Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

Narapusetty, Bala; Collins, Dan C.; Murtugudde, Raghu; Gottschalck, Jon; Peters‐Lidard, Christa

doi:10.1002/asl.818

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Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

2018
By Narapusetty, Bala ; Collins, Dan C. ; Murtugudde, Raghu ; ...
Source: Atmospheric Science Letters

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Alternative Title:

Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi-model ensemble system
Journal Title:

Atmospheric Science Letters
Personal Author:

Narapusetty, Bala ; Collins, Dan C. ; Murtugudde, Raghu ; Gottschalck, Jon ; Peters‐Lidard, Christa

Narapusetty, Bala ; Collins, Dan C. ; Murtugudde, Raghu ; Gottschalck, Jon ; Peters‐Lidard, Christa Less -
NOAA Program & Office:

NWS (National Weather Service) ; CPC (Climate Prediction Center) ; OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)

NWS (National Weather Service) ; CPC (Climate Prediction Center) ; OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) Less -
Description:

Improvements in skill of summer forecasted precipitation as produced by the North American multi-model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2-m air temperature (T2m) forecasts to fully exploit the stronger co-variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real-time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m-precipitation relation will offer a framework for diagnosing poor model skill.
Keywords:

[+]

Atmospheric Science Bias Climatology Conus Corrections Droughts El Niño Current Forecasting Geochemistry Geophysics Improvements Long-range Weather Forecasting Meteorology Models Moisture Precipitation Forecasting Seasonal Variations (Economics) Southern Oscillation Structural Frames Surfaces Temperature Upper Air Temperature
Source:

Atmospheric Science Letters
DOI:

https://doi.org/10.1002/asl.818
Document Type:

Journal Article
Place as Subject:

United States ; North America

United States ; North America Less -
Rights Information:

CC BY
Compliance:

PMC
Main Document Checksum:

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urn:sha256:1166f9bd63d80f95d4560e757e8ed5595d025f946d3c01a7f6bb79fa7e252832
Download URL:

https://repository.library.noaa.gov/view/noaa/26023/noaa_26023_DS1.pdf
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Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system

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