Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3-LAM Model

Park, J.; Xue, M.; Liu, C.

doi:https://doi.org/10.1029/2022GL102709

i

Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3-LAM Model

2023
By Park, J. ; Xue, M. ; Liu, C.

Details

Journal Title:

Geophysical Research Letters
Personal Author:

Park, J. ; Xue, M. ; Liu, C.
NOAA Program & Office:

CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations)
Description:

Capabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain-form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3-LAM) from a recent release for Short-Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short-range forecasts comparable to those using the ensemble square-root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model-space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error-based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF.
Keywords:

Algorithms Kalman Filtering Radar Kalman Filtering Data Assimilation Joint Effort For Data Assimilation Integration (JEDI) Local Ensemble Transform Kalman Filter (LETKF)
Source:

Geophysical Research Letters, 50, e2022GL102709
DOI:

 https://doi.org/10.1029/2022GL102709
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA21ONR4590171 ; Grant no. NA16OAR4320115
License:

https://creativecommons.org/licenses/by-nc/4.0/
Rights Information:

CC BY-NC
Compliance:

Submitted
Main Document Checksum:

urn:sha256:d41fafa128fddefa263935511c87c1a14f6f508114e50add8ec9aecafd4ab723
Download URL:

https://repository.library.noaa.gov/view/noaa/51847/noaa_51847_DS1.pdf
File Type:

[PDF - 3.42 MB ]

ON THIS PAGE

Details

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.