![]() ![]() ![]() CHIRPS and CHIRPS-GEFS data are produced by the University of California Santa Barbara (UCSB) Climate Hazards Center (CHC).ĬHIRPS-GEFS is designed to support operational seasonal precipitation monitoring and impact forecasting by providing a version of GEFS forecasts that are compatible with CHIRPS data. CHIRPS-GEFS provides daily-updated 0.05-degree resolution forecasts for 1-day, 5-day, 10-day, and 15-day precipitation totals, as well as for pentads-the primary periodicity for CHIRPS data. ![]() CHIRPS is a gridded merged satellite-station precipitation data product with a 40+ year record, quasi-global extent, and ~5 km resolution. CHIRPS-GEFS uses the Climate Hazards InfraRed Precipitation with Stations precipitation data product 4 (CHIRPS) and GEFS v12 2000 to 2019 reforecast data 5 for spatial downscaling and bias correction of 0.25-degree to 0.5-degree resolution GEFS v12 real-time ensemble mean forecasts. CHIRPS-GEFS is an operational data set that uses quantile matching to increase the spatial resolution, remove systematic bias, and adjust the variance of deterministic precipitation forecasts from the newest version of this state-of-the-art numerical weather prediction system, GEFS version 12 (v12) 3. In this data descriptor article, we present CHIRPS-GEFS, a precipitation forecast data product based on predictions from the widely used National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). While numerical weather prediction is imperfect, users’ tolerance for error is situation-dependent, and, in some cases, a moderately skillful forecast can be invaluable. Statistical post processing of NWP data is commonly done to correct for systematic errors that arise from resolution limitations and errors in the mean state and ensemble spread 2. Inaccurate representation of the current state and absent or poor parametrization of complex atmospheric processes in the NWP system also yield inaccuracies. A major challenge to weather prediction, and a fundamental limit to predicting weather patterns and precipitation, is the so-called “butterfly effect,” named for the chaotic fluctuations in the atmosphere that amplify small anomalies over time 1. ![]() Weather prediction centers usually run a NWP model multiple times with perturbed initial conditions to characterize uncertainty. To help set the initial atmospheric state and to provide up-to-date information about how the current atmospheric state is evolving, satellite data and weather observations are assimilated into the model simulations several times a day. Numerical weather prediction systems use a powerful computing framework to model key atmospheric processes. NWP data is a crucial resource for short-term disaster planning, and accessibility of weather forecasts has led to uptake in an ever-increasing number of applications. The hope in creating this product is that people engaged in weather and climate-impact assessments, particularly those doing so to assist vulnerable communities, can more easily take advantage of the value of NWP data for new scientific applications, and for timely assessments and communication about high-risk situations. This data set bridges a gap between a resource that is actively used for monitoring agro-climatic conditions and the forward-looking information that modern numerical weather prediction (NWP) systems provide. This article describes a precipitation forecast data set designed to support drought early warning and anticipate weather impacts across many regions of the globe. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. A rank-based quantile matching procedure is used to transform GEFS v12 “reforecast” and “real-time” forecast ensemble means to CHIRPS spatial-temporal characteristics. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. ![]()
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