Medium-range Precipitation Forecasts Research Articles

The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort.

Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products

Abstract. Accurate weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 d) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams (Selingue Dam, Markala Dam, Goronyo Dam, Bakolori Dam, Kainji Dam, Jebba Dam, Dadin Kowa Dam, and Lagdo Dam) in the Niger river basin using NASA's Integrated Multi-satellitE Retrievals (IMERG) Final Run merged satellite gauge rainfall observations. The results indicate that the accuracy of GFS forecast varies depending on climatic regime, lead time, accumulation timescale, and spatial scale. The GFS forecast has large overestimation bias in the Guinea region of the basin (wet climatic regime), moderate overestimation bias in the Savannah region (moderately wet climatic regime), but has no bias in the Sahel region (dry climate). Averaging the forecasts at coarser spatial scales leads to increased forecast accuracy. For daily rainfall forecasts, the performance of GFS is very low for almost all watersheds, except for Markala and Kainji dams, both of which have much larger watershed areas compared to the other watersheds. Averaging the forecasts at longer timescales also leads to increased forecast accuracy. The GFS forecasts, at 15 d accumulation timescale, have better performance but tend to overestimate high rain rates. Additionally, the performance assessment of two other satellite products was conducted using IMERG Final estimates as reference. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) merged satellite gauge product has similar rainfall characteristics to IMERG Final, indicating the robustness of IMERG Final. The IMERG Early Run satellite-only rainfall product is biased in the dry Sahel region; however, in the wet Guinea and Savannah regions, IMERG Early Run outperforms GFS in terms of bias.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

Abstract Reliable weather forecasts are valuable in a number of applications, such as agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1–15 day) precipitation forecasts in the nine subbasins of the Nile basin using NASA’s Integrated Multisatellite Retrievals (IMERG) Final Run satellite–gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3–6 h and a spatial resolution of 0.25°, and the version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1–15 days, a spatial scale from 0.25° to all the way to the subbasin scale, and for a period of one year (15 June 2019–15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling–Gupta efficiency (KGE), is poor (0 < KGE < 0.5) for most of the subbasins. The factors contributing to the low performance are 1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara and Setit watershed, and Khashm El Gibra watershed), and 2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14 110 km2 and Sennar at 13 895 km2). GFS has better bias for watersheds located in the dry parts of the Northern Hemisphere or wet parts of the Southern Hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara and Setit and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara and Setit and Khashm El Gibra watershed. A simple climatological bias correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including postprocessing techniques through the use of both near-real-time and research-version satellite rainfall products.

An Effective Approach to Reduce the Ensemble Members in a Multi-Physics Convection-Permitting Scale Framework and Investigating its Consistency in Generating Medium-Range Precipitation Forecasts

An Effective Approach to Reduce the Ensemble Members in a Multi-Physics Convection-Permitting Scale Framework and Investigating its Consistency in Generating Medium-Range Precipitation Forecasts

A novel hybrid artificial neural network - Parametric scheme for postprocessing medium-range precipitation forecasts

Many present-day statistical schemes for postprocessing weather forecasts, in particular precipitation forecasts, rely on calibration using prescribed statistical models to relate forecast statistics to distributional parameters. The efficacy of such schemes is often constrained not only by prescribed predictor-predictand relation, but also by arbitrary choices of temporal window and lead time range for training. To address this limitation, we propose an end-to-end, computationally efficient hybrid postprocessing scheme capable of producing full predictive distributions of precipitation accumulation without explicit stratification of forecast-observation pairs by forecast lead time and season. The proposed framework uses the censored, shifted gamma distribution (CSGD) as the predictive distribution but uses an artificial neural network (ANN) to estimate the distributional parameters of CSGD through a unified approach. This approach, referred to as ANN-CSGD, allows for simultaneous estimation of distributional parameters over multiple lead times and seasons in a single model by incorporating the latter variables as predictors to the ANN. We test our proposed ANN-CSGD model for postprocessing of ensemble mean forecasts of 24-h precipitation totals over selected river basins in California, at one- to seven-day lead times, from the Global Ensemble Forecast System (GEFS). The probabilistic quantitative precipitation forecasts (PQPFs) from the ANN-CSGD, are more skillful overall than those from the benchmark CSGD and the Mixed-type meta-Gaussian distribution (MMGD) models. The ANN-CSGD PQPFs highly improve the performance of those from CSGD in predicting the probability of precipitation (PoP) and are also much sharper and reliable at higher precipitation thresholds. We demonstrate how the hybrid approach, by using the entire available training data and its modified formulation, efficiently represents interactions between GEFS forecasts and season/lead times, thus leading to enhanced predictive performance.

Potential skill of continental-scale, medium-range ensemble streamflow forecasts for flood prediction in South America

Recent scientific and computational developments have pushed hydrological forecasting systems up to continental and global scales. In this study, we evaluate the potential skill of ensemble streamflow forecasts for flood prediction in South America (SA), with a closer look on some of the large basins of the continent. Medium-range precipitation forecasts from the ECMWF Ensemble Prediction System were used to force a continental-scale, fully coupled hydrologic-hydrodynamic model to produce streamflow forecasts up to 15 days in advance. Forecasts were evaluated over a 4-year period against a reference simulation, derived from the hydrological model forced with a gridded precipitation dataset that merges data from multiple sources (MSWEP). To provide a benchmark for assessing the potential skill, forecasts based on the Ensemble Streamflow Prediction (ESP) technique were produced by running the hydrological model with precipitation data resampled from past MSWEP observations. Overall, ECMWF-based forecasts were skillful mainly at the eastern and southeastern regions of the continent (e.g., São Francisco, Parnaíba, Upper Paraná, Tocantins-Araguaia and Uruguay basins), showing little or no added skill in large rivers draining the Andes such as the Magdalena, Orinoco, Madeira (Amazon) and right-bank tributaries of the Paraguay. In the latter basins, ECMWF-based forecasts largely overestimated the exceedance probabilities of critical thresholds due to a positive bias in the predicted precipitation (in relation to MSWEP), visible mostly during the wet season. In addition, forecasts were found to perform better than ESP in terms of both overall quality and discrimination skill at regions characterized by a warm temperate, fully-humid climate. A direct relation between forecast skill and basin size was not always valid, since the former can vary geographically along tributaries. Limitations and future developments needed for continental-scale hydrological forecasting in SA are also discussed.

Evaluation of the effective forecast and decision horizon in optimal hydropower generation considering medium-range precipitation forecasts

Abstract This paper presents a rolling horizon control (RHC) model to evaluate the effective forecast horizon (EFH) of 10-day forecast inflows derived from quantitative precipitation forecasts (QPFs) and the effective decision horizon (EDH) for hydropower generation. This paper takes the Huanren hydropower reservoir located in the northeast of China as a case study. Firstly, the 10-day forecast inflows are derived from the QPFs. Then the hydropower generation processes are simulated by the RHC model, and the performances of hydropower generation with different EFHs and EDHs are evaluated, respectively. The results show that: (1) the RHC can adapt to varying conditions by re-optimizing the decisions during the EFH; (2) with the EFH increasing, the hydroelectric reliability increases and the efficiency decreases, while the efficiency and reliability are improved with shortened the EDH.

Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

Verification of High-Resolution Medium-Range Precipitation Forecasts from Global Environmental Multiscale Model over China during 2009–2013

Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of Numerical Weather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0–96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well.

Sensitivity Study on the Influence of Parameterization Schemes in WRF_ARW Model on Short- and Medium-Range Precipitation Forecasts in the Central Andes of Peru

A sensitivity study of the performance of the Weather Research and Forecasting regional model (WRF, version 3.7) to the use of different microphysics, cumulus, and boundary layer parameterizations for short- and medium-term precipitation forecast is conducted in the Central Andes of Peru. Lin-Purdué, Thompson, and Morrison microphysics schemes were tested, as well as the Grell–Freitas, Grell 3d, and Betts–Miller–Janjic cumulus parameterizations. The tested boundary layer schemes were the Yonsei University and Mellor–Yamada–Janjic. A control configuration was defined, using the Thompson, Grell–Freitas, and Yonsei University schemes, and a set of numerical experiments is made, using different combinations of parameterizations. Data from 19 local meteorological stations and regional and global gridded were used for verification. It was concluded that all the configurations overestimate precipitation, but the one using the Morrison microphysical scheme had the best performance, based on the indicators of bias (B) and root mean square error (RMSE). It is recommended not to use the Betts–Miller–Janjic scheme in this region for low resolution domains. Categorical forecast verification of the occurrence of rainfall as a binary variable showed detection rates higher than 85%. According to this criterion, the best performing configuration was the combination of Betts–Miller–Janjic and Morrison. Spatial verification showed that, even if all the configurations overestimated precipitation in some degree, spatial patterns of rainfall match the TRMM and PISCO rainfall data. Morrison’s microphysics scheme shows the best results, and consequently, this configuration is recommended for short- and medium-term rainfall forecasting tasks in the Central Andes of Peru and particularly in the Mantaro basin. The results of a special sensitivity experiment showed that the activation or not of cumulus parametrization for the domain of 3 km resolution is not relevant for the precipitation forecast in the study region.

An aggregation-decomposition bayesian stochastic optimization model for cascade hydropower reservoirs using medium-range precipitation forecasts

The forecast information is essential to improve the utilization efficiency of hydropower resources. To address the uncertainties of forecasting inflow, the Aggregation-Decomposition Bayesian Stochastic Dynamic Programming (AD-BSDP) model is presented in the present paper by using the 10-days precipitation value of the Quantitative Precipitation Forecasts from Global Forecast System (GFS-QPFs). The application in China’s Hun River cascade hydropower reservoirs shows that the GFS-QPFs are beneficial for hydropower generation and the performance of AD-BSDP is more efficiency and reliability than the others models.

Eastern U.S. Verification of Ensemble Precipitation Forecasts

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

Verification of short- and medium-range precipitation forecasts from the ensemble modeling system of the hydrometcenter of Russia

Precipitation forecasts from short- and medium-range ensemble prediction system of the Hydrometeorological Research Center of the Russian Federation (Hydrometcenter of Russia) are verified. The verification system includes probabilistic and deterministic scores. The precipitation forecast quality is analyzed for different seasons and large-scale circulation types. Further development of ensemble modeling and verification at the Hydrometcenter of Russia is discussed.

Value of Medium-range Precipitation Forecasts in Inflow Prediction and Hydropower Optimization

This paper presents an inflow-forecasting model and a Piecewise Stochastic Dynamic Programming model (PSDP) to investigate the value of the Quantitative Precipitation Forecasts (QPFs) comprehensively. Recently medium-range quantitative precipitation forecasts are addressed to improve inflow forecasts accuracy. Revising the Ertan operation, a simple hydrological model is proposed to predict 10-day average inflow into the Ertan dam using GFS-QPFs of 10-day total precipitation during wet season firstly. Results show that the reduction of average absolute errors (ABE) is of the order of 15% and the improvement in other statistics is similar, compared with those from the currently used AR model. Then an improved PSDP is proposed to generate monthly or 10-day operating policies to incorporate forecasts with various lead-times as hydrologic state variables. Finally performance of the PSDP is compared with alternative SDP models to evaluate the value of the GFS-QPFs in hydropower generation. The simulation results demonstrate that including the GFS-QPFs is beneficial to the Ertan reservoir inflow forecasting and hydropower generation dispatch.

Dynamically vs. empirically downscaled medium-range precipitation forecasts

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

The Anomalous Rainfall over the United States during July 1993: Sensitivity to Land Surface Parameterization and Soil Moisture Anomalies

Abstract This paper discusses the sensitivity of short- and medium-range precipitation forecasts for the central United States to land surface parametrization and soil moisture anomalies. Two forecast systems with different land surface and boundary layer schemes were running in parallel during the extreme rainfall events of July 1993. One forecast system produces much better precipitation forecasts due to a more realistic thermodynamic structure resulting from improved evaporation in an area that is about 1 day upstream from the area of heaviest rain. The paper also discusses two ensembles of 30-day integrations for July 1993. In the first ensemble, soil moisture is initialized at field capacity (100% availability); in the second ensemble it is at 25% of soil moisture availability. It is shown that the moist integrations produce a much more realistic precipitation pattern than the dry integrations. These results suggest that there may be some predictive skill in the monthly range related to the time-scal...