Skip to main content
U.S. flag

An official website of the United States government

Dot Gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

HTTPS

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Partition of Forecast Error Into Positional and Structural Components

Abstract

Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters (cf., e.g., Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error. The Forecast Error Decomposition (FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components: (a) large scale positional, (b) large scale structural, and (c) small scale error variance. The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored.

Article / Publication Data
Active/Online
YES
Status
FINAL PRINT PUBLICATION
Available Metadata
Accepted On
February 18, 2021
DOI ↗
Fiscal Year
NOAA IR URL ↗
Peer Reviewed
YES
Publication Name
Advances In Atmospheric Sciences
Published On
May 09, 2021
Final Online Publication On
April 01, 2021
Final Print Publication On
May 01, 2021
Publisher Name
Springer
Print Volume
38
Issue
6
Project Type
LAB SUPPORTED
URL ↗

Authors

Authors who have authored or contributed to this publication.