A new methodology to produce more skillful United States cool season precipitation forecasts

Switanek, Matthew B.; Hamill, Thomas M.

doi:10.1175/JHM-D-21-0235.1

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A new methodology to produce more skillful United States cool season precipitation forecasts

2022
By Switanek, Matthew B. ; Hamill, Thomas M.
Source: Journal of Hydrometeorology, 23(6), 991-1005

[PDF-4.58 MB]

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Details:

Journal Title:

Journal of Hydrometeorology
Personal Author:

Switanek, Matthew B. ; Hamill, Thomas M.

Switanek, Matthew B. ; Hamill, Thomas M. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:

The water resources of the western United States have enormous agricultural and municipal demands. At the same time, droughts like the one enveloping the West in the summer of 2021 have disrupted supply of this strained and precious resource. Historically, seasonal forecasts of cool-season (November–March) precipitation from dynamical models such as North American Multi-Model Ensemble (NMME) and the Seasonal Forecasting System 5 (SEAS5) from the European Centre for Medium-Range Weather Forecasts have lacked sufficient skill to aid in Western stakeholders’ and water managers’ decision-making. Here, we propose a new empirical–statistical framework to improve cool-season precipitation forecasts across the contiguous United States (CONUS). This newly developed framework is called the Statistical Climate Ensemble Forecast (SCEF) model. The SCEF framework applies a principal component regression model to predictors and predictands that have undergone dimensionality reduction, where the predictors are large-scale meteorological variables that have been prefiltered in space. The forecasts of the SCEF model captures 12.0% of the total CONUS-wide standardized observed variance over the period 1982/83–2019/20, whereas NMME captures 7.2%. Over the more recent period 2000/01–2019/20, the SCEF, NMME, and SEAS5 models respectively capture 11.8%, 4.0%, and 4.1% of the total CONUS-wide standardized observed variance. An important finding is that much of the improved skill in the SCEF, with respect to models such as NMME and SEAS5, can be attributed to better forecasts across most of the western United States.
Keywords:

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Forecasting Hydrology Precipiation
Source:

Journal of Hydrometeorology, 23(6), 991-1005
DOI:

https://doi.org/10.1175/JHM-D-21-0235.1
Document Type:

Journal Article
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

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urn:sha256:46671e03eb01fb0d74f2af5507a51a19bfc338b07fc0353b2cb01d0fc3d7e68f
Download URL:

https://repository.library.noaa.gov/view/noaa/45145/noaa_45145_DS1.pdf
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