i
Correcting Systematic and State‐Dependent Errors in the NOAA FV3‐GFS Using Neural Networks
-
2022
-
-
Source: Journal of Advances in Modeling Earth Systems, 14(11)
Details:
-
Journal Title:Journal of Advances in Modeling Earth Systems
-
Personal Author:
-
NOAA Program & Office:
-
Description:Weather forecasts made with imperfect models contain state-dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments,” produced by the DA process embed information about model errors. An attempt is made here to extract that information to improve numerical weather prediction. Neural networks (NNs) are trained to predict corrections to the systematic error in the National Oceanic and Atmospheric Administration's FV3-GFS model based on a large set of analysis increments. A simple NN focusing on an atmospheric column significantly improves the estimated model error correction relative to a linear baseline. Leveraging large-scale
-
Keywords:
-
Source:Journal of Advances in Modeling Earth Systems, 14(11)
-
DOI:
-
ISSN:1942-2466;1942-2466;
-
Format:
-
Publisher:
-
Document Type:
-
Funding:
-
License:
-
Rights Information:CC BY
-
Compliance:Library
-
Main Document Checksum:
-
Download URL:
-
File Type: