Details:

Journal Title:
Monthly Weather Review
Personal Author:
Kay, Junkyung ; Weckwerth, Tammy M. ; Lee, Wen-Chau ; Sun, Jenny ; Romine, Glen
Kay, Junkyung ; Weckwerth, Tammy M. ; Lee, Wen-Chau ; Sun, Jenny ; Romine, Glen Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.
Keywords:
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Convection (Meteorology) Numerical Weather Forecasting Remote Sensing Atmospheric Science Data Assimilation Deep Convection Lidars/Lidar Observations Numerical Weather Prediction/Forecasting Profilers, Atmospheric
Source:
Monthly Weather Review, 150(10), 2787-2811
DOI:
https://doi.org/10.1175/MWR-D-21-0284.1
Document Type:
Journal Article
Funding:
Grant no. NA19OAR4590320
Rights Information:
Other
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:7f1c72f399c29df13046638bfb7f02ac892dbc52ee95244c2c9228698384ecd5
Download URL:
https://repository.library.noaa.gov/view/noaa/47756/noaa_47756_DS1.pdf
File Type:

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