictures. They are absent on the Lyman alpha pictures. Their physical characteristics are compatible with those of gravitational and acoustic waves generated by exploding granules.

Forecasting Skill

Science.gov (United States)

1981-01-01

for the third and fourth day precipitation forecasts. A marked improvement was shown for the consensus 24 hour precipitation forecast, and small... Zuckerberg (1980) found a small long term skill increase in forecasts of heavy snow events for nine eastern cities. Other National Weather Service...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I

Seasonal maximum temperature prediction skill over Southern Africa: 1- vs 2-tiered forecasting systems

CSIR Research Space (South Africa)

Lazenby, MJ

2011-09-01

Full Text Available TEMPERATURE PREDICTION SKILL OVER SOUTHERN AFRICA: 1- VS. 2-TIERED FORECASTING SYSTEMS Melissa J. Lazenby University of Pretoria, Private Bag X20, Pretoria, 0028, South Africa Willem A. Landman Council for Scientific and Industrial....J., Tyson, P.D. and Tennant, W.J., 2001. Retro-active skill of multi- tiered forecasts of summer rainfall over southern Africa. International Journal of Climatology, 21, 1- 19. Mason, S.J. and Graham, N.E., 2002. Areas beneath the relative operating...

Future changes over the Himalayas: Maximum and minimum temperature

Science.gov (United States)

Dimri, A. P.; Kumar, D.; Choudhary, A.; Maharana, P.

2018-03-01

An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23-0.52 °C/decade) for both minimum and maximum air temperature (Tmin and Tmax) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of Tmin may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of Tmax over southern flank may effectively melt more snow cover. Such combined effect of rising trend of Tmin and Tmax may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with

Diagnosing Geospatial Uncertainty Visualization Challenges in Seasonal Temperature and Precipitation Forecasts

Science.gov (United States)

Speciale, A.; Kenney, M. A.; Gerst, M.; Baer, A. E.; DeWitt, D.; Gottschalk, J.; Handel, S.

2017-12-01

The uncertainty of future weather and climate conditions is important for many decisions made in communities and economic sectors. One tool that decision-makers use in gauging this uncertainty is forecasts, especially maps (or visualizations) of probabilistic forecast results. However, visualizing geospatial uncertainty is challenging because including probability introduces an extra variable to represent and probability is often poorly understood by users. Using focus group and survey methods, this study seeks to understand the barriers to using probabilistic temperature and precipitation visualizations for specific decisions in the agriculture, energy, emergency management, and water resource sectors. Preliminary results shown here focus on findings of emergency manager needs. Our experimental design uses National Oceanic and Atmospheric Administration (NOAA's) Climate Prediction Center (CPC) climate outlooks, which produce probabilistic temperature and precipitation forecast visualizations at the 6-10 day, 8-14 day, 3-4 week, and 1 and 3 month timeframes. Users were asked to complete questions related to how they use weather information, how uncertainty is represented, and design elements (e.g., color, contour lines) of the visualizations. Preliminary results from the emergency management sector indicate there is significant confusion on how "normal" weather is defined, boundaries between probability ranges, and meaning of the contour lines. After a complete understandability diagnosis is made using results from all sectors, we will collaborate with CPC to suggest modifications to the climate outlook visualizations. These modifications will then be retested in similar focus groups and web-based surveys to confirm they better meet the needs of users.

Physiological minimum temperatures for root growth in seven common European broad-leaved tree species.

Science.gov (United States)

Schenker, Gabriela; Lenz, Armando; Körner, Christian; Hoch, Günter

2014-03-01

Temperature is the most important factor driving the cold edge distribution limit of temperate trees. Here, we identified the minimum temperatures for root growth in seven broad-leaved tree species, compared them with the species' natural elevational limits and identified morphological changes in roots produced near their physiological cold limit. Seedlings were exposed to a vertical soil-temperature gradient from 20 to 2 °C along the rooting zone for 18 weeks. In all species, the bulk of roots was produced at temperatures above 5 °C. However, the absolute minimum temperatures for root growth differed among species between 2.3 and 4.2 °C, with those species that reach their natural distribution limits at higher elevations also tending to have lower thermal limits for root tissue formation. In all investigated species, the roots produced at temperatures close to the thermal limit were pale, thick, unbranched and of reduced mechanical strength. Across species, the specific root length (m g(-1) root) was reduced by, on average, 60% at temperatures below 7 °C. A significant correlation of minimum temperatures for root growth with the natural high elevation limits of the investigated species indicates species-specific thermal requirements for basic physiological processes. Although these limits are not necessarily directly causative for the upper distribution limit of a species, they seem to belong to a syndrome of adaptive processes for life at low temperatures. The anatomical changes at the cold limit likely hint at the mechanisms impeding meristematic activity at low temperatures.

Maximum And Minimum Temperature Trends In Mexico For The Last 31 Years

Science.gov (United States)

Romero-Centeno, R.; Zavala-Hidalgo, J.; Allende Arandia, M. E.; Carrasco-Mijarez, N.; Calderon-Bustamante, O.

2013-05-01

Based on high-resolution (1') daily maps of the maximum and minimum temperatures in Mexico, an analysis of the last 31-year trends is performed. The maps were generated using all the available information from more than 5,000 stations of the Mexican Weather Service (Servicio Meteorológico Nacional, SMN) for the period 1979-2009, along with data from the North American Regional Reanalysis (NARR). The data processing procedure includes a quality control step, in order to eliminate erroneous daily data, and make use of a high-resolution digital elevation model (from GEBCO), the relationship between air temperature and elevation by means of the average environmental lapse rate, and interpolation algorithms (linear and inverse-distance weighting). Based on the monthly gridded maps for the mentioned period, the maximum and minimum temperature trends calculated by least-squares linear regression and their statistical significance are obtained and discussed.

Load forecasting for supermarket refrigeration

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Aalborg Nielsen, Henrik

This report presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. The load for refrigeration is the sum of all cabinets in the supermarket, both low and medium temperature cabinets, and spans a period of one year. As input to the forecasting models the ambient temperature observed near the supermarket together with weather forecasts are used. Every hour...

Investigation on the minimum film boiling temperature on metallic and ceramic heaters

International Nuclear Information System (INIS)

Ladisch, R.

1980-06-01

The minimum film boiling temperature on ceramic and metallic heaters has been experimentally studied. The knowledge of this temperature boundary is important in safety considerations on all liquid cooled nuclear reactors. The experiments have been carried out by quenching a hot metal cylinder with and without ceramic coating of aluminium in water. Results show that the minimum film boiling temperature Tsub(min) increases with water subcooling and is dependend upon the thermophysical properties of the heating surface. The roughness of the heater does not affect Tsub(min). At low subcoolings the vapour film is more stable and seems to break down when the specific heatflux upon liquid solid contact is lower than a threshold value above which film boiling can be reestablished. At higher subcoolings instead the vapour film is thinner and more stable. In this case the surface temperature decreases beyond the value by which the specific heatflux upon liquid solid contact would be lower than the threshold value. As soon as the vapour film becomes unstable, it collapses. (orig.) [de

Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

Directory of Open Access Journals (Sweden)

Ruche Guy

2011-06-01

Full Text Available Abstract Background During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. Methods The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. Results The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85. Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11 but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72. Conclusion Temperature improves dengue outbreaks forecasts

Trends in mean maximum temperature, mean minimum temperature and mean relative humidity for Lautoka, Fiji during 2003 – 2013

Directory of Open Access Journals (Sweden)

Syed S. Ghani

2017-12-01

Full Text Available The current work observes the trends in Lautoka’s temperature and relative humidity during the period 2003 – 2013, which were analyzed using the recently updated data obtained from Fiji Meteorological Services (FMS. Four elements, mean maximum temperature, mean minimum temperature along with diurnal temperature range (DTR and mean relative humidity are investigated. From 2003–2013, the annual mean temperature has been enhanced between 0.02 and 0.080C. The heating is more in minimum temperature than in maximum temperature, resulting in a decrease of diurnal temperature range. The statistically significant increase was mostly seen during the summer months of December and January. Mean Relative Humidity has also increased from 3% to 8%. The bases of abnormal climate conditions are also studied. These bases were defined with temperature or humidity anomalies in their appropriate time sequences. These established the observed findings and exhibited that climate has been becoming gradually damper and heater throughout Lautoka during this period. While we are only at an initial phase in the probable inclinations of temperature changes, ecological reactions to recent climate change are already evidently noticeable. So it is proposed that it would be easier to identify climate alteration in a small island nation like Fiji.

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

Science.gov (United States)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

Effects of Forecasted Climate Change on Stream Temperatures in the Nooksack River Basin

Science.gov (United States)

Truitt, S. E.; Mitchell, R. J.; Yearsley, J. R.; Grah, O. J.

2017-12-01

The Nooksack River in northwest Washington State provides valuable habitat for endangered salmon species, as such it is critical to understand how stream temperatures will be affected by forecasted climate change. The Middle and North Forks basins of the Nooksack are high-relief and glaciated, whereas the South Fork is a lower relief rain and snow dominated basin. Due to a moderate Pacific maritime climate, snowpack in the basins is sensitive to temperature increases. Previous modeling studies in the upper Nooksack basins indicate a reduction in snowpack and spring runoff, and a recession of glaciers into the 21st century. How stream temperatures will respond to these changes is unknown. We use the Distributed Hydrology Soil Vegetation Model (DHSVM) coupled with a glacier dynamics model and the River Basin Model (RBM) to simulate hydrology and stream temperature from present to the year 2100. We calibrate the DHSVM and RBM to the three forks in the upper 1550 km2 of the Nooksack basin, which contain an estimated 3400 hectares of glacial ice. We employ observed stream-temperature data collected over the past decade and hydrologic data from the four USGS streamflow monitoring sites within the basin and observed gridded climate data developed by Linveh et al. (2013). Field work was conducted in the summer of 2016 to determine stream morphology, discharge, and stream temperatures at a number of stream segments for the RBM calibration. We simulate forecast climate change impacts, using gridded daily downscaled data from global climate models of the CMIP5 with RCP4.5 and RCP8.5 forcing scenarios developed using the multivariate adaptive constructed analogs method (MACA; Abatzoglou and Brown, 2011). Simulation results project a trending increase in stream temperature as a result of lower snowmelt and higher air temperatures into the 21st century, especially in the lower relief, unglaciated South Fork basin.

Improving the health forecasting alert system for cold weather and heat-waves in England: a case-study approach using temperature-mortality relationships

Science.gov (United States)

Masato, Giacomo; Cavany, Sean; Charlton-Perez, Andrew; Dacre, Helen; Bone, Angie; Carmicheal, Katie; Murray, Virginia; Danker, Rutger; Neal, Rob; Sarran, Christophe

2015-04-01

The health forecasting alert system for cold weather and heatwaves currently in use in the Cold Weather and Heatwave plans for England is based on 5 alert levels, with levels 2 and 3 dependent on a forecast or actual single temperature action trigger. Epidemiological evidence indicates that for both heat and cold, the impact on human health is gradual, with worsening impact for more extreme temperatures. The 60% risk of heat and cold forecasts used by the alerts is a rather crude probabilistic measure, which could be substantially improved thanks to the state-of-the-art forecast techniques. In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. The prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. The prototype shows some clear improvements over the current alert system. It allows for a much greater

Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

Science.gov (United States)

Del Rio Amador, Lenin; Lovejoy, Shaun

2016-04-01

Traditionally, most of the models for prediction of the atmosphere behavior in the macroweather and climate regimes follow a deterministic approach. However, modern ensemble forecasting systems using stochastic parameterizations are in fact deterministic/ stochastic hybrids that combine both elements to yield a statistical distribution of future atmospheric states. Nevertheless, the result is both highly complex (both numerically and theoretically) as well as being theoretically eclectic. In principle, it should be advantageous to exploit higher level turbulence type scaling laws. Concretely, in the case for the Global Circulation Models (GCM's), due to sensitive dependence on initial conditions, there is a deterministic predictability limit of the order of 10 days. When these models are coupled with ocean, cryosphere and other process models to make long range, climate forecasts, the high frequency "weather" is treated as a driving noise in the integration of the modelling equations. Following Hasselman, 1976, this has led to stochastic models that directly generate the noise, and model the low frequencies using systems of integer ordered linear ordinary differential equations, the most well-known are the Linear Inverse Models (LIM). For annual global scale forecasts, they are somewhat superior to the GCM's and have been presented as a benchmark for surface temperature forecasts with horizons up to decades. A key limitation for the LIM approach is that it assumes that the temperature has only short range (exponential) decorrelations. In contrast, an increasing body of evidence shows that - as with the models - the atmosphere respects a scale invariance symmetry leading to power laws with potentially enormous memories so that LIM greatly underestimates the memory of the system. In this talk we show that, due to the relatively low macroweather intermittency, the simplest scaling models - fractional Gaussian noise - can be used for making greatly improved forecasts

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

OpenAIRE

Eynard , Julien; Grieu , Stéphane; Polit , Monique

2011-01-01

15 pages; International audience; As part of the OptiEnR research project, the present paper deals with outdoor temperature and thermal power consumption forecasting. This project focuses on optimizing the functioning of a multi-energy district boiler (La Rochelle, west coast of France), adding to the plant a thermal storage unit and implementing a model-based predictive controller. The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based mu...

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

Science.gov (United States)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum

Time trends in minimum mortality temperatures in Castile-La Mancha (Central Spain): 1975-2003

Science.gov (United States)

Miron, Isidro J.; Criado-Alvarez, Juan José; Diaz, Julio; Linares, Cristina; Mayoral, Sheila; Montero, Juan Carlos

2008-03-01

The relationship between air temperature and human mortality is described as non-linear, with mortality tending to rise in response to increasingly hot or cold ambient temperatures from a given minimum mortality or optimal comfort temperature, which varies from some areas to others according to their climatic and socio-demographic characteristics. Changes in these characteristics within any specific region could modify this relationship. This study sought to examine the time trend in the maximum temperature of minimum organic-cause mortality in Castile-La Mancha, from 1975 to 2003. The analysis was performed by using daily series of maximum temperatures and organic-cause mortality rates grouped into three decades (1975-1984, 1985-1994, 1995-2003) to compare confidence intervals ( p ARIMA models (Box-Jenkins) and cross-correlation functions (CCF) at seven lags. We observed a significant decrease in comfort temperature (from 34.2°C to 27.8°C) between the first two decades in the Province of Toledo, along with a growing number of significant lags in the summer CFF (1, 3 and 5, respectively). The fall in comfort temperature is attributable to the increase in the effects of heat on mortality, due, in all likelihood, to the percentage increase in the elderly population.

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

Science.gov (United States)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

Medium Range Forecasts Representation (and Long Range Forecasts?)

Science.gov (United States)

Vincendon, J.-C.

2009-09-01

The progress of the numerical forecasts urges us to interest us in more and more distant ranges. We thus supply more and more forecasts with term of some days. Nevertheless, precautions of use are necessary to give the most reliable and the most relevant possible information. Available in a TV bulletin or on quite other support (Internet, mobile phone), the interpretation and the representation of a medium range forecast (5 - 15 days) must be different from those of a short range forecast. Indeed, the "foresee-ability” of a meteorological phenomenon decreases gradually in the course of the ranges, it decreases all the more quickly that the phenomenon is of small scale. So, at the end of some days, the probability character of a forecast becomes very widely dominating. That is why in Meteo-France the forecasts of D+4 to D+7 are accompanied with a confidence index since around ten years. It is a figure between 1 and 5: the more we approach 5, the more the confidence in the supplied forecast is good. In the practice, an indication is supplied for period D+4 / D+5, the other one for period D+6 / D+7, every day being able to benefit from a different forecast, that is be represented in a independent way. We thus supply a global tendency over 24 hours with less and less precise symbols as the range goes away. Concrete examples will be presented. From now on two years, we also publish forecasts to D+8 / J+9, accompanied with a sign of confidence (" good reliability " or " to confirm "). These two days are grouped together on a single map because for us, the described tendency to this term is relevant on a duration about 48 hours with a spatial scale slightly superior to the synoptic scale. So, we avoid producing more than two zones of types of weather over France and we content with giving an evolution for the temperatures (still, in increase or in decline). Newspapers began to publish this information, it should soon be the case of televisions. It is particularly

Multiplexed FBG Monitoring System for Forecasting Coalmine Water Inrush Disaster

Directory of Open Access Journals (Sweden)

B. Liu

2012-01-01

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

Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain

Directory of Open Access Journals (Sweden)

Miao Tian

2016-08-01

Full Text Available This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI. About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models’ development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1 models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1 models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.

Minimum indoor temperature threshold recommendations for English homes in winter - A systematic review.

Science.gov (United States)

Jevons, R; Carmichael, C; Crossley, A; Bone, A

2016-07-01

To identify and assess the available evidence on the impacts of cold indoor temperature thresholds on human health and make evidence-based recommendations for English homes. Systematic literature review. A systematic search of peer-reviewed published literature from the UK and countries with similar climates, and grading of the evidence using the National Institute of Health (NIH) framework was followed by a discussion with experts and formulation of recommendations. Twenty papers were included. Studies were included if they were conducted outside England but were from countries considered to have similar climates. Studies included two small randomised controlled trials, two cohort studies and one case control study; other studies were cross-sectional, largely laboratory-based studies. Health effects in the general population start to occur at around 18 °C. Effects in older people are more profound than in younger adults. Older people are less able to perceive low temperatures. Although evidence was limited, a strong argument for setting thresholds remains. The effects observed on the general population and the effects on those more vulnerable makes a case for a recommended minimum temperature for all. Health messages should be clear and simple, allowing informed choices to be made. A threshold of 18 °C was considered the evidence based and practical minimum temperature at which a home should be kept during winter in England. There is limited evidence available on minimum temperature thresholds for homes. However a recommendation of at least 18 °C for the whole population with nuancing of messages for those more vulnerable to the effects of cold can be made from the results of the retrieved studies. Heating homes to at least 18 °C (65 °F) in winter poses minimal risk to the health of a sedentary person, wearing suitable clothing. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Solid low-level waste forecasting guide

International Nuclear Information System (INIS)

Templeton, K.J.; Dirks, L.L.

1995-03-01

Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford's experience within the last six years. Hanford's forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford's annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford's forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data

Finite temperature grand canonical ensemble study of the minimum electrophilicity principle.

Science.gov (United States)

Miranda-Quintana, Ramón Alain; Chattaraj, Pratim K; Ayers, Paul W

2017-09-28

We analyze the minimum electrophilicity principle of conceptual density functional theory using the framework of the finite temperature grand canonical ensemble. We provide support for this principle, both for the cases of systems evolving from a non-equilibrium to an equilibrium state and for the change from one equilibrium state to another. In doing so, we clearly delineate the cases where this principle can, or cannot, be used.

Seasonal prediction skill of winter temperature over North India

Science.gov (United States)

Tiwari, P. R.; Kar, S. C.; Mohanty, U. C.; Dey, S.; Kumari, S.; Sinha, P.

2016-04-01

The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December-January-February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982-2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

Science.gov (United States)

Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

2016-11-01

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

Method of statistical estimation of temperature minimums in binary systems

International Nuclear Information System (INIS)

Mireev, V.A.; Safonov, V.V.

1985-01-01

On the basis of statistical processing of literature data the technique for evaluation of temperature minima on liquidus curves in binary systems with common ion chloride systems being taken as an example, is developed. The systems are formed by 48 chlorides of 45 chemical elements including alkali, alkaline earth, rare earth and transition metals as well as Cd, In, Th. It is shown that calculation error in determining minimum melting points depends on topology of the phase diagram. The comparison of calculated and experimental data for several previously nonstudied systems is given

Production data from five major geothermal fields in Nevada analysed using a physiostatistical algorithm developed for oil and gas: temperature decline forecasts and type curves

Science.gov (United States)

Kuzma, H. A.; Golubkova, A.; Eklund, C.

2015-12-01

Nevada has the second largest output of geothermal energy in the United States (after California) with 14 major power plants producing over 425 megawatts of electricity meeting 7% of the state's total energy needs. A number of wells, particularly older ones, have shown significant temperature and pressure declines over their lifetimes, adversely affecting economic returns. Production declines are almost universal in the oil and gas (O&G) industry. BetaZi (BZ) is a proprietary algorithm which uses a physiostatistical model to forecast production from the past history of O&G wells and to generate "type curves" which are used to estimate the production of undrilled wells. Although BZ was designed and calibrated for O&G, it is a general purpose diffusion equation solver, capable of modeling complex fluid dynamics in multi-phase systems. In this pilot study, it is applied directly to the temperature data from five Nevada geothermal fields. With the data appropriately normalized, BZ is shown to accurately predict temperature declines. The figure shows several examples of BZ forecasts using historic data from Steamboat Hills field near Reno. BZ forecasts were made using temperature on a normalized scale (blue) with two years of data held out for blind testing (yellow). The forecast is returned in terms of percentiles of probability (red) with the median forecast marked (solid green). Actual production is expected to fall within the majority of the red bounds 80% of the time. Blind tests such as these are used to verify that the probabilistic forecast can be trusted. BZ is also used to compute and accurate type temperature profile for wells that have yet to be drilled. These forecasts can be combined with estimated costs to evaluate the economics and risks of a project or potential capital investment. It is remarkable that an algorithm developed for oil and gas can accurately predict temperature in geothermal wells without significant recasting.

Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events

Science.gov (United States)

Seo, Eunkyo; Lee, Myong-In; Jeong, Jee-Hoon; Koster, Randal D.; Schubert, Siegfried D.; Kim, Hye-Mi; Kim, Daehyun; Kang, Hyun-Suk; Kim, Hyun-Kyung; MacLachlan, Craig; Scaife, Adam A.

2018-05-01

This study uses a global land-atmosphere coupled model, the land-atmosphere component of the Global Seasonal Forecast System version 5, to quantify the degree to which soil moisture initialization could potentially enhance boreal summer surface air temperature forecast skill. Two sets of hindcast experiments are performed by prescribing the observed sea surface temperature as the boundary condition for a 15-year period (1996-2010). In one set of the hindcast experiments (noINIT), the initial soil moisture conditions are randomly taken from a long-term simulation. In the other set (INIT), the initial soil moisture conditions are taken from an observation-driven offline Land Surface Model (LSM) simulation. The soil moisture conditions from the offline LSM simulation are calibrated using the forecast model statistics to minimize the inconsistency between the LSM and the land-atmosphere coupled model in their mean and variability. Results show a higher boreal summer surface air temperature prediction skill in INIT than in noINIT, demonstrating the potential benefit from an accurate soil moisture initialization. The forecast skill enhancement appears especially in the areas in which the evaporative fraction—the ratio of surface latent heat flux to net surface incoming radiation—is sensitive to soil moisture amount. These areas lie in the transitional regime between humid and arid climates. Examination of the extreme 2003 European and 2010 Russian heat wave events reveal that the regionally anomalous soil moisture conditions during the events played an important role in maintaining the stationary circulation anomalies, especially those near the surface.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

Science.gov (United States)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Impact of MODIS High-Resolution Sea-Surface Temperatures on WRF Forecasts at NWS Miami, FL

Science.gov (United States)

Case, Jonathan L.; LaCasse, Katherine M.; Dembek, Scott R.; Santos, Pablo; Lapenta, William M.

2007-01-01

Over the past few years,studies at the Short-term Prediction Research and Transition (SPoRT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) composite sea-surface temperature (SST) products in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. The recent paper by LaCasse et al. (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPoRT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The scientific hypothesis being tested is: More accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running the WRF system in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software; The EMS is a standalone modeling system capable of downloading the necessary daily datasets, and initializing, running and displaying WRF forecasts in the NWS Advanced Weather Interactive Processing System (AWIPS) with little intervention required by forecasters. Twenty-seven hour forecasts are run daily with start times of 0300,0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and the far

Seasonal regional forecast of the minimum sea ice extent in the LapteV Sea

Science.gov (United States)

Tremblay, B.; Brunette, C.; Newton, R.

2017-12-01

Late winter anomaly of sea ice export from the peripheral seas of the Atctic Ocean was found to be a useful predictor for the minimum sea ice extent (SIE) in the Arctic Ocean (Williams et al., 2017). In the following, we present a proof of concept for a regional seasonal forecast of the min SIE for the Laptev Sea based on late winter coastal divergence quantified using a Lagrangian Ice Tracking System (LITS) forced with satellite derived sea-ice drifts from the Polar Pathfinder. Following Nikolaeva and Sesterikov (1970), we track an imaginary line just offshore of coastal polynyas in the Laptev Sea from December of the previous year to May 1 of the following year using LITS. Results show that coastal divergence in the Laptev Sea between February 1st and May 1st is best correlated (r = -0.61) with the following September minimum SIE in accord with previous results from Krumpen et al. (2013, for the Laptev Sea) and Williams et a. (2017, for the pan-Arctic). This gives a maximum seasonal predictability of Laptev Sea min SIE anomalies from observations of approximately 40%. Coastal ice divergence leads to formation of thinner ice that melts earlier in early summer, hence creating areas of open water that have a lower albedo and trigger an ice-albedo feedback. In the Laptev Sea, we find that anomalies of coastal divergence in late winter are amplified threefold to result in the September SIE. We also find a correlation coefficient r = 0.49 between February-March-April (FMA) anomalies of coastal divergence with the FMA averaged AO index. Interestingly, the correlation is stronger, r = 0.61, when comparing the FMA coastal divergence anomalies to the DJFMA averaged AO index. It is hypothesized that the AO index at the beginning of the winter (and the associated anomalous sea ice export) also contains information that impact the magnitude of coastal divergence opening later in the winter. Our approach differs from previous approaches (e.g. Krumpen et al and Williams et al

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

Directory of Open Access Journals (Sweden)

Murat Luy

2018-05-01

Full Text Available The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014. The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

Oxygen sensors for Heavy Liquid Metal coolants: Calibration and assessment of the minimum reading temperature

Energy Technology Data Exchange (ETDEWEB)

Bassini, S., E-mail: serena.bassini@enea.it; Antonelli, A.; Di Piazza, I.; Tarantino, M.

2017-04-01

Oxygen sensors for Heavy Liquid Metals (HLMs) such as lead and LBE (lead-bismuth eutectic) will be essential devices in future Lead Fast Reactor (LFR) and Accelerator Driven System (ADS). Potentiometric sensors based on solid electrolytes were developed in recent years to this purpose. Internal reference electrodes such as Pt-air and Bi/Bi{sub 2}O{sub 3} liquid metal/metal-oxide are among the most used but they both have a weak point: Pt-air sensor has a high minimum reading temperature around 400 °C whereas Bi/Bi{sub 2}O{sub 3} suffers from internal stresses induced by Bi volume variations with temperature, which may lead to the sensor failure in the long-term. The present work describes the performance of standard Pt-air and Bi/Bi{sub 2}O{sub 3} sensors and compares them with recent Cu/Cu{sub 2}O sensor. Sensors with Yttria Partially Stabilized Zirconia (YPSZ) electrolyte were calibrated in oxygen-saturated HLM between 160 and 550 °C and the electric potential compared to the theoretical one to define the accuracy and the minimum reading temperature. Standard Pt-air sensor were also tested using Yttria Totally Stabilized Zirconia (YTSZ) to assess the effect of a different electrolyte on the minimum reading temperature. The performance of Pt-air and Cu/Cu{sub 2}O sensors with YPSZ electrolyte were then tested together in low-oxygen HLM between 200 and 450 °C. The results showed that Pt-air, Bi/Bi{sub 2}O{sub 3} and Cu/Cu{sub 2}O sensors with YPSZ measured oxygen in HLMs down to 400 °C, 290 °C and 200 °C respectively. When the YTSZ electrolyte was used in place of the YPSZ, the Pt-air sensor measured correctly down to at least 350 °C thanks to the superior ionic conductivity of the YTSZ. When Cu/Cu{sub 2}O and Pt-air sensors were tested together in the same low-oxygen HLM between 200 and 450 °C, Cu/Cu{sub 2}O sensor worked predictably in the whole temperature range whereas Pt-air sensor exhibited a correct output only above 400 °C. - Highlights: �

Application of the North American Multi-Model Ensemble to seasonal water supply forecasting in the Great Lakes basin through the use of the Great Lakes Seasonal Climate Forecast Tool

Science.gov (United States)

Gronewold, A.; Apps, D.; Fry, L. M.; Bolinger, R.

2017-12-01

The U.S. Army Corps of Engineers (USACE) contribution to the internationally coordinated 6-month forecast of Great Lakes water levels relies on several water supply models, including a regression model relating a coming month's water supply to past water supplies, previous months' precipitation and temperature, and forecasted precipitation and temperature. Probabilistic forecasts of precipitation and temperature depicted in the Climate Prediction Center's seasonal outlook maps are considered to be standard for use in operational forecasting for seasonal time horizons, and have provided the basis for computing a coming month's precipitation and temperature for use in the USACE water supply regression models. The CPC outlook maps are a useful forecast product offering insight into interpretation of climate models through the prognostic discussion and graphical forecasts. However, recent evolution of USACE forecast procedures to accommodate automated data transfer and manipulation offers a new opportunity for direct incorporation of ensemble climate forecast data into probabilistic outlooks of water supply using existing models that have previously been implemented in a deterministic fashion. We will present results from a study investigating the potential for applying data from the North American Multi-Model Ensemble to operational water supply forecasts. The use of NMME forecasts is facilitated by a new, publicly available, Great Lakes Seasonal Climate Forecast Tool that provides operational forecasts of monthly average temperatures and monthly total precipitation summarized for each lake basin.

The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico

DEFF Research Database (Denmark)

Puig, Daniel; Morales-Nápoles, Oswaldo; Bakhtiari, Fatemeh

2017-01-01

forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic...... forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should...... be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive...

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.

Sub-Seasonal Climate Forecast Rodeo

Science.gov (United States)

Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

2017-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise

NEWLY DISCOVERED GLOBAL TEMPERATURE STRUCTURES IN THE QUIET SUN AT SOLAR MINIMUM

Energy Technology Data Exchange (ETDEWEB)

Huang Zhenguang; Frazin, Richard A.; Landi, Enrico; Manchester, Ward B.; Gombosi, Tamas I. [Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI 48109 (United States); Vasquez, Alberto M. [Instituto de Astronomia y Fisica del Espacio, CONICET-University of Buenos Aires, Ciudad de Buenos Aires, CC 67-Suc 28 (Argentina)

2012-08-20

Magnetic loops are building blocks of the closed-field corona. While active region loops are readily seen in images taken at EUV and X-ray wavelengths, quiet-Sun (QS) loops are seldom identifiable and are therefore difficult to study on an individual basis. The first analysis of solar minimum (Carrington Rotation 2077) QS coronal loops utilizing a novel technique called the Michigan Loop Diagnostic Technique (MLDT) is presented. This technique combines Differential Emission Measure Tomography and a potential field source surface (PFSS) model, and consists of tracing PFSS field lines through the tomographic grid on which the local differential emission measure is determined. As a result, the electron temperature T{sub e} and density N{sub e} at each point along each individual field line can be obtained. Using data from STEREO/EUVI and SOHO/MDI, the MLDT identifies two types of QS loops in the corona: so-called up loops in which the temperature increases with height and so-called down loops in which the temperature decreases with height. Up loops are expected, however, down loops are a surprise, and furthermore, they are ubiquitous in the low-latitude corona. Up loops dominate the QS at higher latitudes. The MLDT allows independent determination of the empirical pressure and density scale heights, and the differences between the two remain to be explained. The down loops appear to be a newly discovered property of the solar minimum corona that may shed light on the physics of coronal heating. The results are shown to be robust to the calibration uncertainties of the EUVI instrument.

West-WRF Sensitivity to Sea Surface Temperature Boundary Condition in California Precipitation Forecasts of AR Related Events

Science.gov (United States)

Zhang, X.; Cornuelle, B. D.; Martin, A.; Weihs, R. R.; Ralph, M.

2017-12-01

We evaluated the merit in coastal precipitation forecasts by inclusion of high resolution sea surface temperature (SST) from blended satellite and in situ observations as a boundary condition (BC) to the Weather Research and Forecast (WRF) mesoscale model through simple perturbation tests. Our sensitivity analyses shows that the limited improvement of watershed scale precipitation forecast is credible. When only SST BC is changed, there is an uncertainty introduced because of artificial model state equilibrium and the nonlinear nature of the WRF model system. With the change of SST on the order of a fraction of a degree centigrade, we found that the part of random perturbation forecast response is saturated after 48 hours when it reaches to the order magnitude of the linear response. It is important to update the SST at a shorter time period, so that the independent excited nonlinear modes can cancel each other. The uncertainty in our SST configuration is quantitatively equivalent to adding to a spatially uncorrelated Guasian noise of zero mean and 0.05 degree of standard deviation to the SST. At this random noise perturbation magnitude, the ensemble average behaves well within a convergent range. It is also found that the sensitivity of forecast changes in response to SST changes. This is measured by the ratio of the spatial variability of mean of the ensemble perturbations over the spatial variability of the corresponding forecast. The ratio is about 10% for surface latent heat flux, 5 % for IWV, and less than 1% for surface pressure.

Short-term forecasts of district heating load and outdoor temperature by use of on-line connected computers; Korttidsprognoser foer fjaerrvaermelast och utetemperatur med on-linekopplade datorer

Energy Technology Data Exchange (ETDEWEB)

Malmstroem, B; Ernfors, P; Nilsson, Daniel; Vallgren, H [Chalmers Tekniska Hoegskola, Goeteborg (Sweden). Institutionen foer Energiteknik

1996-10-01

In this report the available methods for forecasting weather and district heating load have been studied. A forecast method based on neural networks has been tested against the more common statistical methods. The accuracy of the weather forecasts from the SMHI (Swedish Meteorological and Hydrological Institute) has been estimated. In connection with these tests, the possibilities of improving the forecasts by using on-line connected computers has been analysed. The most important results from the study are: Energy company staff generally look upon the forecasting of district heating load as a problem of such a magnitude that computer support is needed. At the companies where computer calculated forecasts are in use, their accuracy is regarded as quite satisfactory; The interest in computer produced load forecasts among energy company staff is increasing; At present, a sufficient number of commercial suppliers of weather forecasts as well as load forecasts is available to fulfill the needs of energy companies; Forecasts based on neural networks did not attain any precision improvement in comparison to more traditional statistical methods. There may though be other types of neural networks, not tested in this study, that are possibly capable of improving the forecast precision; Forecasts of outdoor temperature and district heating load can be significantly improved through the use of on-line-connected computers supplied with instantaneous measurements of temperature and load. This study shows that a general reduction of the load prediction errors by approximately 15% is attainable. For short time horizons (less than 5 hours), more extensive load prediction error reductions can be reached. For the 1-hour time horizon, the possible reduction amounts to up to 50%. 21 refs, 4 figs, 7 appendices

Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

Science.gov (United States)

Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

2015-12-01

Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

Solar ultraviolet continuum radiation: The photosphere, the low chromosphere, and the temperature-minimum region

International Nuclear Information System (INIS)

Samain, D.

1980-01-01

A comparison of solar disk-center intensity measurements with theoretical values calculated for atmospheric models derived from the temperature distributions found by J. Vernazza and his colleagues indicates that generally good agreement is found with an atmospheric model having a minimum temperature of about 4150 K or possibly higher. Empirical opacity values including LTE departures and absorption coefficients which best represent the radiation field in the range 1460 A-2100 A are given. Precise values are obtained for the required opacity distribution, presumably due to lines, longward of 1682 A. It is found that a contribution to the opacity from Fe I almost equal to the Si I opacity allows to explain the observed center-to-limb contrast between 1525 A and 1570 A and its fast change through 1570 A. However, the strong measured limb-darkening as compared with the calculated variation from 1600 A to 1682 A cannot completely be accounted for in terms of opacity, and still preserve the agreement with the absolute center intensities. These differences might be interpreted as having been caused by solar inhomogeneities. Alternatively the differences may indicate that the UV continuum is closer to LTE than current theoretical calculations indicate. If so, our Sun center data would imply a minimum temperature higher than 4150 K

What Fraction of Global Fire Activity Can Be Forecast Using Sea Surface Temperatures?

Science.gov (United States)

Chen, Y.; Randerson, J. T.; Morton, D. C.; Andela, N.; Giglio, L.

2015-12-01

Variations in sea surface temperatures (SSTs) can influence climate dynamics in local and remote land areas, and thus influence fire-climate interactions that govern burned area. SST information has been recently used in statistical models to create seasonal outlooks of fire season severity in South America and as the initial condition for dynamical model predictions of fire activity in Indonesia. However, the degree to which large-scale ocean-atmosphere interactions can influence burned area in other continental regions has not been systematically explored. Here we quantified the amount of global burned area that can be predicted using SSTs in 14 different oceans regions as statistical predictors. We first examined lagged correlations between GFED4s burned area and the 14 ocean climate indices (OCIs) individually. The maximum correlations from different OCIs were used to construct a global map of fire predictability. About half of the global burned area can be forecast by this approach 3 months before the peak burning month (with a Pearson's r of 0.5 or higher), with the highest levels of predictability in Central America and Equatorial Asia. Several hotspots of predictability were identified using k-means cluster analysis. Within these regions, we tested the improvements of the forecast by using two OCIs from different oceans. Our forecast models were based on near-real-time SST data and may therefore support the development of new seasonal outlooks for fire activity that can aid the sustainable management of these fire-prone ecosystems.

EXTREME MAXIMUM AND MINIMUM AIR TEMPERATURE IN MEDİTERRANEAN COASTS IN TURKEY

Directory of Open Access Journals (Sweden)

Barbaros Gönençgil

2016-01-01

Full Text Available In this study, we determined extreme maximum and minimum temperatures in both summer and winter seasons at the stations in the Mediterranean coastal areas of Turkey.In the study, the data of 24 meteorological stations for the daily maximum and minimumtemperatures of the period from 1970–2010 were used. From this database, a set of four extreme temperature indices applied warm (TX90 and cold (TN10 days and warm spells (WSDI and cold spell duration (CSDI. The threshold values were calculated for each station to determine the temperatures that were above and below the seasonal norms in winter and summer. The TX90 index displays a positive statistically significant trend, while TN10 display negative nonsignificant trend. The occurrence of warm spells shows statistically significant increasing trend while the cold spells shows significantly decreasing trend over the Mediterranean coastline in Turkey.

The Art and Science of Long-Range Space Weather Forecasting

Science.gov (United States)

Hathaway, David H.; Wilson, Robert M.

2006-01-01

Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

Estimation of daily minimum land surface air temperature using MODIS data in southern Iran

Science.gov (United States)

Didari, Shohreh; Norouzi, Hamidreza; Zand-Parsa, Shahrokh; Khanbilvardi, Reza

2017-11-01

Land surface air temperature (LSAT) is a key variable in agricultural, climatological, hydrological, and environmental studies. Many of their processes are affected by LSAT at about 5 cm from the ground surface (LSAT5cm). Most of the previous studies tried to find statistical models to estimate LSAT at 2 m height (LSAT2m) which is considered as a standardized height, and there is not enough study for LSAT5cm estimation models. Accurate measurements of LSAT5cm are generally acquired from meteorological stations, which are sparse in remote areas. Nonetheless, remote sensing data by providing rather extensive spatial coverage can complement the spatiotemporal shortcomings of meteorological stations. The main objective of this study was to find a statistical model from the previous day to accurately estimate spatial daily minimum LSAT5cm, which is very important in agricultural frost, in Fars province in southern Iran. Land surface temperature (LST) data were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites at daytime and nighttime periods with normalized difference vegetation index (NDVI) data. These data along with geometric temperature and elevation information were used in a stepwise linear model to estimate minimum LSAT5cm during 2003-2011. The results revealed that utilization of MODIS Aqua nighttime data of previous day provides the most applicable and accurate model. According to the validation results, the accuracy of the proposed model was suitable during 2012 (root mean square difference ( RMSD) = 3.07 °C, {R}_{adj}^2 = 87 %). The model underestimated (overestimated) high (low) minimum LSAT5cm. The accuracy of estimation in the winter time was found to be lower than the other seasons ( RMSD = 3.55 °C), and in summer and winter, the errors were larger than in the remaining seasons.

Visualizing uncertainty : Towards a better understanding of weather forecasts

NARCIS (Netherlands)

Toet, A.; Tak, S.; Erp, J.B.F. van

2016-01-01

Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also

Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States.

Directory of Open Access Journals (Sweden)

Lewis H Ziska

Full Text Available Assessments of climate change and food security often do not consider changes to crop production as a function of altered pest pressures. Evaluation of potential changes may be difficult, in part, because management practices are routinely utilized in situ to minimize pest injury. If so, then such practices, should, in theory, also change with climate, although this has never been quantified. Chemical (pesticide applications remain the primary means of managing pests in industrialized countries. While a wide range of climate variables can influence chemical use, minimum daily temperature (lowest 24 h recorded temperature in a given year can be associated with the distribution and thermal survival of many agricultural pests in temperate regions. The current study quantifies average pesticide applications since 1999 for commercial soybean grown over a 2100 km North-South latitudinal transect for seven states that varied in minimum daily temperature (1999-2013 from -28.6°C (Minnesota to -5.1°C (Louisiana. Although soybean yields (per hectare did not vary by state, total pesticide applications (kg of active ingredient, ai, per hectare increased from 4.3 to 6.5 over this temperature range. Significant correlations were observed between minimum daily temperatures and kg of ai for all pesticide classes. This suggested that minimum daily temperature could serve as a proxy for pesticide application. Longer term temperature data (1977-2013 indicated greater relative increases in minimum daily temperatures for northern relative to southern states. Using these longer-term trends to determine short-term projections of pesticide use (to 2023 showed a greater comparative increase in herbicide use for soybean in northern; but a greater increase in insecticide and fungicide use for southern states in a warmer climate. Overall, these data suggest that increases in pesticide application rates may be a means to maintain soybean production in response to rising

Toward Sub-seasonal to Seasonal Arctic Sea Ice Forecasting Using the Regional Arctic System Model (RASM)

Science.gov (United States)

Kamal, S.; Maslowski, W.; Roberts, A.; Osinski, R.; Cassano, J. J.; Seefeldt, M. W.

2017-12-01

The Regional Arctic system model has been developed and used to advance the current state of Arctic modeling and increase the skill of sea ice forecast. RASM is a fully coupled, limited-area model that includes the atmosphere, ocean, sea ice, land hydrology and runoff routing components and the flux coupler to exchange information among them. Boundary conditions are derived from NCEP Climate Forecasting System Reanalyses (CFSR) or Era Iterim (ERA-I) for hindcast simulations or from NCEP Coupled Forecast System Model version 2 (CFSv2) for seasonal forecasts. We have used RASM to produce sea ice forecasts for September 2016 and 2017, in contribution to the Sea Ice Outlook (SIO) of the Sea Ice Prediction Network (SIPN). Each year, we produced three SIOs for the September minimum, initialized on June 1, July 1 and August 1. In 2016, predictions used a simple linear regression model to correct for systematic biases and included the mean September sea ice extent, the daily minimum and the week of the minimum. In 2017, we produced a 12-member ensemble on June 1 and July 1, and 28-member ensemble August 1. The predictions of September 2017 included the pan-Arctic and regional Alaskan sea ice extent, daily and monthly mean pan-Arctic maps of sea ice probability, concentration and thickness. No bias correction was applied to the 2017 forecasts. Finally, we will also discuss future plans for RASM forecasts, which include increased resolution for model components, ecosystem predictions with marine biogeochemistry extensions (mBGC) to the ocean and sea ice components, and feasibility of optional boundary conditions using the Navy Global Environmental Model (NAVGEM).

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

Science.gov (United States)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Forecast models for urban extreme temperatures : Karachi region as a case study

International Nuclear Information System (INIS)

Hussain, M.A.; Abbas, A.; Ansari, M.R.K.

2010-01-01

The climatic signature of global warming is both local and global. The forcing by increasing greenhouse gases is global, so there is clearly a global component to the climatic signature. Moreover, the damaging impacts of global warming are manifesting themselves around the world in the form of extreme weather events like storms, tornadoes, floods and droughts, all of which have been escalating in frequency and intensity. Furthermore, it is a well-known fact that there is high degree of uncertainty surrounding projections of basic climate variables, such as temperature and precipitation. However, numerous authors have explored many of these effects individually and have begun exploring the interactions between climate change-induced impacts in different sectors of urban activities. Therefore, it is safe to say that an attempt to conduct a definitive, comprehensive analysis of all the potential impacts of climate change on the urban structure is premature at present. This communication attempts to examine the trends in maximum monthly urban temperature fluctuations. Analysis reveals increasing trends in urban temperature fluctuations showing effect of Karachi industrializations. Forecast models also suggest future scenario with respect to occurrence of extreme temperature. The analysis carried out in this work would be useful for urban planners for sustainable future development, economists and environmentalists etc. (author)

The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico

International Nuclear Information System (INIS)

Puig, Daniel; Bakhtiari, Fatemeh; Morales-Napoles, Oswaldo; Landa Rivera, Gissela

2017-01-01

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of greenhouse-gas emissions in the country. We use those projections to produce probabilistic forecasts of greenhouse-gas emissions for Mexico. We contrast our probabilistic forecasts with Mexico's governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets. (authors)

Change In Minimum Temperature As A Response To Land Cover Change In South Florida

Science.gov (United States)

Kandel, H. P.; Melesse, A. M.

2012-12-01

Replacement of higher evapotranspirative surface materials such as water and vegetation cover by other materials such as buildings, roads, and pavements increases the Bowen's ratio from about 0.5-2.0 in rural to about ≈ 5.0 in urban areas resulting in higher surface and near surface atmospheric temperatures in the urban areas (Taha, 1997). This effect is intensified by low emissivity surfaces of the urban covers storing more heat energy during day time, but emitting less during night compared to the energy emitted by rural covers causing higher night time temperatures in urban centers, an effect called Urban Heat Island (UHI). South Florida has undergone tremendous land cover change from its pre-drainage vegetated and wetlands to post drainage agricultural and urban lands, especially after late 20th century. The objective of this study was to simultaneously analyze the land use/ land cover change and the rural/ urban minimum temperatures in south Florida for the period representing pre and post drainage states. The result shows urban sprawl increased from 8% at the beginning of the analysis period to about 14% at the end. Green vegetated areas, shrubs, and forests are found to be declined. The minimum temperature is found increased as maximum as 2°F in the urbanized stations, which remained constant or shows negligible increase in rural stations. The study dictates further micro level scrutiny in order to reach a conclusion on the development of UHI in south Florida. Key words: Bowen's ratio, emissivity, urban heat island

Adaptive Weather Forecasting using Local Meteorological Information

NARCIS (Netherlands)

Doeswijk, T.G.; Keesman, K.J.

2005-01-01

In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

Science.gov (United States)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-23

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Fuzzy rule-based forecast of meteorological drought in western Niger

Science.gov (United States)

Abdourahamane, Zakari Seybou; Acar, Reşat

2018-01-01

Understanding the causes of rainfall anomalies in the West African Sahel to effectively predict drought events remains a challenge. The physical mechanisms that influence precipitation in this region are complex, uncertain, and imprecise in nature. Fuzzy logic techniques are renowned to be highly efficient in modeling such dynamics. This paper attempts to forecast meteorological drought in Western Niger using fuzzy rule-based modeling techniques. The 3-month scale standardized precipitation index (SPI-3) of four rainfall stations was used as predictand. Monthly data of southern oscillation index (SOI), South Atlantic sea surface temperature (SST), relative humidity (RH), and Atlantic sea level pressure (SLP), sourced from the National Oceanic and Atmosphere Administration (NOAA), were used as predictors. Fuzzy rules and membership functions were generated using fuzzy c-means clustering approach, expert decision, and literature review. For a minimum lead time of 1 month, the model has a coefficient of determination R 2 between 0.80 and 0.88, mean square error (MSE) below 0.17, and Nash-Sutcliffe efficiency (NSE) ranging between 0.79 and 0.87. The empirical frequency distributions of the predicted and the observed drought classes are equal at the 99% of confidence level based on two-sample t test. Results also revealed the discrepancy in the influence of SOI and SLP on drought occurrence at the four stations while the effect of SST and RH are space independent, being both significantly correlated (at α based forecast model shows better forecast skills.

Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Science.gov (United States)

Hong, Mei; Chen, Xi; Zhang, Ren; Wang, Dong; Shen, Shuanghe; Singh, Vijay P.

2018-04-01

With the objective of tackling the problem of inaccurate long-term El Niño-Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Directory of Open Access Journals (Sweden)

Tadesse Kassahun Birhanu

2017-12-01

Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

Forecasting of superconducting compounds

International Nuclear Information System (INIS)

Savitskii, E.M.; Gribulya, V.G.; Kiseleva, N.N.

1981-01-01

In forecasting new superconducting intermetallic compounds of the A15 and Mo 3 Se types most promising from the viewpoint of high critical temperature Tsub(c), high critical magnetic fields Hsub(c), and high critical currents and in estimating their transition temperature it is proposed to apply cybernetic methods of computer learning

Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

Science.gov (United States)

Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

2014-01-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both

Fuzzy approach for short term load forecasting

Energy Technology Data Exchange (ETDEWEB)

Chenthur Pandian, S.; Duraiswamy, K.; Kanagaraj, N. [Electrical and Electronics Engg., K.S. Rangasamy College of Technology, Tiruchengode 637209, Tamil Nadu (India); Christober Asir Rajan, C. [Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry (India)

2006-04-15

The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. The STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. In this paper, the 'time' and 'temperature' of the day are taken as inputs for the fuzzy logic controller and the 'forecasted load' is the output. The input variable 'time' has been divided into eight triangular membership functions. The membership functions are Mid Night, Dawn, Morning, Fore Noon, After Noon, Evening, Dusk and Night. Another input variable 'temperature' has been divided into four triangular membership functions. They are Below Normal, Normal, Above Normal and High. The 'forecasted load' as output has been divided into eight triangular membership functions. They are Very Low, Low, Sub Normal, Moderate Normal, Normal, Above Normal, High and Very High. Case studies have been carried out for the Neyveli Thermal Power Station Unit-II (NTPS-II) in India. The fuzzy forecasted load values are compared with the conventional forecasted values. The forecasted load closely matches the actual one within +/-3%. (author)

Long-range forecasting of intermittent streamflow

Science.gov (United States)

van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A.

2011-11-01

Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

Long-range forecasting of intermittent streamflow

Directory of Open Access Journals (Sweden)

F. F. van Ogtrop

2011-11-01

Full Text Available Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill.

Modeling monthly meteorological and agronomic frost days, based on minimum air temperature, in Center-Southern Brazil

Science.gov (United States)

Alvares, Clayton Alcarde; Sentelhas, Paulo César; Stape, José Luiz

2017-09-01

Although Brazil is predominantly a tropical country, frosts are observed with relative high frequency in the Center-Southern states of the country, affecting mainly agriculture, forestry, and human activities. Therefore, information about the frost climatology is of high importance for planning of these activities. Based on that, the aims of the present study were to develop monthly meteorological (F MET) and agronomic (F AGR) frost day models, based on minimum shelter air temperature (T MN), in order to characterize the temporal and spatial frost days variability in Center-Southern Brazil. Daily minimum air temperature data from 244 weather stations distributed across the study area were used, being 195 for developing the models and 49 for validating them. Multivariate regression models were obtained to estimate the monthly T MN, once the frost day models were based on this variable. All T MN regression models were statistically significant (p Brazilian region are the first zoning of these variables for the country.

Temperature response to future urbanization and climate change

Science.gov (United States)

Argüeso, Daniel; Evans, Jason P.; Fita, Lluís; Bormann, Kathryn J.

2014-04-01

This study examines the impact of future urban expansion on local near-surface temperature for Sydney (Australia) using a future climate scenario (A2). The Weather Research and Forecasting model was used to simulate the present (1990-2009) and future (2040-2059) climates of the region at 2-km spatial resolution. The standard land use of the model was replaced with a more accurate dataset that covers the Sydney area. The future simulation incorporates the projected changes in the urban area of Sydney to account for the expected urban expansion. A comparison between areas with projected land use changes and their surroundings was conducted to evaluate how urbanization and global warming will act together and to ascertain their combined effect on the local climate. The analysis of the temperature changes revealed that future urbanization will strongly affect minimum temperature, whereas little impact was detected for maximum temperature. The minimum temperature changes will be noticeable throughout the year. However, during winter and spring these differences will be particularly large and the increases could be double the increase due to global warming alone at 2050. Results indicated that the changes were mostly due to increased heat capacity of urban structures and reduced evaporation in the city environment.

Diagnostics comparing sea surface temperature feedbacks from operational hurricane forecasts to observations

Directory of Open Access Journals (Sweden)

Ian D. Lloyd

2011-11-01

Full Text Available This paper examines the ability of recent versions of the Geophysical Fluid Dynamics Laboratory Operational Hurricane Forecast Model (GHM to reproduce the observed relationship between hurricane intensity and hurricane-induced Sea Surface Temperature (SST cooling. The analysis was performed by taking a Lagrangian composite of all hurricanes in the North Atlantic from 1998–2009 in observations and 2005–2009 for the GHM. A marked improvement in the intensity-SST relationship for the GHM compared to observations was found between the years 2005 and 2006–2009 due to the introduction of warm-core eddies, a representation of the loop current, and changes to the drag coefficient parameterization for bulk turbulent flux computation. A Conceptual Hurricane Intensity Model illustrates the essential steady-state characteristics of the intensity-SST relationship and is explained by two coupled equations for the atmosphere and ocean. The conceptual model qualitatively matches observations and the 2006–2009 period in the GHM, and presents supporting evidence for the conclusion that weaker upper oceanic thermal stratification in the Gulf of Mexico, caused by the introduction of the loop current and warm core eddies, is crucial to explaining the observed SST-intensity pattern. The diagnostics proposed by the conceptual model offer an independent set of metrics for comparing operational hurricane forecast models to observations.

Toward a Marine Ecological Forecasting System

Science.gov (United States)

2010-06-01

coral bleaching , living resource distribution, and pathogen progression). An operational ecological forecasting system depends upon the assimilation of...space scales (e.g., harmful algal blooms, dissolved oxygen concentration (hypoxia), water quality/beach closures, coral bleaching , living resource...advance. Two beaches in Lake Michigan have been selected for initial implementation. Forecasting Coral Bleaching in relation to Ocean Temperatures

Method of forecasting power distribution

International Nuclear Information System (INIS)

Kaneto, Kunikazu.

1981-01-01

Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

Monthly ENSO Forecast Skill and Lagged Ensemble Size

Science.gov (United States)

Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

2018-04-01

The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

Skill prediction of local weather forecasts based on the ECMWF ensemble

Directory of Open Access Journals (Sweden)

C. Ziehmann

2001-01-01

Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

Satellite-derived vertical profiles of temperature and dew point for mesoscale weather forecast

Science.gov (United States)

Masselink, Thomas; Schluessel, P.

1995-12-01

Weather forecast-models need spatially high resolutioned vertical profiles of temperature and dewpoint for their initialisation. These profiles can be supplied by a combination of data from the Tiros-N Operational Vertical Sounder (TOVS) and the imaging Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbiting sate!- lites. In cloudy cases the profiles derived from TOVS data only are of insufficient accuracy. The stanthrd deviations from radiosonde ascents or numerical weather analyses likely exceed 2 K in temperature and 5Kin dewpoint profiles. It will be shown that additional cloud information as retrieved from AVHIRR allows a significant improvement in theaccuracy of vertical profiles. The International TOVS Processing Package (ITPP) is coupled to an algorithm package called AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) where parameters like cloud fraction and cloud-top temperature are determined with higher accuracy than obtained from TOVS retrieval alone. Furthermore, a split-window technique is applied to the cloud-free AVHRR imagery in order to derive more accurate surface temperatures than can be obtained from the pure TOVS retrieval. First results of the impact of AVHRR cloud detection on the quality of the profiles are presented. The temperature and humidity profiles of different retrieval approaches are validated against analyses of the European Centre for Medium-Range Weatherforecasts.

Black Sea coastal forecasting system

Directory of Open Access Journals (Sweden)

A. I. Kubryakov

2012-03-01

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

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Energy Technology Data Exchange (ETDEWEB)

Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

Agricultural losses related to frost events: use of the 850 hPa level temperature as an explanatory variable of the damage cost

Science.gov (United States)

Papagiannaki, K.; Lagouvardos, K.; Kotroni, V.; Papagiannakis, G.

2014-09-01

The objective of this study is the analysis of damaging frost events in agriculture, by examining the relationship between the daily minimum temperature in the lower atmosphere (at an isobaric level of 850 hPa) and crop production losses. Furthermore, the study suggests a methodological approach for estimating agriculture risk due to frost events, with the aim of estimating the short-term probability and magnitude of frost-related financial losses for different levels of 850 hPa temperature. Compared with near-surface temperature forecasts, temperature forecasts at the level of 850 hPa are less influenced by varying weather conditions or by local topographical features; thus, they constitute a more consistent indicator of the forthcoming weather conditions. The analysis of the daily monetary compensations for insured crop losses caused by weather events in Greece shows that, during the period 1999-2011, frost caused more damage to crop production than any other meteorological phenomenon. Two regions of different geographical latitudes are examined further, to account for the differences in the temperature ranges developed within their ecological environment. Using a series of linear and logistic regressions, we found that minimum temperature (at an 850 hPa level), grouped into three categories according to its magnitude, and seasonality, are significant variables when trying to explain crop damage costs, as well as to predict and quantify the likelihood and magnitude of damaging frost events.

Empirical seasonal forecasts of the NAO

Science.gov (United States)

Sanchezgomez, E.; Ortizbevia, M.

2003-04-01

We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-07-26

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

Investigation of the minimum film boiling temperature of water during rewetting under forced convective conditions

International Nuclear Information System (INIS)

Huang, X.C.; Bartsch, G.; Wang, B.X.

1992-01-01

The minimum film boiling temperature of water has been measured on a copper hollow cylinder of 50 mm length with the mass flux rate ranging from 25 to 500 kg/m 2 s and the pressure from 0.1 to 1.0 MPa at subcoolings of 5 to 50 K. Film boiling is established with help of a temperature-controlled system. Rewetting can be initiated by cutting off or very gradually reducing the power supply to the test section. A numerical method for solving the two-dimensional nonlinear inverse heat conduction problem is utilized in the data reduction, taking into account the axial heat conduction. The results are compared with the steady-state maximum transition boiling temperatures measured on the same test section and with the true quench temperatures available in the literature so far. (4 figures, 1 table) (Author)

Verification of different forecasts of Hungarian Meteorological Service

Science.gov (United States)

Feher, B.

2009-09-01

In this paper I show the results of the forecasts made by the Hungarian Meteorological Service. I focus on the general short- and medium-range forecasts, which contains cloudiness, precipitation, wind speed and temperature for six regions of Hungary. I would like to show the results of some special forecasts as well, such as precipitation predictions which are made for the catchment area of Danube and Tisza rivers, and daily mean temperature predictions used by Hungarian energy companies. The product received by the user is made by the general forecaster, but these predictions are based on the ALADIN and ECMWF outputs. Because of these, the product of the forecaster and the models were also verified. Method like this is able to show us, which weather elements are more difficult to forecast or which regions have higher errors. During the verification procedure the basic errors (mean error, mean absolute error) are calculated. Precipitation amount is classified into five categories, and scores like POD, TS, PC,…etc. were defined by contingency table determined by these categories. The procedure runs fully automatically, all the things forecasters have to do is to print the daily result each morning. Beside the daily result, verification is also made for longer periods like week, month or year. Analyzing the results of longer periods we can say that the best predictions are made for the first few days, and precipitation forecasts are less good for mountainous areas, even, the scores of the forecasters sometimes are higher than the errors of the models. Since forecaster receive results next day, it can helps him/her to reduce mistakes and learn the weakness of the models. This paper contains the verification scores, their trends, the method by which these scores are calculated, and some case studies on worse forecasts.

CMB lensing forecasts for constraining the primordial perturbations: adding to the CMB temperature and polarization information

Energy Technology Data Exchange (ETDEWEB)

Kasanda, Simon Muya; Moodley, Kavilan, E-mail: simon.muya.kasanda@gmail.com, E-mail: moodleyk41@ukzn.ac.za [Astrophysics and Cosmology Research Unit and School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Road, Durban, 4041 (South Africa)

2014-12-01

We forecast how current (PLANCK) and future (PRISM) cosmic microwave background (CMB) experiments constrain the adiabatic mode and its admixtures with primordial isocurvature modes. The forecasts are based on measurements of the reconstructed CMB lensing potential and lensing-induced CMB B-mode polarization anisotropies in combination with the CMB temperature and E-mode polarization anisotropies. We first study the characteristic features of the CMB temperature, polarization and lensing spectra for adiabatic and isocurvature modes. We then consider how information from the CMB lensing potential and B-mode polarization induced by lensing can improve constraints on an admixture of adiabatic and three correlated isocurvature modes. We find that the CMB lensing spectrum improves constraints on isocurvature modes by at most 10% for the PLANCK and PRISM experiments. The limited improvement is a result of the low amplitude of isocurvature lensing spectra and cancellations between these spectra that render them only slightly detectable. There is a larger gain from using the lensing-induced B-mode polarization spectrum measured by PRISM. In this case constraints on isocurvature mode amplitudes improve by as much as 40% relative to the CMB temperature and E-mode polarization constraints. The addition of both lensing and lensing-induced B-mode polarization information constrains isocurvature mode amplitudes at the few percent level or better. In the case of admixtures of the adiabatic mode with one or two correlated isocurvature modes we find that constraints at the percent level or better are possible. We investigate the dependence of our results to various assumptions in our analysis, such as the inclusion of dark energy parameters, the CMB temperature-lensing correlation, and the presence of primordial tensor modes, and find that these assumptions do not significantly change our main results.

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

Science.gov (United States)

Prikryl, P.; Jayachandran, P.T.; Mushini, S. C.; Richardson, I. G.

2012-01-01

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

On the reliability of seasonal climate forecasts

Science.gov (United States)

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

Mean Bias in Seasonal Forecast Model and ENSO Prediction Error.

Science.gov (United States)

Kim, Seon Tae; Jeong, Hye-In; Jin, Fei-Fei

2017-07-20

This study uses retrospective forecasts made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in predicting the amplitude of El Niño Southern Oscillation (ENSO)-driven sea surface temperature variability. When utilizing Bjerknes coupled stability (BJ) index analysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by those in the growth rate estimated by the BJ index. ENSO amplitude forecast errors are most strongly associated with the errors in both the thermocline slope response and surface wind response to forcing over the tropical Pacific, leading to errors in thermocline feedback. This study concludes that upper ocean temperature bias in the equatorial Pacific, which becomes more intense with increasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in ENSO amplitude.

Forecasting summertime surface temperature and precipitation in the Mexico City metropolitan area: sensitivity of the WRF model to land cover changes

Science.gov (United States)

López-Bravo, Clemente; Caetano, Ernesto; Magaña, Víctor

2018-02-01

Changes in the frequency and intensity of severe hydrometeorological events in recent decades in the Mexico City Metropolitan Area have motivated the development of weather warning systems. The weather forecasting system for this region was evaluated in sensitivity studies using the Weather Research and Forecasting Model (WRF) for July 2014, a summer time month. It was found that changes in the extent of the urban area and associated changes in thermodynamic and dynamic variables have induced local circulations that affect the diurnal cycles of temperature, precipitation, and wind fields. A newly implemented configuration (land cover update and Four-Dimensional Data Assimilation (FDDA)) of the WRF model has improved the adjustment of the precipitation field to the orography. However, errors related to the depiction of convection due to parameterizations and microphysics remains a source of uncertainty in weather forecasting in this region.

Hourly weather forecasts for gas turbine power generation

Directory of Open Access Journals (Sweden)

G. Giunta

2017-06-01

Full Text Available An hourly short-term weather forecast can optimize processes in Combined Cycle Gas Turbine (CCGT plants by helping to reduce imbalance charges on the national power grid. Consequently, a reliable meteorological prediction for a given power plant is crucial for obtaining competitive prices for the electric market, better planning and stock management, sales and supplies of energy sources. The paper discusses the short-term hourly temperature forecasts, at lead time day+1 and day+2, over a period of thirteen months in 2012 and 2013 for six Italian CCGT power plants of 390 MW each (260 MW from the gas turbine and 130 MW from the steam turbine. These CCGT plants are placed in three different Italian climate areas: the Po Valley, the Adriatic coast, and the North Tyrrhenian coast. The meteorological model applied in this study is the eni-Kassandra Meteo Forecast (e‑kmf™, a multi-model approach system to provide probabilistic forecasts with a Kalman filter used to improve accuracy of local temperature predictions. Performance skill scores, computed by the output data of the meteorological model, are compared with local observations, and used to evaluate forecast reliability. In the study, the approach has shown good overall scores encompassing more than 50,000 hourly temperature values. Some differences from one site to another, due to local meteorological phenomena, can affect the short-term forecast performance, with consequent impacts on gas-to-power production and related negative imbalances. For operational application of the methodology in CCGT power plant, the benefits and limits have been successfully identified.

Forecasting metal prices: Do forecasters herd?

DEFF Research Database (Denmark)

Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

2013-01-01

We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

KAUST Repository

Zhu, Xinxin; Bowman, Kenneth P.; Genton, Marc G.

2014-01-01

pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal

Communicating weather forecast uncertainty: Do individual differences matter?

Science.gov (United States)

Grounds, Margaret A; Joslyn, Susan L

2018-03-01

Research suggests that people make better weather-related decisions when they are given numeric probabilities for critical outcomes (Joslyn & Leclerc, 2012, 2013). However, it is unclear whether all users can take advantage of probabilistic forecasts to the same extent. The research reported here assessed key cognitive and demographic factors to determine their relationship to the use of probabilistic forecasts to improve decision quality. In two studies, participants decided between spending resources to prevent icy conditions on roadways or risk a larger penalty when freezing temperatures occurred. Several forecast formats were tested, including a control condition with the night-time low temperature alone and experimental conditions that also included the probability of freezing and advice based on expected value. All but those with extremely low numeracy scores made better decisions with probabilistic forecasts. Importantly, no groups made worse decisions when probabilities were included. Moreover, numeracy was the best predictor of decision quality, regardless of forecast format, suggesting that the advantage may extend beyond understanding the forecast to general decision strategy issues. This research adds to a growing body of evidence that numerical uncertainty estimates may be an effective way to communicate weather danger to general public end users. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Effects of Data Gaps on the Calculated Monthly Mean Maximum and Minimum Temperatures in the Continental United States: A Spatial and Temporal Study.

Science.gov (United States)

Stooksbury, David E.; Idso, Craig D.; Hubbard, Kenneth G.

1999-05-01

Gaps in otherwise regularly scheduled observations are often referred to as missing data. This paper explores the spatial and temporal impacts that data gaps in the recorded daily maximum and minimum temperatures have on the calculated monthly mean maximum and minimum temperatures. For this analysis 138 climate stations from the United States Historical Climatology Network Daily Temperature and Precipitation Data set were selected. The selected stations had no missing maximum or minimum temperature values during the period 1951-80. The monthly mean maximum and minimum temperatures were calculated for each station for each month. For each month 1-10 consecutive days of data from each station were randomly removed. This was performed 30 times for each simulated gap period. The spatial and temporal impact of the 1-10-day data gaps were compared. The influence of data gaps is most pronounced in the continental regions during the winter and least pronounced in the southeast during the summer. In the north central plains, 10-day data gaps during January produce a standard deviation value greater than 2°C about the `true' mean. In the southeast, 10-day data gaps in July produce a standard deviation value less than 0.5°C about the mean. The results of this study will be of value in climate variability and climate trend research as well as climate assessment and impact studies.

Spatial forecast of landslides in three gorges based on spatial data mining.

Science.gov (United States)

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

EVOLUTION OF THE GLOBAL TEMPERATURE STRUCTURE OF THE SOLAR CORONA DURING THE MINIMUM BETWEEN SOLAR CYCLES 23 AND 24

International Nuclear Information System (INIS)

Nuevo, Federico A.; Vásquez, Alberto M.; Huang Zhenguang; Frazin, Richard; Manchester, Ward B. IV; Jin Meng

2013-01-01

The combination of differential emission measure tomography with extrapolation of the photospheric magnetic field allows determination of the electron density and electron temperature along individual magnetic field lines. This is especially useful in quiet-Sun (QS) plasmas where individual loops cannot otherwise be identified. In Paper I, this approach was applied to study QS plasmas during Carrington rotation (CR) 2077 at the minimum between solar cycles (SCs) 23 and 24. In that work, two types of QS coronal loops were identified: ''up'' loops in which the temperature increases with height, and ''down'' loops in which the temperature decreases with height. While the first ones were expected, the latter ones were a surprise and, furthermore, were found to be ubiquitous in the low-latitude corona. In the present work, we extend the analysis to 11 CRs around the last solar minimum. We found that the ''down'' population, always located at low latitudes, was maximum at the time when the sunspot number was minimum, and the number of down loops systematically increased during the declining phase of SC-23 and diminished during the rising phase of SC-24. ''Down'' loops are found to have systematically larger values of β than do ''up'' loops. These discoveries are interpreted in terms of excitation of Alfvén waves in the photosphere, and mode conversion and damping in the low corona

Frost Forecasting for Fruitgrowers

Science.gov (United States)

Martsolf, J. D.; Chen, E.

1983-01-01

Progress in forecasting from satellite data reviewed. University study found data from satellites displayed in color and used to predict frost are valuable aid to agriculture. Study evaluated scheme to use Earth-temperature data from Geostationary Operational Environmental Satellite in computer model that determines when and where freezing temperatures endanger developing fruit crops, such as apples, peaches and cherries in spring and citrus crops in winter.

Operational aerosol and dust storm forecasting

International Nuclear Information System (INIS)

Westphal, D L; Curtis, C A; Liu, M; Walker, A L

2009-01-01

The U. S. Navy now conducts operational forecasting of aerosols and dust storms on global and regional scales. The Navy Aerosol Analysis and Prediction System (NAAPS) is run four times per day and produces 6-day forecasts of sulfate, smoke, dust and sea salt aerosol concentrations and visibility for the entire globe. The Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS (registered) ) is run twice daily for Southwest Asia and produces 3-day forecasts of dust, smoke, and visibility. The graphical output from these models is available on the Internet (www.nrlmry.navy.mil/aerosol/). The aerosol optical properties are calculated for each specie for each forecast output time and used for sea surface temperature (SST) retrieval corrections, regional electro-optical (EO) propagation assessments, and the development of satellite algorithms. NAAPS daily aerosol optical depth (AOD) values are compared with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD values. Visibility forecasts are compared quantitatively with surface synoptic reports.

Multi-Annual Climate Predictions for Fisheries: An Assessment of Skill of Sea Surface Temperature Forecasts for Large Marine Ecosystems

Directory of Open Access Journals (Sweden)

Desiree Tommasi

2017-06-01

Full Text Available Decisions made by fishers and fisheries managers are informed by climate and fisheries observations that now often span more than 50 years. Multi-annual climate forecasts could further inform such decisions if they were skillful in predicting future conditions relative to the 50-year scope of past variability. We demonstrate that an existing multi-annual prediction system skillfully forecasts the probability of next year, the next 1–3 years, and the next 1–10 years being warmer or cooler than the 50-year average at the surface in coastal ecosystems. Probabilistic forecasts of upper and lower seas surface temperature (SST terciles over the next 3 or 10 years from the GFDL CM 2.1 10-member ensemble global prediction system showed significant improvements in skill over the use of a 50-year climatology for most Large Marine Ecosystems (LMEs in the North Atlantic, the western Pacific, and Indian oceans. Through a comparison of the forecast skill of initialized and uninitialized hindcasts, we demonstrate that this skill is largely due to the predictable signature of radiative forcing changes over the 50-year timescale rather than prediction of evolving modes of climate variability. North Atlantic LMEs stood out as the only coastal regions where initialization significantly contributed to SST prediction skill at the 1 to 10 year scale.

Forecasting risks of natural gas consumption in Slovenia

Energy Technology Data Exchange (ETDEWEB)

Potocnik, Primoz; Govekar, Edvard; Grabec, Igor [Laboratory of Synergetics, Ljubljana (Slovenia). Faculty of Mechanical Engineering; Thaler, Marko; Poredos, Alojz [Laboratory for Refrigeration, Ljubljana (Slovenia). Faculty of Mechanical Engineering

2007-08-15

Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company. (author)

Forecasting risks of natural gas consumption in Slovenia

International Nuclear Information System (INIS)

Potocnik, Primoz; Thaler, Marko; Govekar, Edvard; Grabec, Igor; Poredos, Alojz

2007-01-01

Efficient operation of modern energy distribution systems often requires forecasting future energy demand. This paper proposes a strategy to estimate forecasting risk. The objective of the proposed method is to improve knowledge about expected forecasting risk and to estimate the expected cash flow in advance, based on the risk model. The strategy combines an energy demand forecasting model, an economic incentive model and a risk model. Basic guidelines are given for the construction of a forecasting model that combines past energy consumption data, weather data and weather forecast. The forecasting model is required to estimate expected forecasting errors that are the basis for forecasting risk estimation. The risk estimation strategy also requires an economic incentive model that describes the influence of forecasting accuracy on the energy distribution systems' cash flow. The economic model defines the critical forecasting error levels that most strongly influence cash flow. Based on the forecasting model and the economic model, the development of a risk model is proposed. The risk model is associated with critical forecasting error levels in the context of various influential parameters such as seasonal data, month, day of the week and temperature. The risk model is applicable to estimating the daily forecasting risk based on the influential parameters. The proposed approach is illustrated by a case study of a Slovenian natural gas distribution company

Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece

Science.gov (United States)

Panagoulia, Dionysia; Vlahogianni, Eleni I.

2018-06-01

A methodological framework based on nonlinear recurrence analysis is proposed to examine the historical data evolution of extremes of maximum and minimum daily mean areal temperature patterns over time under different climate scenarios. The methodology is based on both historical data and atmospheric General Circulation Model (GCM) produced climate scenarios for the periods 1961-2000 and 2061-2100 which correspond to 1 × CO2 and 2 × CO2 scenarios. Historical data were derived from the actual daily observations coupled with atmospheric circulation patterns (CPs). The dynamics of the temperature was reconstructed in the phase-space from the time series of temperatures. The statistically comparing different temperature patterns were based on some discriminating statistics obtained by the Recurrence Quantification Analysis (RQA). Moreover, the bootstrap method of Schinkel et al. (2009) was adopted to calculate the confidence bounds of RQA parameters based on a structural preserving resampling. The overall methodology was implemented to the mountainous Mesochora catchment in Central-Western Greece. The results reveal substantial similarities between the historical maximum and minimum daily mean areal temperature statistical patterns and their confidence bounds, as well as the maximum and minimum temperature patterns in evolution under the 2 × CO2 scenario. A significant variability and non-stationary behaviour characterizes all climate series analyzed. Fundamental differences are produced from the historical and maximum 1 × CO2 scenarios, the maximum 1 × CO2 and minimum 1 × CO2 scenarios, as well as the confidence bounds for the two CO2 scenarios. The 2 × CO2 scenario reflects the strongest shifts in intensity, duration and frequency in temperature patterns. Such transitions can help the scientists and policy makers to understand the effects of extreme temperature changes on water resources, economic development, and health of ecosystems and hence to proceed to

Forecasting the Seasonal Timing of Maine's Lobster Fishery

Directory of Open Access Journals (Sweden)

Katherine E. Mills

2017-11-01

Full Text Available The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3–4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

Science.gov (United States)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

Science.gov (United States)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

EVOLUTION OF THE GLOBAL TEMPERATURE STRUCTURE OF THE SOLAR CORONA DURING THE MINIMUM BETWEEN SOLAR CYCLES 23 AND 24

Energy Technology Data Exchange (ETDEWEB)

Nuevo, Federico A.; Vasquez, Alberto M. [Instituto de Astronomia y Fisica del Espacio (CONICET-UBA) and FCEN (UBA), CC 67-Suc 28, Ciudad de Buenos Aires (Argentina); Huang Zhenguang; Frazin, Richard; Manchester, Ward B. IV; Jin Meng [Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI 48109 (United States)

2013-08-10

The combination of differential emission measure tomography with extrapolation of the photospheric magnetic field allows determination of the electron density and electron temperature along individual magnetic field lines. This is especially useful in quiet-Sun (QS) plasmas where individual loops cannot otherwise be identified. In Paper I, this approach was applied to study QS plasmas during Carrington rotation (CR) 2077 at the minimum between solar cycles (SCs) 23 and 24. In that work, two types of QS coronal loops were identified: ''up'' loops in which the temperature increases with height, and ''down'' loops in which the temperature decreases with height. While the first ones were expected, the latter ones were a surprise and, furthermore, were found to be ubiquitous in the low-latitude corona. In the present work, we extend the analysis to 11 CRs around the last solar minimum. We found that the ''down'' population, always located at low latitudes, was maximum at the time when the sunspot number was minimum, and the number of down loops systematically increased during the declining phase of SC-23 and diminished during the rising phase of SC-24. ''Down'' loops are found to have systematically larger values of {beta} than do ''up'' loops. These discoveries are interpreted in terms of excitation of Alfven waves in the photosphere, and mode conversion and damping in the low corona.

Surrogate reservoir models for CSI well probabilistic production forecast

Directory of Open Access Journals (Sweden)

Saúl Buitrago

2017-09-01

Full Text Available The aim of this work is to present the construction and use of Surrogate Reservoir Models capable of accurately predicting cumulative oil production for every well stimulated with cyclic steam injection at any given time in a heavy oil reservoir in Mexico considering uncertain variables. The central composite experimental design technique was selected to capture the maximum amount of information from the model response with a minimum number of reservoir models simulations. Four input uncertain variables (the dead oil viscosity with temperature, the reservoir pressure, the reservoir permeability and oil sand thickness hydraulically connected to the well were selected as the ones with more impact on the initial hot oil production rate according to an analytical production prediction model. Twenty five runs were designed and performed with the STARS simulator for each well type on the reservoir model. The results show that the use of Surrogate Reservoir Models is a fast viable alternative to perform probabilistic production forecasting of the reservoir.

Online short-term heat load forecasting for single family houses

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

2013-01-01

. Every hour the hourly heat load for each house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation, and wind speed. A computationally efficient recursive least squares scheme is used......This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts...... variations in the heat load signal (predominant only for some houses), peaks presumably from showers, shifts in resident behavior, and uncertainty of the weather forecasts for longer horizons, especially for the solar radiation....

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

Directory of Open Access Journals (Sweden)

Xianmin Wang

2009-03-01

Full Text Available The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc. China-Brazil Earth Resources Satellite (Cbers images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

An artificial neural network model for rainfall forecasting in Bangkok, Thailand

Directory of Open Access Journals (Sweden)

N. Q. Hung

2009-08-01

Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

Forecasting Tools Point to Fishing Hotspots

Science.gov (United States)

2009-01-01

Private weather forecaster WorldWinds Inc. of Slidell, Louisiana has employed satellite-gathered oceanic data from Marshall Space Flight Center to create a service that is every fishing enthusiast s dream. The company's FishBytes system uses information about sea surface temperature and chlorophyll levels to forecast favorable conditions for certain fish populations. Transmitting the data to satellite radio subscribers, FishBytes provides maps that guide anglers to the areas they are most likely to make their favorite catch.

An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature

Directory of Open Access Journals (Sweden)

J. Zhang

2016-05-01

Full Text Available A major continental-scale biomass burning smoke event from 28–30 June 2015, spanning central Canada through the eastern seaboard of the United States, resulted in unforecasted drops in daytime high surface temperatures on the order of 2–5  °C in the upper Midwest. This event, with strong smoke gradients and largely cloud-free conditions, provides a natural laboratory to study how aerosol radiative effects may influence numerical weather prediction (NWP forecast outcomes. Here, we describe the nature of this smoke event and evaluate the differences in observed near-surface air temperatures between Bismarck (clear and Grand Forks (overcast smoke, to evaluate to what degree solar radiation forcing from a smoke plume introduces daytime surface cooling, and how this affects model bias in forecasts and analyses. For this event, mid-visible (550 nm smoke aerosol optical thickness (AOT, τ reached values above 5. A direct surface cooling efficiency of −1.5 °C per unit AOT (at 550 nm, τ550 was found. A further analysis of European Centre for Medium-Range Weather Forecasts (ECMWF, National Centers for Environmental Prediction (NCEP, United Kingdom Meteorological Office (UKMO near-surface air temperature forecasts for up to 54 h as a function of Moderate Resolution Imaging Spectroradiometer (MODIS Dark Target AOT data across more than 400 surface stations, also indicated the presence of the daytime aerosol direct cooling effect, but suggested a smaller aerosol direct surface cooling efficiency with magnitude on the order of −0.25 to −1.0 °C per unit τ550. In addition, using observations from the surface stations, uncertainties in near-surface air temperatures from ECMWF, NCEP, and UKMO model runs are estimated. This study further suggests that significant daily changes in τ550 above 1, at which the smoke-aerosol-induced direct surface cooling effect could be comparable in magnitude with model uncertainties, are rare events

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

DEFF Research Database (Denmark)

Ranaboldo, Matteo; Giebel, Gregor; Codina, Bernat

2013-01-01

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique....... The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained...

The potential value of seasonal forecasts in a changing climate

CSIR Research Space (South Africa)

Winsemius, HC

2013-12-01

Full Text Available -range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases...

Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

DEFF Research Database (Denmark)

Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

2017-01-01

In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21...

Short-term heat load forecasting for single family houses

DEFF Research Database (Denmark)

Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg

2013-01-01

This paper presents a method for forecasting the load for space heating in a single-family house. The forecasting model is built using data from sixteen houses located in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each...... house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the individual...... noise and that practically all correlation to the climate variables are removed. Furthermore, the results show that the forecasting errors mainly are related to: unpredictable high frequency variations in the heat load signal (predominant only for some houses), shifts in resident behavior patterns...

A hierarchical spatiotemporal analog forecasting model for count data.

Science.gov (United States)

McDermott, Patrick L; Wikle, Christopher K; Millspaugh, Joshua

2018-01-01

Analog forecasting is a mechanism-free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model-based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.

Energy Consumption Forecasting for University Sector Buildings

Directory of Open Access Journals (Sweden)

Khuram Pervez Amber

2017-10-01

Full Text Available Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type. A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed.

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

Directory of Open Access Journals (Sweden)

Hossein Foroozand

2018-03-01

Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power

NARCIS (Netherlands)

Tascikaraoglu, A.; Sanandaji, B.M.; Chicco, G.; Cocina, V.; Spertino, F.; Erdinç, O.; Paterakis, N.G.; Catalaõ, J.P.S.

2016-01-01

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind

Added value of dynamical downscaling of winter seasonal forecasts over North America

Science.gov (United States)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

Science.gov (United States)

Mishra, D.; Goyal, P.

2014-12-01

Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

House Price Forecasts, Forecaster Herding, and the Recent Crisis

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Ruelke

2013-01-01

We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

KAUST Repository

Zhu, Xinxin

2014-09-01

Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

Science.gov (United States)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

House Price Forecasts, Forecaster Herding, and the Recent Crisis

Directory of Open Access Journals (Sweden)

Christian Pierdzioch

2012-11-01

Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

Road icing forecasting and detecting system

Science.gov (United States)

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

2017-05-01

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

Forecast of Antarctic Sea Ice and Meteorological Fields

Science.gov (United States)

Barreira, S.; Orquera, F.

2017-12-01

Since 2001, we have been forecasting the climatic fields of the Antarctic sea ice (SI) and surface air temperature, surface pressure and precipitation anomalies for the Southern Hemisphere at the Meteorological Department of the Argentine Naval Hydrographic Service with different techniques that have evolved with the years. Forecast is based on the results of Principal Components Analysis applied to SI series (S-Mode) that gives patterns of temporal series with validity areas (these series are important to determine which areas in Antarctica will have positive or negative SI anomalies based on what happen in the atmosphere) and, on the other hand, to SI fields (T-Mode) that give us the form of the SI fields anomalies based on a classification of 16 patterns. Each T-Mode pattern has unique atmospheric fields associated to them. Therefore, it is possible to forecast whichever atmosphere variable we decide for the Southern Hemisphere. When the forecast is obtained, each pattern has a probability of occurrence and sometimes it is necessary to compose more than one of them to obtain the final result. S-Mode and T-Mode are monthly updated with new data, for that reason the forecasts improved with the increase of cases since 2001. We used the Monthly Polar Gridded Sea Ice Concentrations database derived from satellite information generated by NASA Team algorithm provided monthly by the National Snow and Ice Data Center of USA that begins in November 1978. Recently, we have been experimenting with multilayer Perceptron (neuronal network) with supervised learning and a back-propagation algorithm to improve the forecast. The Perceptron is the most common Artificial Neural Network topology dedicated to image pattern recognition. It was implemented through the use of temperature and pressure anomalies field images that were associated with a the different sea ice anomaly patterns. The variables analyzed included only composites of surface air temperature and pressure anomalies

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

Power distribution forecasting device for reactors

International Nuclear Information System (INIS)

Tsukii, Makoto

1981-01-01

Purpose: To save expensive calculations on the forecasting of reactor power distribution. Constitution: Core status (CSD) such as entire coolant flow rate, pressures in the reactor, temperatures at the outlet and inlet and positions for control rods are inputted into a power distribution calculation device to calculate the power distribution based on physical models intermittently. Further, present power distribution is calculated based on in-core neutron flux measured values and CSD in a process control computer. Further, the ratio of the calculation results of the latter to those of the former is calculated, stored and inputted into a correction device to correct the forecast power distribution obtained by the power distribution calculation device. This enables to forecast the power distribution with excellent responsivity in the reactor site. (Furukawa, Y.)

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.

Temperature dependence of the minimum in AC power losses of (Nb/sub 0.99/Zr/sub 0.01/)3Sn in parallel AC and DC magnetic fields

International Nuclear Information System (INIS)

Kovachev, V.T.

1980-01-01

ac losses P/sub L/ of bronze-processed (Nb/sub 0.99/Zr/sub 0.01/) 3 Sn strips have been measured between 4.2 and 16.5 K in the presence of a dc magnetic field H 0 . The measurements were performed using an electronic wattmeter with both ac and dc fields parallel to the long flat surfaces of the sample. A minimum in the function P/sub L/(H 0 ) was observed for fixed ac amplitudes h 0 . This minimum was found to occur in the entire temperature range between 4.2 and 16.5 K. A similar minimum was recently reported in Nb 3 Ge [Thompson et al., J. Appl. Phys. 50, 3514 (1979)] at 4.2 K. The position of the minimum is explained here by the same physical model as in Thompson et al. [J. Appl. Phys. 50, 3514 (1979)]; and Clem (ibid. 3518), but extending the model to include the temperature dependence of the entry surface shielding fields ΔH/sub en/(B,T) for flux density in the sample B=0. It is also shown here that loss minimum measurements can be used for the determination of ΔH/sub en/(0,T) in the temperature range 4.2--16.5 K

Flood forecasting and uncertainty of precipitation forecasts

International Nuclear Information System (INIS)

Kobold, Mira; Suselj, Kay

2004-01-01

The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

Analysis of clinical data to determine the minimum number of sensors required for adequate skin temperature monitoring of superficial hyperthermia treatments.

Science.gov (United States)

Bakker, Akke; Holman, Rebecca; Rodrigues, Dario B; Dobšíček Trefná, Hana; Stauffer, Paul R; van Tienhoven, Geertjan; Rasch, Coen R N; Crezee, Hans

2018-04-27

Tumor response and treatment toxicity are related to minimum and maximum tissue temperatures during hyperthermia, respectively. Using a large set of clinical data, we analyzed the number of sensors required to adequately monitor skin temperature during superficial hyperthermia treatment of breast cancer patients. Hyperthermia treatments monitored with >60 stationary temperature sensors were selected from a database of patients with recurrent breast cancer treated with re-irradiation (23 × 2 Gy) and hyperthermia using single 434 MHz applicators (effective field size 351-396 cm 2 ). Reduced temperature monitoring schemes involved randomly selected subsets of stationary skin sensors, and another subset simulating continuous thermal mapping of the skin. Temperature differences (ΔT) between subsets and complete sets of sensors were evaluated in terms of overall minimum (T min ) and maximum (T max ) temperature, as well as T90 and T10. Eighty patients were included yielding a total of 400 hyperthermia sessions. Median ΔT was 50 sensors were used. Subsets of sensors result in underestimation of T max up to -2.1 °C (ΔT 95%CI), which decreased to -0.5 °C when >50 sensors were used. Thermal profiles (8-21 probes) yielded a median ΔT 50 stationary sensors or thermal profiles. Adequate coverage of the skin temperature distribution during superficial hyperthermia treatment requires the use of >50 stationary sensors per 400 cm 2 applicator. Thermal mapping is a valid alternative.

Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008-2015

Science.gov (United States)

Min, Young-Mi; Kryjov, Vladimir N.; Oh, Sang Myeong; Lee, Hyun-Ju

2017-12-01

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008-2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012-2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008-2015 is similar to that of hindcast.

Surface wave effect on the upper ocean in marine forecast

Science.gov (United States)

Wang, Guansuo; Qiao, Fangli; Xia, Changshui; Zhao, Chang

2015-04-01

An Operational Coupled Forecast System for the seas off China and adjacent (OCFS-C) is constructed based on the paralleled wave-circulation coupled model, which is tested with comprehensive experiments and operational since November 1st, 2007. The main feature of the system is that the wave-induced mixing is considered in circulation model. Daily analyses and three day forecasts of three-dimensional temperature, salinity, currents and wave height are produced. Coverage is global at 1/2 degreed resolution with nested models up to 1/24 degree resolution in China Sea. Daily remote sensing sea surface temperatures (SST) are taken to relax to an analytical product as hot restarting fields for OCFS-C by the Nudging techniques. Forecasting-data inter-comparisons are performed to measure the effectiveness of OCFS-C in predicting upper-ocean quantities including SST, mixed layer depth (MLD) and subsurface temperature. The variety of performance with lead time and real-time is discussed as well using the daily statistic results for SST between forecast and satellite data. Several buoy observations and many Argo profiles are used for this validation. Except the conventional statistical metrics, non-dimension skill scores (SS) is taken to estimate forecast skill. Model SST comparisons with more one year-long SST time series from 2 buoys given a large SS value (more than 0.90). And skill in predicting the seasonal variability of SST is confirmed. Model subsurface temperature comparisons with that from a lot of Argo profiles indicated that OCFS-C has low skill in predicting subsurface temperatures between 80m and 120m. Inter-comparisons of MLD reveal that MLD from model is shallower than that from Argo profiles by about 12m. QCFS-C is successful and steady in predicting MLD. The daily statistic results for SST between 1-d, 2-d and 3-d forecast and data is adopted to describe variability of Skill in predicting SST with lead time or real time. In a word QCFS-C shows reasonable

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

Science.gov (United States)

Percival, Donald B.; Denbo, Donald W.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.

2018-03-01

A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network's ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

Science.gov (United States)

Percival, Donald B.; Denbo, Donald W.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.

2018-04-01

A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network's ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

Satellite based Ocean Forecasting, the SOFT project

Science.gov (United States)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

Seasonal forecasting of discharge for the Raccoon River, Iowa

Science.gov (United States)

Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Science.gov (United States)

Dutton, John A.; James, Richard P.; Ross, Jeremy D.

2013-06-01

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.

Relationship of magnetic field strength and brightness of fine-structure elements in the solar temperature minimum region

Science.gov (United States)

Cook, J. W.; Ewing, J. A.

1990-01-01

A quantitative relationship was determined between magnetic field strength (or magnetic flux) from photospheric magnetograph observations and the brightness temperature of solar fine-structure elements observed at 1600 A, where the predominant flux source is continuum emission from the solar temperature minimum region. A Kitt Peak magnetogram and spectroheliograph observations at 1600 A taken during a sounding rocket flight of the High Resolution Telescope and Spectrograph from December 11, 1987 were used. The statistical distributions of brightness temperature in the quiet sun at 1600 A, and absolute value of magnetic field strength in the same area were determined from these observations. Using a technique which obtains the best-fit relationship of a given functional form between these two histogram distributions, a quantitative relationship was determined between absolute value of magnetic field strength B and brightness temperature which is essentially linear from 10 to 150 G. An interpretation is suggested, in which a basal heating occurs generally, while brighter elements are produced in magnetic regions with temperature enhancements proportional to B.

Improved Weather Forecasting for the Dynamic Scheduling System of the Green Bank Telescope

Science.gov (United States)

Henry, Kari; Maddalena, Ronald

2018-01-01

The Robert C Byrd Green Bank Telescope (GBT) uses a software system that dynamically schedules observations based on models of vertical weather forecasts produced by the National Weather Service (NWS). The NWS provides hourly forecasted values for ~60 layers that extend into the stratosphere over the observatory. We use models, recommended by the Radiocommunication Sector of the International Telecommunications Union, to derive the absorption coefficient in each layer for each hour in the NWS forecasts and for all frequencies over which the GBT has receivers, 0.1 to 115 GHz. We apply radiative transfer models to derive the opacity and the atmospheric contributions to the system temperature, thereby deriving forecasts applicable to scheduling radio observations for up to 10 days into the future. Additionally, the algorithms embedded in the data processing pipeline use historical values of the forecasted opacity to calibrate observations. Until recently, we have concentrated on predictions for high frequency (> 15 GHz) observing, as these need to be scheduled carefully around bad weather. We have been using simple models for the contribution of rain and clouds since we only schedule low-frequency observations under these conditions. In this project, we wanted to improve the scheduling of the GBT and data calibration at low frequencies by deriving better algorithms for clouds and rain. To address the limitation at low frequency, the observatory acquired a Radiometrics Corporation MP-1500A radiometer, which operates in 27 channels between 22 and 30 GHz. By comparing 16 months of measurements from the radiometer against forecasted system temperatures, we have confirmed that forecasted system temperatures are indistinguishable from those measured under good weather conditions. Small miss-calibrations of the radiometer data dominate the comparison. By using recalibrated radiometer measurements, we looked at bad weather days to derive better models for forecasting the

Global-warming forecasting models

International Nuclear Information System (INIS)

Moeller, K.P.

1992-01-01

In spite of an annual man-made quantity of about 20 billion tons, carbon dioxide has remained a trace gas in the atmosphere (350 ppm at present). The reliability of model calculations which forecast temperatures is dicussed in view of the world-wide increase in carbon dioxides. Computer simulations reveal a general, serious threat to the future of mankind. (DG) [de

Forecasting behavior in smart homes based on sleep and wake patterns.

Science.gov (United States)

Williams, Jennifer A; Cook, Diane J

2017-01-01

The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa.

Impact of solar versus volcanic activity variations on tropospheric temperatures and precipitation during the Dalton Minimum

Science.gov (United States)

Anet, J. G.; Muthers, S.; Rozanov, E. V.; Raible, C. C.; Stenke, A.; Shapiro, A. I.; Brönnimann, S.; Arfeuille, F.; Brugnara, Y.; Beer, J.; Steinhilber, F.; Schmutz, W.; Peter, T.

2014-05-01

The aim of this work is to elucidate the impact of changes in solar irradiance and energetic particles versus volcanic eruptions on tropospheric global climate during the Dalton Minimum (DM, AD 1780-1840). Separate variations in the (i) solar irradiance in the UV-C with wavelengths λ 250 nm, (iii) in energetic particle spectrum, and (iv) volcanic aerosol forcing were analyzed separately, and (v) in combination, by means of small ensemble calculations using a coupled atmosphere-ocean chemistry-climate model. Global and hemispheric mean surface temperatures show a significant dependence on solar irradiance at λ > 250 nm. Also, powerful volcanic eruptions in 1809, 1815, 1831 and 1835 significantly decreased global mean temperature by up to 0.5 K for 2-3 years after the eruption. However, while the volcanic effect is clearly discernible in the Southern Hemispheric mean temperature, it is less significant in the Northern Hemisphere, partly because the two largest volcanic eruptions occurred in the SH tropics and during seasons when the aerosols were mainly transported southward, partly because of the higher northern internal variability. In the simulation including all forcings, temperatures are in reasonable agreement with the tree ring-based temperature anomalies of the Northern Hemisphere. Interestingly, the model suggests that solar irradiance changes at λ Dalton Minimum. This downscales the importance of top-down processes (stemming from changes at λ 250 nm). Reduction of irradiance at λ > 250 nm leads to a significant (up to 2%) decrease in the ocean heat content (OHC) between 0 and 300 m in depth, whereas the changes in irradiance at λ < 250 nm or in energetic particles have virtually no effect. Also, volcanic aerosol yields a very strong response, reducing the OHC of the upper ocean by up to 1.5%. In the simulation with all forcings, the OHC of the uppermost levels recovers after 8-15 years after volcanic eruption, while the solar signal and the different

Impact of solar vs. volcanic activity variations on tropospheric temperatures and precipitation during the Dalton Minimum

Science.gov (United States)

Anet, J. G.; Muthers, S.; Rozanov, E. V.; Raible, C. C.; Stenke, A.; Shapiro, A. I.; Brönnimann, S.; Arfeuille, F.; Brugnara, Y.; Beer, J.; Steinhilber, F.; Schmutz, W.; Peter, T.

2013-11-01

The aim of this work is to elucidate the impact of changes in solar irradiance and energetic particles vs. volcanic eruptions on tropospheric global climate during the Dalton Minimum (DM, 1780-1840 AD). Separate variations in the (i) solar irradiance in the UV-C with wavelengths λ 250 nm, (iii) in energetic particle spectrum, and (iv) volcanic aerosol forcing were analyzed separately, and (v) in combination, by means of small ensemble calculations using a coupled atmosphere-ocean chemistry-climate-model. Global and hemispheric mean surface temperatures show a significant dependence on solar irradiance at λ > 250 nm. Also, powerful volcanic eruptions in 1809, 1815, 1831 and 1835 significantly decrease global mean temperature by up to 0.5 K for 2-3 yr after the eruption. However, while the volcanic effect is clearly discernible in the southern hemispheric mean temperature, it is less significant in the Northern Hemisphere, partly because the two largest volcanic eruptions occurred in the SH tropics and during seasons when the aerosols were mainly transported southward, partly because of the higher northern internal variability. In the simulation including all forcings, temperatures are in reasonable agreement with the tree-ring-based temperature anomalies of the Northern Hemisphere. Interestingly, the model suggests that solar irradiance changes at λ Dalton Minimum. This downscales the importance of top-down processes (stemming from changes at λ 250 nm). Reduction of irradiance at λ > 250 nm leads to a significant (up to 2%) decrease of the ocean heat content (OHC) between the 0 and 300 m of depth, whereas the changes in irradiance at λ < 250 nm or in energetic particle have virtually no effect. Also, volcanic aerosol yields a very strong response, reducing the OHC of the upper ocean by up to 1.5%. In the simulation with all forcings, the OHC of the uppermost levels recovers after 8-15 yr after volcanic eruption, while the solar signal and the different

Neural network based photovoltaic electrical forecasting in south Algeria

International Nuclear Information System (INIS)

Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

2014-01-01

Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

Experimental investigation on the minimum ignition temperature of hybrid mixtures of dusts and gases or solvents.

Science.gov (United States)

Addai, Emmanuel Kwasi; Gabel, Dieter; Krause, Ulrich

2016-01-15

Investigations on the minimum ignition temperatures (MIT) of hybrid mixtures of dusts with gases or solvents were performed in the modified Godbert-Greenwald (GG) furnace. Five combustible dusts and six flammable gases (three ideal and three real) were used. The test protocol was according to EN 50281-2-1 for dust-air mixtures whereas in the case of gases, solvents and hybrid mixtures this standard was used with slight modification. The experimental results demonstrated a significant decrease of the MIT of gas, solvent or dust and an increase in the likelihood of explosion when a small amount of dust, which was either below the minimum explosion concentration or not ignitable by itself, was mixed with gas and vice versa. For example, the MIT of toluene decreased from 540°C to 455°C when small amount of lycopodium was added. It was also confirmed that a hybrid mixture explosion is possible even when both dust and vapour or gas concentrations are respectively lower than their minimum explosion concentration (MEC) and lower explosion limit (LEL). Another example is CN4, the MEC of which of 304 g/m(3) decreased to 37 g/m(3) when propane was added, even though the concentrations of the gas was below its LEL. Copyright © 2015 Elsevier B.V. All rights reserved.

Forecaster Behaviour and Bias in Macroeconomic Forecasts

OpenAIRE

Roy Batchelor

2007-01-01

This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

Perturbations of modeling and forecast of karachi coastal region seawater

International Nuclear Information System (INIS)

Hussain, M.A.; Abbas, S.; Ansari, M.R.K.; Zaffar, A.

2013-01-01

Global warming is now a stark reality affecting the humanity in many hazardous ways. Continuous floods in Pakistan in past two years are an eye opener in this regard. A great loss of property, agriculture and life as a result of these floods suggests for an intelligent monitoring of the future projections of climate change and global warming. This is necessary because the harmful impacts of natural hazards can be coped and alleviated with a good planning in advance. This monitoring demands for enhanced forecasting capabilities, use of better analytical techniques and a clear determination and study of the controlling factors. Karachi is a coastal city which is also the industrial hub of Pakistan. Moreover, it is among one of the largest metropolitans of the world. So expectedly is most suitable for the study of high level of complex natural and anthropogenic activities. It is peculiar in the sense that it has two summer seasons, a situation scarcely observable on the globe. Here, summer season seawater temperature fluctuations are studied with the help of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and short- and long-term forecasts are made. Our short-term forecasts determine months for the summer wise temperature extremes. It appears that the months of May, June, July and August are the months of extreme temperature for the first summer and October is the month of extreme temperature for the second summer. The long-term forecasts predict that 2014, 2016, 2018, and 2019 will be the years of warm summers. The analysis appearing here would be useful for coastal-urban planners in emphasizing the impact of seawater extreme temperatures on urban industrial activities, etc. (author)

Statistical Studies of Mesoscale Forecast Models MM5 and WRF

National Research Council Canada - National Science Library

Henmi, Teizi

2004-01-01

... models were carried out and the results were compared with surface observation data. Both models tended to overforecast temperature and dew-point temperature, although the correlation coefficients between forecast and observations were fairly high...

Using ensemble forecasting for wind power

Energy Technology Data Exchange (ETDEWEB)

Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

2003-07-01

Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

Forecast combinations

OpenAIRE

Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

2010-01-01

We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

MOnthly TEmperature DAtabase of Spain 1951-2010: MOTEDAS (2): The Correlation Decay Distance (CDD) and the spatial variability of maximum and minimum monthly temperature in Spain during (1981-2010).

Science.gov (United States)

Cortesi, Nicola; Peña-Angulo, Dhais; Simolo, Claudia; Stepanek, Peter; Brunetti, Michele; Gonzalez-Hidalgo, José Carlos

2014-05-01

One of the key point in the develop of the MOTEDAS dataset (see Poster 1 MOTEDAS) in the framework of the HIDROCAES Project (Impactos Hidrológicos del Calentamiento Global en España, Spanish Ministery of Research CGL2011-27574-C02-01) is the reference series for which no generalized metadata exist. In this poster we present an analysis of spatial variability of monthly minimum and maximum temperatures in the conterminous land of Spain (Iberian Peninsula, IP), by using the Correlation Decay Distance function (CDD), with the aim of evaluating, at sub-regional level, the optimal threshold distance between neighbouring stations for producing the set of reference series used in the quality control (see MOTEDAS Poster 1) and the reconstruction (see MOREDAS Poster 3). The CDD analysis for Tmax and Tmin was performed calculating a correlation matrix at monthly scale between 1981-2010 among monthly mean values of maximum (Tmax) and minimum (Tmin) temperature series (with at least 90% of data), free of anomalous data and homogenized (see MOTEDAS Poster 1), obtained from AEMEt archives (National Spanish Meteorological Agency). Monthly anomalies (difference between data and mean 1981-2010) were used to prevent the dominant effect of annual cycle in the CDD annual estimation. For each station, and time scale, the common variance r2 (using the square of Pearson's correlation coefficient) was calculated between all neighbouring temperature series and the relation between r2 and distance was modelled according to the following equation (1): Log (r2ij) = b*°dij (1) being Log(rij2) the common variance between target (i) and neighbouring series (j), dij the distance between them and b the slope of the ordinary least-squares linear regression model applied taking into account only the surrounding stations within a starting radius of 50 km and with a minimum of 5 stations required. Finally, monthly, seasonal and annual CDD values were interpolated using the Ordinary Kriging with a

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

Science.gov (United States)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

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.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0

Directory of Open Access Journals (Sweden)

A. Pasternack

2018-01-01

Full Text Available Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt, a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS. Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Science.gov (United States)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

International Nuclear Information System (INIS)

Halepoto, I.A.; Uqaili, M.A.

2014-01-01

Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

Forecasting short-run crude oil price using high- and low-inventory variables

International Nuclear Information System (INIS)

Ye, Michael; Zyren, John; Shore, Joanne

2006-01-01

Since inventories have a lower bound or a minimum operating level, economic literature suggests a nonlinear relationship between inventory level and commodity prices. This was found to be the case in the short-run crude oil market. In order to explore this inventory-price relationship, two nonlinear inventory variables are defined and derived from the monthly normal level and relative level of OECD crude oil inventories from post 1991 Gulf War to October 2003: one for the low inventory state and another for the high inventory state of the crude oil market. Incorporation of low- and high-inventory variables in a single equation model to forecast short-run WTI crude oil prices enhances the model fit and forecast ability

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

Science.gov (United States)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

Miscanthus establishment and overwintering in the Midwest USA: a regional modeling study of crop residue management on critical minimum soil temperatures.

Directory of Open Access Journals (Sweden)

Christopher J Kucharik

Full Text Available Miscanthus is an intriguing cellulosic bioenergy feedstock because its aboveground productivity is high for low amounts of agrochemical inputs, but soil temperatures below -3.5 °C could threaten successful cultivation in temperate regions. We used a combination of observed soil temperatures and the Agro-IBIS model to investigate how strategic residue management could reduce the risk of rhizome threatening soil temperatures. This objective was addressed using a historical (1978-2007 reconstruction of extreme minimum 10 cm soil temperatures experienced across the Midwest US and model sensitivity studies that quantified the impact of crop residue on soil temperatures. At observation sites and for simulations that had bare soil, two critical soil temperature thresholds (50% rhizome winterkill at -3.5 °C and -6.0 °C for different Miscanthus genotypes were reached at rhizome planting depth (10 cm over large geographic areas. The coldest average annual extreme 10 cm soil temperatures were between -8 °C to -11 °C across North Dakota, South Dakota, and Minnesota. Large portions of the region experienced 10 cm soil temperatures below -3.5 °C in 75% or greater for all years, and portions of North and South Dakota, Minnesota, and Wisconsin experienced soil temperatures below -6.0 °C in 50-60% of all years. For simulated management options that established varied thicknesses (1-5 cm of miscanthus straw following harvest, extreme minimum soil temperatures increased by 2.5 °C to 6 °C compared to bare soil, with the greatest warming associated with thicker residue layers. While the likelihood of 10 cm soil temperatures reaching -3.5 °C was greatly reduced with 2-5 cm of surface residue, portions of the Dakotas, Nebraska, Minnesota, and Wisconsin still experienced temperatures colder than -3.5 °C in 50-80% of all years. Nonetheless, strategic residue management could help increase the likelihood of overwintering of miscanthus rhizomes in the first few

Fuel cycle forecasting - there are forecasts and there are forecasts

International Nuclear Information System (INIS)

Puechl, K.H.

1975-01-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

Fuel cycle forecasting - there are forecasts and there are forecasts

Energy Technology Data Exchange (ETDEWEB)

Puechl, K H

1975-12-01

The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis.

Space power needs and forecasted technologies for the 1990s and beyond

International Nuclear Information System (INIS)

Buden, D.; Albert, T.

1987-01-01

A new generation of reactors for electric power will be available for space missions to satisfy military and civilian needs in the 1990s and beyond. To ensure a useful product, nuclear power plant development must be cognizant of other space power technologies. Major advances in solar and chemical technologies need to be considered in establishing the goals of future nuclear power plants. In addition, the mission needs are evolving into new regimes. Civilian and military power needs are forecasted to exceed anything used in space to date. Technology trend forecasts have been mapped as a function of time for solar, nuclear, chemical, and storage systems to illustrate areas where each technology provides minimum mass. Other system characteristics may dominate the usefulness of a technology on a given mission. This paper will discuss some of these factors, as well as forecast future military and civilian power needs and the status of technologies for the 1990s and 2000s. 6 references

Seasonal Forecast Skill And Teleconnections Over East Africa

Science.gov (United States)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Effects of Recent Minimum Temperature and Water Deficit Increases on Pinus pinaster Radial Growth and Wood Density in Southern Portugal.

Science.gov (United States)

Kurz-Besson, Cathy B; Lousada, José L; Gaspar, Maria J; Correia, Isabel E; David, Teresa S; Soares, Pedro M M; Cardoso, Rita M; Russo, Ana; Varino, Filipa; Mériaux, Catherine; Trigo, Ricardo M; Gouveia, Célia M

2016-01-01

Western Iberia has recently shown increasing frequency of drought conditions coupled with heatwave events, leading to exacerbated limiting climatic conditions for plant growth. It is not clear to what extent wood growth and density of agroforestry species have suffered from such changes or recent extreme climate events. To address this question, tree-ring width and density chronologies were built for a Pinus pinaster stand in southern Portugal and correlated with climate variables, including the minimum, mean and maximum temperatures and the number of cold days. Monthly and maximum daily precipitations were also analyzed as well as dry spells. The drought effect was assessed using the standardized precipitation-evapotranspiration (SPEI) multi-scalar drought index, between 1 to 24-months. The climate-growth/density relationships were evaluated for the period 1958-2011. We show that both wood radial growth and density highly benefit from the strong decay of cold days and the increase of minimum temperature. Yet the benefits are hindered by long-term water deficit, which results in different levels of impact on wood radial growth and density. Despite of the intensification of long-term water deficit, tree-ring width appears to benefit from the minimum temperature increase, whereas the effects of long-term droughts significantly prevail on tree-ring density. Our results further highlight the dependency of the species on deep water sources after the juvenile stage. The impact of climate changes on long-term droughts and their repercussion on the shallow groundwater table and P. pinaster's vulnerability are also discussed. This work provides relevant information for forest management in the semi-arid area of the Alentejo region of Portugal. It should ease the elaboration of mitigation strategies to assure P. pinaster's production capacity and quality in response to more arid conditions in the near future in the region.

Effects of Recent Minimum Temperature and Water Deficit Increases on Pinus pinaster Radial Growth and Wood Density in Southern Portugal

Science.gov (United States)

Kurz-Besson, Cathy B.; Lousada, José L.; Gaspar, Maria J.; Correia, Isabel E.; David, Teresa S.; Soares, Pedro M. M.; Cardoso, Rita M.; Russo, Ana; Varino, Filipa; Mériaux, Catherine; Trigo, Ricardo M.; Gouveia, Célia M.

2016-01-01

Western Iberia has recently shown increasing frequency of drought conditions coupled with heatwave events, leading to exacerbated limiting climatic conditions for plant growth. It is not clear to what extent wood growth and density of agroforestry species have suffered from such changes or recent extreme climate events. To address this question, tree-ring width and density chronologies were built for a Pinus pinaster stand in southern Portugal and correlated with climate variables, including the minimum, mean and maximum temperatures and the number of cold days. Monthly and maximum daily precipitations were also analyzed as well as dry spells. The drought effect was assessed using the standardized precipitation-evapotranspiration (SPEI) multi-scalar drought index, between 1 to 24-months. The climate-growth/density relationships were evaluated for the period 1958-2011. We show that both wood radial growth and density highly benefit from the strong decay of cold days and the increase of minimum temperature. Yet the benefits are hindered by long-term water deficit, which results in different levels of impact on wood radial growth and density. Despite of the intensification of long-term water deficit, tree-ring width appears to benefit from the minimum temperature increase, whereas the effects of long-term droughts significantly prevail on tree-ring density. Our results further highlight the dependency of the species on deep water sources after the juvenile stage. The impact of climate changes on long-term droughts and their repercussion on the shallow groundwater table and P. pinaster’s vulnerability are also discussed. This work provides relevant information for forest management in the semi-arid area of the Alentejo region of Portugal. It should ease the elaboration of mitigation strategies to assure P. pinaster’s production capacity and quality in response to more arid conditions in the near future in the region. PMID:27570527

Forecasting giant, catastrophic slope collapse: lessons from Vajont, Northern Italy

Science.gov (United States)

Kilburn, Christopher R. J.; Petley, David N.

2003-08-01

Rapid, giant landslides, or sturzstroms, are among the most powerful natural hazards on Earth. They have minimum volumes of ˜10 6-10 7 m 3 and, normally preceded by prolonged intervals of accelerating creep, are produced by catastrophic and deep-seated slope collapse (loads ˜1-10 MPa). Conventional analyses attribute rapid collapse to unusual mechanisms, such as the vaporization of ground water during sliding. Here, catastrophic collapse is related to self-accelerating rock fracture, common in crustal rocks at loads ˜1-10 MPa and readily catalysed by circulating fluids. Fracturing produces an abrupt drop in resisting stress. Measured stress drops in crustal rock account for minimum sturzstrom volumes and rapid collapse accelerations. Fracturing also provides a physical basis for quantitatively forecasting catastrophic slope failure.

Short term and medium term power distribution load forecasting by neural networks

International Nuclear Information System (INIS)

Yalcinoz, T.; Eminoglu, U.

2005-01-01

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

Directory of Open Access Journals (Sweden)

Jinyin Ye

2016-01-01

Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

Science.gov (United States)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

Forecasting Zakat collection using artificial neural network

Science.gov (United States)

Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

2013-04-01

'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

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

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

Assimilation scheme of the Mediterranean Forecasting System: operational implementation

Directory of Open Access Journals (Sweden)

E. Demirov

2003-01-01

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

Forecasting conditional climate-change using a hybrid approach

Science.gov (United States)

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Forecasting Housing Approvals in Australia: Do Forecasters Herd?

DEFF Research Database (Denmark)

Stadtmann, Georg; Pierdzioch; Rülke

2012-01-01

Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

Safe Minimum Internal Temperature Chart

Science.gov (United States)

... Internal Temperature Chart Safe steps in food handling, cooking, and storage are essential in preventing foodborne illness. You can't see, smell, or taste harmful bacteria that may cause illness. In every step of food preparation, follow the four guidelines to keep food safe: ...

Simulation of a model predictive room temperature control by use of an ideal weather forecast; Simulation einer praediktiven Raumtemperaturregelung unter Verwendung einer idealen Wettervorhersage

Energy Technology Data Exchange (ETDEWEB)

Goertler, Gregor [Fachhochschulstudiengaenge Burgenland GesmbH, Pinkafeld (Austria). Kernkompetenzbereich Energie- und Umweltmanagement; Beigelboeck, Barbara

2010-12-15

Due to the use of MPC (Model Predictive Control) for room heating applications users and constructors expect nameable energy savings. By usage of a simulation for a special case the energy saving potential of predictive control algorithm for room temperature control in connection with an ideal weather forecast, in comparison to established algorithms (PI-control, two level controller) is estimated. The controlled system with the control variable room temperature is a room with floor heating which was modelled in TRNSYS. A linear state space model of the controlled system was derived with suitable identification methods. This model was used by the predictive control algorithm, which was programmed in MATLAB. The weather data was taken from the TRNSYS library and has been made available also for the control algorithm, so that an ideal weather forecast was established. For the example considered in this paper, the amount of energy saving was 10 % per year with the MPC-controller compared to a PI-controller. (Copyright copyright 2010 Ernst and Sohn Verlag fuer Architektur und technische Wissenschaften GmbH and Co. KG, Berlin)

Modelling the occurrence of heat waves in maximum and minimum temperatures over Spain and projections for the period 2031-60

Science.gov (United States)

Abaurrea, J.; Asín, J.; Cebrián, A. C.

2018-02-01

The occurrence of extreme heat events in maximum and minimum daily temperatures is modelled using a non-homogeneous common Poisson shock process. It is applied to five Spanish locations, representative of the most common climates over the Iberian Peninsula. The model is based on an excess over threshold approach and distinguishes three types of extreme events: only in maximum temperature, only in minimum temperature and in both of them (simultaneous events). It takes into account the dependence between the occurrence of extreme events in both temperatures and its parameters are expressed as functions of time and temperature related covariates. The fitted models allow us to characterize the occurrence of extreme heat events and to compare their evolution in the different climates during the observed period. This model is also a useful tool for obtaining local projections of the occurrence rate of extreme heat events under climate change conditions, using the future downscaled temperature trajectories generated by Earth System Models. The projections for 2031-60 under scenarios RCP4.5, RCP6.0 and RCP8.5 are obtained and analysed using the trajectories from four earth system models which have successfully passed a preliminary control analysis. Different graphical tools and summary measures of the projected daily intensities are used to quantify the climate change on a local scale. A high increase in the occurrence of extreme heat events, mainly in July and August, is projected in all the locations, all types of event and in the three scenarios, although in 2051-60 the increase is higher under RCP8.5. However, relevant differences are found between the evolution in the different climates and the types of event, with a specially high increase in the simultaneous ones.

Evaluation of daily maximum and minimum 2-m temperatures as simulated with the Regional Climate Model COSMO-CLM over Africa

Directory of Open Access Journals (Sweden)

Stefan Krähenmann

2013-07-01

Full Text Available The representation of the diurnal 2-m temperature cycle is challenging because of the many processes involved, particularly land-atmosphere interactions. This study examines the ability of the regional climate model COSMO-CLM (version 4.8 to capture the statistics of daily maximum and minimum 2-m temperatures (Tmin/Tmax over Africa. The simulations are carried out at two different horizontal grid-spacings (0.22° and 0.44°, and are driven by ECMWF ERA-Interim reanalyses as near-perfect lateral boundary conditions. As evaluation reference, a high-resolution gridded dataset of daily maximum and minimum temperatures (Tmin/Tmax for Africa (covering the period 2008–2010 is created using the regression-kriging-regression-kriging (RKRK algorithm. RKRK applies, among other predictors, the remotely sensed predictors land surface temperature and cloud cover to compensate for the missing information about the temperature pattern due to the low station density over Africa. This dataset allows the evaluation of temperature characteristics like the frequencies of Tmin/Tmax, the diurnal temperature range, and the 90th percentile of Tmax. Although the large-scale patterns of temperature are reproduced well, COSMO-CLM shows significant under- and overestimation of temperature at regional scales. The hemispheric summers are generally too warm and the day-to-day temperature variability is overestimated over northern and southern extra-tropical Africa. The average diurnal temperature range is underestimated by about 2°C across arid areas, yet overestimated by around 2°C over the African tropics. An evaluation based on frequency distributions shows good model performance for simulated Tmin (the simulated frequency distributions capture more than 80% of the observed ones, but less well performance for Tmax (capture below 70%. Further, over wide parts of Africa a too large fraction of daily Tmax values exceeds the observed 90th percentile of Tmax, particularly

Evaluation of daily maximum and minimum 2-m temperatures as simulated with the regional climate model COSMO-CLM over Africa

Energy Technology Data Exchange (ETDEWEB)

Kraehenmann, Stefan; Kothe, Steffen; Ahrens, Bodo [Frankfurt Univ. (Germany). Inst. for Atmospheric and Environmental Sciences; Panitz, Hans-Juergen [Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen (Germany)

2013-10-15

The representation of the diurnal 2-m temperature cycle is challenging because of the many processes involved, particularly land-atmosphere interactions. This study examines the ability of the regional climate model COSMO-CLM (version 4.8) to capture the statistics of daily maximum and minimum 2-m temperatures (Tmin/Tmax) over Africa. The simulations are carried out at two different horizontal grid-spacings (0.22 and 0.44 ), and are driven by ECMWF ERA-Interim reanalyses as near-perfect lateral boundary conditions. As evaluation reference, a high-resolution gridded dataset of daily maximum and minimum temperatures (Tmin/Tmax) for Africa (covering the period 2008-2010) is created using the regression-kriging-regression-kriging (RKRK) algorithm. RKRK applies, among other predictors, the remotely sensed predictors land surface temperature and cloud cover to compensate for the missing information about the temperature pattern due to the low station density over Africa. This dataset allows the evaluation of temperature characteristics like the frequencies of Tmin/Tmax, the diurnal temperature range, and the 90{sup th} percentile of Tmax. Although the large-scale patterns of temperature are reproduced well, COSMO-CLM shows significant under- and overestimation of temperature at regional scales. The hemispheric summers are generally too warm and the day-to-day temperature variability is overestimated over northern and southern extra-tropical Africa. The average diurnal temperature range is underestimated by about 2 C across arid areas, yet overestimated by around 2 C over the African tropics. An evaluation based on frequency distributions shows good model performance for simulated Tmin (the simulated frequency distributions capture more than 80% of the observed ones), but less well performance for Tmax (capture below 70%). Further, over wide parts of Africa a too large fraction of daily Tmax values exceeds the observed 90{sup th} percentile of Tmax, particularly across

Forecasting Behavior in Smart Homes Based on Sleep and Wake Patterns

Science.gov (United States)

Williams, Jennifer A.; Cook, Diane J.

2017-01-01

Background The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. Objective We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. Methods This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. Results The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. Conclusions The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa. PMID:27689555

Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

Science.gov (United States)

Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

2016-04-01

Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

Forecasting freight flows

DEFF Research Database (Denmark)

Lyk-Jensen, Stéphanie

2011-01-01

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...... constitute a valuable input to freight models for forecasting future capacity problems.......Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment...

Online updating procedures for a real-time hydrological forecasting system

International Nuclear Information System (INIS)

Kahl, B; Nachtnebel, H P

2008-01-01

Rainfall-runoff-models can explain major parts of the natural runoff pattern but never simulate the observed hydrograph exactly. Reasons for errors are various sources of uncertainties embedded in the model forecasting system. Errors are due to measurement errors, the selected time period for calibration and validation, the parametric uncertainty and the model imprecision. In on-line forecasting systems forecasted input data is used which additionally generates a major uncertainty for the hydrological forecasting system. Techniques for partially compensating these uncertainties are investigated in the recent study in a medium sized catchment in the Austrian part of the Danube basin. The catchment area is about 1000 km2. The forecasting system consists of a semi-distributed continuous rainfall-runoff model that uses quantitative precipitation and temperature forecasts. To provide adequate system states at the beginning of the forecasting period continuous simulation is required, especially in winter. In this study two online updating methods are used and combined for enhancing the runoff forecasts. The first method is used for updating the system states at the beginning of the forecasting period by changing the precipitation input. The second method is an autoregressive error model, which is used to eliminate systematic errors in the model output. In combination those two methods work together well as each method is more effective in different runoff situations.

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Directory of Open Access Journals (Sweden)

Y. Tulunay

2008-12-01

Full Text Available Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M. The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS, a data driven Neural Network module (METU-FNN of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC and cloud top temperatures (CTT are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

Robust forecast comparison

OpenAIRE

Jin, Sainan; Corradi, Valentina; Swanson, Norman

2015-01-01

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

Science.gov (United States)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

The practical performance forecast and analysis of thermoelectric module from macro to micro

International Nuclear Information System (INIS)

Shen, Limei; Chen, Huanxin; Xiao, Fu; Wang, Shengwei

2015-01-01

Highlights: • We analyze the practical performance of TEMs to meet specific requirements. • The influence of different input power sources are discussed. • The step-change phenomena of thermoelectric cooling are found and discussed. • The influence ratio of hot side heat exchanger and input power source is compared. - Abstract: The practical operating conditions of thermoelectric products, such as the input power source and the thermal resistance of hot side heat exchanger, are different from the theoretical study. Thus the equations, which are used to estimate the practical maximum cooling performance just according to the datum in datasheet of commercial thermoelectric module (TEM), are given. The nested loop method is adopted to solve the numerical model. This study provides a method to choose a suitable TEM for thermoelectric product to meet the application requirement. It finds that the minimum cold side temperature increase and the voltage for achieving the minimum cold side temperature step decrease with the increase of thermal resistance of hot side heat exchanger, respectively. The maximum temperature difference increase and the voltage for achieving the maximum temperature difference step increase with the increase of thermal resistance of hot side heat exchanger, respectively. According to the dimension, three kinds of thermoelectric module, bulk TEM, miniature TEM and micro TEM, are studied. The novel scale effect are discovered by comparing these TEMs. It found that the step-change phenomenon become more and more obvious with the decrease of the dimension of thermoelectric module. The influence ratio of thermal resistance of hot side heat exchanger on the maximum cooling performance increases and the influence ratio of input power source decreases from macro to micro, respectively. It forecasts that there exists a critical value for the dimension of thermoelectric module, when the dimension of thermoelectric module is smaller than this critical

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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...

Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

International Nuclear Information System (INIS)

Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

2009-01-01

The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

Verification of ECMWF and ECMWF/MACC's global and direct irradiance forecasts with respect to solar electricity production forecasts

Directory of Open Access Journals (Sweden)

M. Schroedter-Homscheidt

2017-02-01

conversion scheme. Cloudy situations and especially thin ice cloud cases are forecasted much better with respect to biases and RMSE, but large biases are introduced in clear sky cases. When applying the MACC aerosol scheme to include aerosol direct effects, an improvement especially in DNI biases is found for cloud free cases as expected. However, a performance decrease is found for water cloud cases. It is assumed that this is caused by the lack of an explicit modelling of cloud-aerosol interactions, while other meteorological forcings for cloud processes like the temperature field are modified by the aerosols.

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

National Forecast Charts

Science.gov (United States)

code. Press enter or select the go button to submit request Local forecast by "City, St" or Prediction Center on Twitter NCEP Quarterly Newsletter WPC Home Analyses and Forecasts National Forecast to all federal, state, and local government web resources and services. National Forecast Charts

The impact of different sea-surface temperature prediction scenarios on Southern African seasonal climate forecast skill

CSIR Research Space (South Africa)

Landman, WA

2009-12-01

Full Text Available Retro-active forecasts produced at a 1-month lead-time by the ECHAM4.5 AGCM are statistically downscaled to South African district rainfall totals for the austral mid-summer season of December to February. The AGCM is forced with SST forecasts...

A stochastic HMM-based forecasting model for fuzzy time series.

Science.gov (United States)

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

COMBINING LONG MEMORY AND NONLINEAR MODEL OUTPUTS FOR INFLATION FORECAST

OpenAIRE

Heri Kuswanto; Irhamah Alimuhajin; Laylia Afidah

2014-01-01

Long memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast is to obtain the future condition with minimum error. Some researches argued that no matter what the model is, the important thi...

Inflight fuel tank temperature survey data

Science.gov (United States)

Pasion, A. J.

1979-01-01

Statistical summaries of the fuel and air temperature data for twelve different routes and for different aircraft models (B747, B707, DC-10 and DC-8), are given. The minimum fuel, total air and static air temperature expected for a 0.3% probability were summarized in table form. Minimum fuel temperature extremes agreed with calculated predictions and the minimum fuel temperature did not necessarily equal the minimum total air temperature even for extreme weather, long range flights.

Forecasting telecommunication new service demand by analogy method and combined forecast

Directory of Open Access Journals (Sweden)

Lin Feng-Jenq

2005-01-01

Full Text Available In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.

Exploring the interactions between forecast accuracy, risk perception and perceived forecast reliability in reservoir operator's decision to use forecast

Science.gov (United States)

Shafiee-Jood, M.; Cai, X.

2017-12-01

Advances in streamflow forecasts at different time scales offer a promise for proactive flood management and improved risk management. Despite the huge potential, previous studies have found that water resources managers are often not willing to incorporate streamflow forecasts information in decisions making, particularly in risky situations. While low accuracy of forecasts information is often cited as the main reason, some studies have found that implementation of streamflow forecasts sometimes is impeded by institutional obstacles and behavioral factors (e.g., risk perception). In fact, a seminal study by O'Connor et al. (2005) found that risk perception is the strongest determinant of forecast use while managers' perception about forecast reliability is not significant. In this study, we aim to address this issue again. However, instead of using survey data and regression analysis, we develop a theoretical framework to assess the user-perceived value of streamflow forecasts. The framework includes a novel behavioral component which incorporates both risk perception and perceived forecast reliability. The framework is then used in a hypothetical problem where reservoir operator should react to probabilistic flood forecasts with different reliabilities. The framework will allow us to explore the interactions among risk perception and perceived forecast reliability, and among the behavioral components and information accuracy. The findings will provide insights to improve the usability of flood forecasts information through better communication and education.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

Science.gov (United States)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation

Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

Directory of Open Access Journals (Sweden)

Zied Ben Bouallègue

2013-02-01

Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.

Regression trees modeling and forecasting of PM10 air pollution in urban areas

Science.gov (United States)

Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.

2017-10-01

Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.

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

Science.gov (United States)

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

2017-04-01

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

A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

International Nuclear Information System (INIS)

Deo, Ravinesh C.; Wen, Xiaohu; Qi, Feng

2016-01-01

Highlights: • A forecasting model for short- and long-term global incident solar radiation (R_n) has been developed. • The support vector machine and discrete wavelet transformation algorithm has been integrated. • The precision of the wavelet-coupled hybrid model is assessed using several prediction score metrics. • The proposed model is an appealing tool for forecasting R_n in the present study region. - Abstract: A solar radiation forecasting model can be utilized is a scientific contrivance for investigating future viability of solar energy potentials. In this paper, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (S_t), minimum temperature (T_m_a_x), maximum temperature (T_m_a_x), windspeed (U), evaporation (E) and precipitation (P) as the predictor variables. To ascertain conclusive results, the merit of the W-SVM was benchmarked with the classical SVM model. For daily forecasting, sixteen months of data (01-March-2014 to 30-June-2015) partitioned into the train (65%) and test (35%) set for the three metropolitan stations (Brisbane City, Cairns Aero and Townsville Aero) were utilized. Data were decomposed into their wavelet sub-series by discrete wavelet transformation algorithm and summed up to create new series with one approximation and four levels of detail using Daubechies-2 mother wavelet. For daily forecasting, six model scenarios were formulated where the number of input was increased and the forecast was assessed by statistical metrics (correlation coefficient r; Willmott’s index d; Nash-Sutcliffe coefficient E_N_S; peak deviation P_d_v), distribution statistics and prediction errors (mean absolute error MAE; root mean square error RMSE; mean absolute percentage error MAPE; relative root mean square error RMSE). Results for daily forecasts showed that the W-SVM model outperformed the classical SVM model for optimum input combinations. A sensitivity

Minimum airflow reset of single-duct VAV terminal boxes

Science.gov (United States)

Cho, Young-Hum

Single duct Variable Air Volume (VAV) systems are currently the most widely used type of HVAC system in the United States. When installing such a system, it is critical to determine the minimum airflow set point of the terminal box, as an optimally selected set point will improve the level of thermal comfort and indoor air quality (IAQ) while at the same time lower overall energy costs. In principle, this minimum rate should be calculated according to the minimum ventilation requirement based on ASHRAE standard 62.1 and maximum heating load of the zone. Several factors must be carefully considered when calculating this minimum rate. Terminal boxes with conventional control sequences may result in occupant discomfort and energy waste. If the minimum rate of airflow is set too high, the AHUs will consume excess fan power, and the terminal boxes may cause significant simultaneous room heating and cooling. At the same time, a rate that is too low will result in poor air circulation and indoor air quality in the air-conditioned space. Currently, many scholars are investigating how to change the algorithm of the advanced VAV terminal box controller without retrofitting. Some of these controllers have been found to effectively improve thermal comfort, indoor air quality, and energy efficiency. However, minimum airflow set points have not yet been identified, nor has controller performance been verified in confirmed studies. In this study, control algorithms were developed that automatically identify and reset terminal box minimum airflow set points, thereby improving indoor air quality and thermal comfort levels, and reducing the overall rate of energy consumption. A theoretical analysis of the optimal minimum airflow and discharge air temperature was performed to identify the potential energy benefits of resetting the terminal box minimum airflow set points. Applicable control algorithms for calculating the ideal values for the minimum airflow reset were developed and

On the effect of model parameters on forecast objects

Science.gov (United States)

Marzban, Caren; Jones, Corinne; Li, Ning; Sandgathe, Scott

2018-04-01

Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Neural networks to predict exosphere temperature corrections

Science.gov (United States)

Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

2013-10-01

Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

Science.gov (United States)

Kim, Whan-Seon

2013-06-01

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.

Development and testing of improved statistical wind power forecasting methods.

Energy Technology Data Exchange (ETDEWEB)

Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

2011-12-06

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

Operational Hydrologic Forecasts in the Columbia River Basin

Science.gov (United States)

Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

2013-12-01

The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of

A robust method to forecast volcanic ash clouds

Science.gov (United States)

Denlinger, Roger P.; Pavolonis, Mike; Sieglaff, Justin

2012-01-01

Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an

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

Science.gov (United States)

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

2014-05-01

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

Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

Science.gov (United States)

Yoo, Cheolhee; Im, Jungho; Park, Seonyoung; Quackenbush, Lindi J.

2018-03-01

Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with Tmax/Tmin for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R2 of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for Tmax and Tmin in Los Angeles, and R2 of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for Tmax and Tmin in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that Tmax was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while Tmin showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.

Short-range quantitative precipitation forecasting using Deep Learning approaches

Science.gov (United States)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Predicting short-range quantitative precipitation is very important for flood forecasting, early flood warning and other hydrometeorological purposes. This study aims to improve the precipitation forecasting skills using a recently developed and advanced machine learning technique named Long Short-Term Memory (LSTM). The proposed LSTM learns the changing patterns of clouds from Cloud-Top Brightness Temperature (CTBT) images, retrieved from the infrared channel of Geostationary Operational Environmental Satellite (GOES), using a sophisticated and effective learning method. After learning the dynamics of clouds, the LSTM model predicts the upcoming rainy CTBT events. The proposed model is then merged with a precipitation estimation algorithm termed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) to provide precipitation forecasts. The results of merged LSTM with PERSIANN are compared to the results of an Elman-type Recurrent Neural Network (RNN) merged with PERSIANN and Final Analysis of Global Forecast System model over the states of Oklahoma, Florida and Oregon. The performance of each model is investigated during 3 storm events each located over one of the study regions. The results indicate the outperformance of merged LSTM forecasts comparing to the numerical and statistical baselines in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), RMSE and correlation coefficient especially in convective systems. The proposed method shows superior capabilities in short-term forecasting over compared methods.

Overview of Hydrometeorologic Forecasting Procedures at BC Hydro

Science.gov (United States)

McCollor, D.

2004-12-01

Energy utility companies must balance production from limited sources with increasing demand from industrial, business, and residential consumers. The utility planning process requires a balanced, efficient, and effective distribution of energy from source to consumer. Therefore utility planners must consider the impact of weather on energy production and consumption. Hydro-electric companies should be particularly tuned to weather because their source of energy is water, and water supply depends on precipitation. BC Hydro operates as the largest hydro-electric company in western Canada, managing over 30 reservoirs within the province of British Columbia, and generating electricity for 1.6 million people. BC Hydro relies on weather forecasts of watershed precipitation and temperature to drive hydrologic reservoir inflow models and of urban temperatures to meet energy demand requirements. Operations and planning specialists in the company rely on current, value-added weather forecasts for extreme high-inflow events, daily reservoir operations planning, and long-term water resource management. Weather plays a dominant role for BC Hydro financial planners in terms of sensitive economic responses. For example, a two percent change in hydropower generation, due in large part to annual precipitation patterns, results in an annual net change of \\50 million in earnings. A five percent change in temperature produces a \\5 million change in yearly earnings. On a daily basis, significant precipitation events or temperature extremes involve potential profit/loss decisions in the tens of thousands of dollars worth of power generation. These factors are in addition to environmental and societal costs that must be considered equally as part of a triple bottom line reporting structure. BC Hydro water resource managers require improved meteorological information from recent advancements in numerical weather prediction. At BC Hydro, methods of providing meteorological forecast data

Inflation Forecast Contracts

OpenAIRE

Gersbach, Hans; Hahn, Volker

2012-01-01

We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers’ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balanc...

Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

Energy Technology Data Exchange (ETDEWEB)

Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

2013-11-01

As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

The effects of the recent minimum temperature and water deficit increases on Pinus pinaster wood radial growth and density in southern Portugal.

Directory of Open Access Journals (Sweden)

Cathy Béatrice Kurz Besson

2016-08-01

Full Text Available Western Iberia has recently shown increasing frequency of drought conditions coupled with heatwave events, leading to exacerbated limiting climatic conditions for plant growth. It is not clear to what extent wood growth and density of agroforestry species have suffered from such changes or recent extreme climate events.To address this question, tree-ring width and density chronologies were built for a P. pinaster stand in southern Portugal and correlated with climate variables, including the minimum, mean and maximum temperatures and the number of cold days. Monthly and maximum daily precipitations were also analyzed as well as dry spells. The drought effect was assessed using the standardized precipitation-evapotranspiration (SPEI multi-scalar drought index, between 1 to 24-months. The climate-growth/density relationships were evaluated for the period 1958-2011.We show that both wood radial growth and density highly benefit from the strong decay of cold days and the increase of minimum temperature. Yet the benefits are hindered by long-term water deficit, which results in different levels of impact on wood radial growth and density. Despite of the intensification of long-term water deficit, tree-ring width appears to benefit from the minimum temperature increase, whereas the effects of long-term droughts significantly prevail on tree-ring density. Our results further highlight the dependency of the species on deep water sources after the juvenile stage. The impact of climate changes on long-term droughts and their repercussion on the shallow groundwater table and P. pinaster’s vulnerability are also discussed. This work provides relevant information for forest management in the semi-arid area of the Alentejo region of Portugal. It should ease the elaboration of mitigation strategies to assure P. pinaster’s production capacity and quality in response to more arid conditions in the near future in the region.

Early forecasting of Indian Summer Monsoon: case study 2016

Science.gov (United States)

Surovyatkina, Elena; Stolbova, Veronika; Kurths, Jurgen

2017-04-01

The prior knowledge of dates of onset and withdrawal of monsoon is of vital importance for the population of the Indian subcontinent. In May 2016 before monsoon season, India recorded its highest-ever temperature of 51C. Hot waves have decimated crops, killed livestock and left 330 million people without enough water. At the end of monsoon season the floods in Indian this year have also broken previous records. Severe and devastating rainfall poured down, triggering dams spilling and floods. Such extreme conditions pose the vital questions such as: When will the monsoon come? When will the monsoon withdraw? More lead time in monsoon forecast warning is crucial for taking appropriate decisions at various levels - from the farmer's field (e.g. plowing day, seeding) to the central government (e.g. managing water and energy resources, food procurement policies). The Indian Meteorological Department issues forecasts of onset of monsoon for Kerala state in South India on May 15-th. It does not give such predictions for the other 28 states of the country. Our study concerns the central part of India. We made the monsoon forecast using our recently developed method which focuses on Tipping elements of the Indian monsoon [1]. Our prediction relies on observations of near-surface air temperature and relative humidity from both the ERA-40 and NCEP/NCAR reanalyses. We performed both of our forecasts for the onset and withdrawal of monsoon for the central part of India, the Eastern Ghats (20N,80E). We predicted the monsoon arrival to the Eastern Ghats (20N,80E) on the 13th of June with a deviation of +/-4 days. The prediction was made on May 6-th, 2016 [2], that is 40 days in advance of the date of the forecast. The actual monsoon arrival was June 17-th. In this day near-surface air temperature and relative humidity overcame the critical values and the monsoon season started, that was confirmed by observations of meteorological stations located around the EG-region. We

Cloud Impacts on Pavement Temperature in Energy Balance Models

Science.gov (United States)

Walker, C. L.

2013-12-01

Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex, yet direct relationship exists between tire and pavement temperatures. Literature has shown that as tire temperature increases, friction decreases which affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focused on forecast improvement by determining how cloud type impacts the amount of shortwave radiation reaching the surface and subsequent pavement temperatures. The study region was the Great Plains where surface solar radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Road pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud properties and radiative transfer quantities were obtained from the Clouds and Earth's Radiant Energy System mission via Aqua and Terra Moderate Resolution Imaging Spectroradiometer satellite products. An additional cloud data set was incorporated from the Naval Research Laboratory Cloud Classification algorithm. Statistical analyses using a modified nearest neighbor approach were first performed relating shortwave radiation variability with road pavement temperature fluctuations. Then statistical associations were determined between the shortwave radiation and cloud property data sets. Preliminary results suggest that substantial pavement forecasting improvement is possible with the inclusion of cloud-specific information. Future model sensitivity testing seeks to quantify the magnitude of forecast improvement.

Historical effects of temperature and precipitation on California crop yields

Energy Technology Data Exchange (ETDEWEB)

Lobell, D.B. [Energy and Environment Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550 (United States); Cahill, K.N. [Interdisciplinary Graduate Program in Environment and Resources, Stanford University, Stanford, CA 94305 (United States); Field, C.B. [Department of Global Ecology, Carnegie Institution, Stanford, CA 94305 (United States)

2007-03-15

For the 1980-2003 period, we analyzed the relationship between crop yield and three climatic variables (minimum temperature, maximum temperature, and precipitation) for 12 major Californian crops: wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios. The months and climatic variables of greatest importance to each crop were used to develop regressions relating yield to climatic conditions. For most crops, fairly simple equations using only 2-3 variables explained more than two-thirds of observed yield variance. The types of variables and months identified suggest that relatively poorly understood processes such as crop infection, pollination, and dormancy may be important mechanisms by which climate influences crop yield. Recent climatic trends have had mixed effects on crop yields, with orange and walnut yields aided, avocado yields hurt, and most crops little affected by recent climatic trends. Yield-climate relationships can provide a foundation for forecasting crop production within a year and for projecting the impact of future climate changes.

Forecasting short-term wind farm production in complex terrain. Volume 1

International Nuclear Information System (INIS)

LeBlanc, M.

2005-01-01

Wind energy forecasting adds financial value to wind farms and may soon become a regulatory requirement. A robust information technology system is essential for addressing industry demands. Various forecasting methodologies for short-term wind production in complex terrain were presented. Numerical weather predictions were discussed with reference to supervisory control and data acquisition (SCADA) system site measurements. Forecasting methods using wind speed, direction, temperature and pressure, as well as issues concerning statistical modelling were presented. Model output statistics and neural networks were reviewed, as well as significant components of error. Results from a Garrad Hassan forecaster with a European wind farm were presented, including wind speed evaluation, and forecast horizon for T + 1 hours, T + 12 hours, and T + 36 hours. It was suggested that buy prices often reflect the cost of under-prediction, and that forecasting has more potential where the spread is greatest. Accurate T + 19 hours to T + 31 hours could enable participation in the day-ahead market, which is less volatile and prices are usually better. Estimates of possible profits per annum through the use of GH forecaster power predictions were presented, calculated over and above spilling power to the grid. It was concluded that accurate forecasts combined with certainty evaluation enables the optimization of wind energy in the market, and is applicable to a wide range of weather regimes and terrain types. It was suggested that site feedback is essential for good forecasts at short horizons, and that the value of forecasting is dependent on the market. refs., tabs., figs

East Asian winter monsoon forecasting schemes based on the NCEP's climate forecast system

Science.gov (United States)

Tian, Baoqiang; Fan, Ke; Yang, Hongqing

2017-12-01

The East Asian winter monsoon (EAWM) is the major climate system in the Northern Hemisphere during boreal winter. In this study, we developed two schemes to improve the forecasting skill of the interannual variability of the EAWM index (EAWMI) using the interannual increment prediction method, also known as the DY method. First, we found that version 2 of the NCEP's Climate Forecast System (CFSv2) showed higher skill in predicting the EAWMI in DY form than not. So, based on the advantage of the DY method, Scheme-I was obtained by adding the EAWMI DY predicted by CFSv2 to the observed EAWMI in the previous year. This scheme showed higher forecasting skill than CFSv2. Specifically, during 1983-2016, the temporal correlation coefficient between the Scheme-I-predicted and observed EAWMI was 0.47, exceeding the 99% significance level, with the root-mean-square error (RMSE) decreased by 12%. The autumn Arctic sea ice and North Pacific sea surface temperature (SST) are two important external forcing factors for the interannual variability of the EAWM. Therefore, a second (hybrid) prediction scheme, Scheme-II, was also developed. This scheme not only involved the EAWMI DY of CFSv2, but also the sea-ice concentration (SIC) observed the previous autumn in the Laptev and East Siberian seas and the temporal coefficients of the third mode of the North Pacific SST in DY form. We found that a negative SIC anomaly in the preceding autumn over the Laptev and the East Siberian seas could lead to a significant enhancement of the Aleutian low and East Asian westerly jet in the following winter. However, the intensity of the winter Siberian high was mainly affected by the third mode of the North Pacific autumn SST. Scheme-I and Scheme-II also showed higher predictive ability for the EAWMI in negative anomaly years compared to CFSv2. More importantly, the improvement in the prediction skill of the EAWMI by the new schemes, especially for Scheme-II, could enhance the forecasting skill of

Using Collar worn Sensors to Forecast Thermal Strain in Military Working Dogs

Science.gov (United States)

2016-04-22

Using Collar-worn Sensors to Forecast Thermal Strain in Military Working Dogs James R. Williamson, Austin R. Hess, Christopher J. Smalt, Delsey M...these estimates for forecasting and monitoring thermal strain is assessed based on performance in out of sample prediction of core temperature (Tc...time step (100 Hz) from the magnitude of the three- dimensional acceleration vector, ai , which is independent of sensor orientation. Next, the

High-Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

Science.gov (United States)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.; Kotamarthi, V. Rao

2017-12-01

The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary conditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal-length time slices (2045-2054 and 2085-2094) are compared with a historical decade (1995-2004). Probability density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm-side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm-side shift with decreased variability. Using "business-as-usual" scenario, 5-day heat waves are projected to occur at least 5-10 times per year in most CONUS and ≥95°F days will increase by 1-2 months by the end of the century.

A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

Directory of Open Access Journals (Sweden)

Chengshi Tian

2018-03-01

Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

Sales Forecasting System for Newspaper Distribution Companies in Turkey

Directory of Open Access Journals (Sweden)

Gencay İncesu

2012-07-01

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

Approximation to a model for forecasting radiation fog in Rionegro-Antioquia

International Nuclear Information System (INIS)

Aguilar Gonzalez, Ana Isabel; Leon Aristizabal, Gloria

2004-01-01

Based on the discriminant analysis method, the modeling of the fog was carried out in order to forecast its occurrence with 3, 5 and 6 hours of anticipation, considering as the predictor variables: air temperature, dew point depression, strength of the surface wind, heat flow in the ground, latent heat flow, sensible heat flow, and the friction velocity. The values of these variables are taken from the hourly surface weather data at the airport in question, whereas the micro meteorological type variables are obtained through the program CAPLIM, thus enabling this model to be applied with operative aims. The forecast skill of the model depends on the prior time span of prediction. Three hours ahead of the occurrence of the phenomenon, the forecasts are right in 76% of cases, whereas if forecast five to six hours in advance the skill scores 53%

AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis in a Global Data Assimilation and Forecasting System

Science.gov (United States)

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

2009-01-01

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

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

Directory of Open Access Journals (Sweden)

Jan D. Keller

2008-12-01

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

Short-Term Load Forecast in Electric Energy System in Bulgaria

Directory of Open Access Journals (Sweden)

Irina Asenova

2010-01-01

Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

Science.gov (United States)

Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

2014-01-01

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

Science.gov (United States)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Impact of data assimilation on ocean current forecasts in the Angola Basin

Science.gov (United States)

Phillipson, Luke; Toumi, Ralf

2017-06-01

The ocean current predictability in the data limited Angola Basin was investigated using the Regional Ocean Modelling System (ROMS) with four-dimensional variational data assimilation. Six experiments were undertaken comprising a baseline case of the assimilation of salinity/temperature profiles and satellite sea surface temperature, with the subsequent addition of altimetry, OSCAR (satellite-derived sea surface currents), drifters, altimetry and drifters combined, and OSCAR and drifters combined. The addition of drifters significantly improves Lagrangian predictability in comparison to the baseline case as well as the addition of either altimetry or OSCAR. OSCAR assimilation only improves Lagrangian predictability as much as altimetry assimilation. On average the assimilation of either altimetry or OSCAR with drifter velocities does not significantly improve Lagrangian predictability compared to the drifter assimilation alone, even degrading predictability in some cases. When the forecast current speed is large, it is more likely that the combination improves trajectory forecasts. Conversely, when the currents are weaker, it is more likely that the combination degrades the trajectory forecast.

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

Science.gov (United States)

Barghouty, Nasser; Falconer, David

2015-01-01

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

The strategy of professional forecasting

DEFF Research Database (Denmark)

Ottaviani, Marco; Sørensen, Peter Norman

2006-01-01

We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...... contest with pre-specified rules. In a winner-take-all contest, equilibrium forecasts are excessively differentiated...

Are Forecast Updates Progressive?

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)

2010-01-01

textabstractMacro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average,

Forecasting the Reference Evapotranspiration Using Time Series Model

Directory of Open Access Journals (Sweden)

H. Zare Abyaneh

2016-10-01

Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

Seasonal UK Drought Forecasting using Statistical Methods

Science.gov (United States)

Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco

2016-04-01

In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.

Load forecasting

International Nuclear Information System (INIS)

Mak, H.

1995-01-01

Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

Relating Tropical Cyclone Track Forecast Error Distributions with Measurements of Forecast Uncertainty

Science.gov (United States)

2016-03-01

CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS WITH MEASUREMENTS OF FORECAST UNCERTAINTY by Nicholas M. Chisler March 2016 Thesis Advisor...March 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE RELATING TROPICAL CYCLONE TRACK FORECAST ERROR DISTRIBUTIONS...WITH MEASUREMENTS OF FORECAST UNCERTAINTY 5. FUNDING NUMBERS 6. AUTHOR(S) Nicholas M. Chisler 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES

Deformed special relativity with an energy barrier of a minimum speed

International Nuclear Information System (INIS)

Nassif, Claudio

2011-01-01

Full text: This research aims to introduce a new principle of symmetry in the flat space-time by means of the elimination of the classical idea of rest, and by including a universal minimum limit of speed in the quantum world. Such a limit, unattainable by the particles, represents a preferred inertial reference frame associated with a universal background field that breaks Lorentz symmetry. So there emerges a new relativistic dynamics where a minimum speed forms an inferior energy barrier. One of the interesting implications of the existence of such a minimum speed is that it prevents the absolute zero temperature for an ultracold gas, according to the third law of thermodynamics. So we will be able to provide a fundamental dynamical explanation for the third law by means of a connection between such a phenomenological law and the new relativistic dynamics with a minimum speed. In other words we say that our relevant investigation is with respect to the problem of the absolute zero temperature in the thermodynamics of an ideal gas. We have made a connection between the 3 rd law of Thermodynamics and the new dynamics with a minimum speed by means of a relation between the absolute zero temperature (T = 0 deg K) and a minimum average speed (V) for a gas with N particles (molecules or atoms). Since T = 0 deg K is thermodynamically unattainable, we have shown this is due to the impossibility of reaching V from the new dynamics standpoint. (author)

Seasonal forecasts: communicating current climate variability in southern Africa

CSIR Research Space (South Africa)

Landman, WA

2011-11-01

Full Text Available seasonal time scale. Seasonal climate forecasts are defined as probabilistic predictions of how much rain is expected during the season and how warm or cool it will be, based primarily on the principle that the ocean (sea-surface temperatures) influences...

Optimal temperature profiles for minimum residual stress in the cure process of polymer composites

CSIR Research Space (South Africa)

Gopal, AK

2000-01-01

Full Text Available include the minimum residual stresses, minimum cure cycle lime and full degree of cure. The development of residual stresses during the cure cycle is one of the most important problems as they affect the strength and the mechanical properties of the final...

Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting

Directory of Open Access Journals (Sweden)

Danladi Ali

2018-03-01

Full Text Available Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy – neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE of 1.22%. This confirms that fuzzy – neuro is a good tool for load forecasting. Keywords: Electrical load, Load forecasting, Fuzzy logic, Back propagation, Neuro-fuzzy, Weather parameter

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Directory of Open Access Journals (Sweden)

Jui-Yi Ho

2015-04-01

Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

Large-scale analysis and forecast experiments with wind data from the Seasat A scatterometer

Science.gov (United States)

Baker, W. E.; Atlas, R.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.; Edelmann, D.

1984-01-01

A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

Challenges for operational forecasting and early warning of rainfall induced landslides

Science.gov (United States)

Guzzetti, Fausto

2017-04-01

models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

Use of Quality Controlled AIRS Temperature Soundings to Improve Forecast Skill

Science.gov (United States)

Susskind, Joel; Reale, Oreste; Iredell, Lena

2010-01-01

on use of a Standard profile dependent threshold (Delta)T(p). These Standard thresholds were designed as a compromise between optimal use for data assimilation purposes, which requires highest accuracy (tighter Quality Control), and climate purposes, which requires more spatial coverage (looser Quality Control). Subsequent research using Version 5 sounding and error estimates showed that tighter Quality Control performs better for data assimilation proposes, while looser Quality Control better spatial coverage) performs better for climate purposes. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 degree latitude x 0.67 degree longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates (delta)T(p) were used as the uncertainty for each measurement in the data assimilation process.

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

DEFF Research Database (Denmark)

Lusis, Peter; Khalilpour, Kaveh Rajab; Andrew, Lachlan

2017-01-01

forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies...... how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector...... regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power...

Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa

Science.gov (United States)

Shukla, Shraddhanand; Roberts, Jason B.; Hoell. Andrew,; Funk, Chris; Robertson, Franklin R.; Kirtmann, Benjamin

2016-01-01

The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

International Nuclear Information System (INIS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Chenwei, Nie; Dong, Ren

2014-01-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps

Results from Evaluations of Gridded CrIS/ATMS Visualization for Operational Forecasting

Science.gov (United States)

Stevens, E.; Zavodsky, B.; Dostalek, J.; Berndt, E.; Hoese, D.; White, K.; Bowlan, M.; Gambacorta, A.; Wheeler, A.; Haisley, C.; Smith, N.

2017-12-01

For forecast challenges which require diagnosis of the three-dimensional atmosphere, current observations, such as radiosondes, may not offer enough information. Satellite data can help fill the spatial and temporal gaps between soundings. In particular, temperature and moisture retrievals from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), which combines infrared soundings from the Cross-track Infrared Sounder (CrIS) with the Advanced Technology Microwave Sounder (ATMS) to retrieve profiles of temperature and moisture. NUCAPS retrievals are available in a wide swath with approximately 45-km spatial resolution at nadir and a local Equator crossing time of 1:30 A.M./P.M. enabling three-dimensional observations at asynoptic times. This abstract focuses on evaluation of a new visualization for NUCAPS within the operational National Weather Service Advanced Weather Interactive Processing System (AWIPS) decision support system that allows these data to be viewed in gridded horizontal maps or vertical cross sections. Two testbed evaluations have occurred in 2017: a Cold Air Aloft (CAA) evaluation at the Alaska Center Weather Service Unit and a Convective Potential evaluation at the NOAA Hazardous Weather Testbed. For CAA, at high latitudes during the winter months, the air at altitudes used by passenger and cargo aircraft can reach temperatures cold enough (-65°C) to begin to freeze jet fuel, and Gridded NUCAPS visualization was shown to help fill in the spatial and temporal gaps in data-sparse areas across the Alaskan airspace by identifying the 3D spatial extent of cold air features. For convective potential, understanding the vertical distribution of temperature and moisture is also very important for forecasting the potential for convection related to severe weather such as lightning, large hail, and tornadoes. The Gridded NUCAPS visualization was shown to aid forecasters in understanding temperature and moisture characteristics at critical levels

Strategic Forecasting

DEFF Research Database (Denmark)

Duus, Henrik Johannsen

2016-01-01

Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...... as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts...

Spatial electric load forecasting

CERN Document Server

Willis, H Lee

2002-01-01

Containing 12 new chapters, this second edition contains offers increased-coverage of weather correction and normalization of forecasts, anticipation of redevelopment, determining the validity of announced developments, and minimizing risk from over- or under-planning. It provides specific examples and detailed explanations of key points to consider for both standard and unusual utility forecasting situations, information on new algorithms and concepts in forecasting, a review of forecasting pitfalls and mistakes, case studies depicting challenging forecast environments, and load models illustrating various types of demand.

Electricity demand forecasting techniques

International Nuclear Information System (INIS)

Gnanalingam, K.

1994-01-01

Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

Institute of Scientific and Technical Information of China (English)

John ABBOT; Jennifer MAROHASY

2012-01-01

In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error (RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

Air Pollution Forecasts: An Overview.

Science.gov (United States)

Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

2018-04-17

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Air Pollution Forecasts: An Overview

Directory of Open Access Journals (Sweden)

Lu Bai

2018-04-01

Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

Air Pollution Forecasts: An Overview

Science.gov (United States)

Bai, Lu; Wang, Jianzhou; Lu, Haiyan

2018-01-01

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

Did a skillful prediction of sea surface temperatures help or hinder forecasting of the 2012 Midwestern US drought?

International Nuclear Information System (INIS)

Kam, Jonghun; Sheffield, Justin; Yuan, Xing; Wood, Eric F

2014-01-01

The latest drought to hit the Midwestern (MW) US region, in 2012, was driven by the least summer precipitation for the last three decades with $20 billion in agriculture losses. For 2012, the summer forecast skill for Pacific and Atlantic sea surface temperature (SST) anomalies and low MW precipitation is remarkably good for some National Multi-Model Ensemble (NMME) models, but this is not generally repeated for other drought years, with some models predicting extreme wet anomalies, despite skill in predicting Pacific and Atlantic SST anomalies. In order to diagnose the origins of the limited skill of the NMME models, we use singular value decomposition (SVD) for global SSTs and continental US (CONUS) precipitation from observational data and NMME hindcasts (1982–2012). Observational data indicate that there is an insignificant coupling between global SSTs and MW precipitation during summer over the last 30 years. However, the NMME climate forecast models show strong coupling and therefore predicted the 2012 drought fortuitously for the wrong reason (a strong pan-Pacific El Niño–Southern Oscillation (ENSO)-like pattern). The observational data indicate that the strength of ENSO teleconnections with CONUS precipitation has weakened and the precipitation footprint has shifted over the past decades, suggesting that the transient nature of teleconnections may play a role in poor model skill. (paper)

Forecast of sea surface temperature off the Peruvian coast using an autoregressive integrated moving average model

Directory of Open Access Journals (Sweden)

Carlos Quispe

2013-04-01

Full Text Available El Niño connects globally climate, ecosystems and socio-economic activities. Since 1980 this event has been tried to be predicted, but until now the statistical and dynamical models are insuffi cient. Thus, the objective of the present work was to explore using an autoregressive moving average model the effect of El Niño over the sea surface temperature (TSM off the Peruvian coast. The work involved 5 stages: identifi cation, estimation, diagnostic checking, forecasting and validation. Simple and partial autocorrelation functions (FAC and FACP were used to identify and reformulate the orders of the model parameters, as well as Akaike information criterium (AIC and Schwarz criterium (SC for the selection of the best models during the diagnostic checking. Among the main results the models ARIMA(12,0,11 were proposed, which simulated monthly conditions in agreement with the observed conditions off the Peruvian coast: cold conditions at the end of 2004, and neutral conditions at the beginning of 2005.

Forecast Accuracy Uncertainty and Momentum

OpenAIRE

Bing Han; Dong Hong; Mitch Warachka

2009-01-01

We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Mome...

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

Science.gov (United States)

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

2014-01-01

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

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

Science.gov (United States)

Zavodsky, Bradley; Kozlowski, Danielle; Case, Jonathan; Molthan, Andrew

2012-01-01

Short-term Prediction Research and Transition (SPoRT) seeks to improve short-term, regional weather forecasts using unique NASA products and capabilities SPoRT has developed a unique, real-time configuration of the NASA Unified Weather Research and Forecasting (WRF)WRF (ARW) that integrates all SPoRT modeling research data: (1) 2-km SPoRT Sea Surface Temperature (SST) Composite, (2) 3-km LIS with 1-km Greenness Vegetation Fraction (GVFs) (3) 45-km AIRS retrieved profiles. Transitioned this real-time forecast to NOAA's Hazardous Weather Testbed (HWT) as deterministic model at Experimental Forecast Program (EFP). Feedback from forecasters/participants and internal evaluation of SPoRT-WRF shows a cool, dry bias that appears to suppress convection likely related to methodology for assimilation of AIRS profiles Version 2 of the SPoRT-WRF will premier at the 2012 EFP and include NASA physics, cycling data assimilation methodology, better coverage of precipitation forcing, and new GVFs

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

Science.gov (United States)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation.

Science.gov (United States)

Tippett, Michael K.; Goddard, Lisa; Barnston, Anthony G.

2005-06-01

Interannual precipitation variability in central-southwest (CSW) Asia has been associated with East Asian jet stream variability and western Pacific tropical convection. However, atmospheric general circulation models (AGCMs) forced by observed sea surface temperature (SST) poorly simulate the region's interannual precipitation variability. The statistical-dynamical approach uses statistical methods to correct systematic deficiencies in the response of AGCMs to SST forcing. Statistical correction methods linking model-simulated Indo-west Pacific precipitation and observed CSW Asia precipitation result in modest, but statistically significant, cross-validated simulation skill in the northeast part of the domain for the period from 1951 to 1998. The statistical-dynamical method is also applied to recent (winter 1998/99 to 2002/03) multimodel, two-tier December-March precipitation forecasts initiated in October. This period includes 4 yr (winter of 1998/99 to 2001/02) of severe drought. Tercile probability forecasts are produced using ensemble-mean forecasts and forecast error estimates. The statistical-dynamical forecasts show enhanced probability of below-normal precipitation for the four drought years and capture the return to normal conditions in part of the region during the winter of 2002/03.May Kabul be without gold, but not without snow.—Traditional Afghan proverb

klax Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kprc Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

katl Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kmcn Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kogb Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kama Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ptkk Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kiwa Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kavp Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kdca Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbwg Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kdfw Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kssi Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

pahn Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksrq Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kpvd Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kisp Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kttd Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

pmdy Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kmgm Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

khib Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

pavd Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kfar Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kluk Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kwwr Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

klse Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksts Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

koth Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbfl Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksgf Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

klch Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kpkb Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

krog Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbjc Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksea Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbwi Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kftw Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kpuw Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kabq Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksny Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

khio Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

klaf Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kfoe Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ksmx Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kipt Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ktrk Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kwmc Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

katy Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

tjmz Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kdet Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kcxp Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbur Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

krkd Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

pawg Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kloz Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kcec Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kdec Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

paor Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kavl Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kdrt Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kstl Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kbfi Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

khsv Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

pafa Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kekn Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

tncm Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kith Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kgnv Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

ktoi Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

kgso Terminal Aerodrome Forecast

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...