Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part II: Results

Stovern, Diana R.; Hamill, Thomas M.; Smith, Lesley L.

doi:10.1175/mwr-d-22-0310.1

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part II: Results

2023
By Stovern, Diana R. ; Hamill, Thomas M. ; Smith, Lesley L.
Source: Monthly Weather Review, 151(6), 1535-1550

[PDF-5.47 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

Details You May Also Like

Details:

Journal Title:

Monthly Weather Review
Personal Author:

Stovern, Diana R. ; Hamill, Thomas M. ; Smith, Lesley L.

Stovern, Diana R. ; Hamill, Thomas M. ; Smith, Lesley L. Less -
NOAA Program & Office:

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

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

This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Keywords:

[+]

Atmospheric Science
Source:

Monthly Weather Review, 151(6), 1535-1550
DOI:

https://doi.org/10.1175/mwr-d-22-0310.1
ISSN:

0027-0644;1520-0493;
Format:

PDF
Publisher:

American Meteorological Society
Document Type:

Journal Article
Funding:

Grant no. NA17OAR4320101
Rights Information:

Other
Compliance:

Library
Main Document Checksum:

[+]

urn:sha256:36b9d829529c7870391d25626e8539a77ba2c1cee08fe90fb95bd0155046ad8c
Download URL:

https://repository.library.noaa.gov/view/noaa/54781/noaa_54781_DS1.pdf
File Type:

No Additional Files

More +

Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part II: Results

Details:

Supporting Files

You May Also Like