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Multimodel Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing

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  • 1 aSchool of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, China
  • | 2 bDepartment of Crop, Soil and Environmental Sciences, Auburn University, Auburn, Alabama
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Abstract

This study assessed multimodel subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multimodel ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill was relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multimodel ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2–4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3–4, while the ENSO phases influenced the skill mostly in the southern regions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Di Tian, tiandi@auburn.edu

Abstract

This study assessed multimodel subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multimodel ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill was relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multimodel ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2–4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3–4, while the ENSO phases influenced the skill mostly in the southern regions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Di Tian, tiandi@auburn.edu
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