WorldWideScience

Sample records for quantitative precipitation forecasting

  1. Validating quantitative precipitation forecast for the Flood ...

    Indian Academy of Sciences (India)

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitationis required. In view of this, the present study intends to validate the quantitative precipitationforecast (QPF) issued during southwest monsoon season for six river catchments (basin) under theflood meteorological ...

  2. 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.

  3. Bayesian quantitative precipitation forecasts in terms of quantiles

    Science.gov (United States)

    Bentzien, Sabrina; Friederichs, Petra

    2014-05-01

    Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into

  4. Wavelet-based verification of the quantitative precipitation forecast

    Science.gov (United States)

    Yano, Jun-Ichi; Jakubiak, Bogumil

    2016-06-01

    This paper explores the use of wavelets for spatial verification of quantitative precipitation forecasts (QPF), and especially the capacity of wavelets to provide both localization and scale information. Two 24-h forecast experiments using the two versions of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) on 22 August 2010 over Poland are used to illustrate the method. Strong spatial localizations and associated intermittency of the precipitation field make verification of QPF difficult using standard statistical methods. The wavelet becomes an attractive alternative, because it is specifically designed to extract spatially localized features. The wavelet modes are characterized by the two indices for the scale and the localization. Thus, these indices can simply be employed for characterizing the performance of QPF in scale and localization without any further elaboration or tunable parameters. Furthermore, spatially-localized features can be extracted in wavelet space in a relatively straightforward manner with only a weak dependence on a threshold. Such a feature may be considered an advantage of the wavelet-based method over more conventional "object" oriented verification methods, as the latter tend to represent strong threshold sensitivities. The present paper also points out limits of the so-called "scale separation" methods based on wavelets. Our study demonstrates how these wavelet-based QPF verifications can be performed straightforwardly. Possibilities for further developments of the wavelet-based methods, especially towards a goal of identifying a weak physical process contributing to forecast error, are also pointed out.

  5. Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

    Science.gov (United States)

    Huang, Ling; Luo, Yali

    2017-08-01

    Based on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium-Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April-June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013-2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light-to-heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm-moist air into south China by the low-level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30-36 h to 18-24 h to 6-12 h for the cases with large-scale forcing, but not for the locally produced cases.

  6. Operational 0-3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

    Science.gov (United States)

    Sokol, Z.; Kitzmiller, D.; Pešice, P.; Guan, S.

    2009-05-01

    The NOAA National Weather Service has maintained an automated, centralized 0-3 h prediction system for probabilistic quantitative precipitation forecasts since 2001. This advective-statistical system (ADSTAT) produces probabilities that rainfall will exceed multiple threshold values up to 50 mm at some location within a 40-km grid box. Operational characteristics and development methods for the system are described. Although development data were stratified by season and time of day, ADSTAT utilizes only a single set of nation-wide equations that relate predictor variables derived from radar reflectivity, lightning, satellite infrared temperatures, and numerical prediction model output to rainfall occurrence. A verification study documented herein showed that the operational ADSTAT reliably models regional variations in the relative frequency of heavy rain events. This was true even in the western United States, where no regional-scale, gridded hourly precipitation data were available during the development period in the 1990s. An effort was recently launched to improve the quality of ADSTAT forecasts by regionalizing the prediction equations and to adapt the model for application in the Czech Republic. We have experimented with incorporating various levels of regional specificity in the probability equations. The geographic localization study showed that in the warm season, regional climate differences and variations in the diurnal temperature cycle have a marked effect on the predictor-predictand relationships, and thus regionalization would lead to better statistical reliability in the forecasts.

  7. Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

    Directory of Open Access Journals (Sweden)

    Y. H. Lee

    2006-12-01

    Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

  8. Skill Assessment of An Hybrid Technique To Estimate Quantitative Precipitation Forecast For Galicia (nw Spain)

    Science.gov (United States)

    Lage, A.; Taboada, J. J.

    Precipitation is the most obvious of the weather elements in its effects on normal life. Numerical weather prediction (NWP) is generally used to produce quantitative precip- itation forecast (QPF) beyond the 1-3 h time frame. These models often fail to predict small-scale variations of rain because of spin-up problems and their coarse spatial and temporal resolution (Antolik, 2000). Moreover, there are some uncertainties about the behaviour of the NWP models in extreme situations (de Bruijn and Brandsma, 2000). Hybrid techniques, combining the benefits of NWP and statistical approaches in a flexible way, are very useful to achieve a good QPF. In this work, a new technique of QPF for Galicia (NW of Spain) is presented. This region has a percentage of rainy days per year greater than 50% with quantities that may cause floods, with human and economical damages. The technique is composed of a NWP model (ARPS) and a statistical downscaling process based on an automated classification scheme of at- mospheric circulation patterns for the Iberian Peninsula (J. Ribalaygua and R. Boren, 1995). Results show that QPF for Galicia is improved using this hybrid technique. [1] Antolik, M.S. 2000 "An Overview of the National Weather Service's centralized statistical quantitative precipitation forecasts". Journal of Hydrology, 239, pp:306- 337. [2] de Bruijn, E.I.F and T. Brandsma "Rainfall prediction for a flooding event in Ireland caused by the remnants of Hurricane Charley". Journal of Hydrology, 239, pp:148-161. [3] Ribalaygua, J. and Boren R. "Clasificación de patrones espaciales de precipitación diaria sobre la España Peninsular". Informes N 3 y 4 del Servicio de Análisis e Investigación del Clima. Instituto Nacional de Meteorología. Madrid. 53 pp.

  9. Development of Radar-Satellite Blended QPF (Quantitative Precipitation Forecast) Technique for heavy rainfall

    Science.gov (United States)

    Jang, Sangmin; Yoon, Sunkwon; Rhee, Jinyoung; Park, Kyungwon

    2016-04-01

    Due to the recent extreme weather and climate change, a frequency and size of localized heavy rainfall increases and it may bring various hazards including sediment-related disasters, flooding and inundation. To prevent and mitigate damage from such disasters, very short range forecasting and nowcasting of precipitation amounts are very important. Weather radar data very useful in monitoring and forecasting because weather radar has high resolution in spatial and temporal. Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems' direction and speed of movement and blended radar-COMS rain field is used for initial data. Since the original horizontal resolution of COMS is 4 km while that of radar is about 1 km, spatial downscaling technique is used to downscale the COMS data from 4 to 1 km pixels in order to match with the radar data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred in the southern part of Korean Peninsula on 25 August 2014. The results of this study will be used as input data for an urban stream real-time flood early warning system and a prediction model of landslide. Acknowledgement This research was supported by a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by

  10. Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system

    Science.gov (United States)

    Bera, Maitreyee; Ortel, Terry W.

    2018-01-12

    The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.

  11. Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN from a Geostationary Satellite.

    Directory of Open Access Journals (Sweden)

    Yu Liu

    Full Text Available The prediction of the short-term quantitative precipitation nowcasting (QPN from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN using images of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC; the Horn-Schunck optical-flow scheme (PHS; and the Pyramid Lucas-Kanade Optical Flow method (PPLK, which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6. The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

  12. Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite.

    Science.gov (United States)

    Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Ji, Wei

    2015-01-01

    The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN using images of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

  13. 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

  14. Sensitivity of quantitative precipitation forecasts to boundary layer parameterization: a flash flood case study in the Western Mediterranean

    Directory of Open Access Journals (Sweden)

    M. Zampieri

    2005-01-01

    Full Text Available The 'Montserrat-2000' severe flash flood event which occurred over Catalonia on 9 and 10 June 2000 is analyzed. Strong precipitation was generated by a mesoscale convective system associated with the development of a cyclone. The location of heavy precipitation depends on the position of the cyclone, which, in turn, is found to be very sensitive to various model characteristics and initial conditions. Numerical simulations of this case study using the hydrostatic BOLAM and the non-hydrostatic MOLOCH models are performed in order to test the effects of different formulations of the boundary layer parameterization: a modified version of the Louis (order 1 model and a custom version of the E-ℓ (order 1.5 model. Both of them require a diagnostic formulation of the mixing length, but the use of the turbulent kinetic energy equation in the E-ℓ model allows to represent turbulence history and non-locality effects and to formulate a more physically based mixing length. The impact of the two schemes is different in the two models. The hydrostatic model, run at 1/5 degree resolution, is less sensitive, but the quantitative precipitation forecast is in any case unsatisfactory in terms of localization and amount. Conversely, the non-hydrostatic model, run at 1/50 degree resolution, is capable of realistically simulate timing, position and amount of precipitation, with the apparently superior results obtained with the E-ℓ parameterization model.

  15. Operational 0–3 h probabilistic quantitative precipitation forecasts: Recent performance and potential enhancements

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Kitzmiller, D.; Pešice, Petr; Guan, S.

    2009-01-01

    Roč. 92, č. 3 (2009), s. 318-330 ISSN 0169-8095. [International workshop on precipitation in urban areas /7./. St. Moritz, 07.12.2006-10.12.2006] R&D Projects: GA MŠk 1P05ME748 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation * Prediction * Convection * Radar * Nowcasting Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.811, year: 2009

  16. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    2014-02-14

    Feb 14, 2014 ... Application of probabilistic precipitation forecasts from a deterministic model ... aim of this paper is to investigate the increase in the lead-time of flash flood warnings of the SAFFG using probabilistic precipitation forecasts ... The procedure is applied to a real flash flood event and the ensemble-based.

  17. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    Application of probabilistic precipitation forecasts from a deterministic model towards increasing the lead-time of flash flood forecasts in South Africa. ... The procedure is applied to a real flash flood event and the ensemble-based rainfall forecasts are verified against rainfall estimated by the SAFFG system. The approach ...

  18. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models

    Science.gov (United States)

    Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals...

  19. 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.

  20. Assessing Hourly Precipitation Forecast Skill with the Fractions Skill Score

    Science.gov (United States)

    Zhao, Bin; Zhang, Bo

    2018-02-01

    Statistical methods for category (yes/no) forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called "double-penalty" problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score (FSS), a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts. We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES (Global/Regional Assimilation Prediction System) regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation. Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.

  1. Forecasting Monsoon Precipitation Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

  2. Nowcasting of precipitation – Advective statistical forecast model (SAM) for the Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Pešice, Petr

    2012-01-01

    Roč. 103, - (2012), s. 70-79 ISSN 0169-8095 R&D Projects: GA MŠk ME09033; GA ČR GA205/07/0905 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Statistical models * Regression * Quantitative precipitation forecast * Extrapolation forecast Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.200, year: 2012 http://dx.doi.org/10.1016/j. atm osres.2011.07.013

  3. Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

    Directory of Open Access Journals (Sweden)

    Sheng-Chi Yang

    2014-01-01

    Full Text Available During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.

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

    Data.gov (United States)

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

  5. Regime-dependent forecast uncertainty of convective precipitation

    Energy Technology Data Exchange (ETDEWEB)

    Keil, Christian; Craig, George C. [Muenchen Univ. (Germany). Meteorologisches Inst.

    2011-04-15

    Forecast uncertainty of convective precipitation is influenced by all scales, but in different ways in different meteorological situations. Forecasts of the high resolution ensemble prediction system COSMO-DE-EPS of Deutscher Wetterdienst (DWD) are used to examine the dominant sources of uncertainty of convective precipitation. A validation with radar data using traditional as well as spatial verification measures highlights differences in precipitation forecast performance in differing weather regimes. When the forecast uncertainty can primarily be associated with local, small-scale processes individual members run with the same variation of the physical parameterisation driven by different global models outperform all other ensemble members. In contrast when the precipitation is governed by the large-scale flow all ensemble members perform similarly. Application of the convective adjustment time scale confirms this separation and shows a regime-dependent forecast uncertainty of convective precipitation. (orig.)

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

    Science.gov (United States)

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

    2017-04-01

    specific atmospheric variable without a complete description of the weather situation. In the first phase, the method considers a selection of analogous situations in terms of mean sea level pressure, specific humidity and total precipitation. In the second one, a subset of observations data is extracted according to the analogues found. The research of analogues consists of cascading filters designed to find the most similar weather situation in a historical archive of ECMWF analysis. The method has been calibrated in the period between 2008 and 2011, over different France weather stations (Paris, Meaux, La Londe Les Maures etc) in order to forecast extreme rainfall events. The results of the operational demonstrator, which has been running since September 2016 over the same France weather stations, show good performances in terms of prediction of extreme events at 24hrs horizon, meant as daily quantitative precipitation greater than 93th percentile of distribution, with a relative low false alarm rate.

  7. A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

    Science.gov (United States)

    Li, Xiang; He, Hongrang; Chen, Chaohui; Miao, Ziqing; Bai, Shigang

    2017-10-01

    A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

  8. Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

    Directory of Open Access Journals (Sweden)

    S. K. Jha

    2018-03-01

    Full Text Available Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP, developed in Australia (Robertson et al., 2013; Shrestha et al., 2015, has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS, from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

  9. Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

    Science.gov (United States)

    Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

    2018-03-01

    Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

  10. Seasonal precipitation forecast skill over Iran

    CSIR Research Space (South Africa)

    Shirvani, A

    2015-07-01

    Full Text Available . For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January (NDJ), December-January-February (DJF) and January-February-March (JFM) seasons were 0.68, 0...

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

    Directory of Open Access Journals (Sweden)

    S. Davolio

    2008-02-01

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

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

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

  12. Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts

    Science.gov (United States)

    Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2016-04-01

    Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows

  13. Evaluation of Real-Time Convection-Permitting Precipitation Forecasts in China During the 2013-2014 Summer Season

    Science.gov (United States)

    Zhu, Kefeng; Xue, Ming; Zhou, Bowen; Zhao, Kun; Sun, Zhengqi; Fu, Peiling; Zheng, Yongguang; Zhang, Xiaoling; Meng, Qingtao

    2018-01-01

    Forecasts at a 4 km convection-permitting resolution over China during the summer season have been produced with the Weather Research and Forecasting model at Nanjing University since 2013. Precipitation forecasts from 2013 to 2014 are evaluated with dense rain gauge observations and compared with operational global model forecasts. Overall, the 4 km forecasts show very good agreement with observations over most parts of China, outperforming global forecasts in terms of spatial distribution, intensity, and diurnal variation. Quantitative evaluations with the Gilbert skill score further confirm the better performance of the 4 km forecasts over global forecasts for heavy precipitation, especially for the thresholds of 100 and 150 mm d-1. Besides bulk characteristics, the representations of some unique features of summer precipitation in China under the influence of the East Asian summer monsoon are further evaluated. These include the northward progression and southward retreat of the main rainband through the summer season, the diurnal variations of precipitation, and the meridional and zonal propagation of precipitation episodes associated with background synoptic flow and the embedded mesoscale convective systems. The 4 km forecast is able to faithfully reproduce most of the features while overprediction of afternoon convection near the southern China coast is found to be a main deficiency that requires further investigations.

  14. MOS BASED FORECAST OF 6-HOURLY AREA PRECIPITATION

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk

    2006-01-01

    Roč. 50, č. 1 (2006), s. 105-120 ISSN 0039-3169 R&D Projects: GA AV ČR IBS3042101 Institutional research plan: CEZ:AV0Z30420517 Keywords : precipitation forecast * regression * statistical postprocessing * MOS Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 0.603, year: 2006

  15. A test for Improvement of high resolution Quantitative Precipitation Estimation for localized heavy precipitation events

    Science.gov (United States)

    Lee, Jung-Hoon; Roh, Joon-Woo; Park, Jeong-Gyun

    2017-04-01

    Accurate estimation of precipitation is one of the most difficult and significant tasks in the area of weather diagnostic and forecasting. In the Korean Peninsula, heavy precipitations are caused by various physical mechanisms, which are affected by shortwave trough, quasi-stationary moisture convergence zone among varying air masses, and a direct/indirect effect of tropical cyclone. In addition to, various geographical and topographical elements make production of temporal and spatial distribution of precipitation is very complicated. Especially, localized heavy rainfall events in South Korea generally arise from mesoscale convective systems embedded in these synoptic scale disturbances. In weather radar data with high temporal and spatial resolution, accurate estimation of rain rate from radar reflectivity data is too difficult. Z-R relationship (Marshal and Palmer 1948) have adapted representatively. In addition to, several methods such as support vector machine (SVM), neural network, Fuzzy logic, Kriging were utilized in order to improve the accuracy of rain rate. These methods show the different quantitative precipitation estimation (QPE) and the performances of accuracy are different for heavy precipitation cases. In this study, in order to improve the accuracy of QPE for localized heavy precipitation, ensemble method for Z-R relationship and various techniques was tested. This QPE ensemble method was developed by a concept based on utilizing each advantage of precipitation calibration methods. The ensemble members were produced for a combination of different Z-R coefficient and calibration method.

  16. Radar-Derived Quantitative Precipitation Estimation Based on Precipitation Classification

    Directory of Open Access Journals (Sweden)

    Lili Yang

    2016-01-01

    Full Text Available A method for improving radar-derived quantitative precipitation estimation is proposed. Tropical vertical profiles of reflectivity (VPRs are first determined from multiple VPRs. Upon identifying a tropical VPR, the event can be further classified as either tropical-stratiform or tropical-convective rainfall by a fuzzy logic (FL algorithm. Based on the precipitation-type fields, the reflectivity values are converted into rainfall rate using a Z-R relationship. In order to evaluate the performance of this rainfall classification scheme, three experiments were conducted using three months of data and two study cases. In Experiment I, the Weather Surveillance Radar-1988 Doppler (WSR-88D default Z-R relationship was applied. In Experiment II, the precipitation regime was separated into convective and stratiform rainfall using the FL algorithm, and corresponding Z-R relationships were used. In Experiment III, the precipitation regime was separated into convective, stratiform, and tropical rainfall, and the corresponding Z-R relationships were applied. The results show that the rainfall rates obtained from all three experiments match closely with the gauge observations, although Experiment II could solve the underestimation, when compared to Experiment I. Experiment III significantly reduced this underestimation and generated the most accurate radar estimates of rain rate among the three experiments.

  17. ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation

    Science.gov (United States)

    Lavers, David A.; Pappenberger, Florian; Richardson, David S.; Zsoter, Ervin

    2016-11-01

    In winter, heavy precipitation and floods along the west coasts of midlatitude continents are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within the atmospheric river of extratropical cyclones. This study builds on previous findings that showed that forecasts of IVT have higher predictability than precipitation, by applying and evaluating the European Centre for Medium-Range Weather Forecasts Extreme Forecast Index (EFI) for IVT in ensemble forecasts during three winters across Europe. We show that the IVT EFI is more able (than the precipitation EFI) to capture extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase; conversely, the precipitation EFI is better during the negative NAO phase and at shorter leads. An IVT EFI example for storm Desmond in December 2015 highlights its potential to identify upcoming hydrometeorological extremes, which may prove useful to the user and forecasting communities.

  18. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

    Science.gov (United States)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

    2017-04-01

    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  19. GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

    Science.gov (United States)

    Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

    2013-01-01

    Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

  20. Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2016-11-01

    Full Text Available In recent years, the Weather Research and Forecast (WRF model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI, but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS over this region.

  1. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation

    Data.gov (United States)

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

  2. Application study of monthly precipitation forecast in Northeast China based on the cold vortex persistence activity index

    Science.gov (United States)

    Gang, Liu; Meihui, Qu; Guolin, Feng; Qucheng, Chu; Jing, Cao; Jie, Yang; Ling, Cao; Yao, Feng

    2018-03-01

    This paper introduces three quantitative indicators to conduct research for characterizing Northeast China cold vortex persistence activity: cold vortex persistence, generalized "cold vortex," and cold vortex precipitation. As discussed in the first part of paper, a hindcast is performed by multiple regressions using Northeast China precipitation from 2012 to 2014 combination with the previous winter 144 air-sea system factors. The results show that the mentioned three cold vortex index series can reflect the spatial and temporal distributions of observational precipitation in 2012-2014 and obtain results. The cold vortex factors are then added to the Forecast System on Dynamical and Analogy Skills (FODAS) to carry out dynamic statistical hindcast of precipitation in Northeast China from 2003 to 2012. Based on the characteristics and significance of each index, precipitation hindcast is carried out for Northeast China in May, June, July, August, May-June, and July-August. It turns out that the Northeast Cold Vortex Index Series, as defined in this paper, can make positive corrections to the FODAS forecast system, and most of the index correction results are higher than the system's own correction value. This study provides quantitative index products and supplies a solid technical foundation and support for monthly precipitation forecast in Northeast China.

  3. 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.

  4. GPS-based PWV for precipitation forecasting and its application to a typhoon event

    Science.gov (United States)

    Zhao, Qingzhi; Yao, Yibin; Yao, Wanqiang

    2018-01-01

    The temporal variability of precipitable water vapour (PWV) derived from Global Navigation Satellite System (GNSS) observations can be used to forecast precipitation events. A number of case studies of precipitation events have been analysed in Zhejiang Province, and a forecasting method for precipitation events was proposed. The PWV time series retrieved from the Global Positioning System (GPS) observations was processed by using a least-squares fitting method, so as to obtain the line tendency of ascents and descents over PWV. The increment of PWV for a short time (two to six hours) and PWV slope for a longer time (a few hours to more than ten hours) during the PWV ascending period are considered as predictive factors with which to forecast the precipitation event. The numerical results show that about 80%-90% of precipitation events and more than 90% of heavy rain events can be forecasted two to six hours in advance of the precipitation event based on the proposed method. 5-minute PWV data derived from GPS observations based on real-time precise point positioning (RT-PPP) were used for the typhoon event that passed over Zhejiang Province between 10 and 12 July, 2015. A good result was acquired using the proposed method and about 74% of precipitation events were predicted at some ten to thirty minutes earlier than their onset with a false alarm rate of 18%. This study shows that the GPS-based PWV was promising for short-term and now-casting precipitation forecasting.

  5. Monthly Total Precipitation 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 total precipitation for 102 Forecast Divisions within the conterminous U.S. It is derived from the monthly NCDC climate division...

  6. Two quantitative forecasting methods for macroeconomic indicators in Czech Republic

    Directory of Open Access Journals (Sweden)

    Mihaela BRATU (SIMIONESCU

    2012-03-01

    Full Text Available Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2 models, ARMA procedure and models with lagged variables. One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.

  7. 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

  8. CLUJ-NAPOCA PRECIPITATION FORECAST USING WSR-98D DOPPLER RADAR

    Directory of Open Access Journals (Sweden)

    Narcis MAIER

    2011-11-01

    Full Text Available CLUJ-NAPOCA precipitation forecast using WSR-98D Doppler radar. Forecasting inundations requires accurate spatial and temporal estimation of rainfalls in an area. Depending on the Z-R relationship (reflectivity-precipitation rate, the thresholds, maximum reflectivity data processing, VIL, cloud height or speed, provided by the WSR-98D affects the estimated precipitation used in the prediction of inundations. How much precipitation receives a watershed during an extreme event and what response will result depends on the basin hydrographic characteristics. A study of summer weather events between the years 2004-2008 and a new method in establishing relations between the radar estimated and recorded precipitations led to the determination of new relations between them which will balance the connections between them.

  9. Forecasting the distribution of precipitate diameters in the presence of changes in the structure of the material

    Directory of Open Access Journals (Sweden)

    Zieliński A.

    2017-03-01

    Full Text Available The results of investigations on the microstructure of T23 and T24 low-alloy steels as well as P91 and P92 high-chromium steels in the as-received condition and after 70.000 h annealing at 550-650°C are presented. The quantitative analysis of the existing precipitates was performed for representative images of microstructure. The statistical analysis of collected data allowed the parameters of a selected theoretical statistical distribution to be estimated. A forecast of average precipitate diameter and standard deviation of such a distribution for the time of 100,000 hours at 550 and 600°C for T23 and T24 steels and at 600 and 650°C for P91 and P92 steels was calculated. The obtained results of investigations have made it possible to compare changes in the microstructure of various steel grades due to long-term impact of elevated temperature. They have also confirmed the possibility of using, in evaluating the degradation degree of materials in use, the forecasting methods that derive from mathematical statistics, in particular the theory of stochastic processes and forecast by analogy methods. The presented approach allows the development of a forecast of precipitate diameter probability density under the microstructure instability conditions for selected steel grades. The assessment of material condition that takes into consideration, but is not limited to, the precipitate diameter measurement is useful as an assessment component in estimating the time of safe service of power unit elements working under creep conditions.

  10. Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

    Science.gov (United States)

    Aminyavari, Saleh; Saghafian, Bahram; Delavar, Majid

    2018-04-01

    The application of numerical weather prediction (NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous (yes/no), and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

  11. Fine-Tuning Nonhomogeneous Regression for Probabilistic Precipitation Forecasts: Unanimous Predictions, Heavy Tails, and Link Functions

    DEFF Research Database (Denmark)

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

    2017-01-01

    functions for the optimization of regression coefficients for the scale parameter. These three refinements are tested for 10 stations in a small area of the European Alps for lead times from +24 to +144 h and accumulation periods of 24 and 6 h. Together, they improve probabilistic forecasts...... to obtain automatically corrected weather forecasts. This study applies the nonhomogenous regression framework as a state-of-the-art ensemble postprocessing technique to predict a full forecast distribution and improves its forecast performance with three statistical refinements. First of all, a novel split...... for precipitation amounts as well as the probability of precipitation events over the default postprocessing method. The improvements are largest for the shorter accumulation periods and shorter lead times, where the information of unanimous ensemble predictions is more important....

  12. Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

    Directory of Open Access Journals (Sweden)

    M. P. Mittermaier

    2008-05-01

    Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.

    The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

  13. Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory.

    Science.gov (United States)

    Pérez-Jordán, G.; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

    2018-04-01

    We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R =0.951 and R =0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

  14. The Relative Performance of High Resolution Quantitative Precipitation Estimates in the Russian River Basin

    Science.gov (United States)

    Bytheway, J. L.; Biswas, S.; Cifelli, R.; Hughes, M.

    2017-12-01

    The Russian River carves a 110 mile path through Mendocino and Sonoma counties in western California, providing water for thousands of residents and acres of agriculture as well as a home for several species of endangered fish. The Russian River basin receives almost all of its precipitation during the October through March wet season, and the systems bringing this precipitation are often impacted by atmospheric river events as well as the complex topography of the region. This study will examine the performance of several high resolution (hourly, products and forecasts over the 2015-2016 and 2016-2017 wet seasons. Comparisons of event total rainfall as well as hourly rainfall will be performed using 1) rain gauges operated by the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Division (PSD), 2) products from the Multi-Radar/Multi-Sensor (MRMS) QPE dataset, and 3) quantitative precipitation forecasts from the High Resolution Rapid Refresh (HRRR) model at 1, 3, 6, and 12 hour lead times. Further attention will be given to cases or locations representing large disparities between the estimates.

  15. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    Science.gov (United States)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  16. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Boogaard, H.L.; Diepen, van C.A.

    2005-01-01

    This paper explores the effect of uncertainty in precipitation and radiation on crop simulation results at local (50 × 50 km grids) and regional scale (NUTS1 regions) and on the crop yield forecasts for Germany and France. Two experiments were carried out where crop yields for winter-wheat and grain

  17. The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

    Directory of Open Access Journals (Sweden)

    Amir Givati

    2017-01-01

    Full Text Available This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME. The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

  18. quantitative precipitation forecasts during the Indian Summer Monsoon

    Indian Academy of Sciences (India)

    65

    the Indian Summer Monsoon: Contiguous Rain Area (CRA) Approach ... 1Centre for Australian Weather and Climate Research, Melbourne, Australia ... are evaluated over India using the Contiguous Rainfall Area (CRA) verification technique.

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

    Directory of Open Access Journals (Sweden)

    Hans-Stefan Bauer

    2015-04-01

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

  20. Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

    Science.gov (United States)

    Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

    2013-12-01

    Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so

  1. Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

    Science.gov (United States)

    Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

    2017-11-01

    Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed

  2. Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

    Directory of Open Access Journals (Sweden)

    M. Jiang

    2017-11-01

    Full Text Available Aerosol–cloud interactions (ACIs have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 % and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE, and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m−2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and

  3. On evaluation of ensemble precipitation forecasts with observation-based ensembles

    Directory of Open Access Journals (Sweden)

    S. Jaun

    2007-04-01

    Full Text Available Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS. The observational references in the evaluation are (a analyzed rain gauge data by ordinary Kriging and (b ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2 of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

  4. Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

    Science.gov (United States)

    Beria, H.; Nanda, T., Sr.; Chatterjee, C.

    2015-12-01

    High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

  5. Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

    Science.gov (United States)

    Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

    2017-12-01

    The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

  6. 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.

  7. Assessment of the Effects of Various Precipitation Forcings on Flood Forecasting Potential Using WRF-Hydro Modeling

    Science.gov (United States)

    Zhang, J.; Fang, N. Z.

    2017-12-01

    A potential flood forecast system is under development for the Upper Trinity River Basin (UTRB) in North Central of Texas using the WRF-Hydro model. The Routing Application for the Parallel Computation of Discharge (RAPID) is utilized as channel routing module to simulate streamflow. Model performance analysis was conducted based on three quantitative precipitation estimates (QPE): the North Land Data Assimilation System (NLDAS) rainfall, the Multi-Radar Multi-Sensor (MRMS) QPE and the National Centers for Environmental Prediction (NCEP) quality-controlled stage IV estimates. Prior to hydrologic simulation, QPE performance is assessed on two time scales (daily and hourly) using the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Hydrometeorological Automated Data System (HADS) hourly products. The calibrated WRF-Hydro model was then evaluated by comparing the simulated against the USGS observed using various QPE products. The results imply that the NCEP stage IV estimates have the best accuracy among the three QPEs on both time scales, while the NLDAS rainfall performs poorly because of its coarse spatial resolution. Furthermore, precipitation bias demonstrates pronounced impact on flood forecasting skills, as the root mean squared errors are significantly reduced by replacing NLDAS rainfall with NCEP stage IV estimates. This study also demonstrates that accurate simulated results can be achieved when initial soil moisture values are well understood in the WRF-Hydro model. Future research effort will therefore be invested on incorporating data assimilation with focus on initial states of the soil properties for UTRB.

  8. 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.)

  9. Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

    Science.gov (United States)

    Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

    2016-01-01

    Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

  10. Forecasting Precipitation over the MENA Region: A Data Mining and Remote Sensing Based Approach

    Science.gov (United States)

    Elkadiri, R.; Sultan, M.; Elbayoumi, T.; Chouinard, K.

    2015-12-01

    We developed and applied an integrated approach to construct predictive tools with lead times of 1 to 12 months to forecast precipitation amounts over the Middle East and North Africa (MENA) region. The following steps were conducted: (1) acquire and analyze temporal remote sensing-based precipitation datasets (i.e. Tropical Rainfall Measuring Mission [TRMM]) over five main water source regions in the MENA area (i.e. Atlas Mountains in Morocco, Southern Sudan, Red Sea Hills of Yemen, and Blue Nile and White Nile source areas) throughout the investigation period (1998 to 2015), (2) acquire and extract monthly values for all of the climatic indices that are likely to influence the climatic patterns over the MENA region (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); and (3) apply data mining methods to extract relationships between the observed precipitation and the controlling factors (climatic indices) and use predictive tools to forecast monthly precipitation over each of the identified pilot study areas. Preliminary results indicate that by using the period from January 1998 until August 2012 for model training and the period from September 2012 to January 2015 for testing, precipitation can be successfully predicted with a three-months lead over South West Yemen, Atlas Mountains in Morocco, Southern Sudan, Blue Nile sources and White Nile sources with confidence (Pearson correlation coefficient: 0.911, 0.823, 0.807, 0.801 and 0.895 respectively). Future work will focus on applying this technique for prediction of precipitation over each of the climatically contiguous areas of the MENA region. If our efforts are successful, our findings will lead the way to the development and implementation of sound water management scenarios for the MENA countries.

  11. Precipitation observations for operational flood forecasting in Scotland: Data availability, limitations and the impact of observational uncertainty

    Science.gov (United States)

    Parry, Louise; Neely, Ryan, III; Bennett, Lindsay; Collier, Chris; Dufton, David

    2017-04-01

    The Scottish Environment Protection Agency (SEPA) has a statutory responsibility to provide flood warning across Scotland. It achieves this through an operational partnership with the UK Met Office wherein meteorological forecasts are applied to a national distributed hydrological model, Grid- to- Grid (G2G), and catchment specific lumped PDM models. Both of these model types rely on observed precipitation input for model development and calibration, and operationally for historical runs to generate initial conditions. Scotland has an average annual precipitation of 1430mm per annum (1971-2000), but the spatial variability in totals is high, predominantly in relation to the topography and prevailing winds, which poses different challenges to both radar and point measurement methods of observation. In addition, the high elevations mean that in winter a significant proportion of precipitation falls as snow. For the operational forecasting models, observed rainfall data is provided in Near Real Time (NRT) from SEPA's network of approximately 260 telemetered TBR gauges and 4 UK Met Office C-band radars. Both data sources have their strengths and weaknesses, particularly in relation to the orography and spatial representativeness, but estimates of rainfall from the two methods can vary greatly. Northern Scotland, particularly near Inverness, is a comparatively sparse part of the radar network. Rainfall totals and distribution in this area are determined by the Northern Western Highlands and Cairngorms mountain ranges, which also have a negative impact on radar observations. In recognition of this issue, the NCAS mobile X-band weather radar (MXWR) was deployed in this area between February and August 2016. This study presents a comparison of rainfall estimates for the Inverness and Moray Firth region generated from the operational radar network, the TBR network, and the MXWR. Quantitative precipitation estimates (QPEs) from both sources of radar data were compared to

  12. Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

    Science.gov (United States)

    Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

    2018-02-01

    Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

  13. Forecasting with quantitative methods the impact of special events in time series

    OpenAIRE

    Nikolopoulos, Konstantinos

    2010-01-01

    Abstract Quantitative methods are very successful for producing baseline forecasts of time series; however these models fail to forecast neither the timing nor the impact of special events such as promotions or strikes. In most of the cases the timing of such events is not known so they are usually referred as shocks (economics) or special events (forecasting). Sometimes the timing of such events is known a priori (i.e. a future promotion); but even then the impact of the forthcom...

  14. 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

  15. 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.

  16. Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

    Science.gov (United States)

    Singh, Sanjeev Kumar; Prasad, V. S.

    2018-02-01

    This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June-September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble-variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.

  17. Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling

    Directory of Open Access Journals (Sweden)

    Chien-Lin Huang

    2015-01-01

    Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.

  18. Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites

    Science.gov (United States)

    Marín, Julio C.; Pozo, Diana; Curé, Michel

    2015-01-01

    In this work, we describe a method to estimate the precipitable water vapor (PWV) from Geostationary Observational Environmental Satellite (GOES) data at high altitude sites. The method was applied at Atacama Pathfinder Experiment (APEX) and Cerro Toco sites, located above 5000 m altitude in the Chajnantor plateau, in the north of Chile. It was validated using GOES-12 satellite data over the range 0-1.2 mm since submillimeter/millimeter astronomical observations are only useful within this PWV range. The PWV estimated from GOES and the Final Analyses (FNL) at APEX for 2007 and 2009 show root mean square error values of 0.23 mm and 0.36 mm over the ranges 0-0.4 mm and 0.4-1.2 mm, respectively. However, absolute relative errors of 51% and 33% were shown over these PWV ranges, respectively. We recommend using high-resolution thermodynamic profiles from the Global Forecast System (GFS) model to estimate the PWV from GOES data since they are available every three hours and at an earlier time than the FNL data. The estimated PWV from GOES/GFS agrees better with the observed PWV at both sites during night time. The largest errors are shown during daytime. Short-term PWV forecasts were implemented at both sites, applying a simple persistence method to the PWV estimated from GOES/GFS. The 12 h and 24 h PWV forecasts evaluated from August to October 2009 indicates that 25% of them show a very good agreement with observations whereas 50% of them show reasonably good agreement with observations. Transmission uncertainties calculated for PWV estimations and forecasts over the studied sites are larger over the range 0-0.4 mm than over the range 0.4-1.2 mm. Thus, the method can be used over the latter interval with more confidence.

  19. State of the Science for Sub-Seasonal to Seasonal Precipitation Forecasting in Support of Water Resource Managers

    Science.gov (United States)

    DeWitt, D. G.

    2017-12-01

    Water resource managers are one of the communities that would strongly benefit from highly-skilled sub-seasonal to seasonal precipitation forecasts. Unfortunately, the current state of the art prediction tools frequently fail to provide a level of skill sufficient to meet the stakeholders needs, especially on the monthly and seasonal timescale. On the other hand, the skill of precipitation forecasts on the week-2 timescale are relatively high and arguably useful in many decision-making contexts. This talk will present a comparison of forecast skill for the week-2 through the first season timescale and describe current efforts within NOAA and elsewhere to try to improve forecast skill beyond week-2, including research gaps that need to be addressed in order to make progress.

  20. Evaluation of TRMM satellite-based precipitation indexes for flood forecasting over Riyadh City, Saudi Arabia

    Science.gov (United States)

    Tekeli, Ahmet Emre; Fouli, Hesham

    2016-10-01

    Floods are among the most common disasters harming humanity. In particular, flash floods cause hazards to life, property and any type of structures. Arid and semi-arid regions are equally prone to flash floods like regions with abundant rainfall. Despite rareness of intensive and frequent rainfall events over Kingdom of Saudi Arabia (KSA); an arid/semi-arid region, occasional flash floods occur and result in large amounts of damaging surface runoff. The flooding of 16 November, 2013 in Riyadh; the capital city of KSA, resulted in killing some people and led to much property damage. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Real Time (RT) data (3B42RT) are used herein for flash flood forecasting. 3B42RT detected high-intensity rainfall events matching with the distribution of observed floods over KSA. A flood early warning system based on exceedance of threshold limits on 3B42RT data is proposed for Riyadh. Three different indexes: Constant Threshold (CT), Cumulative Distribution Functions (CDF) and Riyadh Flood Precipitation Index (RFPI) are developed using 14-year 3B42RT data from 2000 to 2013. RFPI and CDF with 90% captured the three major flooding events that occurred in February 2005, May 2010 and November 2013 in Riyadh. CT with 3 mm/h intensity indicated the 2013 flooding, but missed those of 2005 and 2010. The methodology implemented herein is a first-step simple and accurate way for flash flood forecasting over Riyadh. The simplicity of the methodology enables its applicability for the TRMM follow-on missions like Global Precipitation Measurement (GPM) mission.

  1. Local short-duration precipitation extremes in Sweden: observations, forecasts and projections

    Science.gov (United States)

    Olsson, Jonas; Berg, Peter; Simonsson, Lennart

    2015-04-01

    Local short-duration precipitation extremes (LSPEs) are a key driver of hydrological hazards, notably in steep catchments with thin soils and in urban environments. The triggered floodings, landslides, etc., have large consequences for society in terms of both economy and health. Accurate estimations of LSPEs on both climatological time-scales (past, present, future) and in real-time is thus of great importance for improved hydrological predictions as well as design of constructions and infrastructure affected by hydrological fluxes. Analysis of LSPEs is, however, associated with various limitations and uncertainties. These are to a large degree associated with the small-scale nature of the meteorological processes behind LSPEs and the associated requirements on observation sensors as well as model descriptions. Some examples of causes for the limitations involved are given in the following. - Observations: High-resolution data sets available for LSPE analyses are often limited to either relatively long series from one or a few stations or relatively short series from larger station networks. Radar data have excellent resolutions in both time and space but the estimated local precipitation intensity is still highly uncertain. New and promising techniques (e.g. microwave links) are still in their infancy. - Weather forecasts (short-range): Although forecasts with the required spatial resolution for potential generation of LSPEs (around 2-4 km) are becoming operationally available, the actual forecast precision of LSPEs is largely unknown. Forecasted LSPEs may be displaced in time or, more critically, in space which strongly affects the possibility to assess hydrological risk. - Climate projections: The spatial resolution of the current RCM generation (around 25 km) is not sufficient for proper description of LSPEs. Statistical post-processing (i.e. downscaling) is required which adds substantial uncertainty to the final result. Ensemble generation of sufficiently

  2. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    Science.gov (United States)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  3. Improving Radar Quantitative Precipitation Estimation over Complex Terrain in the San Francisco Bay Area

    Science.gov (United States)

    Cifelli, R.; Chen, H.; Chandrasekar, V.

    2017-12-01

    A recent study by the State of California's Department of Water Resources has emphasized that the San Francisco Bay Area is at risk of catastrophic flooding. Therefore, accurate quantitative precipitation estimation (QPE) and forecast (QPF) are critical for protecting life and property in this region. Compared to rain gauge and meteorological satellite, ground based radar has shown great advantages for high-resolution precipitation observations in both space and time domain. In addition, the polarization diversity shows great potential to characterize precipitation microphysics through identification of different hydrometeor types and their size and shape information. Currently, all the radars comprising the U.S. National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network are operating in dual-polarization mode. Enhancement of QPE is one of the main considerations of the dual-polarization upgrade. The San Francisco Bay Area is covered by two S-band WSR-88D radars, namely, KMUX and KDAX. However, in complex terrain like the Bay Area, it is still challenging to obtain an optimal rainfall algorithm for a given set of dual-polarization measurements. In addition, the accuracy of rain rate estimates is contingent on additional factors such as bright band contamination, vertical profile of reflectivity (VPR) correction, and partial beam blockages. This presentation aims to improve radar QPE for the Bay area using advanced dual-polarization rainfall methodologies. The benefit brought by the dual-polarization upgrade of operational radar network is assessed. In addition, a pilot study of gap fill X-band radar performance is conducted in support of regional QPE system development. This paper also presents a detailed comparison between the dual-polarization radar-derived rainfall products with various operational products including the NSSL's Multi-Radar/Multi-Sensor (MRMS) system. Quantitative evaluation of various rainfall products is achieved

  4. Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

    Directory of Open Access Journals (Sweden)

    Jen-Chi Hu

    2009-01-01

    Full Text Available We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI, with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR, the Goddard Profiling Algorithm (GPROF, and a multi-channel linear regression statistical method (MLRS. We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS error against rain gauge data for 16 typhoon over passes in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals out perform those retrieved from GPROF and MLRS. Over all, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Ac cu rate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

  5. WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

    Science.gov (United States)

    Zhou, Jianzhong; Zhang, Hairong; Zhang, Jianyun; Zeng, Xiaofan; Ye, Lei; Liu, Yi; Tayyab, Muhammad; Chen, Yufan

    2017-07-01

    An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

  6. Quantitative forecasting of the 27-day recurrent magnetic activity

    International Nuclear Information System (INIS)

    Akasofu, S.-I.; Olmsted, C.

    1988-01-01

    It is shown that forecasting of the recurrent geomagnetic activity is reduced to predicting the solar wind speed V and the magnitude B of the Interplanetary Magnetic Field IMF for a 27-day period. It is shown further that the prediction of V and B is reduced to inferring the geometry of the neutral line on the source surface which is an imaginary spherical surface of radius 2.5 solar radii. For this purpose, we note that it has recently been found that the neutral line on the source surface can be reproduced fairly accurately by a dipole at the center of the Sun and a few dipoles on the photosphere. This finding provides us with an opportunity to predict the geometry of the neutral line on the source surface by extrapolating time variations of the magnitude and orientation of the dipoles. We show that time variations of the dipoles are fairly systematic, making the extrapolation possible. (author)

  7. Forecasting gastrointestinal precipitation and oral pharmacokinetics of dantrolene in dogs using an in vitro precipitation testing coupled with in silico modeling and simulation.

    Science.gov (United States)

    Kambayashi, Atsushi; Dressman, Jennifer B

    2017-10-01

    The aim of the current research was to determine the precipitation kinetics of dantrolene sodium using canine biorelevant in vitro testing and to model the precipitation kinetics by appropriately coupling the data with an in silico tool adapted for dogs. The precipitation profiles of dantrolene sodium solutions were obtained with the in vitro paddle apparatus at a revolution rate of 50rpm. The in silico prediction tool was designed using STELLA software and the predicted plasma concentration profiles of dantrolene using the in vitro precipitation data were compared with the observed in vivo pharmacokinetics in beagle dogs. The plasma profiles of dantrolene, which served as a model weakly acidic drug which precipitates in the upper gastrointestinal tract, was successfully predicted using the in vitro precipitation testing coupled with the in silico modeling and simulation approach. The approach was subsequently used to forecast the effect of pharmaceutical excipients (HPMC/PG) on the ability of the drug to supersaturate in the gut and the resulting pharmacokinetics. The agreement of the simulated pharmacokinetics with the observed values confirms the ability of canine biorelevant media to predict oral performance of enhanced dosage forms in dogs. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. A quantitative phase field model for hydride precipitation in zirconium alloys: Part I. Development of quantitative free energy functional

    International Nuclear Information System (INIS)

    Shi, San-Qiang; Xiao, Zhihua

    2015-01-01

    A temperature dependent, quantitative free energy functional was developed for the modeling of hydride precipitation in zirconium alloys within a phase field scheme. The model takes into account crystallographic variants of hydrides, interfacial energy between hydride and matrix, interfacial energy between hydrides, elastoplastic hydride precipitation and interaction with externally applied stress. The model is fully quantitative in real time and real length scale, and simulation results were compared with limited experimental data available in the literature with a reasonable agreement. The work calls for experimental and/or theoretical investigations of some of the key material properties that are not yet available in the literature

  9. Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

    Directory of Open Access Journals (Sweden)

    Brent D. Winslow

    2017-04-01

    Full Text Available Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

  10. On the forecast of runoff based on the harmonic analysis of time series of precipitation in the catchment area

    Science.gov (United States)

    Cherednichenko, A. V.; Cherednichenko, A. V.; Cherednichenko, V. S.

    2018-01-01

    It is shown that a significant connection exists between the most important harmonics, extracted in the process of harmonic analysis of time series of precipitation in the catchment area of rivers and the amount of runoff. This allowed us to predict the size of the flow for a period of up to 20 years, assuming that the main parameters of the harmonics are preserved at the predicted time interval. The results of such a forecast for three river basins of Kazakhstan are presented.

  11. Assimilation of extrapolated radar reflectivity into a NWP model and its impact on a precipitation forecast at high resolution

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk

    2011-01-01

    Roč. 100, 2-3 (2011), s. 201-212 ISSN 0169-8095 R&D Projects: GA ČR GA205/07/0905; GA MŠk ME09033 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * Nowcasting * Assimilation of radar reflectivity * Numerical weather prediction * Convective storms Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.911, year: 2011 http://www.sciencedirect.com/science/article/pii/S0169809510002462

  12. High resolution forecast of heavy precipitation with Lokal Modell: analysis of two case studies in the Alpine area

    Directory of Open Access Journals (Sweden)

    M. Elementi

    2005-01-01

    Full Text Available Northern Italy is frequently affected by severe precipitation conditions often inducing flood events with associated loss of properties, damages and casualties. The capability of correctly forecast these events, strongly required for an efficient support to civil protection actions, is still nowadays a challenge. This difficulty is also related with the complex structure of the precipitation field in the Alpine area and, more generally, over the Italian territory. Recently a new generation of non-hydrostatic meteorological models, suitable to be used at very high spatial resolution, has been developed. In this paper the performance of the non-hydrostatic Lokal Modell developed by the COSMO Consortium, is analysed with regard to a couple of intense precipitation events occurred in the Piemonte region in Northern Italy. These events were selected among the reference cases of the Hydroptimet/INTERREG IIIB project. LM run at the operational resolution of 7km provides a good forecast of the general rain structure, with an unsatisfactory representation of the precipitation distribution across the mountain ranges. It is shown that the inclusion of the new prognostic equations for cloud ice, rain and snow produces a remarkable improvement, reducing the precipitation in the upwind side and extending the intense rainfall area to the downwind side. The unrealistic maxima are decreased towards observed values. The use of very high horizontal resolution (2.8 km improves the general shape of the precipitation field in the flat area of the Piemonte region but, keeping active the moist convection scheme, sparse and more intense rainfall peaks are produced. When convective precipitation is not parametrised but explicitly represented by the model, this negative effect is removed.

  13. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

    International Nuclear Information System (INIS)

    Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple ‘hybrid’ forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble’s (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The ‘hybrid approach’ described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45. (letter)

  14. Comparison of precipitation nowcasting by extrapolation and statistical-advection methods

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Kitzmiller, D.; Pešice, Petr; Mejsnar, Jan

    2013-01-01

    Roč. 123, 1 April (2013), s. 17-30 ISSN 0169-8095 R&D Projects: GA MŠk ME09033 Institutional support: RVO:68378289 Keywords : Precipitation forecast * Statistical models * Regression * Quantitative precipitation forecast * Extrapolation forecast Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.421, year: 2013 http://www.sciencedirect.com/science/article/pii/S0169809512003390

  15. A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach

    Science.gov (United States)

    Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Hong, Yang

    2015-10-01

    Short-term high-resolution Quantitative Precipitation Nowcasting (QPN) has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This study proposes a new algorithm called Pixel-based QPN using the Pyramid Lucas-Kanade Optical Flow method (PPLK), which comprises three steps: employing a Pyramid Lucas-Kanade Optical Flow method (PLKOF) to estimate precipitation advection, projecting rainy clouds by considering the advection and evolution pixel by pixel, and interpolating QPN imagery based on the space-time continuum of cloud patches. The PPLK methodology was evaluated with 2338 images from the geostationary meteorological satellite Fengyun-2F (FY-2F) of China and compared with two other advection-based methods, i.e., the maximum correlation method and the Horn-Schunck Optical Flow scheme. The data sample covered all intensive observations since the launch of FY-2F, despite covering a total of only approximately 10 days. The results show that the PPLK performed better than the algorithms used for comparison, demonstrating less time expenditure, more effective cloud tracking, and improved QPN accuracy.

  16. Optimal Physics Parameterization Scheme Combination of the Weather Research and Forecasting Model for Seasonal Precipitation Simulation over Ghana

    Directory of Open Access Journals (Sweden)

    Richard Yao Kuma Agyeman

    2017-01-01

    Full Text Available Seasonal predictions of precipitation, among others, are important to help mitigate the effects of drought and floods on agriculture, hydropower generation, disasters, and many more. This work seeks to obtain a suitable combination of physics schemes of the Weather Research and Forecasting (WRF model for seasonal precipitation simulation over Ghana. Using the ERA-Interim reanalysis as forcing data, simulation experiments spanning eight months (from April to November were performed for two different years: a dry year (2001 and a wet year (2008. A double nested approach was used with the outer domain at 50 km resolution covering West Africa and the inner domain covering Ghana at 10 km resolution. The results suggest that the WRF model generally overestimated the observed precipitation by a mean value between 3% and 64% for both years. Most of the scheme combinations overestimated (underestimated precipitation over coastal (northern zones of Ghana for both years but estimated precipitation reasonably well over forest and transitional zones. On the whole, the combination of WRF Single-Moment 6-Class Microphysics Scheme, Grell-Devenyi Ensemble Cumulus Scheme, and Asymmetric Convective Model Planetary Boundary Layer Scheme simulated the best temporal pattern and temporal variability with the least relative bias for both years and therefore is recommended for Ghana.

  17. Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

    Science.gov (United States)

    Pandey, Dhruva Kumar; Rai, Shailendra; Sahai, A. K.; Abhilash, S.; Shahi, N. K.

    2016-02-01

    This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001-2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3-5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.

  18. 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

  19. A method to forecast quantitative variables relating to nuclear public acceptance

    International Nuclear Information System (INIS)

    Ohnishi, T.

    1992-01-01

    A methodology is proposed for forecasting the future trend of quantitative variables profoundly related to the public acceptance (PA) of nuclear energy. The social environment influencing PA is first modeled by breaking it down into a finite number of fundamental elements and then the interactive formulae between the quantitative variables, which are attributed to and characterize each element, are determined by using the actual values of the variables in the past. Inputting the estimated values of exogenous variables into these formulae, the forecast values of endogenous variables can finally be obtained. Using this method, the problem of nuclear PA in Japan is treated as, for example, where the context is considered to comprise a public sector and the general social environment and socio-psychology. The public sector is broken down into three elements of the general public, the inhabitants living around nuclear facilities and the activists of anti-nuclear movements, whereas the social environment and socio-psychological factors are broken down into several elements, such as news media and psychological factors. Twenty-seven endogenous and seven exogenous variables are introduced to quantify these elements. After quantitatively formulating the interactive features between them and extrapolating the exogenous variables into the future estimates are made of the growth or attenuation of the endogenous variables, such as the pro- and anti-nuclear fractions in public opinion polls and the frequency of occurrence of anti-nuclear movements. (author)

  20. Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau

    Science.gov (United States)

    Gou, Yabin; Ma, Yingzhao; Chen, Haonan; Wen, Yixin

    2018-05-01

    Quantitative precipitation estimation (QPE) is one of the important applications of weather radars. However, in complex terrain such as Tibetan Plateau, it is a challenging task to obtain an optimal Z-R relation due to the complex spatial and temporal variability in precipitation microphysics. This paper develops two radar QPE schemes respectively based on Reflectivity Threshold (RT) and Storm Cell Identification and Tracking (SCIT) algorithms using observations from 11 Doppler weather radars and 3264 rain gauges over the Eastern Tibetan Plateau (ETP). These two QPE methodologies are evaluated extensively using four precipitation events that are characterized by different meteorological features. Precipitation characteristics of independent storm cells associated with these four events, as well as the storm-scale differences, are investigated using short-term vertical profile of reflectivity (VPR) clusters. Evaluation results show that the SCIT-based rainfall approach performs better than the simple RT-based method for all precipitation events in terms of score comparison using validation gauge measurements as references. It is also found that the SCIT-based approach can effectively mitigate the local error of radar QPE and represent the precipitation spatiotemporal variability better than the RT-based scheme.

  1. The Impacts of Satellite Remotely Sensed Winds and Total Precipitable Vapour in WRF Tropical Cyclone Track Forecasts

    Directory of Open Access Journals (Sweden)

    Diandong Ren

    2016-01-01

    Full Text Available This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs. These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005, the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks.

  2. Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

    Energy Technology Data Exchange (ETDEWEB)

    Stenz, Ronald; Dong, Xiquan; Xi, Baike; Feng, Zhe; Kuligowski, Robert J.

    2016-02-01

    To address significant gaps in ground-based radar coverage and rain gauge networks in the U.S., geostationary satellite quantitative precipitation estimates (QPEs) such as the Self-Calibrating Multivariate Precipitation Retrievals (SCaMPR) can be used to fill in both the spatial and temporal gaps of ground-based measurements. Additionally, with the launch of GOES-R, the temporal resolution of satellite QPEs may be comparable to that of Weather Service Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every five minutes. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events. Deep Convective Systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain-cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth (τ) has been found to reduce overestimates of precipitation in anvil regions (Stenz et al. 2014). A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. (2012) classification algorithm. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

  3. A quantitative comparison of lightning-induced electron precipitation and VLF signal perturbations

    Science.gov (United States)

    Peter, W. B.; Inan, U. S.

    2007-12-01

    VLF signal perturbations recorded on the Holographic Array for Ionospheric/Lightning Research (HAIL) are quantitatively related to a comprehensive model of lightning-induced electron precipitation (LEP) events. The model consists of three major components: a test-particle model of gyroresonant whistler-induced electron precipitation, a Monte Carlo simulation of energy deposition into the ionosphere, and a model of VLF subionospheric signal propagation. For the two representative LEP events studied, the model calculates peak VLF amplitude perturbations within a factor of three of those observed, well within the expected variability of radiation belt flux levels. The phase response of the observed VLF signal to precipitation varied dramatically over the course of the two nights and this variability in phase response is not properly reproduced by the model. The model calculates a peak in the precipitation that is poleward displaced ~6° from the causative lightning flash, consistent with observations. The modeled precipitated energy flux (E > 45 keV) peaks at ~1 × 10-2 (ergs s-1 cm-2), resulting in a peak loss of ~0.001% from a single flux tube at L ~ 2.2, consistent with previous satellite measurements of LEP events. The precipitation calculated by the model is highly dependent on the near-loss-cone trapped radiation belt flux levels assumed, and hence our main objective is not to compare the model calculations and the VLF signal observations on an absolute basis but is rather to develop metrics with which we can characterize the VLF signal perturbations recorded on HAIL in terms of the associated precipitation flux. Metrics quantifying the ionospheric density enhancement (N ILDE) and the electron precipitation (Γ) along a VLF signal path are strongly correlated with the VLF signal perturbations calculated by the model. A conversion ratio Ψ, relating VLF signal amplitude perturbations (ΔA) to the time-integrated precipitation (100-300 keV) along the VLF path (

  4. Generating Southern Africa Precipitation Forecast Using the FEWS Engine, a New Application for the Google Earth Engine

    Science.gov (United States)

    Landsfeld, M. F.; Hegewisch, K.; Daudert, B.; Morton, C.; Husak, G. J.; Friedrichs, M.; Funk, C. C.; Huntington, J. L.; Abatzoglou, J. T.; Verdin, J. P.

    2016-12-01

    The Famine Early Warning Systems Network (FEWS NET) focuses on food insecurity in developing nations and provides objective, evidence-based analysis to help government decision-makers and relief agencies plan for and respond to humanitarian emergencies. The network of FEWS NET analysts and scientists require flexible, interactive tools to aid in their monitoring and research efforts. Because they often work in bandwidth-limited regions, lightweight Internet tools and services that bypass the need for downloading massive datasets are preferred for their work. To support food security analysis FEWS NET developed a custom interface for the Google Earth Engine (GEE). GEE is a platform developed by Google to support scientific analysis of environmental data in their cloud computing environment. This platform allows scientists and independent researchers to mine massive collections of environmental data, leveraging Google's vast computational resources for purposes of detecting changes and monitoring the Earth's surface and climate. GEE hosts an enormous amount of satellite imagery and climate archives, one of which is the Climate Hazards Group Infrared Precipitation with Stations dataset (CHIRPS). CHIRPS precipitation dataset is a key input for FEWS NET monitoring and forecasting efforts. In this talk we introduce the FEWS Engine interface. We present an application that highlights the utility of FEWS Engine for forecasting the upcoming seasonal precipitation of southern Africa. Specifically, the current state of ENSO is assessed and used to identify similar historical seasons. The FEWS Engine compositing tool is used to examine rainfall and other environmental data for these analog seasons. The application illustrates the unique benefits of using FEWS Engine for on-the-fly food security scenario development.

  5. Quantitative measurement of lightning-induced electron precipitation using VLF remote sensing

    Science.gov (United States)

    Peter, William Bolton

    This dissertation examines the detection of lightning-induced energetic electron precipitation via subionospheric Very Low Frequency (VLF) remote sensing. The primary measurement tool used is a distributed set of VLF observing sites, the Holographic Array for Ionospheric/Lightning Research (HAIL), located along the eastern side of the Rocky Mountains in the Central United States. Measurements of the VLF signal perturbations indicate that 90% of the precipitation occurs over a region ˜8 degrees in latitudinal extent, with the peak of the precipitation poleward displaced ˜7 degrees from the causative discharge. A comparison of the VLF signal perturbations recorded on the HAIL array with a comprehensive model of LEP events allows for the quantitative measurement of electron precipitation and ionospheric density enhancement with unprecedented quantitative detail. The model consists of three major components: a test-particle model of gyroresonant whistler-induced electron precipitation; a Monte Carlo simulation of energy deposition into the ionosphere; and a model of VLF subionospheric signal propagation. For the two representative LEP events studied, the model calculates peak VLF amplitude and phase perturbations within a factor of three of those observed, well within the expected variability of radiation belt flux levels. The modeled precipitated energy flux (E>45 keV) peaks at ˜1 x 10-2 [ergs s-1 cm -2], resulting in a peak loss of ˜0.001% from a single flux tube at L˜2.2, consistent with previous satellite measurements of LEP events. Metrics quantifying the ionospheric density enhancement (N ILDE) and the electron precipitation (Gamma) are strongly correlated with the VLF signal perturbations calculated by the model. A conversion ratio Psi relates VLF signal amplitude perturbations (DeltaA) to the time-integrated precipitation (100-300 keV) along the VLF path (Psi=Gamma / DeltaA). The total precipitation (100-300 keV) induced by one of the representative LEP

  6. The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes.

    Science.gov (United States)

    Teh, Boon Kin; Cheong, Siew Ann

    2016-01-01

    The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.

  7. The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes.

    Directory of Open Access Journals (Sweden)

    Boon Kin Teh

    Full Text Available The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009. Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.

  8. Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

    Science.gov (United States)

    Spero, Tanya L.; Otte, Martin J.; Bowden, Jared H.; Nolte, Christopher G.

    2014-10-01

    Spectral nudging—a scale-selective interior constraint technique—is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonstrated that spectral nudging improves the representation of regional climate in reanalysis-forced simulations compared with not using nudging in the interior of the domain. However, in the Weather Research and Forecasting (WRF) model, spectral nudging tends to produce degraded precipitation simulations when compared to analysis nudging—an interior constraint technique that is scale indiscriminate but also operates on moisture fields which until now could not be altered directly by spectral nudging. Since analysis nudging is less desirable for regional climate modeling because it dampens fine-scale variability, changes are proposed to the spectral nudging methodology to capitalize on differences between the nudging techniques and aim to improve the representation of clouds, radiation, and precipitation without compromising other fields. These changes include adding spectral nudging toward moisture, limiting nudging to below the tropopause, and increasing the nudging time scale for potential temperature, all of which collectively improve the representation of mean and extreme precipitation, 2 m temperature, clouds, and radiation, as demonstrated using a model-simulated 20 year historical period. Such improvements to WRF may increase the fidelity of regional climate data used to assess the potential impacts of climate change on human health and the environment and aid in climate change mitigation and adaptation studies.

  9. Combining weather radar nowcasts and numerical weather prediction models to estimate short-term quantitative precipitation and uncertainty

    DEFF Research Database (Denmark)

    Jensen, David Getreuer

    The topic of this Ph.D. thesis is short term forecasting of precipitation for up to 6 hours called nowcasts. The focus is on improving the precision of deterministic nowcasts, assimilation of radar extrapolation model (REM) data into Danish Meteorological Institutes (DMI) HIRLAM numerical weather...

  10. Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

    Directory of Open Access Journals (Sweden)

    Chih-Chien Tsai

    2014-03-01

    Full Text Available This study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN are evaluated with observing system simulation experiments on Typhoon Morakot (2009, which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.

  11. Quantitative analysis of precipitation over Fukushima to understand the wet deposition process in March 2011

    Science.gov (United States)

    Yatagai, A.; Onda, Y.; Watanabe, A.

    2012-04-01

    The Great East Japan Earthquake caused a severe accident at the Fukushima-Daiichi nuclear power plant (NPP), leading to the emission of large amounts of radioactive pollutants into the environment. The transport and diffusion of these radioactive pollutants in the atmosphere caused a disaster for residents in and around Fukushima. Studies have sought to understand the transport, diffusion, and deposition process, and to understand the movement of radioactive pollutants through the soil, vegetation, rivers, and groundwater. However, a detailed simulation and understanding of the distribution of radioactive compounds depend on a simulation of precipitation and on the information on the timing of the emission of these radioactive pollutants from the NPP. Past nuclear expansion studies have demonstrated the importance of wet deposition in distributing pollutants. Hence, this study examined the quantitative precipitation pattern in March 2011 using rain-gauge observations and X-band radar data from Fukushima University. We used the AMeDAS rain-gauge network data of 1) the Japan Meteorological Agency (1273 stations in Japan) and 2) the Water Information System (47 stations in Fukushima prefecture) and 3) the rain-gauge data of the Environmental Information Network of NTT Docomo (30 stations in Fukushima) to construct 0.05-degree mesh data using the same method used to create the APHRODITE daily grid precipitation data (Yatagai et al., 2009). Since some AMeDAS data for the coastal region were lost due to the earthquake, the complementary network of 2) and 3) yielded better precipitation estimates. The data clarified that snowfall was observed on the night of Mar 15 into the morning of Mar 16 throughout Fukushima prefecture. This had an important effect on the radioactive contamination pattern in Fukushima prefecture. The precipitation pattern itself does not show one-on-one correspondence with the contamination pattern. While the pollutants transported northeast of the

  12. 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.

  13. Quantitative atom probe analysis of nanostructure containing clusters and precipitates with multiple length scales

    International Nuclear Information System (INIS)

    Marceau, R.K.W.; Stephenson, L.T.; Hutchinson, C.R.; Ringer, S.P.

    2011-01-01

    A model Al-3Cu-(0.05 Sn) (wt%) alloy containing a bimodal distribution of relatively shear-resistant θ' precipitates and shearable GP zones is considered in this study. It has recently been shown that the addition of the GP zones to such microstructures can lead to significant increases in strength without a decrease in the uniform elongation. In this study, atom probe tomography (APT) has been used to quantitatively characterise the evolution of the GP zones and the solute distribution in the bimodal microstructure as a function of applied plastic strain. Recent nuclear magnetic resonance (NMR) analysis has clearly shown strain-induced dissolution of the GP zones, which is supported by the current APT data with additional spatial information. There is significant repartitioning of Cu from the GP zones into the solid solution during deformation. A new approach for cluster finding in APT data has been used to quantitatively characterise the evolution of the sizes and shapes of the Cu containing features in the solid solution solute as a function of applied strain. -- Research highlights: → A new approach for cluster finding in atom probe tomography (APT) data has been used to quantitatively characterise the evolution of the sizes and shapes of the Cu containing features with multiple length scales. → In this study, a model Al-3Cu-(0.05 Sn) (wt%) alloy containing a bimodal distribution of relatively shear-resistant θ' precipitates and shearable GP zones is considered. → APT has been used to quantitatively characterise the evolution of the GP zones and the solute distribution in the bimodal microstructure as a function of applied plastic strain. → It is clearly shown that there is strain-induced dissolution of the GP zones with significant repartitioning of Cu from the GP zones into the solid solution during deformation.

  14. Application of Quantitative Models, MNLR and ANN in Short Term Forecasting of Ship Data

    OpenAIRE

    P.Oliver Jayaprakash; K. Gunasekaran

    2011-01-01

    Forecasting has been the trouble-free way for the port authorities to derive the future expected values of service time of Bulk cargo ships handled at ports of South India. The short term forecasting could be an effective tool for estimating the resource requirements of recurring ships of similar tonnage and Cargo. Forecasting the arrival data related to port based ship operations customarily done using the standard algorithms and assumptions. The regular forecasting methods were decompositio...

  15. Quantitative characterization and comparison of precipitate and grain shape in Nickel -base superalloys using moment invariants

    Science.gov (United States)

    Callahan, Patrick Gregory

    A fundamental objective of materials science and engineering is to understand the structure-property-processing-performance relationship. We need to know the true 3-D microstructure of a material to understand certain geometric properties of a material, and thus fulfill this objective. Focused ion beam (FIB) serial sectioning allows us to find the true 3-D microstructure of Ni-base superalloys. Once the true 3-D microstructure is obtained, an accurate quantitative description and characterization of precipitate and/or grain shapes is needed to understand the microstructure and describe it in an unbiased way. In this thesis, second order moment invariants, the shape quotient Q, a convexity measure relating the volume of an object to the volume of its convex hull, V/Vconv, and Gaussian curvature have been used to compare an experimentally observed polycrystalline IN100 microstructure to three synthetic microstructures. The three synthetic microstructures used different shape classes to produce starting grain shapes. The three shape classes are ellipsoids, superellipsoids, and the shapes generated when truncating a cube with an octahedron. The microstructures are compared using a distance measure, the Hellinger distance. The Hellinger distance is used to compare distributions of shape descriptors for the grains in each microstructure. The synthetic microstructure that has the smallest Hellinger distance, and so best matched the experimentally observed microstructure is the microstructure that used superellipsoids as a starting grain shape. While it has the smallest Hellinger distance, and is approaching realistic grain morphologies, the superellipsoidal microstructure is still not realistic. Second order moment invariants, Q, and V/V conv have also been used to characterize the γ' precipitate shapes from four experimental Ru-containing Ni-base superalloys with differences in alloying additions. The superalloys are designated UM-F9, UM-F18, UM-F19, and UM-F22. The

  16. THE QUANTITATIVE COMPONENT’S DIAGNOSIS OF THE ATMOSPHERIC PRECIPITATION CONDITION IN BAIA MARE URBAN AREA

    Directory of Open Access Journals (Sweden)

    S. ZAHARIA

    2012-12-01

    Full Text Available The atmospheric precipitation, an essential meteorological element for defining the climatic potential of a region, presents through its general and local particularities a defining influence for the evolution of the other climatic parameters, conditioning the structure of the overall geographic landscape. Their quantitative parameters sets up the regional natural setting and differentiation of water resources, soil, vegetation and fauna, in the same time influencing the majority of human activities’ aspects, through the generated impact over the agriculture, transportation, construction, for tourism etc. Especially, through the evolution of the related climatic parameters (production type, quantity, duration, frequency, intensity and their spatial and temporal fluctuations, the pluviometric extremes set out the maxim manifestation of the energy gap of the hydroclimatic hazards/risks which induce unfavourable or even damaging conditions for the human activities’ progress. Hence, the production of atmospheric precipitation surpluses conditions the triggering, or reactivation of some intense erosion processes, landslides, and last but not least, floods. Just as dangerous are the adverse amounts of precipitation or their absence on longer periods, determining the appearance of droughts, aridity phenomena, which if associated with the sharp anthropic pressure over the environment, favours the expansion of desertification, with the whole process of the arising negative effects. In this context, this paper aims to perform the diagnosis of atmospheric precipitation condition in Baia Mare urban area, through its quantitative component, in multiannual condition (1971-2007, underlining through the results of the analyzed climatic data and their interpretation, the main characteristics that define it. The data bank from Baia Mare station from the National Meteorological Administration network, representative for the chosen study area, was used. Baia

  17. The development of pan-African food forecasting and the exploration of satellite-based precipitation estimates

    NARCIS (Netherlands)

    Thiemig, Vera

    2014-01-01

    The main objective of this PhD is to contribute to the development of a pan-African flood forecasting system in order to enhance flood forecasting for the whole of Africa. In view of the dimension and complexity of this goal, this research focused on particular aspects of flood forecasting,

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-11-15

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

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

    Science.gov (United States)

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

    2014-04-01

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

  20. 3800 Years of Quantitative Precipitation Reconstruction from the Northwest Yucatan Peninsula

    Science.gov (United States)

    Carrillo-Bastos, Alicia; Islebe, Gerald A.; Torrescano-Valle, Nuria

    2013-01-01

    Precipitation over the last 3800 years has been reconstructed using modern pollen calibration and precipitation data. A transfer function was then performed via the linear method of partial least squares. By calculating precipitation anomalies, it is estimated that precipitation deficits were greater than surpluses, reaching 21% and <9%, respectively. The period from 50 BC to 800 AD was the driest of the record. The drought related to the abandonment of the Maya Preclassic period featured a 21% reduction in precipitation, while the drought of the Maya collapse (800 to 860 AD) featured a reduction of 18%. The Medieval Climatic Anomaly was a period of positive phases (3.8–7.6%). The Little Ice Age was a period of climatic variability, with reductions in precipitation but without deficits. PMID:24391940

  1. Quantitative precipitation estimation in complex orography using quasi-vertical profiles of dual polarization radar variables

    Science.gov (United States)

    Montopoli, Mario; Roberto, Nicoletta; Adirosi, Elisa; Gorgucci, Eugenio; Baldini, Luca

    2017-04-01

    Weather radars are nowadays a unique tool to estimate quantitatively the rain precipitation near the surface. This is an important task for a plenty of applications. For example, to feed hydrological models, mitigate the impact of severe storms at the ground using radar information in modern warning tools as well as aid the validation studies of satellite-based rain products. With respect to the latter application, several ground validation studies of the Global Precipitation Mission (GPM) products have recently highlighted the importance of accurate QPE from ground-based weather radars. To date, a plenty of works analyzed the performance of various QPE algorithms making use of actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization variables not only to ensure a good level of radar data quality but also as a direct input in the rain estimation equations. Among others, one of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution that affects at different levels, all the radar variables acquired as well as rain rates. This is particularly impactful in mountainous areas where the altitudes of the radar sampling is likely several hundred of meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested a in complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that make use of the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered because in that case all the radar variables used in the rain estimation process should be consistently extrapolated at the surface

  2. Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions

    Science.gov (United States)

    Mark B. Green; John L. Campbell; Ruth D. Yanai; Scott W. Bailey; Amey S. Bailey; Nicholas Grant; Ian Halm; Eric P. Kelsey; Lindsey E. Rustad

    2018-01-01

    The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been...

  3. Identification and uncertainty estimation of vertical reflectivity profiles using a Lagrangian approach to support quantitative precipitation measurements by weather radar

    Science.gov (United States)

    Hazenberg, P.; Torfs, P. J. J. F.; Leijnse, H.; Delrieu, G.; Uijlenhoet, R.

    2013-09-01

    This paper presents a novel approach to estimate the vertical profile of reflectivity (VPR) from volumetric weather radar data using both a traditional Eulerian as well as a newly proposed Lagrangian implementation. For this latter implementation, the recently developed Rotational Carpenter Square Cluster Algorithm (RoCaSCA) is used to delineate precipitation regions at different reflectivity levels. A piecewise linear VPR is estimated for either stratiform or neither stratiform/convective precipitation. As a second aspect of this paper, a novel approach is presented which is able to account for the impact of VPR uncertainty on the estimated radar rainfall variability. Results show that implementation of the VPR identification and correction procedure has a positive impact on quantitative precipitation estimates from radar. Unfortunately, visibility problems severely limit the impact of the Lagrangian implementation beyond distances of 100 km. However, by combining this procedure with the global Eulerian VPR estimation procedure for a given rainfall type (stratiform and neither stratiform/convective), the quality of the quantitative precipitation estimates increases up to a distance of 150 km. Analyses of the impact of VPR uncertainty shows that this aspect accounts for a large fraction of the differences between weather radar rainfall estimates and rain gauge measurements.

  4. Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

    Directory of Open Access Journals (Sweden)

    Mario Montopoli

    2017-02-01

    Full Text Available Near surface quantitative precipitation estimation (QPE from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR, i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain when dealing with

  5. Precipitation evidences on X-Band Synthetic Aperture Radar imagery: an approach for quantitative detection and estimation

    Science.gov (United States)

    Mori, Saverio; Marzano, Frank S.; Montopoli, Mario; Pulvirenti, Luca; Pierdicca, Nazzareno

    2017-04-01

    Spaceborne synthetic aperture radars (SARs) operating at L-band and above are nowadays a well-established tool for Earth remote sensing; among the numerous civil applications we can indicate flood areas detection and monitoring, earthquakes analysis, digital elevation model production, land use monitoring and classification. Appealing characteristics of this kind of instruments is the high spatial resolution ensured in almost all-weather conditions and with a reasonable duty cycle and coverage. This result has achieved by the by the most recent generation of SAR missions, which moreover allow polarimetric observation of the target. Nevertheless, atmospheric clouds, in particular the precipitating ones, can significantly affect the signal backscattered from the ground surface (e.g. Ferrazzoli and Schiavon, 1997), on both amplitude and phase, with effects increasing with the operating frequency. In this respect, proofs are given by several recent works (e.g. Marzano et al., 2010, Baldini et al., 2014) using X-Band SAR data by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. On the other hand, this sensitivity open interesting perspectives towards the SAR observation, and eventually quantification, of precipitations. In this respect, a proposal approach for X-SARs precipitation maps production and cloud masking arise from our work. Cloud masking allows detection of precipitation compromised areas. Respect precipitation maps, satellite X-SARs offer the unique possibility to ingest within flood forecasting model precipitation data at the catchment scale. This aspect is particularly innovative, even if work has been done the late years, and some aspects need to still address. Our developed processing framework allows, within the cloud masking stage, distinguishing flooded areas, precipitating clouds together with permanent water bodies, all appearing dark in the SAR image. The procedure is mainly based on image segmentation techniques and fuzzy logic (e.g. Pulvirenti et

  6. Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

    Science.gov (United States)

    Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

    2016-12-01

    The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

  7. Quantitative study of substorm-associated VLF phase anomalies and precipitating energetic electrons on November 13, 1979

    International Nuclear Information System (INIS)

    Kikuchi, T.; Evans, D.S.

    1983-01-01

    The phase anomalies associated with substorms are observed on VLF signals propagating on transauroral paths (transmitters at OMEGA-ALDRA (13.6 kHz), GBR (16.0 kHz), and OMEGA--NORTH DAKOTA (13.6 kHz)) which were continually received at Inubo, Japan, during the events on November 13, 1979. Detailed comparisons are made between these phase anomalies and geomagnetic bays, and quantitative relations are obtained with precipitating energetic electrons (E>30, E>100, and E>300 keV) detected on board the TIROS-N and NOAA 6 satellites. It is concluded that two types of VLF phase anomalies exist which, in turn, are associated with two phases in the history of energetic electron precipitation into the atmosphere. The first type of phase anomaly is associated with direct injection of energetic electrons into the outer magnetosphere and atmosphere which, in turn, is completely correlated in time with development of the auroral electrojet current system. The second type arises from energetic electrons which subsequently precipitate from a trapped electron population and has a delayed onset and prolonged duration. An excellent quantitative correlation is obtained between the logarithm of the electron flux and the magnitude of the phase anomaly on the OMEGA-ALDRA signal. From the local time characteristics of this quantitative relation it is deduced that the electrons with E>300 keV are the main source of D region ionization responsible for the VLF phase anomaly

  8. Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application

    DEFF Research Database (Denmark)

    Galatzer-Levy, I. R.; Karstoft, K. I.; Statnikov, A.

    2014-01-01

    -traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics...... algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder......). The feature selection algorithm identified 16 predictors, present in >= 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC...

  9. Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

    Science.gov (United States)

    Green, Mark B; Campbell, John L; Yanai, Ruth D; Bailey, Scott W; Bailey, Amey S; Grant, Nicholas; Halm, Ian; Kelsey, Eric P; Rustad, Lindsey E

    2018-01-01

    The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.

  10. Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.

    Science.gov (United States)

    Galatzer-Levy, Isaac R; Karstoft, Karen-Inge; Statnikov, Alexander; Shalev, Arieh Y

    2014-12-01

    There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The

  11. Quantitative diagnosis of moisture sources and transport pathways for summer precipitation over the mid-lower Yangtze River Basin

    Science.gov (United States)

    Wang, Ning; Zeng, Xin-Min; Guo, Wei-Dong; Chen, Chaohui; You, Wei; Zheng, Yiqun; Zhu, Jian

    2018-04-01

    Using a moisture tracking model with 32-year reanalysis data and station precipitation observations, we diagnosed the sources of moisture for summer (June 1-August 31) precipitation in mid-lower reaches of the Yangtze River Basin (YRB). Results indicate the dominant role of oceanic evaporation compared to terrestrial evapotranspiration, and the previously overlooked southern Indian Ocean, as a source region, is found to contribute more moisture than the well-known Arabian Sea or Bay of Bengal. Terrestrial evapotranspiration appears to be important for summer precipitation, especially in early June when moisture contribution is more than 50%. The terrestrial contribution then decreases and is generally less than 40% after late June. The Indian Ocean is the most important oceanic source before mid-July, with its largest contribution during the period of heavy precipitation, while the Pacific Ocean becomes the more important oceanic source after mid-July. To quantitatively analyze paths of moisture transport to YRB, we proposed the Trajectory Frequency Method. The most intense branch of water vapor transport to YRB stretches from the Arabian Sea through the Bay of Bengal, the Indochina Peninsula, the South China Sea, and South China. The other main transport branches are westerly moisture fluxes to the south of the Tibetan Plateau, cross-equatorial flows north of Australia, and separate branches located in the north and equatorial Pacific Ocean. Significant intraseasonal variability for these branches is presented. Additionally, the importance of the South China Sea for moisture transport to YRB, especially from the sea areas, is emphasized.

  12. Observation-based Quantitative Uncertainty Estimation for Realtime Tsunami Inundation Forecast using ABIC and Ensemble Simulation

    Science.gov (United States)

    Takagawa, T.

    2016-12-01

    An ensemble forecasting scheme for tsunami inundation is presented. The scheme consists of three elemental methods. The first is a hierarchical Bayesian inversion using Akaike's Bayesian Information Criterion (ABIC). The second is Montecarlo sampling from a probability density function of multidimensional normal distribution. The third is ensamble analysis of tsunami inundation simulations with multiple tsunami sources. Simulation based validation of the model was conducted. A tsunami scenario of M9.1 Nankai earthquake was chosen as a target of validation. Tsunami inundation around Nagoya Port was estimated by using synthetic tsunami waveforms at offshore GPS buoys. The error of estimation of tsunami inundation area was about 10% even if we used only ten minutes observation data. The estimation accuracy of waveforms on/off land and spatial distribution of maximum tsunami inundation depth is demonstrated.

  13. Trend analysis and forecast of precipitation, reference evapotranspiration and rainfall deficit in the Blackland Prairie of eastern Mississippi

    Science.gov (United States)

    Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration (ETo) and rainfall deficit are essential for water resources management and cropping system design. Rainfall, ETo, and water deficit patterns and trends in eastern Mississippi USA for a 120-year period (1...

  14. Qualitative and Quantitative Analysis of Heparin during Precipitation by Near-Infrared Spectroscopy

    OpenAIRE

    Lian Li; Jinfeng Wang; Hengchang Zang; Hui Zhang; Wei Jiang; Shang Chen; Fengshan Wang

    2016-01-01

    Heparin is a glycosaminoglycan (GAG) that plays an important role in the blood coagulation system. Its quality is of great importance, so it is necessary to develop a fast analytical method during the manufacture process to analyse the quality of heparin produced. In this study, the heparin contents of 80 samples collected from five batches during the precipitation process were analysed using nearinfrared (NIR) spectroscopy and a chemometrics approach. This was done in order to improve the ef...

  15. Quantitative Analysis on Carbide Precipitation in V-Ti Microalloyed TRIP Steel Containing Aluminum

    Directory of Open Access Journals (Sweden)

    Fu Shiyu

    2016-01-01

    Full Text Available Introducing fine precipitates is an important way to enhance the properties of transformation-induced plasticity (TRIP steels. In present work, two V-Ti microalloyed TRIP steels containing aluminum with different content were compared. The average size, size distribution and numbers of vanadium-titanium carbides in samples cold rolled, quenched after being held at 800°C and quenched after intercritical annealing at 800°C and being held at bainitic isothermal transformation temperature of 400°C were investigated by using the technique of carbon extraction replica, twin jet chemical polishing thinning and transmission electron microscopy. The carbides were identified to be (Ti,VC precipitates in steel A and VC in steel B respectively, precipitated mainly from ferrites grains. The average equivalent radius was 3~6nm. Comparison of the experimental results in A and B steel revealed low carbon diffusion rate caused by aluminum inhibited the coarsening of vanadium-titanium carbides. The experimental results also showed that VC carbides dissolution occurred during the intercritical annealing at 800°C.

  16. Quantitative examination of carbide and sulphide precipitates in chemically complex steels processed by direct strip casting

    Energy Technology Data Exchange (ETDEWEB)

    Dorin, Thomas, E-mail: thomas.dorin@deakin.edu.au [Deakin University, Pigdons Road, Geelong, Victoria, 3216 (Australia); Wood, Kathleen [Australian Nuclear Science and Technology Organisation, Bragg Institute, New South Wales, 2234, Menai (Australia); Taylor, Adam; Hodgson, Peter; Stanford, Nicole [Deakin University, Pigdons Road, Geelong, Victoria, 3216 (Australia)

    2016-02-15

    A high strength low alloy steel composition has been melted and processed by two different routes: simulated direct strip casting and slow cooled ingot casting. The microstructures were examined with scanning and transmission electron microscopy, atom probe tomography and small angle neutron scattering (SANS). The formation of cementite (Fe{sub 3}C), manganese sulphides (MnS) and niobium carbo-nitrides (Nb(C,N)) was investigated in both casting conditions. The sulphides were found to be significantly refined by the higher cooling rate, and developed an average diameter of only 100 nm for the fast cooled sample, and a diameter too large to be measured with SANS in the slow cooled condition (> 1.1 μm). Slow cooling resulted in the development of classical Nb(C,N) precipitation, with an average diameter of 7.2 nm. However, after rapid cooling both the SANS and atom probe tomography data indicated that the Nb was retained in the matrix as a random solid solution. There was also some evidence that O, N and S are also retained in solid solution in levels not found during conventional processing. - Highlights: • The influence of cooling rate on microstructure is investigated in a HSLA steel. • SANS, TEM and APT are used to characterise the sulphides and Nb(C,N) precipitates. • The slow cooling rate result in the formation of Nb(C,N) precipitates. • The fast cooling rate results in a microstructure supersaturated in Nb, C and N. • The sulphides are 100 nm in the fast cooled sample and > 1 μm in the slow cooled one.

  17. Disdrometer-based C-Band Radar Quantitative Precipitation Estimation (QPE) in a highly complex terrain region in tropical Colombia.

    Science.gov (United States)

    Sepúlveda, J.; Hoyos Ortiz, C. D.

    2017-12-01

    An adequate quantification of precipitation over land is critical for many societal applications including agriculture, hydroelectricity generation, water supply, and risk management associated with extreme events. The use of rain gauges, a traditional method for precipitation estimation, and an excellent one, to estimate the volume of liquid water during a particular precipitation event, does not allow to fully capture the highly spatial variability of the phenomena which is a requirement for almost all practical applications. On the other hand, the weather radar, an active remote sensing sensor, provides a proxy for rainfall with fine spatial resolution and adequate temporary sampling, however, it does not measure surface precipitation. In order to fully exploit the capabilities of the weather radar, it is necessary to develop quantitative precipitation estimation (QPE) techniques combining radar information with in-situ measurements. Different QPE methodologies are explored and adapted to local observations in a highly complex terrain region in tropical Colombia using a C-Band radar and a relatively dense network of rain gauges and disdrometers. One important result is that the expressions reported in the literature for extratropical locations are not representative of the conditions found in the tropical region studied. In addition to reproducing the state-of-the-art techniques, a new multi-stage methodology based on radar-derived variables and disdrometer data is proposed in order to achieve the best QPE possible. The main motivation for this new methodology is based on the fact that most traditional QPE methods do not directly take into account the different uncertainty sources involved in the process. The main advantage of the multi-stage model compared to traditional models is that it allows assessing and quantifying the uncertainty in the surface rain rate estimation. The sub-hourly rainfall estimations using the multi-stage methodology are realistic

  18. New Approaches to Transport Project Assessment: Reference Scenario Forecasting and Quantitative Risk Analysis

    DEFF Research Database (Denmark)

    Salling, Kim Bang

    2010-01-01

    however has proved that the point estimates derived from such analyses are embedded with a large degree of uncertainty. Thus, a new scheme was proposed in terms of applying quantitative risk analysis (QRA) and Monte Carlo simulation in order to represent the uncertainties within the cost-benefit analysis....... Additionally, the handling of uncertainties is supplemented by making use of the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits (user demands i.e. travel time savings) and underestimating investment costs....

  19. Merging Radar Quantitative Precipitation Estimates (QPEs) from the High-resolution NEXRAD Reanalysis over CONUS with Rain-gauge Observations

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Nickl, E.; Seo, D. J.; Kim, B.; Zhang, J.; Qi, Y.

    2015-12-01

    The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over the Continental United States (CONUS) is completed for the period covering from 2002 to 2011. While this constitutes a unique opportunity to study precipitation processes at higher resolution than conventionally possible (1-km, 5-min), the long-term radar-only product needs to be merged with in-situ information in order to be suitable for hydrological, meteorological and climatological applications. The radar-gauge merging is performed by using rain gauge information at daily (Global Historical Climatology Network-Daily: GHCN-D), hourly (Hydrometeorological Automated Data System: HADS), and 5-min (Automated Surface Observing Systems: ASOS; Climate Reference Network: CRN) resolution. The challenges related to incorporating differing resolution and quality networks to generate long-term large-scale gridded estimates of precipitation are enormous. In that perspective, we are implementing techniques for merging the rain gauge datasets and the radar-only estimates such as Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and Conditional Bias-Penalized Kriging (CBPK). An evaluation of the different radar-gauge merging techniques is presented and we provide an estimate of uncertainty for the gridded estimates. In addition, comparisons with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) are provided in order to give a detailed picture of the improvements and remaining challenges.

  20. Quantitative transmission electron microscopy and atom probe tomography study of Ag-dependent precipitation of Ω phase in Al-Cu-Mg alloys

    Energy Technology Data Exchange (ETDEWEB)

    Bai, Song; Ying, Puyou [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China); Liu, Zhiyi, E-mail: liuzhiyi@csu.edu.cn [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China); Wang, Jian; Li, Junlin [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China)

    2017-02-27

    The close association between the Ω precipitation and various Ag additions is systematically investigated by quantitative transmission electron microscopy and atom probe tomography analysis. Our results suggest that the precipitation of Ω phase is strongly dependent on Ag variations. Increasing the bulk Ag content favors a denser Ω precipitation and hence leads to a greater age-hardening response of Al-Cu-Mg-Ag alloy. This phenomenon, as proposed by proximity histograms, is directly related to the greater abundance of Ag solutes within Ω precursors. This feature lowers its nucleation barrier and increases the nucleation rate of Ω phase, finally contributes to the enhanced Ω precipitation. Also, it is noted that increasing Ag remarkably restricts the precipitation of θ' phase.

  1. Quantitative reconstruction of precipitation and runoff during MIS 5a, MIS 3a, and Holocene, arid China

    Science.gov (United States)

    Liu, Yuan; Li, Yu

    2017-11-01

    Marine oxygen isotope stage 5a (MIS 5a), MIS 3a, and Holocene were highlighted periods in paleoclimate studies. Many scientists have published a great number of studies in this regard, but they paid more attention to qualitative research, and there was often a lack of quantitative data. In this paper, based on chronological evidence from a paleolake in arid China, MIS 5a, MIS 3a, and Holocene lake area, the precipitation of the drainage area and the runoff of the inflowing rivers of the lake were reconstructed with ArcGIS spatial analysis software and the improved water and energy balance model which was calibrated by modern meteorological and hydrological data in the Shiyang River drainage basin. The results showed that the paleolake areas were 1824, 1124, and 628 km2 for MIS 5a, MIS 3a, and Holocene; meanwhile, the paleoprecipitation and runoff were 293.992-297.433, 271.105-274.294, and 249.431-252.373 mm and 29.103 × 108-29.496 × 108, 18.810 × 108-18.959 × 108, and 10.637 × 108-10.777 × 108 mm, respectively. The quantitative data can help us not only strengthen the understanding of paleoclimatic characteristics but also recognize the complexity and diversity of the climate system.

  2. Precipitable water and surface humidity over global oceans from special sensor microwave imager and European Center for Medium Range Weather Forecasts

    Science.gov (United States)

    Liu, W. T.; Tang, Wenqing; Wentz, Frank J.

    1992-01-01

    Global fields of precipitable water W from the special sensor microwave imager were compared with those from the European Center for Medium Range Weather Forecasts (ECMWF) model. They agree over most ocean areas; both data sets capture the two annual cycles examined and the interannual anomalies during an ENSO episode. They show significant differences in the dry air masses over the eastern tropical-subtropical oceans, particularly in the Southern Hemisphere. In these regions, comparisons with radiosonde data indicate that overestimation by the ECMWF model accounts for a large part of the differences. As a check on the W differences, surface-level specific humidity Q derived from W, using a statistical relation, was compared with Q from the ECMWF model. The differences in Q were found to be consistent with the differences in W, indirectly validating the Q-W relation. In both W and Q, SSMI was able to discern clearly the equatorial extension of the tongues of dry air in the eastern tropical ocean, while both ECMWF and climatological fields have reduced spatial gradients and weaker intensity.

  3. Uncertainty in the area-related QPF for heavy convective precipitation

    Czech Academy of Sciences Publication Activity Database

    Řezáčová, Daniela; Zacharov, Petr, jr.; Sokol, Zbyněk

    2009-01-01

    Roč. 93, 1-3 (2009), s. 238-246 ISSN 0169-8095. [European Conference on Severe Storms /4./. Miramare -Trieste, 10.09.2007-14.09.2007] R&D Projects: GA ČR GA205/07/0905; GA MŠk OC 112 Institutional research plan: CEZ:AV0Z30420517 Keywords : Convective storm * Quantitative precipitation forecast * Uncertainty in precipitation forecasting * Ensemble forecasting * Numerical weather prediction model Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.811, year: 2009 http://www.sciencedirect.com/science/journal/01698095

  4. Quantitative measurement of precipitation using radar in comparison with ground-level measurements, taking orographic influences into account; Quantitative Niederschlagsmessung mit Radar im Vergleich mit Bodenmessungen in orographisch gegliedertem Gelaende

    Energy Technology Data Exchange (ETDEWEB)

    Gysi, H. [Radar-Info, Karlsruhe (Germany)

    1998-01-01

    The methods of correction applied to the determination of the spatial distribution of precipitation on the basis of the volumes established by the Karlsruhe C-band precipitation radar distinctly enhance the quality of statements regarding precipitation intensities and their time integration both in summer and winter. (orig./KW) [Deutsch] Die fuer die Bestimmung der raeumlichen Niederschlagsverteilung aus Volumendaten des Karlsruher C-Band Niederschlagradars angewandten Korrekturverfahren verbessern sowohl im Sommer als auch im Winter deutlich die Qualitaet und quantitative Aussagekraft der dargestellten Niederschlagsintensitaeten und deren zeitlichen Integrationen. (orig./KW)

  5. Quantitative assessment of intermetallic phase precipitation in a super duplex stainless steel weld metal using automatic image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gregori, A. [AB Sandvik Steel, Sandviken (Sweden). R and D Centre; Nilsson, J.-O. [AB Sandvik Steel, R and D Centre, Sandviken (Sweden); Bonollo, F. [Univ. di Padova, DTGSI, Vicenza (Italy)

    1999-07-01

    The microstructure of weld metal of the type 25%Cr-10%Ni-4%Mo-0.28%N in both as-welded and isothermally heat treated (temperature range: 700-1050 C: time range: 10s-72h) conditions has been investigated. Multipass welding was performed in Ar+2%N{sub 2} atmosphere using GTAW. By means of the electron diffraction technique. {sigma}-phase and {chi}-phase were detected and investigated. {chi}-phase precipitated more readily than {sigma}-phase and was found to be a precursor to {sigma}-phase by providing suitable nucleation sites. Quantitative image analysis of ferrite and intermetallic phases was performed as well as manual point counting (ISO 9042). Automatic image analysis was found to be more accurate. The results were used to assess the TTT-diagram with respect to intermetallic phase formation. On the basis of these results a CCT-diagram was computed, considering the intermetallic phase formation described by an Avrami type equation and adopting the additivity rule. (orig.)

  6. Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

    Directory of Open Access Journals (Sweden)

    Basile Pauthier

    2016-01-01

    Full Text Available A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1 PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2 both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3 PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE. This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

  7. A hybrid spatiotemporal drought forecasting model for operational use

    Science.gov (United States)

    Vasiliades, L.; Loukas, A.

    2010-09-01

    Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

  8. Quantitative TEM study of the precipitation microstructure in aluminium alloy Al(MgSiCu) 6056 T6

    International Nuclear Information System (INIS)

    Delmas, F.; Casanove, M.J.; Lours, P.; Couret, A.; Coujou, A.

    2004-01-01

    The precipitate microstructure in the last-generation aluminium alloy 6056 T6 [AlMgSiCu] is investigated using three complementary techniques of transmission electron microscopy (TEM) with a special focus on the density and volume fraction of strengthening particles. High-resolution TEM allows the identification of the precipitates and the measurement of the precipitate sizes to be performed. Conventional TEM is used to evaluate the number of precipitates in the investigated area as well as their distribution in the matrix. In situ TEM straining, via the analysis of the dislocation slip traces, permits to determine precisely the thickness and the volume of the foil in the region where the precipitates are analysed. Taking into account the shape and the dimensions of precipitates with respect to the foil thickness, a novel methodology for measuring the volume density and the volume fraction of precipitates is proposed

  9. Forecasting in Planning

    OpenAIRE

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forec...

  10. Quantitative precipitation climatology over the Himalayas by using Precipitation Radar on Tropical Rainfall Measuring Mission (TRMM) and a dense network of rain-gauges

    Science.gov (United States)

    Yatagai, A.

    2010-09-01

    Quantified grid observation data at a reasonable resolution are indispensable for environmental monitoring as well as for predicting future change of mountain environment. However quantified datasets have not been available for the Himalayan region. Hence we evaluate climatological precipitation data around the Himalayas by using Precipitation Radar (PR) data acquired by the Tropical Rainfall Measuring Mission (TRMM) over 10 years of observation. To validate and adjust these patterns, we used a dense network of rain gauges collected by the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE Water Resources) project (http://www.chikyu.ac.jp/precip/). We used more than 2600 stations which have more than 10-year monthly precipitation over the Himalayan region (75E-105E, 20-36N) including country data of Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. The region we studied is so topographically complicated that horizontal patterns are not uniform. Therefore, every path data of PR2A25 (near-surface rain) was averaged in a 0.05-degree grid and a 10-year monthly average was computed (hereafter we call PR). On the other hand, for rain-gauge, we first computed cell averages if each 0.05-degree grid cell has 10 years observation or more. Here we refer to the 0.05-degree rain-gauge climatology data as RG data. On the basis of comparisons between the RG and PR composite values, we defined the parameters of the regressions to correct the monthly climatology value based on the rain gauge observations. Compared with the RG, the PR systematically underestimated precipitation by 28-38% in summer (July-September). Significant correlation between TRMM/PR and rain-gauge data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in

  11. Coupling meteorological and hydrological models for flood forecasting

    Directory of Open Access Journals (Sweden)

    Bartholmes

    2005-01-01

    Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

  12. On the quality of the precipitation forecast of the Boundary Layer Model (BLM) in the Geophysical Consulting Service of the Bundeswehr. February to August 1994; Zur Guete der Niederschlagsvorhersage des Boundary Layer Model (BLM) im Geophysikalischen Beratungsdienst der Bundeswehr. Februar bis August 1994

    Energy Technology Data Exchange (ETDEWEB)

    Arck, J.

    1995-04-01

    A study was made of various aspects of the Boundary Layer Model`s precipitation forecast. The following trends were found: Precipitation-free events are underestimated by the BLM. This happens more frequently in the valleys of the highlands than in coastal regions. There is a correlation between forecast quality and the start of the forecast period. Skill scores for forecasts beginning at 12Z are generally higher than those for forecasts beginning at 00Z. Skill scores for forecasts for the second half of the day are higher than those for the first half of the day. For forecast periods of up to 36 hours the daytime-dependent differences in forecast quality appear to have a greater effect than the variation of forecast quality with the duration of the forecast period. (orig.) [Deutsch] Es wurden einzelne Aspekte der BLM Niederschlagsvorhersagen untersucht. Es ergeben sich folgende Trends: - Niederschlagsfreie Ereignisse werden vom BLM unterschaetzt. Dies kommt in den Tallagen der Mittelgebirge haeufiger vor als an der Kueste. - Es besteht eine Abhaengigkeit zwischen Vorhersageguete und Startzeitpunkt der Vorhersage: Fuer die von 12Z ausgehenden Vorhersagen ist der Skill Score in der Regel groesser als fuer die von 00Z ausgehenden Vorhersagen. - Die Vorhersagen fuer die 2. Tageshaelfte haben meist einen hoeheren Skill Score als die Vorhersagen fuer die erste Tageshaelfte. - Die vom Tagesgang abhaengigen Unterschiede in der Vorhersageguete scheinen innerhalb einer 36 Std Vorhersagedauer einen groesseren Effekt zu haben als die Variation der Vorhersageguete mit der Vorhersagedauer. (orig.)

  13. Dynamical contibution of Mean Potential Vorticity pseudo-observations derived from MetOp/GOME2 Ozone data into weather forecast, a Mediterranean High Precipitation Event study.

    Science.gov (United States)

    Sbii, Siham; Zazoui, Mimoun; Semane, Noureddine

    2015-04-01

    In the absence of observations covering the upper troposphere - lower stratophere, headquarters of several disturbances, and knowing that satellites are uniquely capable of providing uniform data coverage globally, a methodology is followed [1] to convert Total Column Ozone, observed by MetOp/GOME2, into pseudo-observations of Mean Potential Vorticity (MPV). The aim is to study the dynamical impact of Ozone data in the prediction of a Mediterranean Heavy Precipitation Event observed during 28-29 September 2012 in the context of HYMEX1. This study builds on a previously described methodology [2] that generates numerical weather prediction model initial conditions from ozone data. Indeed, the assimilation of MPV in a 3D-var framework is based on a linear regression between observed Ozone and vertical integrated Ertel PV. The latter is calculated using dynamical fields from the moroccan operational limited area model ALADIN-MAROC according to [3]: δθ fp p0 -R δU δV P V = - gξaδp- g-R-(p )Cp [(δp-)2 + (δp-)2] (1) Where ξa is the vertical component of the absolute vorticity, U and V the horizontal wind components, θ the potential temperature, R gas constant, Cp specific heat at constant pressure, p the pressure, p0 a reference pressure, g the gravity and f is the Coriolis parameter. The MPV is estimated using the following expression: --1--∫ P2 M PV = P1 - P2 P P V.δp 1 (2) With P1 = 500hPa and P2 = 100hPa In the present study, the linear regression is performed over September 2012 with a correlation coefficient of 0.8265 and is described as follows: M P V = 5.314610- 2 *O3 - 13.445 (3) where O3 and MPV are given in Dobson Unit (DU) and PVU (1 PV U = 10-6 m2 K kg-1 s-1), respectively. It is found that the ozone-influenced upper-level initializing fields affect the precipitation forecast, as diagnosed by a comparison with the ECMWF model. References [1] S. Sbii, N. Semane, Y. Michel, P. Arbogast and M. Zazoui (2012). Using METOP/GOME-2 data and MSG ozone

  14. Dosing of low-activity strontium 90 in human bone ashes - A method based on the quantitative precipitation of strontium nitrate

    International Nuclear Information System (INIS)

    Patti, Francois; Bullier, Denise

    1969-02-01

    The specific separation of strontium nitrate in bone ash samples by red fuming nitric acid requires a succession of precipitation varying in number according to the weight of ashes. The interest of the technique is to define the experimental conditions required for a reproducible quantitative separation of strontium. The operating process tested on over 1.500 samples allowed to obtain chemical yields of about 90 per cent. (authors) [fr

  15. Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

    Science.gov (United States)

    Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

    2017-03-01

    Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

  16. Quantitative measurement for the microstructural parameters of nano-precipitates in Al-Mg-Si-Cu alloys

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kai [School of Metallurgy and Environment, Central South University, Changsha 410083 (China); Electron Microscopy for Materials Science (EMAT), University of Antwerp, Antwerp B-2020 (Belgium); State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083 (China); Idrissi, Hosni [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Antwerp B-2020 (Belgium); Institute of Mechanics, Materials and Civil Engineering (iMMC), Université catholique de Louvain, Place Sainte Barbe 2, B-1348 Louvain-la-Neuve (Belgium); Sha, Gang [Gleiter Institute of Nano-science, Nanjing University of Science and Technology, Nanjing 210094 (China); Song, Min, E-mail: msong@csu.edu.cn [State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083 (China); Lu, Jiangbo [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Antwerp B-2020 (Belgium); Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education and International Center for Dielectric Research, Xi' an Jiaotong University, Xi' an 710049 (China); Shi, Hui [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Antwerp B-2020 (Belgium); ArcelorMittal Global R& D Gent, Pres. J.F. Kennedylaan 3 Zelzate, Ghent B-9060 (Belgium); Wang, Wanlin [School of Metallurgy and Environment, Central South University, Changsha 410083 (China); Ringer, Simon P. [Australian Institute for Nanoscale Science and Technology, The University of Sydney, NSW 2006 (Australia); School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006 (Australia); Du, Yong [State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083 (China); Schryvers, Dominique [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Antwerp B-2020 (Belgium)

    2016-08-15

    Size, number density and volume fraction of nano-precipitates are important microstructural parameters controlling the strengthening of materials. In this work a widely accessible, convenient, moderately time efficient method with acceptable accuracy and precision has been provided for measurement of volume fraction of nano-precipitates in crystalline materials. The method is based on the traditional but highly accurate technique of measuring foil thickness via convergent beam electron diffraction. A new equation is proposed and verified with the aid of 3-dimensional atom probe (3DAP) analysis, to compensate for the additional error resulted from the hardly distinguishable contrast of too short incomplete precipitates cut by the foil surface. The method can be performed on a regular foil specimen with a modern LaB{sub 6} or field-emission-gun transmission electron microscope. Precisions around ± 16% have been obtained for precipitate volume fractions of needle-like β″/C and Q precipitates in an aged Al-Mg-Si-Cu alloy. The measured number density is close to that directly obtained using 3DAP analysis by a misfit of 4.5%, and the estimated precision for number density measurement is about ± 11%. The limitations of the method are also discussed. - Highlights: •A facile method for measuring volume fraction of nano-precipitates based on CBED •An equation to compensate for small invisible precipitates, with 3DAP verification •Precisions around ± 16% for volume fraction and ± 11% for number density.

  17. An operational weather radar-based Quantitative Precipitation Estimation and its application in catchment water resources modeling

    DEFF Research Database (Denmark)

    He, Xin; Vejen, Flemming; Stisen, Simon

    2011-01-01

    of precipitation compared with rain-gauge-based methods, thus providing the basis for better water resources assessments. The radar QPE algorithm called ARNE is a distance-dependent areal estimation method that merges radar data with ground surface observations. The method was applied to the Skjern River catchment...... in western Denmark where alternative precipitation estimates were also used as input to an integrated hydrologic model. The hydrologic responses from the model were analyzed by comparing radar- and ground-based precipitation input scenarios. Results showed that radar QPE products are able to generate...... reliable simulations of stream flow and water balance. The potential of using radar-based precipitation was found to be especially high at a smaller scale, where the impact of spatial resolution was evident from the stream discharge results. Also, groundwater recharge was shown to be sensitive...

  18. Quantitative investigation of precipitate growth during ageing of Al-(Mg,Si) alloys by energy-filtered electron diffraction

    DEFF Research Database (Denmark)

    Wollgarten, M.; Chang, C. S. T.; Duchstein, Linus Daniel Leonhard

    2011-01-01

    Besides other application fields, light-weight Al-(Mg, Si) (6XXX series) alloys are of substantial importance in automotive industries where they are used for the production of car body panels. The material gains its strength by precipitation of metastable Mg-Si-based phases. Though the general...... accepted that the early stages of precipitate growth are important for the understanding of this peculiar behaviour. During these stages, electron diffraction patterns of Al-(Mg, Si) alloys show diffuse features (Figure 1 (a) and (b)) which can be traced back to originate from β'' Mg5Si6 precipitates [5......-7]. In this paper, we use energy-filtered electron diffraction to determine dimensions of the β'' Mg5Si6 precipitates along their a, b and c-axes as a function of ageing time and alloy composition. In our contribution, we first derive that there is an optimal zone axis - - from the view point of practicability. We...

  19. On the forecast skill of a convection-permitting ensemble

    Science.gov (United States)

    Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander

    2017-01-01

    The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale - Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational - Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

  20. 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.

  1. 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.

  2. Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

    Directory of Open Access Journals (Sweden)

    Xinchi Chen

    2016-01-01

    Full Text Available Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

  3. Comparison of three different methods of perturbing the potential vorticity field in mesoscale forecasts of Mediterranean heavy precipitation events: PV-gradient, PV-adjoint and PV-satellite

    Science.gov (United States)

    Vich, M.; Romero, R.; Richard, E.; Arbogast, P.; Maynard, K.

    2010-09-01

    Heavy precipitation events occur regularly in the western Mediterranean region. These events often have a high impact on the society due to economic and personal losses. The improvement of the mesoscale numerical forecasts of these events can be used to prevent or minimize their impact on the society. In previous studies, two ensemble prediction systems (EPSs) based on perturbing the model initial and boundary conditions were developed and tested for a collection of high-impact MEDEX cyclonic episodes. These EPSs perturb the initial and boundary potential vorticity (PV) field through a PV inversion algorithm. This technique ensures modifications of all the meteorological fields without compromising the mass-wind balance. One EPS introduces the perturbations along the zones of the three-dimensional PV structure presenting the local most intense values and gradients of the field (a semi-objective choice, PV-gradient), while the other perturbs the PV field over the MM5 adjoint model calculated sensitivity zones (an objective method, PV-adjoint). The PV perturbations are set from a PV error climatology (PVEC) that characterizes typical PV errors in the ECMWF forecasts, both in intensity and displacement. This intensity and displacement perturbation of the PV field is chosen randomly, while its location is given by the perturbation zones defined in each ensemble generation method. Encouraged by the good results obtained by these two EPSs that perturb the PV field, a new approach based on a manual perturbation of the PV field has been tested and compared with the previous results. This technique uses the satellite water vapor (WV) observations to guide the correction of initial PV structures. The correction of the PV field intents to improve the match between the PV distribution and the WV image, taking advantage of the relation between dark and bright features of WV images and PV anomalies, under some assumptions. Afterwards, the PV inversion algorithm is applied to run

  4. Comparison Of Quantitative Precipitation Estimates Derived From Rain Gauge And Radar Derived Algorithms For Operational Flash Flood Support.

    Science.gov (United States)

    Streubel, D. P.; Kodama, K.

    2014-12-01

    To provide continuous flash flood situational awareness and to better differentiate severity of ongoing individual precipitation events, the National Weather Service Research Distributed Hydrologic Model (RDHM) is being implemented over Hawaii and Alaska. In the implementation process of RDHM, three gridded precipitation analyses are used as forcing. The first analysis is a radar only precipitation estimate derived from WSR-88D digital hybrid reflectivity, a Z-R relationship and aggregated into an hourly ¼ HRAP grid. The second analysis is derived from a rain gauge network and interpolated into an hourly ¼ HRAP grid using PRISM climatology. The third analysis is derived from a rain gauge network where rain gauges are assigned static pre-determined weights to derive a uniform mean areal precipitation that is applied over a catchment on a ¼ HRAP grid. To assess the effect of different QPE analyses on the accuracy of RDHM simulations and to potentially identify a preferred analysis for operational use, each QPE was used to force RDHM to simulate stream flow for 20 USGS peak flow events. An evaluation of the RDHM simulations was focused on peak flow magnitude, peak flow timing, and event volume accuracy to be most relevant for operational use. Results showed RDHM simulations based on the observed rain gauge amounts were more accurate in simulating peak flow magnitude and event volume relative to the radar derived analysis. However this result was not consistent for all 20 events nor was it consistent for a few of the rainfall events where an annual peak flow was recorded at more than one USGS gage. Implications of this indicate that a more robust QPE forcing with the inclusion of uncertainty derived from the three analyses may provide a better input for simulating extreme peak flow events.

  5. Quantitative estimation of orographic precipitation over the Himalayas by using TRMM/PR and a dense network of rain gauges

    Science.gov (United States)

    Yatagai, A.

    2009-04-01

    Precipitation Radar (PR) data acquired by the Tropical Rainfall Measuring Mission (TRMM) over 10 years of observation were used to show the monthly rainfall patterns over the Himalayas. To validate and adjust these patterns, we used a dense network of rain gauges to measure daily precipitation over Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. We then compared TRMM/PR and rain gauge data in 0.05-degree grid cells (an approximately 5.5-km mesh). Compared with the rain gauge observations, the PR systematically underestimated precipitation by 28-38% in summer (July-September).Significant correlation between TRMM/PR and RG data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in February (250-1000m), March (0-1000m), and April (0-1500m). Monthly PR climatology was adjusted on the basis of monthly regressions between the two sets of data and depicted.

  6. Long-Term Quantitative Precipitation Estimates (QPE) at High Spatial and Temporal Resolution over CONUS: Bias-Adjustment of the Radar-Only National Mosaic and Multi-sensor QPE (NMQ/Q2) Precipitation Reanalysis (2001-2012)

    Science.gov (United States)

    Prat, Olivier; Nelson, Brian; Stevens, Scott; Seo, Dong-Jun; Kim, Beomgeun

    2015-04-01

    The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is completed for the period covering from 2001 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Several in-situ datasets are available to assess the biases of the radar-only product and to adjust for those biases to provide a multi-sensor QPE. The rain gauge networks that are used such as the Global Historical Climatology Network-Daily (GHCN-D), the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), and the Climate Reference Network (CRN), have different spatial density and temporal resolution. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. The objective of this work is threefold. First, we investigate how the different in-situ networks can impact the precipitation estimates as a function of the spatial density, sensor type, and temporal resolution. Second, we assess conditional and un-conditional biases of the radar-only QPE for various time scales (daily, hourly, 5-min) using in-situ precipitation observations. Finally, after assessing the bias and applying reduction or elimination techniques, we are using a unique in-situ dataset merging the different RG networks (CRN, ASOS, HADS, GHCN-D) to

  7. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    forecasts where a dampening of the differences of precipitation quality occurs. Seasonal meteorological forecasts are possible due to changes of large scale patterns of the ocean and land, such as el Niño, that evolve at a much slower pace than the atmosphere, which can have an impact on its evolution later...

  8. Numerical simulation of heavy precipitation events using mesoscale weather forecast models. Validation with radar data and diagnosis of the atmospheric moisture budget; Numerische Simulation von Starkniederschlagsereignissen mit mesoskaligen Wettervorhersagemodellen. Ueberpruefung mit Radar-Daten und Diagnose der atmosphaerischen Wasserbilanz

    Energy Technology Data Exchange (ETDEWEB)

    Keil, C.

    2000-07-01

    Convective precipitation systems contribute substantially to the summertime rainfall maximum in the northern Alpine region. The capability of mesoscale weather forecast models in capturing such heavy precipitation events is investigated. The complementary application of so far hardly used areal radar data and conventional rain gauge observations enables a case-study-type evaluation of summertime precipitation episodes. Different rainfall episodes are simulated with the former operational model (DM, meshsize 14 km) of Deutscher Wetterdienst (DWD). The influence of the horizontal resolution and the parameterization of moist convection is subsequently studied with a higher resolution atmospheric model (MC2, meshsize 2 km). Diagnostic studies on the atmospheric water budget regarding the rainfall episode, which instigated the Oder-flood in summer 1997, allow an examination of the origin of the moisture and the genesis of the copious precipitation. (orig.) [German] Konvektive Niederschlagssysterne tragen im Nordalpenraum wesentlich zum sommerlichen Niederschlagsmaximum bei. Die Faehigkeit mesoskaliger Wettervorhersagemodelle, solche Starkniederschlagsereignisse zu erfassen, wird in dieser Arbeit untersucht. Durch den komplementaeren Gebrauch von, bisher kaum genutzten, flaechendeckenden Radardaten und konventionellen Niederschlagsmessungen des Bodenmessnetzes werden Modellergebnisse sommerlicher Niederschlagssysteme fallstudienhaft detailliert ueberprueft. Fuer verschiedene Starkniederschlagsereignisse werden dazu Modellsimulationen mit dem in den 90er Jahren operationellen Modell (DM, Maschenweite 14 km) des Deutschen Wetterdienstes (DWD) durchgefuehrt. Zur Untersuchung des Einflusses der horizontalen Maschenweite und der Niederschlagsparametrisierung werden ferner numerische Simulationen mit einem hoeher aufloesdenden Atmosphaerenmodell (MC2, Maschenweite 2 km) behandelt. Anhand diagnostischer Untersuchungen der atmosphaerischen Wasserbilanz laesst sich ausserdem die

  9. Extreme Precipitation events over North China in August 2010 and their link to eastward-propagating wave-trains across Eurasia: observations and monthly forecasting

    NARCIS (Netherlands)

    Orsolini, Y.J.; Zhang, L.; Peters, D.H.W.; Fraedrich, K.; Zhu, X.; Schneidereit, A.; van den Hurk, B.J.J.M.

    2015-01-01

    Over the Far East in summer, climate is strongly influenced by the fluctuating Western Pacific Subtropical High (WPSH), and strong precipitation is often associated with southeasterly low-level wind that brings moist air from the southern China seas. The WPSH intraseasonal variability is partly

  10. Combination of methylated-DNA precipitation and methylation-sensitive restriction enzymes (COMPARE-MS) for the rapid, sensitive and quantitative detection of DNA methylation.

    Science.gov (United States)

    Yegnasubramanian, Srinivasan; Lin, Xiaohui; Haffner, Michael C; DeMarzo, Angelo M; Nelson, William G

    2006-02-09

    Hypermethylation of CpG island (CGI) sequences is a nearly universal somatic genome alteration in cancer. Rapid and sensitive detection of DNA hypermethylation would aid in cancer diagnosis and risk stratification. We present a novel technique, called COMPARE-MS, that can rapidly and quantitatively detect CGI hypermethylation with high sensitivity and specificity in hundreds of samples simultaneously. To quantitate CGI hypermethylation, COMPARE-MS uses real-time PCR of DNA that was first digested by methylation-sensitive restriction enzymes and then precipitated by methyl-binding domain polypeptides immobilized on a magnetic solid matrix. We show that COMPARE-MS could detect five genome equivalents of methylated CGIs in a 1000- to 10,000-fold excess of unmethylated DNA. COMPARE-MS was used to rapidly quantitate hypermethylation at multiple CGIs in >155 prostate tissues, including benign and malignant prostate specimens, and prostate cell lines. This analysis showed that GSTP1, MDR1 and PTGS2 CGI hypermethylation as determined by COMPARE-MS could differentiate between malignant and benign prostate with sensitivities >95% and specificities approaching 100%. This novel technology could significantly improve our ability to detect CGI hypermethylation.

  11. Flood forecasting and warning systems in Pakistan

    International Nuclear Information System (INIS)

    Ali Awan, Shaukat

    2004-01-01

    Meteorologically, there are two situations which may cause three types of floods in Indus Basin in Pakistan: i) Meteorological Situation for Category-I Floods when the seasonal low is a semi permanent weather system situated over south eastern Balochistan, south western Punjab, adjoining parts of Sindh get intensified and causes the moisture from the Arabian Sea to be brought up to upper catchments of Chenab and Jhelum rivers. (ii) Meteorological Situation for Category-11 and Category-111 Floods, which is linked with monsoon low/depression. Such monsoon systems originate in Bay of Bengal region and then move across India in general west/north westerly direction arrive over Rajasthan or any of adjoining states of India. Flood management in Pakistan is multi-functional process involving a number of different organizations. The first step in the process is issuance of flood forecast/warning, which is performed by Pakistan Meteorological Department (PMD) utilizing satellite cloud pictures and quantitative precipitation measurement radar data, in addition to the conventional weather forecasting facilities. For quantitative flood forecasting, hydrological data is obtained through the Provincial Irrigation Department and WAPDA. Furthermore, improved rainfall/runoff and flood routing models have been developed to provide more reliable and explicit flood information to a flood prone population.(Author)

  12. Case studies of extended model-based flood forecasting: prediction of dike strength and flood impacts

    Science.gov (United States)

    Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet

    2017-04-01

    Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global

  13. The Role of Secondary Frontal Waves in Causing Missed or False Alarm Flood Forecasts During Landfalling Atmospheric Rivers

    Science.gov (United States)

    Martin, A.; Ralph, F. M.; Lavers, D. A.; Kalansky, J.; Kawzenuk, B.

    2015-12-01

    The previous ten years has seen an explosion in research devoted to the Atmospheric River (AR) phenomena, features of the midlatitude circulation responsible for large horizontal water vapor transport. Upon landfall, ARs can be associated with 30-50% of annual precipitation in some regions, while also causing the largest flooding events in places such as coastal California. Little discussed is the role secondary frontal waves play in modulating precipitation during a landfalling AR. Secondary frontal waves develop along an existing cold front in response to baroclinic frontogenesis, often coinciding with a strong upper-tropospheric jet. If the secondary wave develops along a front associated with a landfalling AR, the resulting precipitation may be much greater or much less than originally forecasted - especially in regions where orographic uplift of horizontally transported water vapor is responsible for a large portion of precipitation. In this study, we present several cases of secondary frontal waves that have occurred in conjunction with a landfalling AR on the US West Coast. We put the impact of these cases in historical perspective using quantitative precipitation forecasts, satellite data, reanalyses, and estimates of damage related to flooding. We also discuss the dynamical mechanisms behind secondary frontal wave development and relate these mechanisms to the high spatiotemporal variability in precipitation observed during ARs with secondary frontal waves. Finally, we demonstrate that even at lead times less than 24 hours, current quantitative precipitation forecasting methods have difficulty accurately predicting the rainfall in the area near the secondary wave landfall, in some cases leading to missed or false alarm flood warnings, and suggest methods which may improve quantitative precipitation forecasts for this type of system in the future.

  14. 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...

  15. 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...

  16. Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

    Science.gov (United States)

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

    2016-04-01

    accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.

  17. Continuous hydrological modelling in the context of real time flood forecasting in alpine Danube tributary catchments

    International Nuclear Information System (INIS)

    Stanzel, Ph; Kahl, B; Haberl, U; Herrnegger, M; Nachtnebel, H P

    2008-01-01

    A hydrological modelling framework applied within operational flood forecasting systems in three alpine Danube tributary basins, Traisen, Salzach and Enns, is presented. A continuous, semi-distributed rainfall-runoff model, accounting for the main hydrological processes of snow accumulation and melt, interception, evapotranspiration, infiltration, runoff generation and routing is set up. Spatial discretization relies on the division of watersheds into subbasins and subsequently into hydrologic response units based on spatial information on soil types, land cover and elevation bands. The hydrological models are calibrated with meteorological ground measurements and with meteorological analyses incorporating radar information. Operationally, each forecasting sequence starts with the re-calculation of the last 24 to 48 hours. Errors between simulated and observed runoff are minimized by optimizing a correction factor for the input to provide improved system states. For the hydrological forecast quantitative 48 or 72 hour forecast grids of temperature and precipitation - deterministic and probabilistic - are used as input. The forecasted hydrograph is corrected with an autoregressive model. The forecasting sequences are repeated each 15 minutes. First evaluations of resulting hydrological forecasts are presented and reliability of forecasts with different lead times is discussed.

  18. 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

  19. Daily reservoir inflow forecasting combining QPF into ANNs model

    Science.gov (United States)

    Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

    2009-01-01

    Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

  20. Using inferred probabilities to measure the accuracy of imprecise forecasts

    Directory of Open Access Journals (Sweden)

    Paul Lehner

    2012-11-01

    Full Text Available Research on forecasting is effectively limited to forecasts that are expressed with clarity; which is to say that the forecasted event must be sufficiently well-defined so that it can be clearly resolved whether or not the event occurred and forecasts certainties are expressed as quantitative probabilities. When forecasts are expressed with clarity, then quantitative measures (scoring rules, calibration, discrimination, etc. can be used to measure forecast accuracy, which in turn can be used to measure the comparative accuracy of different forecasting methods. Unfortunately most real world forecasts are not expressed clearly. This lack of clarity extends to both the description of the forecast event and to the use of vague language to express forecast certainty. It is thus difficult to assess the accuracy of most real world forecasts, and consequently the accuracy the methods used to generate real world forecasts. This paper addresses this deficiency by presenting an approach to measuring the accuracy of imprecise real world forecasts using the same quantitative metrics routinely used to measure the accuracy of well-defined forecasts. To demonstrate applicability, the Inferred Probability Method is applied to measure the accuracy of forecasts in fourteen documents examining complex political domains. Key words: inferred probability, imputed probability, judgment-based forecasting, forecast accuracy, imprecise forecasts, political forecasting, verbal probability, probability calibration.

  1. Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

    Directory of Open Access Journals (Sweden)

    J. Schmidt

    2008-04-01

    Full Text Available A project established at the National Institute of Water and Atmospheric Research (NIWA in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO Numerical Weather Prediction model (NWP are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

  2. Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

    Science.gov (United States)

    Restrepo, P.; Schaake, J.

    2009-04-01

    Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result

  3. Effective Assimilation of Global Precipitation

    Science.gov (United States)

    Lien, G.; Kalnay, E.; Miyoshi, T.; Huffman, G. J.

    2012-12-01

    Assimilating precipitation observations by modifying the moisture and sometimes temperature profiles has been shown successful in forcing the model precipitation to be close to the observed precipitation, but only while the assimilation is taking place. After the forecast start, the model tends to "forget" the assimilation changes and lose their extra skill after few forecast hours. This suggests that this approach is not an efficient way to modify the potential vorticity field, since this is the variable that the model would remember. In this study, the ensemble Kalman filter (EnKF) method is used to effectively change the potential vorticity field by allowing ensemble members with better precipitation to receive higher weights. In addition to using an EnKF, two other changes in the precipitation assimilation process are proposed to solve the problems related to the highly non-Gaussian nature of the precipitation variable: a) transform precipitation into a Gaussian distribution based on its climatological distribution, and b) only assimilate precipitation at the location where some ensemble members have positive precipitation. The idea is first tested by the observing system simulation experiments (OSSEs) using SPEEDY, a simplified but realistic general circulation model. When the global precipitation is assimilated in addition to conventional rawinsonde observations, both the analyses and the medium range forecasts are significantly improved as compared to only having rawinsonde observations. The improvement is much reduced when only modifying the moisture field with the same approach, which shows the importance of the error covariance between precipitation and all other model variables. The effect of precipitation assimilation is larger in the Southern Hemisphere than that in the Northern Hemisphere because the Northern Hemisphere analyses are already accurate as a result of denser rawinsonde stations. Assimilation of precipitation using a more comprehensive

  4. A quantitative history of precipitation and hydrologic variability for the last 45 ka: Lake Titicaca, Salar de Coipasa and Salar de Uyuni, Peru and Bolivia

    Science.gov (United States)

    Nunnery, A.; Baker, P. A.; Coe, M. T.; Fritz, S. C.; Rigsby, C. A.

    2011-12-01

    Precipitation on the Bolivian/Peruvian Altiplano is dominantly controlled by the South American summer Monsoon (SASM). Over long timescales moisture transport to the Altiplano by the SASM fluctuates in intensity due to precessional insolation forcing as well as teleconnections to millennial scale abrupt temperature shifts in the North Atlantic. These long-term changes in moisture transport have been observed in multiple paleoclimate and paleo-lake level records as advances and retreats of large lakes in the terminal basin (the Salar de Uyuni). Several previous studies using energy/water balance models have been applied to paleoclimate records in attempts to provide quantitative constraints on past precipitation and temperature (P and T). For example, Blodgett et al. concluded that high paleolake stands, first dated at ca. 16,000 cal. yr BP, required P 20% higher and T 5°C colder than modern. We expand on this work conducting two experiments. The first uses a latitudinal paleohydrologic profile to reconstruct hydrological history. The second uses a terrestrial hydrology model (THMB) to "predict" lake level given changes in P and T. The profile is constructed using records from Lake Titicaca (LT), Salar de Coipasa (SC) and Salar de Uyuni (SU). LT carbonate and diatom records indicate a deep, overflowing lake for much of the last 100 ka with a distinct dry, closed-basin phase in the early to mid Holocene. A continuous sediment core from SC indicates lake level fluctuations between deep and shallow phases for the last 45 ka. A natural gamma radiation log from SU, where large paleolakes alternated with shallow salt pans characteristic of drier and/or warmer periods, shows alternation between wet and dry phases through time. These three records give evidence to the complex nature of Altiplano hydrology, most notably the ability to sustain lakes in the SC basin while exhibiting dry conditions in SU. For the second experiment, THMB, which estimates water balance and

  5. 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

  6. Uncertainties in Forecasting Streamflow using Entropy Theory

    Science.gov (United States)

    Cui, H.; Singh, V. P.

    2017-12-01

    Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

  7. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    Science.gov (United States)

    Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

    2013-04-01

    variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

  8. Effective assimilation of global precipitation: simulation experiments

    Directory of Open Access Journals (Sweden)

    Guo-Yuan Lien

    2013-07-01

    Full Text Available Past attempts to assimilate precipitation by nudging or variational methods have succeeded in forcing the model precipitation to be close to the observed values. However, the model forecasts tend to lose their additional skill after a few forecast hours. In this study, a local ensemble transform Kalman filter (LETKF is used to effectively assimilate precipitation by allowing ensemble members with better precipitation to receive higher weights in the analysis. In addition, two other changes in the precipitation assimilation process are found to alleviate the problems related to the non-Gaussianity of the precipitation variable: (a transform the precipitation variable into a Gaussian distribution based on its climatological distribution (an approach that could also be used in the assimilation of other non-Gaussian observations and (b only assimilate precipitation at the location where at least some ensemble members have precipitation. Unlike many current approaches, both positive and zero rain observations are assimilated effectively. Observing system simulation experiments (OSSEs are conducted using the Simplified Parametrisations, primitivE-Equation DYnamics (SPEEDY model, a simplified but realistic general circulation model. When uniformly and globally distributed observations of precipitation are assimilated in addition to rawinsonde observations, both the analyses and the medium-range forecasts of all model variables, including precipitation, are significantly improved as compared to only assimilating rawinsonde observations. The effect of precipitation assimilation on the analyses is retained on the medium-range forecasts and is larger in the Southern Hemisphere (SH than that in the Northern Hemisphere (NH because the NH analyses are already made more accurate by the denser rawinsonde stations. These improvements are much reduced when only the moisture field is modified by the precipitation observations. Both the Gaussian transformation and

  9. Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

    Science.gov (United States)

    Shi, Zhao; Wei, Fangqiang; Chandrasekar, Venkatachalam

    2018-03-01

    Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity-duration (I-D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall-reflectivity (R - Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the frequentist method is I = 10.1D-0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D thresholds and likewise underestimate I-D thresholds due to undershooting at the core of convective

  10. The Sensitivity of Heavy Precipitation to Horizontal Resolution, Domain Size, and Rain Rate Assimilation: Case Studies with a Convection-Permitting Model

    Directory of Open Access Journals (Sweden)

    Xingbao Wang

    2016-01-01

    Full Text Available The Australian Community Climate and Earth-System Simulator (ACCESS is used to test the sensitivity of heavy precipitation to various model configurations: horizontal resolution, domain size, rain rate assimilation, perturbed physics, and initial condition uncertainties, through a series of convection-permitting simulations of three heavy precipitation (greater than 200 mm day−1 cases in different synoptic backgrounds. The larger disparity of intensity histograms and rainfall fluctuation caused by different model configurations from their mean and/or control run indicates that heavier precipitation forecasts have larger uncertainty. A cross-verification exercise is used to quantify the impacts of different model parameters on heavy precipitation. The dispersion of skill scores with control run used as “truth” shows that the impacts of the model resolution and domain size on the quantitative precipitation forecast are not less than those of perturbed physics and initial field uncertainties in these not intentionally selected heavy precipitation cases. The result indicates that model resolution and domain size should be considered as part of probabilistic precipitation forecasts and ensemble prediction system design besides the model initial field uncertainty.

  11. Quantitative analysis of the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China

    Science.gov (United States)

    Zhao, Yinjun; Deng, Qiyu; Lin, Qing; Cai, Chunting

    2017-03-01

    Taking the Guangxi Beibu Gulf Economic Zone as the study area, this paper utilizes the geographical detector model to quantify the feedback effects from the terrestrial environment on precipitation variation from 1985 to 2010 with a comprehensive consideration of natural factors (forest coverage rate, vegetation type, terrain, terrestrial ecosystem types, land use and land cover change) and social factors (population density, farmland rate, GDP and urbanization rate). First, we found that the precipitation trend rate in the Beibu Gulf Economic Zone is between -47 and 96 mm/10a. Second, forest coverage rate change (FCRC), urbanization rate change (URC), GDP change (GDPC) and population density change (PDC) have a larger contribution to precipitation change through land-surface feedback, which makes them the leading factors. Third, the human element is found to primarily account for the precipitation changes in this region, as humans are the active media linking and enhancing these impact factors. Finally, it can be concluded that the interaction of impact factor pairs has a significant effect compared to the corresponding single factor on precipitation changes. The geographical detector model offers an analytical framework to reveal the terrestrial factors affecting the precipitation change, which gives direction for future work on regional climate modeling and analyses.

  12. Forecasting contaminant concentrations: Spills in the White Oak Creek Basin

    International Nuclear Information System (INIS)

    Borders, D.M.; Hyndman, D.W.; Huff, D.D.

    1987-01-01

    The Streamflow Synthesis and Reservoir Regulation (SSARR) model has been installed and sufficiently calibrated for use in managing accidental release of contaminants in surface waters of the White Oak Creek (WOC) watershed at ORNL. The model employs existing watershed conditions, hydrologic parameters representing basin response to precipitation, and a Quantitative Precipitation Forecast (QPF) to predict variable flow conditions throughout the basin. Natural runoff from each of the hydrologically distinct subbasins is simulated and added to specified plant and process water discharges. The resulting flows are then routed through stream reaches and eventually to White Oak Lake (WOL), which is the outlet from the WOC drainage basin. In addition, the SSARR model is being used to simulate change in storage volumes and pool levels in WOL, and most recently, routing characteristics of contaminant spills through WOC and WOL. 10 figs

  13. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  14. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  15. 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...

  16. Steps toward a CONUS-wide reanalysis with archived NEXRAD data using National Mosaic and Multisensor Quantitative Precipitation Estimation (NMQ/Q2) algorithms

    Science.gov (United States)

    Stevens, S. E.; Nelson, B. R.; Langston, C.; Qi, Y.

    2012-12-01

    The National Mosaic and Multisensor QPE (NMQ/Q2) software suite, developed at NOAA's National Severe Storms Laboratory (NSSL) in Norman, OK, addresses a large deficiency in the resolution of currently archived precipitation datasets. Current standards, both radar- and satellite-based, provide for nationwide precipitation data with a spatial resolution of up to 4-5 km, with a temporal resolution as fine as one hour. Efforts are ongoing to process archived NEXRAD data for the period of record (1996 - present), producing a continuous dataset providing precipitation data at a spatial resolution of 1 km, on a timescale of only five minutes. In addition, radar-derived precipitation data are adjusted hourly using a wide variety of automated gauge networks spanning the United States. Applications for such a product range widely, from emergency management and flash flood guidance, to hydrological studies and drought monitoring. Results are presented from a subset of the NEXRAD dataset, providing basic statistics on the distribution of rainrates, relative frequency of precipitation types, and several other variables which demonstrate the variety of output provided by the software. Precipitation data from select case studies are also presented to highlight the increased resolution provided by this reanalysis and the possibilities that arise from the availability of data on such fine scales. A previously completed pilot project and steps toward a nationwide implementation are presented along with proposed strategies for managing and processing such a large dataset. Reprocessing efforts span several institutions in both North Carolina and Oklahoma, and data/software coordination are key in producing a homogeneous record of precipitation to be archived alongside NOAA's other Climate Data Records. Methods are presented for utilizing supercomputing capability in expediting processing, to allow for the iterative nature of a reanalysis effort.

  17. Forecasting Lightning Threat using Cloud-resolving Model Simulations

    Science.gov (United States)

    McCaul, E. W., Jr.; Goodman, S. J.; LaCasse, K. M.; Cecil, D. J.

    2009-01-01

    As numerical forecasts capable of resolving individual convective clouds become more common, it is of interest to see if quantitative forecasts of lightning flash rate density are possible, based on fields computed by the numerical model. Previous observational research has shown robust relationships between observed lightning flash rates and inferred updraft and large precipitation ice fields in the mixed phase regions of storms, and that these relationships might allow simulated fields to serve as proxies for lightning flash rate density. It is shown in this paper that two simple proxy fields do indeed provide reasonable and cost-effective bases for creating time-evolving maps of predicted lightning flash rate density, judging from a series of diverse simulation case study events in North Alabama for which Lightning Mapping Array data provide ground truth. One method is based on the product of upward velocity and the mixing ratio of precipitating ice hydrometeors, modeled as graupel only, in the mixed phase region of storms at the -15\\dgc\\ level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domainwide statistics of the peak values of simulated flash rate proxy fields against domainwide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. A blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Weather Research and Forecast Model simulations of selected North Alabama cases show that this model can distinguish the general character and intensity of most convective events, and that the proposed methods show promise as a means of generating

  18. Accounting for uncertainties in real time hydrologic forecasting systems for the management of public dams in Quebec; Prise en compte des incertitudes dans la prevision hydrologique pour la gestion en temps reel des barrages publics du Quebec

    Energy Technology Data Exchange (ETDEWEB)

    Turcotte, R; Lafleur, J; Poirier, C; Lacombe, P; Brousseau, J [Quebec Ministere du Developpement durable, de l' Environnement et des Parcs, Quebec, PQ (Canada). Centre d' Expertise Hydrique; Sparks, D; Tapsoba, D [Hydro-Quebec, Montreal, PQ (Canada)

    2006-07-01

    This paper presented details of new approaches to inflow forecast modelling developed by the Centre d'Expertise Hydrique du Quebec (CEHQ) as part its real-time decision-making management of Quebec public dams. The CEHQ now plans to include the impacts of quantitative precipitation and initial snow condition uncertainties within its program. In order to assess precipitation forecast uncertainties, distributions of errors using historical precipitation data and forecasted precipitation data were used. Conditional probabilities were then evaluated within the context of a special weather alert. A distributed hydrological model was used to simulate inflows that corresponded to various precipitation scenarios in order to determine a precipitation threshold which could be managed within dam outflow policies. The temporal evolution and spatial distribution of potential errors for basin snow water equivalent (SWE) were then studied separately. Outputs from a snow model were combined with tools from a geostatic model and used for error analysis. It was concluded that future research will focus on developing a strategy to incorporate SWE within the inflow forecasting model. 10 refs., 4 tabs., 5 figs.

  19. Accounting for uncertainties in real time hydrologic forecasting systems for the management of public dams in Quebec; Prise en compte des incertitudes dans la prevision hydrologique pour la gestion en temps reel des barrages publics du Quebec

    Energy Technology Data Exchange (ETDEWEB)

    Turcotte, R.; Lafleur, J.; Poirier, C.; Lacombe, P.; Brousseau, J. [Quebec Ministere du Developpement durable, de l' Environnement et des Parcs, Quebec, PQ (Canada). Centre d' Expertise Hydrique; Sparks, D.; Tapsoba, D. [Hydro-Quebec, Montreal, PQ (Canada)

    2006-07-01

    This paper presented details of new approaches to inflow forecast modelling developed by the Centre d'Expertise Hydrique du Quebec (CEHQ) as part its real-time decision-making management of Quebec public dams. The CEHQ now plans to include the impacts of quantitative precipitation and initial snow condition uncertainties within its program. In order to assess precipitation forecast uncertainties, distributions of errors using historical precipitation data and forecasted precipitation data were used. Conditional probabilities were then evaluated within the context of a special weather alert. A distributed hydrological model was used to simulate inflows that corresponded to various precipitation scenarios in order to determine a precipitation threshold which could be managed within dam outflow policies. The temporal evolution and spatial distribution of potential errors for basin snow water equivalent (SWE) were then studied separately. Outputs from a snow model were combined with tools from a geostatic model and used for error analysis. It was concluded that future research will focus on developing a strategy to incorporate SWE within the inflow forecasting model. 10 refs., 4 tabs., 5 figs.

  20. 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

  1. 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...

  2. The forecaster's added value in QPF

    Science.gov (United States)

    Turco, M.; Milelli, M.

    2010-03-01

    To the authors' knowledge there are relatively few studies that try to answer this question: "Are humans able to add value to computer-generated forecasts and warnings?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast. Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human Quantitative Precipitation Forecast) in terms of an areal average and maximum value for each of the 13 warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS (Global Telecommunication System) network of rain gauges available that makes possible a high resolution verification. In this work we compare the performances of the latest three years of QPF derived from the meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the skill scores of two competitive forecasts. It is important to underline that the conclusions refer to the analysis of the Piemonte operational alert system, so they cannot be directly taken as universally true. But we think that some of the main lessons that can be derived from this study could be useful for the meteorological community. In details, the main conclusions are the following

  3. Contribution of long-term accounting for raindrop size distribution variations on quantitative precipitation estimation by weather radar: Disdrometers vs parameter optimization

    Science.gov (United States)

    Hazenberg, P.; Uijlenhoet, R.; Leijnse, H.

    2015-12-01

    Volumetric weather radars provide information on the characteristics of precipitation at high spatial and temporal resolution. Unfortunately, rainfall measurements by radar are affected by multiple error sources, which can be subdivided into two main groups: 1) errors affecting the volumetric reflectivity measurements (e.g. ground clutter, vertical profile of reflectivity, attenuation, etc.), and 2) errors related to the conversion of the observed reflectivity (Z) values into rainfall intensity (R) and specific attenuation (k). Until the recent wide-scale implementation of dual-polarimetric radar, this second group of errors received relatively little attention, focusing predominantly on precipitation type-dependent Z-R and Z-k relations. The current work accounts for the impact of variations of the drop size distribution (DSD) on the radar QPE performance. We propose to link the parameters of the Z-R and Z-k relations directly to those of the normalized gamma DSD. The benefit of this procedure is that it reduces the number of unknown parameters. In this work, the DSD parameters are obtained using 1) surface observations from a Parsivel and Thies LPM disdrometer, and 2) a Monte Carlo optimization procedure using surface rain gauge observations. The impact of both approaches for a given precipitation type is assessed for 45 days of summertime precipitation observed within The Netherlands. Accounting for DSD variations using disdrometer observations leads to an improved radar QPE product as compared to applying climatological Z-R and Z-k relations. However, overall precipitation intensities are still underestimated. This underestimation is expected to result from unaccounted errors (e.g. transmitter calibration, erroneous identification of precipitation as clutter, overshooting and small-scale variability). In case the DSD parameters are optimized, the performance of the radar is further improved, resulting in the best performance of the radar QPE product. However

  4. The forecaster's added value

    Science.gov (United States)

    Turco, M.; Milelli, M.

    2009-09-01

    To the authors' knowledge there are relatively few studies that try to answer this topic: "Are humans able to add value to computer-generated forecasts and warnings ?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast (see for instance Baars et al., 2005, Charba et al., 2002, Doswell C., 2003, Roebber et al., 1996, Sanders F., 1986). Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human QPF) in terms of an areal average for each of the 13 regional warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS network of rain gauges available that makes possible a high resolution verification. In this context the most useful verification approach is the measure of the QPF and HQPF skills by first converting precipitation expressed as continuous amounts into ‘‘exceedance'' categories (yes-no statements indicating whether precipitation equals or exceeds selected thresholds) and then computing the performances for each threshold. In particular in this work we compare the performances of the latest three years of QPF derived from two meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the

  5. Methods and Techniques of Enrollment Forecasting.

    Science.gov (United States)

    Brinkman, Paul T.; McIntyre, Chuck

    1997-01-01

    There is no right way to forecast college enrollments; in many instances, it will be prudent to use both qualitative and quantitative methods. Methods chosen must be relevant to questions addressed, policies and decisions at stake, and time and talent required. While it is tempting to start quickly, enrollment forecasting is an area in which…

  6. Precipitation Sedimentation and Advection in GFS

    Science.gov (United States)

    Sun, R.; Tallapragada, V.

    2016-12-01

    Zhao and Carr microphysics scheme as implemented in the NCEP Global Forecasting System (GFS) predicts only the total cloud condensate (cloud water or ice). The precipitation generated in the column fall to the ground instantly. This mean precipitation sedimentation and advection are not considered. As resolution increases the lack of the two physical processes creates problems. The slowly falling precipitation (snow) falls to the wrong surface grid box, which may have led to the observed spotty-precipitation pattern. To solve the problem two prognositic variables, snow and rain, are added. Addition of the two precipitation variable allows their advection. The corresponding sedimentation process are also added. In this study we examine the effect of precipitation advection and sedimentation on the precipitation pattern, associated precipitation skills and clouds.

  7. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

  8. Precipitation and measurements of precipitation

    NARCIS (Netherlands)

    Schmidt, F.H.; Bruin, H.A.R. de; Attmannspacher, W.; Harrold, T.W.; Kraijenhoff van de Leur, D.A.

    1977-01-01

    In Western Europe, precipitation is normal phenomenon; it is of importance to all aspects of society, particularly to agriculture, in cattle breeding and, of course, it is a subject of hydrological research. Precipitation is an essential part in the hydrological cycle. How disastrous local

  9. Data Analysis of GPM Constellation Satellites-IMERG and ERA-Interim precipitation products over West of Iran

    Science.gov (United States)

    Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

    2016-04-01

    Precipitation is a critical component of the Earth's hydrological cycle. The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Especially in data sparse regions with insufficient radar coverage, satellite information can provide a spatial and temporal context. Nonetheless, evaluation of satellite precipitation is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation. Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards. In situ observations over mountainous areas are mostly limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. One of the newest and blended methods that use multi-satellites and multi-sensors has been developed for estimating global precipitation. The considered data set known as Integrated Multi-satellitE Retrievals (IMERG) for GPM (Global Precipitation Measurement) is routinely produced by the GPM constellation satellites. Moreover, recent efforts have been put into the improvement of the precipitation products derived from reanalysis systems, which has led to significant progress. One of the best and a worldwide used model is developed by the European Centre for Medium Range Weather Forecasts (ECMWF). They have produced global reanalysis daily precipitation, known as ERA-Interim. This study has evaluated one year of precipitation data from the GPM-IMERG and ERA-Interim reanalysis daily time series over West of Iran. IMERG and ERA-Interim yield underestimate the observed values while IMERG underestimated slightly and performed better when precipitation is greater than 10mm. Furthermore, with respect to evaluation of probability of detection (POD), threat score (TS), false alarm ratio (FAR) and probability

  10. Four methodologies to improve healthcare demand forecasting.

    Science.gov (United States)

    Côté, M J; Tucker, S L

    2001-05-01

    Forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. This task, which often is assumed by financial managers, first requires the compilation and examination of historical information. Although many quantitative forecasting methods exist, four common methods of forecasting are percent adjustment, 12-month moving average, trendline, and seasonalized forecast. These four methods are all based upon the organization's recent historical demand. Healthcare financial managers who want to project demand for healthcare services in their facility should understand the advantages and disadvantages of each method and then select the method that will best meet the organization's needs.

  11. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  12. Precipitous Birth

    Directory of Open Access Journals (Sweden)

    Jennifer Yee

    2017-09-01

    Full Text Available Audience: This scenario was developed to educate emergency medicine residents on the management of a precipitous birth in the emergency department (ED. The case is also appropriate for teaching of medical students and advanced practice providers, as well as reviewing the principles of crisis resource management, teamwork, and communication. Introduction: Patients with precipitous birth require providers to manage two patients simultaneously with limited time and resources. Crisis resource management skills will be tested once baby is delivered, and the neonate will require assessment for potential neonatal resuscitation. Objectives: At the conclusion of the simulation session, learners will be able to manage women who have precipitous deliveries, as well as perform neonatal assessment and management. Method: This session was conducted using high-fidelity simulation, followed by a debriefing session and lecture on precipitous birth management and neonatal evaluation.

  13. Real time flood forecasting in the Upper Danube basin

    Directory of Open Access Journals (Sweden)

    Nester Thomas

    2016-12-01

    Full Text Available This paper reports on experience with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of hydrological and hydrodynamic components, and involves precipitation forecasts. The model parameters were estimated based on the dominant processes concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the model performance indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. During the 2013 flood, there was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts. It is suggested that the human forecaster plays an essential role in interpreting the model results and, if needed, adjusting them before issuing the forecasts to the general public.

  14. TCA precipitation.

    Science.gov (United States)

    Koontz, Laura

    2014-01-01

    Trichloroacetic acid (TCA) precipitation of proteins is commonly used to concentrate protein samples or remove contaminants, including salts and detergents, prior to downstream applications such as SDS-PAGE or 2D-gels. TCA precipitation denatures the protein, so it should not be used if the protein must remain in its folded state (e.g., if you want to measure a biochemical activity of the protein). © 2014 Elsevier Inc. All rights reserved.

  15. STRONTIUM PRECIPITATION

    Science.gov (United States)

    McKenzie, T.R.

    1960-09-13

    A process is given for improving the precipitation of strontium from an aqueous phosphoric-acid-containing solution with nickel or cobalt ferrocyanide by simultaneously precipitating strontium or calcium phosphate. This is accomplished by adding to the ferrocyanide-containing solution calcium or strontium nitrate in a quantity to yield a concentration of from 0.004 to 0.03 and adjusting the pH of the solution to a value of above 8.

  16. Forecasting the Particle Diameter Size Distribution in P92 (X10CrWMoVNb9-2 Steel After Long-Term Ageing at 600 and 650 °C

    Directory of Open Access Journals (Sweden)

    Zieliński A.

    2016-06-01

    Full Text Available The investigations on microstructure of P92 steel in the as-received condition and after 105 h ageing at 600 and 650 °C were carried out. For the recorded images of microstructure, the quantitative analysis of precipitates was performed. On that basis, a statistical analysis of collected data was made with the aim of estimating parameters of selected theoretical statistical distribution. Then, the forecast for average precipitate diameter and standard deviation of such a distribution for the time of 1,5*105h at 600 and 650 °C was calculated. The obtained results of investigations confirm the possibility of using, in evaluation of degradation degree for materials in use, the forecasting methods derived from mathematical statistics, in particular the theory of stochastic processes and methods of forecasting by analogy.

  17. A precipitation-induced landslide susceptibility model for natural gas transmission pipelines

    Energy Technology Data Exchange (ETDEWEB)

    Finley, Jason P. [Fugro William Lettis and Associates, Inc., Valencia, California (United States); Slayter, David L.; Hitchcock, Chris S. [Fugro William Lettis and Associates, Inc., Walnut Creek, California (United States); Lee, Chih-Hung [Pacific Gas and Electric Company, Gas Systems Integrity Management, Walnut Creek, California (United States)

    2010-07-01

    Landslides related to heavy rainfall can cause extensive damage to natural gas transmission pipelines. Fugro William Lettis and Associates Inc. have developed and implemented a geographic information system (GIS) model that evaluates near real-time precipitation-induced landslide susceptibility. The model incorporates state-wide precipitation data and geologically-based landslide classifications to produce rapid landslide risk evaluation for Pacific Gas and Electric Company's (PGandE) gas transmission system during winter rain storms in California. The precipitation data include pre-storm event quantitative precipitation forecasts (QPF) and post-storm event quantitative precipitation estimate (QPE) from the United States National Oceanic and Atmospheric Administration (NOAA). The geologic classifications are based on slope, susceptible geologic formations, and the locations of historic or known landslide occurrences. Currently the model is calibrated using qualitative measures. This paper describes the development of the model algorithm and input data, model results, calibration efforts, and the on-going research and landslide collection warranted for continued refinement of the model.

  18. Spatio-Temporal Analysis of the Accuracy of Tropical Multisatellite Precipitation Analysis 3B42 Precipitation Data in Mid-High Latitudes of China

    Science.gov (United States)

    Cai, Yancong; Jin, Changjie; Wang, Anzhi; Guan, Dexin; Wu, Jiabing; Yuan, Fenghui; Xu, Leilei

    2015-01-01

    Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998–2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are

  19. Spatio-temporal analysis of the accuracy of tropical multisatellite precipitation analysis 3B42 precipitation data in mid-high latitudes of China.

    Directory of Open Access Journals (Sweden)

    Yancong Cai

    Full Text Available Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS. This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%. Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these

  20. Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

    Directory of Open Access Journals (Sweden)

    Hugo Carrão

    2018-06-01

    Full Text Available Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated. Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin

  1. Performance Evaluation of the Naval Research Laboratory COAMPS on the Forecast of Typhoon Herb in the Western Pacific in 1996

    National Research Council Canada - National Science Library

    Peng, Melinda

    1997-01-01

    ... Herb that hit Taiwan island and caused severe damage. Performance of the COAMPS on the track forecast, wind and precipitation forecast, and interaction of the typhoon with topography is evaluated and analyzed.

  2. kosh 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...

  3. kpdt 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...

  4. kewr 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...

  5. kiso 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...

  6. kpga 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...

  7. kbkw 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...

  8. ktcl 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...

  9. pgwt 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...

  10. kpsp 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...

  11. kbih 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...

  12. kdnl 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...

  13. kart 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...

  14. kilm 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...

  15. kpne 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...

  16. kabi 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...

  17. ptpn 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...

  18. kblf 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...

  19. panc 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...

  20. kpbi 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...

  1. kgdv 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...

  2. kcmx 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...

  3. kdls 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...

  4. koaj 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...

  5. krhi 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...

  6. kbpk 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...

  7. khuf 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...

  8. kbpi 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. nstu 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. kink 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...

  2. krut 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...

  3. kbli 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...

  4. kaoo 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...

  5. klit 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...

  6. ktup 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...

  7. ktop 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. kont 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...

  8. kyng 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...

  9. kcwa 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...

  10. kflg 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...

  11. krsw 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...

  12. kmyl 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...

  13. krbg 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...

  14. kril 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...

  15. ksus 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...

  16. padq 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...

  17. kbil 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...

  18. krfd 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...

  19. kdug 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...

  20. ktix 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...

  1. kcod 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...

  2. kslk 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...

  3. kgfl 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...

  4. kguc 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...

  5. kmlu 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...

  6. kbff 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...

  7. ksmn 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...

  8. kdro 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...

  9. kmce 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...

  10. ktpa 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...

  11. kmot 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...

  12. kcre 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...

  13. klws 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...

  14. kotm 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...

  15. khqm 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...

  16. kabr 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...

  17. klal 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...

  18. kelp 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...

  19. kecg 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...

  20. khbg 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...

  1. kpbf 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...

  2. konp 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...

  3. pkwa 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...

  4. ktvf 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...

  5. paga 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...

  6. khks 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...

  7. kdsm 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...

  8. kpsm 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...

  9. kgrb 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...

  10. kgmu 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...

  11. papg 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...

  12. kbgm 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...

  13. pamc 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...

  14. klrd 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...

  15. ksan 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...

  16. patk 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...

  17. kowb 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...

  18. klru 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...

  19. kfxe 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...

  20. kjct 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...

  1. kcrg 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...

  2. paaq 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...

  3. kaex 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...

  4. klbx 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...

  5. kmia 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...

  6. kpit 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...

  7. kcrw 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...

  8. paen 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...

  9. kast 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...

  10. kuin 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...

  11. kmht 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...

  12. kcys 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...

  13. kflo 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...

  14. pakn 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...

  15. pabt 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...

  16. krdg 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...

  17. khdn 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...

  18. kjac 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...

  19. kphx 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...

  20. Forecasting Lightning Threat using Cloud-Resolving Model Simulations

    Science.gov (United States)

    McCaul, Eugene W., Jr.; Goodman, Steven J.; LaCasse, Katherine M.; Cecil, Daniel J.

    2008-01-01

    Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single

  1. 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 ...

  2. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

    OpenAIRE

    Shi, Xingjian; Chen, Zhourong; Wang, Hao; Yeung, Dit-Yan; Wong, Wai-kin; Woo, Wang-chun

    2015-01-01

    The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (F...

  3. Precipitation Matters

    Science.gov (United States)

    McDuffie, Thomas

    2007-01-01

    Although weather, including its role in the water cycle, is included in most elementary science programs, any further examination of raindrops and snowflakes is rare. Together rain and snow make up most of the precipitation that replenishes Earth's life-sustaining fresh water supply. When viewed individually, raindrops and snowflakes are quite…

  4. 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...

  5. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

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

  6. Merging Satellite Precipitation Products for Improved Streamflow Simulations

    Science.gov (United States)

    Maggioni, V.; Massari, C.; Barbetta, S.; Camici, S.; Brocca, L.

    2017-12-01

    Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. Therefore, we propose to merge SM2RAIN and the widely used TMPA 3B42RT product across Italy for a 6-year period (2010-2015) at daily/0.25deg temporal/spatial scale. Two conceptually different merging techniques are compared to each other and evaluated in terms of different statistical metrics, including hit bias, threat score, false alarm rates, and missed rainfall volumes. The first is based on the maximization of the temporal correlation with a reference dataset, while the second is based on a Bayesian approach, which provides a probabilistic satellite precipitation estimate derived from the joint probability distribution of observations and satellite estimates. The merged precipitation products show a better performance with respect to the parental satellite-based products in terms of categorical

  7. Ensemble methods for seasonal limited area forecasts

    DEFF Research Database (Denmark)

    Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.

    2004-01-01

    The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average...

  8. A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

    Science.gov (United States)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

    2015-04-01

    Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and

  9. 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.

  10. Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

    Science.gov (United States)

    Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

    2017-12-01

    Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

  11. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    Science.gov (United States)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  12. Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives.

    Science.gov (United States)

    Wang, Jiao; Deng, Zhiqiang

    2012-09-01

    Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Early Transition and Use of VIIRS and GOES-R Products by NWS Forecast Offices

    Science.gov (United States)

    Fuell, Kevin K.; Smith, Mathew; Jedlovec, Gary

    2012-01-01

    The Visible Infrared Imaging Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) satellite, part of the Joint Polar Satellite System (JPSS), and the ABI and GLM sensors scheduled for the GOES-R geostationary satellite will bring advanced observing capabilities to the operational weather community. The NASA Short-term Prediction Research and Transition (SPoRT) project at Marshall Space Flight Center has been facilitating the use of real-time experimental and research satellite data by NWS Weather Forecast Offices (WFOs) for a number of years to demonstrate the planned capabilities of future sensors to address particular forecast challenges through improve situational awareness and short-term weather forecasts. For the NOAA GOES-R Proving Ground (PG) activity, SPoRT is developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. SPoRT developed the a pseudo-Geostationary Lightning Mapper product and helped in the transition of the Algorithm Working Group (AWG) Convective Initiation (CI) proxy product for the Hazardous Weather Testbed (HWT) Spring Experiment,. Along with its partner WFOs, SPoRT is evaluating MODIS/GOES Hybrid products, which brings ABI-like data sets from existing NASA instrumentation in front of the forecaster for everyday use. The Hybrid uses near real-time MODIS imagery to demonstrate future ABI capabilities, while utilizing standard GOES imagery to provide the temporal frequency of geostationary imagery expected by operational forecasters. In addition, SPoRT is collaborating with the GOES-R hydrology AWG to transition a baseline proxy product for rainfall rate / quantitative precipitation estimate (QPE) to the OCONUS regions. For VIIRS, SPoRT is demonstrating multispectral observing capabilities and the utility of low-light channels not previously available on operational weather satellites to address a variety of weather forecast challenges. This presentation will discuss the results of

  14. 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

  15. 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,

  16. Predictability and possible earlier awareness of extreme precipitation across Europe

    Science.gov (United States)

    Lavers, David; Pappenberger, Florian; Richardson, David; Zsoter, Ervin

    2017-04-01

    Extreme hydrological events can cause large socioeconomic damages in Europe. In winter, a large proportion of these flood episodes are associated with atmospheric rivers, a region of intense water vapour transport within the warm sector of extratropical cyclones. When preparing for such extreme events, forecasts of precipitation from numerical weather prediction models or river discharge forecasts from hydrological models are generally used. Given the strong link between water vapour transport (integrated vapour transport IVT) and heavy precipitation, it is possible that IVT could be used to warn of extreme events. Furthermore, as IVT is located in extratropical cyclones, it is hypothesized to be a more predictable variable due to its link with synoptic-scale atmospheric dynamics. In this research, we firstly provide an overview of the predictability of IVT and precipitation forecasts, and secondly introduce and evaluate the ECMWF Extreme Forecast Index (EFI) for IVT. The EFI is a tool that has been developed to evaluate how ensemble forecasts differ from the model climate, thus revealing the extremeness of the forecast. The ability of the IVT EFI to capture extreme precipitation across Europe during winter 2013/14, 2014/15, and 2015/16 is presented. The results show that the IVT EFI is more capable than the precipitation EFI of identifying extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase. However, the precipitation EFI is superior during the negative NAO phase and at shorter lead times. An IVT EFI example is shown for storm Desmond in December 2015 highlighting its potential to identify upcoming hydrometeorological extremes.

  17. Status of mineral resources evaluation and forecast

    International Nuclear Information System (INIS)

    Ma Hanfeng; Li Ziying; Luo Yi; Li Shengxiang; Sun Wenpeng

    2007-01-01

    The work of resources evaluation and forecast is a focus to the governments of every country in the world, it is related to the establishment of strategic policy on the national mineral resources. In order to quantitatively evaluate the general potential of uranium resources in China and better forecast uranium deposits, this paper briefly introduces the method of evaluating total amount of mineral resources, especially 6 usual prospective methods which are recommended in international geology comparison programs, as well as principle of usual mineral resources quantitative prediction and its steps. The work history of mineral resources evaluation and forecast is reviewed concisely. Advantages and disadvantages of each method, their application field and condition are also explained briefly. At last, the history of uranium resources evaluation and forecast in China and its status are concisely outlined. (authors)

  18. Towards a better knowledge of flash flood forecasting at the Three Gorges Region: Progress over the past decade and challenges ahead

    Science.gov (United States)

    Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao

    2015-04-01

    The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model

  19. 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...

  20. 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.

  1. Real-time Social Internet Data to Guide Forecasting Models

    Energy Technology Data Exchange (ETDEWEB)

    Del Valle, Sara Y. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-20

    Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.

  2. Spatiotemporal drought forecasting using nonlinear models

    Science.gov (United States)

    Vasiliades, Lampros; Loukas, Athanasios

    2010-05-01

    Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

  3. Issues in Forecasting CMEs

    Science.gov (United States)

    Pizzo, V. J.

    2017-12-01

    I will present my view of the current status of space weather forecasting abilities related to CMEs. This talk will address the large-scale aspects, but specifically not energetic particle phenomena. A key point is that all models, whether sophisticated numerical contraptions or quasi-empirical ones, are only as good as the data you feed them. Hence the emphasis will be on observations and analysis methods. First I will review where we stand with regard to the near-Sun quantitative data needed to drive any model, no matter how complex or simple-minded, and I will discuss technological roadblocks that suggest it may be some time before we see any meaningful improvements beyond what we have today. Then I cover issues related to characterizing CME propagation out through the corona and into interplanetary space, as well as to observational limitations in the vicinity of 1 AU. Since none of these observational constraints are likely to be resolved anytime soon, the real challenge is to make more informed use of what is available. Thus, this talk will focus on how we may identify and pursue the most profitable approaches, for both forecast and research applications. The discussion will highlight a number of promising leads, including those related to inclusion of solar backside information, joint magnetograph observations from L5 and Earth, how to use (not just run) ensembles, more rational use of HI observations, and suggestions for using cube-sats for deep space observations of CMEs and MCs.

  4. The Possibility of Making a Quantitative Study of the Precipitin Reaction by Gamma-Radioactive Tracers; Possibilite d'une Etude Quantitative de la Reaction de Precipitation par Marquage a l'Aide d'Emetteurs Gamma; Vozmozhnost' kolichestvennogo opredeleniya reaktsii osazhdeniya s pomoshch'yu gamma-radioaktivnykh indikatorov; Estudio Cuantitativo de la Reaccion de Precipitacion con Ayuda de Indicadores Gamma

    Energy Technology Data Exchange (ETDEWEB)

    Bonev, L.; Todorov, S.; Robev, S. [Nauchno-Issledovatel' skij Institut Radiologii i Radiacionnoj Bezopasnosti, Sofija (Bulgaria)

    1965-10-15

    The paper presents the first results of the quantitative determination of the precipitin reaction (formation of an antigen-antibody complex) by labelling the precipitating components with gamma-radioactive tracers which do not chemically interact with albuminous molecules. As tracers it is possible to use chrome-manganese and nickel-copper compounds, whose tendency to hydrolyze permits the fixation of the radioactive tracer on the antigen and antibody respectively. The radioactivity of the components is determined by a multichannel pulse-height analyser. The results obtained show that the precipitation curve, plotted on the basis of radiometric data, closely corresponds to the curve plotted by the well-known quantitative methods used to determine albumin. The paper discusses the possibilities of using the method described. (author) [French] Les auteurs communiquent les premiers resultats d'une etude quantitative qu'ils ont faite de la reaction de precipitation (formation du complexe antigene-anticorps) en marquant les composants du precipites avec des emetteurs gamma qui ne reagissent pas chimiquement avec les molecules d'albumine. Pour le marquage, on peut utiliser des composes de chrome/manganese ou de fer qui subissent facilement l'hydrolyse et assurent de ce fait la fixation de la substance radioactive sur l'antigene et, partant, sur l'anticorps. La radioactivite des composants est determinee a l'aide d'un selecteur d'amplitudes a plusieurs canaux. Les resultats montrent que la courbe de precipitation obtenue avec les donnees radiometriques concorde parfaitement avec celle que l'on obtient par les methodes quantitatives connues servant au dosage de l'albumine. Les auteurs discutent les possibilites d'application de la methode etudiee. (author) [Spanish] Los autores presentan los primeros resultados de un estudio cuantitativo de la reaccion de precipitacion (formacion del complejo antigeno-anticuerpo) que efectuaron marcando los componentes de precipitacion con

  5. 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.

  6. 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

  7. 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.

  8. Precipitation and total power consumption in the ionosphere: Global MHD simulation results compared with Polar and SNOE observations

    Directory of Open Access Journals (Sweden)

    M. Palmroth

    2006-05-01

    Full Text Available We compare the ionospheric electron precipitation morphology and power from a global MHD simulation (GUMICS-4 with direct measurements of auroral energy flux during a pair of substorms on 28-29 March 1998. The electron precipitation power is computed directly from global images of auroral light observed by the Polar satellite ultraviolet imager (UVI. Independent of the Polar UVI measurements, the electron precipitation energy is determined from SNOE satellite observations on the thermospheric nitric oxide (NO density. We find that the GUMICS-4 simulation reproduces the spatial variation of the global aurora rather reliably in the sense that the onset of the substorm is shown in GUMICS-4 simulation as enhanced precipitation in the right location at the right time. The total integrated precipitation power in the GUMICS-4 simulation is in quantitative agreement with the observations during quiet times, i.e., before the two substorm intensifications. We find that during active times the GUMICS-4 integrated precipitation is a factor of 5 lower than the observations indicate. However, we also find factor of 2-3 differences in the precipitation power among the three different UVI processing methods tested here. The findings of this paper are used to complete an earlier objective, in which the total ionospheric power deposition in the simulation is forecasted from a mathematical expression, which is a function of solar wind density, velocity and magnetic field. We find that during this event, the correlation coefficient between the outcome of the forecasting expression and the simulation results is 0.83. During the event, the simulation result on the total ionospheric power deposition agrees with observations (correlation coefficient 0.8 and the AE index (0.85.

  9. Statistically extrapolated nowcasting of summertime precipitation over the Eastern Alps

    Science.gov (United States)

    Chen, Min; Bica, Benedikt; Tüchler, Lukas; Kann, Alexander; Wang, Yong

    2017-07-01

    This paper presents a new multiple linear regression (MLR) approach to updating the hourly, extrapolated precipitation forecasts generated by the INCA (Integrated Nowcasting through Comprehensive Analysis) system for the Eastern Alps. The generalized form of the model approximates the updated precipitation forecast as a linear response to combinations of predictors selected through a backward elimination algorithm from a pool of predictors. The predictors comprise the raw output of the extrapolated precipitation forecast, the latest radar observations, the convective analysis, and the precipitation analysis. For every MLR model, bias and distribution correction procedures are designed to further correct the systematic regression errors. Applications of the MLR models to a verification dataset containing two months of qualified samples, and to one-month gridded data, are performed and evaluated. Generally, MLR yields slight, but definite, improvements in the intensity accuracy of forecasts during the late evening to morning period, and significantly improves the forecasts for large thresholds. The structure-amplitude-location scores, used to evaluate the performance of the MLR approach, based on its simulation of morphological features, indicate that MLR typically reduces the overestimation of amplitudes and generates similar horizontal structures in precipitation patterns and slightly degraded location forecasts, when compared with the extrapolated nowcasting.

  10. Transportation forecasting : analysis and quantitative methods

    Science.gov (United States)

    1983-01-01

    This Record contains the following papers: Development of Survey Instruments Suitable for Determining Non-Home Activity Patterns; Sequential, History-Dependent Approach to Trip-Chaining Behavior; Identifying Time and History Dependencies of Activity ...

  11. Global Precipitation Measurement (GPM) Mission: Overview and Status

    Science.gov (United States)

    Hou, Arthur Y.

    2012-01-01

    The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA will deploy a Core Observatory in 2014 to serve as a reference satellite to unify precipitation measurements from the constellation of sensors. The GPM Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The DPR will be the first dual-frequency radar in space to provide not only measurements of 3-D precipitation structures but also quantitative information on microphysical properties of precipitating particles. The DPR and GMI measurements will together provide a database that relates vertical hydrometeor profiles to multi-frequency microwave radiances over a variety of environmental conditions across the globe. This combined database will be used as a common transfer standard for improving the accuracy and consistency of precipitation retrievals from all constellation radiometers. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on the GCOM-W1 satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder

  12. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

    Science.gov (United States)

    Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

    2017-11-01

    Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

  13. Precipitation Nowcast using Deep Recurrent Neural Network

    Science.gov (United States)

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

    2016-12-01

    An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

  14. Medium Range Forecasts Representation (and Long Range Forecasts?)

    Science.gov (United States)

    Vincendon, J.-C.

    2009-09-01

    interesting on Fridays because it gives then a first outlook of the weather for the second weekend. There also, an example will illustrate that. Finally, we lead an experiment for some months to go beyond and supply a tendency of weather forecasts over the period D+10 / D+14, whom we also call " tendency for week 2 ". It is a question at the moment of producing a small text describing the global evolution of the temperatures and the precipitation, there is no graphic production. All this is completed by a sentence summarizing the tendencies expected from the temperature for weeks 3 and 4. We thus begin to think seriously about the production of a monthly forecast for the public within the framework of our operational activities. We have to establish under which graphic shape this one can be made.

  15. Operational hydrological forecasting during the IPHEx-IOP campaign - Meet the challenge

    Science.gov (United States)

    Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa; Barros, Ana P.

    2016-10-01

    An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts

  16. Operational Hydrological Forecasting During the Iphex-iop Campaign - Meet the Challenge

    Science.gov (United States)

    Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa D.; Barros, Ana P.

    2016-01-01

    An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts

  17. A System Dynamics Approach to Modeling Future Climate Scenarios: Quantifying and Projecting Patterns of Evapotranspiration and Precipitation in the Salton Sea Watershed

    Directory of Open Access Journals (Sweden)

    Michael E. Kjelland

    2014-01-01

    Full Text Available The need for improved quantitative precipitation forecasts and realistic assessments of the regional impacts of natural climate variability and climate change has generated increased interest in regional (i.e., systems-scale climate simulation. The Salton Sea Stochastic Simulation Model (S4M was developed to assist planners and residents of the Salton Sea (SS transboundary watershed (USA and Mexico in making sound policy decisions regarding complex water-related issues. In order to develop the S4M with a higher degree of climate forecasting resolution, an in-depth analysis was conducted regarding precipitation and evapotranspiration for the semiarid region of the watershed. Weather station data were compiled for both precipitation and evapotranspiration from 1980 to 2004. Several logistic regression models were developed for determining the relationships among precipitation events, that is, duration and volume, and evapotranspiration levels. These data were then used to develop a stochastic weather generator for S4M. Analyses revealed that the cumulative effects and changes of ±10 percent in SS inflows can have significant effects on sea elevation and salinity. The aforementioned technique maintains the relationships between the historic frequency distributions of both precipitation and evapotranspiration, and not as separate unconnected and constrained variables.

  18. Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.

    2014-10-01

    We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

  19. Improving operational flood forecasting through data assimilation

    Science.gov (United States)

    Rakovec, Oldrich; Weerts, Albrecht; Uijlenhoet, Remko; Hazenberg, Pieter; Torfs, Paul

    2010-05-01

    Accurate flood forecasts have been a challenging topic in hydrology for decades. Uncertainty in hydrological forecasts is due to errors in initial state (e.g. forcing errors in historical mode), errors in model structure and parameters and last but not least the errors in model forcings (weather forecasts) during the forecast mode. More accurate flood forecasts can be obtained through data assimilation by merging observations with model simulations. This enables to identify the sources of uncertainties in the flood forecasting system. Our aim is to assess the different sources of error that affect the initial state and to investigate how they propagate through hydrological models with different levels of spatial variation, starting from lumped models. The knowledge thus obtained can then be used in a data assimilation scheme to improve the flood forecasts. This study presents the first results of this framework and focuses on quantifying precipitation errors and its effect on discharge simulations within the Ourthe catchment (1600 km2), which is situated in the Belgian Ardennes and is one of the larger subbasins of the Meuse River. Inside the catchment, hourly rain gauge information from 10 different locations is available over a period of 15 years. Based on these time series, the bootstrap method has been applied to generate precipitation ensembles. These were then used to simulate the catchment's discharges at the outlet. The corresponding streamflow ensembles were further assimilated with observed river discharges to update the model states of lumped hydrological models (R-PDM, HBV) through Residual Resampling. This particle filtering technique is a sequential data assimilation method and takes no prior assumption of the probability density function for the model states, which in contrast to the Ensemble Kalman filter does not have to be Gaussian. Our further research will be aimed at quantifying and reducing the sources of uncertainty that affect the initial

  20. An Optimization of Inventory Demand Forecasting in University Healthcare Centre

    Science.gov (United States)

    Bon, A. T.; Ng, T. K.

    2017-01-01

    Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

  1. Hydrometeorological aspects of the Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP*

    Directory of Open Access Journals (Sweden)

    R. Benoit

    2003-01-01

    Full Text Available During the Special Observation Period (SOP, 7 September–15 November, 1999 of the Mesoscale Alpine Programme (MAP, the Canadian Mesoscale Compressible Community Model (MC2 was run in real time at a horizontal resolution of 3 km on a computational domain of 350☓300☓50 grid points, covering the whole of the Alpine region. The WATFLOOD model was passively coupled to the MC2; the former is an integrated set of computer programs to forecast flood flows, using all available data, for catchments with response times ranging from one hour to several weeks. The unique aspect of this contribution is the operational application of numerical weather prediction data to forecast flows over a very large, multinational domain. An overview of the system performance from the hydrometeorological aspect is presented, mostly for the real-time results, but also from subsequent analyses. A streamflow validation of the precipitation is included for large basins covering upper parts of the Rhine and the Rhone, and parts of the Po and of the Danube. In general, the MC2/WATFLOOD model underestimated the total runoff because of the under-prediction of precipitation by MC2 during the MAP SOP. After the field experiment, a coding error in the cloud microphysics scheme of MC2 explains this underestimation to a large extent. A sensitivity study revealed that the simulated flows reproduce the major features of the observed flow record for most of the flow stations. The experiment was considered successful because two out of three possible flood events in the Swiss-Italian border region were predicted correctly by data from the numerical weather models linked to the hydrological model and no flow events were missed. This study has demonstrated that a flow forecast from a coupled atmospheric-hydrological model can serve as a useful first alert and quantitative forecast. Keywords: mesoscale atmospheric model, hydrological model, flood forecasting, Alps

  2. 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.

  3. 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)

  4. Clear turbulence forecasting - Towards a union of art and science

    Science.gov (United States)

    Keller, J. L.

    1985-01-01

    The development of clear air turbulence (CAT) forecasting over the last several decades is reviewed in the context of empirical and theoretical research into the nature of nonconvective turbulence in the free atmosphere, particularly at jet stream levels. Various qualitative CAT forecasting techniques are examined, and prospects for an effective quantitative index to aid aviation meteorologists in jet stream level turbulence monitoring and forecasting are examined. Finally, the use of on-board sensors for short-term warning is discussed.

  5. Forecasting for the Small Retail Business: Operational Recommendations

    OpenAIRE

    J. Holton Wilson; Robert H. Miller

    1998-01-01

    Often small business owners/managers feel they have inadequate time for formal forecasting and/or that they lack the expertise to do so. Since forecasting is an important decision-aiding tool, managers in small retail establishments can benefit by implementing a simple forecasting process. Doing so will enable them to better anticipate the firms future level of sales and lead to more successful operations. We suggest the use of relatively simple quantitative methods that have proven to work w...

  6. Ensemble prediction of floods – catchment non-linearity and forecast probabilities

    Directory of Open Access Journals (Sweden)

    C. Reszler

    2007-07-01

    Full Text Available Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less, the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

  7. Robust Approaches to Forecasting

    OpenAIRE

    Jennifer Castle; David Hendry; Michael P. Clements

    2014-01-01

    We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods ar...

  8. 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...

  9. Investigation of Asphaltene Precipitation at Elevated Temperature

    DEFF Research Database (Denmark)

    Andersen, Simon Ivar; Lindeloff, Niels; Stenby, Erling Halfdan

    1998-01-01

    In order to obtain quantitative data on the asphaltene precipitation induced by the addition of n-alkane (heptane) at temperatures above the normal boiling point of the precipitant, a high temperature/high pressure filtration apparatus has been constructed. Oil and alkane are mixed...

  10. 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

  11. A Preliminary Study of Grade Forecasting by Students

    Science.gov (United States)

    Armstrong, Michael J.

    2013-01-01

    This experiment enabled undergraduate business students to better assess their progress in a course by quantitatively forecasting their own end-of-course grades. This innovation provided them with predictive feedback in addition to the outcome feedback they were already receiving. A total of 144 students forecast their grades using an…

  12. Educational Forecasting Methodologies: State of the Art, Trends, and Highlights.

    Science.gov (United States)

    Hudson, Barclay; Bruno, James

    This overview of both quantitative and qualitative methods of educational forecasting is introduced by a discussion of a general typology of forecasting methods. In each of the following sections, discussion follows the same general format: a number of basic approaches are identified (e.g. extrapolation, correlation, systems modelling), and each…

  13. The intersections between TRIZ and forecasting methodology

    Directory of Open Access Journals (Sweden)

    Georgeta BARBULESCU

    2010-12-01

    Full Text Available The authors’ intention is to correlate the basic knowledge in using the TRIZ methodology (Theory of Inventive Problem Solving or in Russian: Teoriya Resheniya Izobretatelskikh Zadatch as a problem solving tools meant to help the decision makers to perform more significant forecasting exercises. The idea is to identify the TRIZ features and instruments (40 inventive principles, i.e. for putting in evidence the noise and signal problem, for trend identification (qualitative and quantitative tendencies and support tools in technological forecasting, to make the decision-makers able to refine and to increase the level of confidence in the forecasting results. The interest in connecting TRIZ to forecasting methodology, nowadays, relates to the massive application of TRIZ methods and techniques for engineering system development world-wide and in growing application of TRIZ’s concepts and paradigms for improvements of non-engineering systems (including the business and economic applications.

  14. 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

  15. 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.

  16. The Forecasting of Adaptation Potential of Herbaceous Perennials

    Directory of Open Access Journals (Sweden)

    Belykh, O. A.

    2013-04-01

    Full Text Available This work investigates the problem of the ecobiomorph productive features formation of perennial herbs Ranunculaceae family forecasting on the basis of quantitative connections of species parameters with the leading geomorphological factors of South Siberia environment.

  17. Operational flood-forecasting in the Piemonte region – development and verification of a fully distributed physically-oriented hydrological model

    Directory of Open Access Journals (Sweden)

    D. Rabuffetti

    2009-03-01

    Full Text Available A hydrological model for real time flood forecasting to Civil Protection services requires reliability and rapidity. At present, computational capabilities overcome the rapidity needs even when a fully distributed hydrological model is adopted for a large river catchment as the Upper Po river basin closed at Ponte Becca (nearly 40 000 km2. This approach allows simulating the whole domain and obtaining the responses of large as well as of medium and little sized sub-catchments. The FEST-WB hydrological model (Mancini, 1990; Montaldo et al., 2007; Rabuffetti et al., 2008 is implemented. The calibration and verification activities are based on more than 100 flood events, occurred along the main tributaries of the Po river in the period 2000–2003. More than 300 meteorological stations are used to obtain the forcing fields, 10 cross sections with continuous and reliable discharge time series are used for calibration while verification is performed on about 40 monitored cross sections. Furthermore meteorological forecasting models are used to force the hydrological model with Quantitative Precipitation Forecasts (QPFs for 36 h horizon in "operational setting" experiments. Particular care is devoted to understanding how QPF affects the accuracy of the Quantitative Discharge Forecasts (QDFs and to assessing the QDF uncertainty impact on the warning system reliability. Results are presented either in terms of QDF and of warning issues highlighting the importance of an "operational based" verification approach.

  18. Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds

    Science.gov (United States)

    Muhlbauer, A.; Hashino, T.; Xue, L.; Teller, A.; Lohmann, U.; Rasmussen, R. M.; Geresdi, I.; Pan, Z.

    2010-09-01

    Anthropogenic aerosols serve as a source of both cloud condensation nuclei (CCN) and ice nuclei (IN) and affect microphysical properties of clouds. Increasing aerosol number concentrations is hypothesized to retard the cloud droplet coalescence and the riming in mixed-phase clouds, thereby decreasing orographic precipitation. This study presents results from a model intercomparison of 2-D simulations of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds. The sensitivity of orographic precipitation to changes in the aerosol number concentrations is analysed and compared for various dynamical and thermodynamical situations. Furthermore, the sensitivities of microphysical processes such as coalescence, aggregation, riming and diffusional growth to changes in the aerosol number concentrations are evaluated and compared. The participating numerical models are the model from the Consortium for Small-Scale Modeling (COSMO) with bulk microphysics, the Weather Research and Forecasting (WRF) model with bin microphysics and the University of Wisconsin modeling system (UWNMS) with a spectral ice habit prediction microphysics scheme. All models are operated on a cloud-resolving scale with 2 km horizontal grid spacing. The results of the model intercomparison suggest that the sensitivity of orographic precipitation to aerosol modifications varies greatly from case to case and from model to model. Neither a precipitation decrease nor a precipitation increase is found robustly in all simulations. Qualitative robust results can only be found for a subset of the simulations but even then quantitative agreement is scarce. Estimates of the aerosol effect on orographic precipitation are found to range from -19% to 0% depending on the simulated case and the model. Similarly, riming is shown to decrease in some cases and models whereas it increases in others, which implies that a decrease in riming with increasing aerosol load is not a robust result

  19. Quantitative analysis chemistry

    International Nuclear Information System (INIS)

    Ko, Wansuk; Lee, Choongyoung; Jun, Kwangsik; Hwang, Taeksung

    1995-02-01

    This book is about quantitative analysis chemistry. It is divided into ten chapters, which deal with the basic conception of material with the meaning of analysis chemistry and SI units, chemical equilibrium, basic preparation for quantitative analysis, introduction of volumetric analysis, acid-base titration of outline and experiment examples, chelate titration, oxidation-reduction titration with introduction, titration curve, and diazotization titration, precipitation titration, electrometric titration and quantitative analysis.

  20. Near-term probabilistic forecast of significant wildfire events for the Western United States

    Science.gov (United States)

    Haiganoush K. Preisler; Karin L. Riley; Crystal S. Stonesifer; Dave E. Calkin; Matt Jolly

    2016-01-01

    Fire danger and potential for large fires in the United States (US) is currently indicated via several forecasted qualitative indices. However, landscape-level quantitative forecasts of the probability of a large fire are currently lacking. In this study, we present a framework for forecasting large fire occurrence - an extreme value event - and evaluating...

  1. Winter Precipitation in North America and the Pacific-North America Pattern in GEOS-S2Sv2 Seasonal Hindcast

    Science.gov (United States)

    Li, Zhao; Molod, Andrea; Schubert, Siegfried

    2018-01-01

    Reliable prediction of precipitation remains one of the most pivotal and complex challenges in seasonal forecasting. Previous studies show that various large-scale climate modes, such as ENSO, PNA and NAO play significant role in winter precipitation variability over the Northern America. The influences are most pronounced in years of strong indices of such climate modes. This study evaluates model bias, predictability and forecast skills of monthly winter precipitation in GEOS5-S2S 2.0 retrospective forecast from 1981 to 2016, with emphasis on the forecast skill of precipitation over North America during the extreme events of ENSO, PNA and NAO by applying EOF and composite analysis.

  2. Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: application to a medium size basin of Mediterranean Spain

    Science.gov (United States)

    Amengual, A.; Romero, R.; Vich, M.; Alonso, S.

    2009-06-01

    The improvement of the short- and mid-range numerical runoff forecasts over the flood-prone Spanish Mediterranean area is a challenging issue. This work analyses four intense precipitation events which produced floods of different magnitude over the Llobregat river basin, a medium size catchment located in Catalonia, north-eastern Spain. One of them was a devasting flash flood - known as the "Montserrat" event - which produced 5 fatalities and material losses estimated at about 65 million euros. The characterization of the Llobregat basin's hydrological response to these floods is first assessed by using rain-gauge data and the Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) runoff model. In second place, the non-hydrostatic fifth-generation Pennsylvania State University/NCAR mesoscale model (MM5) is nested within the ECMWF large-scale forecast fields in a set of 54 h period simulations to provide quantitative precipitation forecasts (QPFs) for each hydrometeorological episode. The hydrological model is forced with these QPFs to evaluate the reliability of the resulting discharge forecasts, while an ensemble prediction system (EPS) based on perturbed atmospheric initial and boundary conditions has been designed to test the value of a probabilistic strategy versus the previous deterministic approach. Specifically, a Potential Vorticity (PV) Inversion technique has been used to perturb the MM5 model initial and boundary states (i.e. ECMWF forecast fields). For that purpose, a PV error climatology has been previously derived in order to introduce realistic PV perturbations in the EPS. Results show the benefits of using a probabilistic approach in those cases where the deterministic QPF presents significant deficiencies over the Llobregat river basin in terms of the rainfall amounts, timing and localization. These deficiences in precipitation fields have a major impact on flood forecasts. Our ensemble strategy has been found useful to reduce the

  3. 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.

  4. New Aspects of Probabilistic Forecast Verification Using Information Theory

    Science.gov (United States)

    Tödter, Julian; Ahrens, Bodo

    2013-04-01

    This work deals with information-theoretical methods in probabilistic forecast verification, particularly concerning ensemble forecasts. Recent findings concerning the "Ignorance Score" are shortly reviewed, then a consistent generalization to continuous forecasts is motivated. For ensemble-generated forecasts, the presented measures can be calculated exactly. The Brier Score (BS) and its generalizations to the multi-categorical Ranked Probability Score (RPS) and to the Continuous Ranked Probability Score (CRPS) are prominent verification measures for probabilistic forecasts. Particularly, their decompositions into measures quantifying the reliability, resolution and uncertainty of the forecasts are attractive. Information theory sets up a natural framework for forecast verification. Recently, it has been shown that the BS is a second-order approximation of the information-based Ignorance Score (IGN), which also contains easily interpretable components and can also be generalized to a ranked version (RIGN). Here, the IGN, its generalizations and decompositions are systematically discussed in analogy to the variants of the BS. Additionally, a Continuous Ranked IGN (CRIGN) is introduced in analogy to the CRPS. The useful properties of the conceptually appealing CRIGN are illustrated, together with an algorithm to evaluate its components reliability, resolution, and uncertainty for ensemble-generated forecasts. This algorithm can also be used to calculate the decomposition of the more traditional CRPS exactly. The applicability of the "new" measures is demonstrated in a small evaluation study of ensemble-based precipitation forecasts.

  5. 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.

  6. Evaluation of the Plant-Craig stochastic convection scheme in an ensemble forecasting system

    Science.gov (United States)

    Keane, R. J.; Plant, R. S.; Tennant, W. J.

    2015-12-01

    The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

  7. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance...... that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  8. FORECASTING MODELS IN MANAGEMENT

    OpenAIRE

    Sindelar, Jiri

    2008-01-01

    This article deals with the problems of forecasting models. First part of the article is dedicated to definition of the relevant areas (vertical and horizontal pillar of definition) and then the forecasting model itself is defined; as article presents theoretical background for further primary research, this definition is crucial. Finally the position of forecasting models within the management system is identified. The paper is a part of the outputs of FEM CULS grant no. 1312/11/3121.

  9. 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...

  10. Real-Time Hydrometeorological Forecasting and Analysis from Radar and Satellite Observations

    National Research Council Canada - National Science Library

    Hoffman, Ross

    2000-01-01

    ...) utilizes state-of-the-art precipitation and hydrological forecasting techniques; but, (c) overcomes the inherent limitations of these approaches by optimally merging the results of the different techniques to provide a robust solution...

  11. 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

  12. Precipitation Dynamical Downscaling Over the Great Plains

    Science.gov (United States)

    Hu, Xiao-Ming; Xue, Ming; McPherson, Renee A.; Martin, Elinor; Rosendahl, Derek H.; Qiao, Lei

    2018-02-01

    Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm-season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic-scale circulations are faithfully reproduced while still allowing WRF to develop small-scale dynamics, thus effectively suppressing the large-scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved.

  13. Error sensitivity analysis in 10-30-day extended range forecasting by using a nonlinear cross-prediction error model

    Science.gov (United States)

    Xia, Zhiye; Xu, Lisheng; Chen, Hongbin; Wang, Yongqian; Liu, Jinbao; Feng, Wenlan

    2017-06-01

    Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-6-10-2), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-1-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.

  14. 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

  15. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

    OpenAIRE

    Chen, Junfei; Li, Ming; Wang, Weiguang

    2012-01-01

    Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has ...

  16. Drought forecasting in Luanhe River basin involving climatic indices

    Science.gov (United States)

    Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

    2017-11-01

    Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the

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

    Science.gov (United States)

    Gawor, J.

    2012-04-01

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

  18. LBA-HMET PC-06 ECMWF Modeled Precipitation and Surface Flux, Rondonia, Brazil: 1999

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: This data set provides the mean diurnal cycle of precipitation, near-surface thermodynamics, and surface fluxes generated from short-term forecasts from...

  19. 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.

  20. 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....

  1. World Area Forecast System (WAFS)

    Data.gov (United States)

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

  2. Radar-driven High-resolution Hydrometeorological Forecasts of the 26 September 2007 Venice flash flood

    Science.gov (United States)

    Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

    2010-05-01

    Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the

  3. Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

    Science.gov (United States)

    Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

    2016-03-01

    This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959-2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in

  4. Forecasting in Planning

    NARCIS (Netherlands)

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively.

  5. Improving Garch Volatility Forecasts

    NARCIS (Netherlands)

    Klaassen, F.J.G.M.

    1998-01-01

    Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH effects are allowed within each regime, so that our model

  6. On density forecast evaluation

    NARCIS (Netherlands)

    Diks, C.

    2008-01-01

    Traditionally, probability integral transforms (PITs) have been popular means for evaluating density forecasts. For an ideal density forecast, the PITs should be uniformly distributed on the unit interval and independent. However, this is only a necessary condition, and not a sufficient one, as

  7. 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

  8. 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...

  9. MOS-based precipitation forecasts for river basins

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk

    2003-01-01

    Roč. 18, č. 5 (2003), s. 769-781 ISSN 0882-8156 R&D Projects: GA ČR GA205/01/1066 Institutional research plan: CEZ:AV0Z3042911 Keywords : cross-validation * prediction * model Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.073, year: 2003

  10. Precipitation Indices Low Countries

    Science.gov (United States)

    van Engelen, A. F. V.; Ynsen, F.; Buisman, J.; van der Schrier, G.

    2009-09-01

    Since 1995, KNMI published a series of books(1), presenting an annual reconstruction of weather and climate in the Low Countries, covering the period AD 763-present, or roughly, the last millennium. The reconstructions are based on the interpretation of documentary sources predominantly and comparison with other proxies and instrumental observations. The series also comprises a number of classifications. Amongst them annual classifications for winter and summer temperature and for winter and summer dryness-wetness. The classification of temperature have been reworked into peer reviewed (2) series (AD 1000-present) of seasonal temperatures and temperature indices, the so called LCT (Low Countries Temperature) series, now incorporated in the Millennium databases. Recently we started a study to convert the dryness-wetness classifications into a series of precipitation; the so called LCP (Low Countries Precipitation) series. A brief outline is given here of the applied methodology and preliminary results. The WMO definition for meteorological drought has been followed being that a period is called wet respectively dry when the amount of precipitation is considerable more respectively less than usual (normal). To gain a more quantitative insight for four locations, geographically spread over the Low Countries area (De Bilt, Vlissingen, Maastricht and Uccle), we analysed the statistics of daily precipitation series, covering the period 1900-present. This brought us to the following definition, valid for the Low Countries: A period is considered as (very) dry respectively (very) wet if over a continuous period of at least 60 days (~two months) cq 90 days (~three months) on at least two out of the four locations 50% less resp. 50% more than the normal amount for the location (based on the 1961-1990 normal period) has been measured. This results into the following classification into five drought classes hat could be applied to non instrumental observations: Very wet period

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

    Science.gov (United States)

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

    2016-12-01

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

  12. Maximizing Statistical Power When Verifying Probabilistic Forecasts of Hydrometeorological Events

    Science.gov (United States)

    DeChant, C. M.; Moradkhani, H.

    2014-12-01

    Hydrometeorological events (i.e. floods, droughts, precipitation) are increasingly being forecasted probabilistically, owing to the uncertainties in the underlying causes of the phenomenon. In these forecasts, the probability of the event, over some lead time, is estimated based on some model simulations or predictive indicators. By issuing probabilistic forecasts, agencies may communicate the uncertainty in the event occurring. Assuming that the assigned probability of the event is correct, which is referred to as a reliable forecast, the end user may perform some risk management based on the potential damages resulting from the event. Alternatively, an unreliable forecast may give false impressions of the actual risk, leading to improper decision making when protecting resources from extreme events. Due to this requisite for reliable forecasts to perform effective risk management, this study takes a renewed look at reliability assessment in event forecasts. Illustrative experiments will be presented, showing deficiencies in the commonly available approaches (Brier Score, Reliability Diagram). Overall, it is shown that the conventional reliability assessment techniques do not maximize the ability to distinguish between a reliable and unreliable forecast. In this regard, a theoretical formulation of the probabilistic event forecast verification framework will be presented. From this analysis, hypothesis testing with the Poisson-Binomial distribution is the most exact model available for the verification framework, and therefore maximizes one's ability to distinguish between a reliable and unreliable forecast. Application of this verification system was also examined within a real forecasting case study, highlighting the additional statistical power provided with the use of the Poisson-Binomial distribution.

  13. Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

    Science.gov (United States)

    Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

    2016-12-01

    Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

  14. Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO

    Science.gov (United States)

    Manatsa, Desmond; Mushore, Terrence; Lenouo, Andre

    2017-01-01

    The provision of timely and reliable climate information on which to base management decisions remains a critical component in drought planning for southern Africa. In this observational study, we have not only proposed a forecasting scheme which caters for timeliness and reliability but improved relevance of the climate information by using a novel drought index called the standardised precipitation evapotranspiration index (SPEI), instead of the traditional precipitation only based index, the standardised precipitation index (SPI). The SPEI which includes temperature and other climatic factors in its construction has a more robust connection to ENSO than the SPI. Consequently, the developed ENSO-SPEI prediction scheme can provide quantitative information about the spatial extent and severity of predicted drought conditions in a way that reflects more closely the level of risk in the global warming context of the sub region. However, it is established that the ENSO significant regional impact is restricted only to the period December-March, implying a revisit to the traditional ENSO-based forecast scheme which essentially divides the rainfall season into the two periods, October to December and January to March. Although the prediction of ENSO events has increased with the refinement of numerical models, this work has demonstrated that the prediction of drought impacts related to ENSO is also a reality based only on observations. A large temporal lag is observed between the development of ENSO phenomena (typically in May of the previous year) and the identification of regional SPEI defined drought conditions. It has been shown that using the Southern Africa Regional Climate Outlook Forum's (SARCOF) traditional 3-month averaged Nino 3.4 SST index (June to August) as a predictor does not have an added advantage over using only the May SST index values. In this regard, the extended lead time and improved skill demonstrated in this study could immensely benefit

  15. Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

    Science.gov (United States)

    Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

    2018-02-13

    Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

  16. 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.

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  18. 3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Science.gov (United States)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-02-01

    We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

  19. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  20. Land surface feedbacks on spring precipitation in the Netherlands

    NARCIS (Netherlands)

    Daniels, E.E.; Hutjes, R.W.A.; Lenderink, G.; Ronda, R.J.; Holtslag, A.A.M.

    2015-01-01

    In this paper the Weather Research and Forecasting (WRF) model is used to investigate the sensitivity of precipitation to soil moisture and urban areas in the Netherlands. We analyze the average output of a four day event from 10-13 May 1999 for which the individual days had similar synoptical