JAWRA Journal of the American Water Resources Association · 2019 · 35 citations · 40 references
Natural SciencesEarth SciencesWater Resources ManagementForecastingHydrologic ForecastingWatershed HydrologyEnsemble ForecastingForecast VerificationHydrometeorological ForecastingPrecipitation ForecastingReal-time ForecastingSeasonal ForecastingForecast Bias DetectionForecast Skill AssessmentSeasonal Forecast SkillSubseasonal Precipitation ForecastingWater Resource ForecastingWatershed Scale MeasurementWatershed Scale ModelingWatershed Scale Prediction
Abstract We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.
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The NCEP Climate Forecast System Version 2
Suranjana Saha, Shrinivas Moorthi, Xingren Wu et al. · Journal of Climate · 2013 · 3.5K citations · Full text
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