Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System

Zhang, Jiaying; Lin, Liao-Fan; Bras, Rafael L.

doi:10.3390/w12071918

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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

Effect of Logarithmically Transformed IMERG Precipitation Observations in WRF 4D-Var Data Assimilation System

2020
By Zhang, Jiaying ; Lin, Liao-Fan ; Bras, Rafael L.
Source: Water, 12(7)

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Details:

Journal Title:

Water
Personal Author:

Zhang, Jiaying ; Lin, Liao-Fan ; Bras, Rafael L.

Zhang, Jiaying ; Lin, Liao-Fan ; Bras, Rafael L. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; CIRA (Cooperative Institute for Research in the Atmosphere)

OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; CIRA (Cooperative Institute for Research in the Atmosphere) Less -
Description:

Precipitation estimates from numerical weather prediction (NWP) models are uncertain. The uncertainties can be reduced by integrating precipitation observations into NWP models. This study assimilates Version 04 Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) (IMERG) Final Run into the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system using a four-dimensional variational (4D-Var) method. Three synoptic-scale convective precipitation events over the central United States during 2015–2017 are used as case studies. To investigate the effect of logarithmically transformed IMERG precipitation in the WRFDA system, this study reports on several experiments with six-hour and hourly assimilation windows, regular (nontransformed) and logarithmically transformed observations, and a constant observation error in regular and logarithmic spaces. Results show that hourly assimilation windows improve precipitation simulations significantly compared to six-hour windows. Logarithmically transformed precipitation does not improve precipitation estimations relative to nontransformed precipitation. However, better predictions of heavy precipitation can be achieved with a constant error in the logarithmic space (corresponding to a linearly increasing error in the regular space), which modifies the threshold of rejecting observations, and thus utilizes more observations. This study provides a cost function with logarithmically transformed observations for the 4D-Var method in the WRFDA system for future investigations.
Keywords:

[+]

GPM; IMERG; WRF; Data assimilation; Precipitation; Satellite retrievals
Source:

Water, 12(7)
DOI:

https://doi.org/10.3390/w12071918
Document Type:

Journal Article
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:1bda8fb445b9164e6fcad51e3532be7fe6d17358131baf080bb438b4802a8da5
Download URL:

https://repository.library.noaa.gov/view/noaa/31973/noaa_31973_DS1.pdf
File Type:

[PDF-2.72 MB]