A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations

A 3.5-dimensional variational method is developed for Doppler radar data assimilation. In this method, incremental analyses are performed in three steps to update the model state upon the background state provided by the model prediction. First, radar radial-velocity observations from three consecut...

Full description

Saved in:
Bibliographic Details
Main Authors: Qin Xu, Li Wei, Wei Gu, Jiandong Gong, Qingyun Zhao
Format: Article
Language:English
Published: Wiley 2010-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2010/797265
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832555997549297664
author Qin Xu
Li Wei
Wei Gu
Jiandong Gong
Qingyun Zhao
author_facet Qin Xu
Li Wei
Wei Gu
Jiandong Gong
Qingyun Zhao
author_sort Qin Xu
collection DOAJ
description A 3.5-dimensional variational method is developed for Doppler radar data assimilation. In this method, incremental analyses are performed in three steps to update the model state upon the background state provided by the model prediction. First, radar radial-velocity observations from three consecutive volume scans are analyzed on the model grid. The analyzed radial-velocity fields are then used in step 2 to produce incremental analyses for the vector velocity fields at two time levels between the three volume scans. The analyzed vector velocity fields are used in step 3 to produce incremental analyses for the thermodynamic fields at the central time level accompanied by the adjustments in water vapor and hydrometeor mixing ratios based on radar reflectivity observations. The finite element B-spline representations and recursive filter are used to reduce the dimension of the analysis space and enhance the computational efficiency. The method is applied to a squall line case observed by the phased-array radar with rapid volume scans at the National Weather Radar Testbed and is shown to be effective in assimilating the phased-array radar observations and improve the prediction of the subsequent evolution of the squall line.
format Article
id doaj-art-796dd7d982e84dcd96c9197d7f1424e6
institution Kabale University
issn 1687-9309
1687-9317
language English
publishDate 2010-01-01
publisher Wiley
record_format Article
series Advances in Meteorology
spelling doaj-art-796dd7d982e84dcd96c9197d7f1424e62025-02-03T05:46:35ZengWileyAdvances in Meteorology1687-93091687-93172010-01-01201010.1155/2010/797265797265A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar ObservationsQin Xu0Li Wei1Wei Gu2Jiandong Gong3Qingyun Zhao4National Severe Storms Laboratory, Norman, OK 73072, USACooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 73072, USAGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Science Application International Corporation, Greenbelt, MD 20771, USANational Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaMarine Meteorology Division, Naval Research Laboratory, Monterey, CA 93943-5502, USAA 3.5-dimensional variational method is developed for Doppler radar data assimilation. In this method, incremental analyses are performed in three steps to update the model state upon the background state provided by the model prediction. First, radar radial-velocity observations from three consecutive volume scans are analyzed on the model grid. The analyzed radial-velocity fields are then used in step 2 to produce incremental analyses for the vector velocity fields at two time levels between the three volume scans. The analyzed vector velocity fields are used in step 3 to produce incremental analyses for the thermodynamic fields at the central time level accompanied by the adjustments in water vapor and hydrometeor mixing ratios based on radar reflectivity observations. The finite element B-spline representations and recursive filter are used to reduce the dimension of the analysis space and enhance the computational efficiency. The method is applied to a squall line case observed by the phased-array radar with rapid volume scans at the National Weather Radar Testbed and is shown to be effective in assimilating the phased-array radar observations and improve the prediction of the subsequent evolution of the squall line.http://dx.doi.org/10.1155/2010/797265
spellingShingle Qin Xu
Li Wei
Wei Gu
Jiandong Gong
Qingyun Zhao
A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
Advances in Meteorology
title A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
title_full A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
title_fullStr A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
title_full_unstemmed A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
title_short A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations
title_sort 3 5 dimensional variational method for doppler radar data assimilation and its application to phased array radar observations
url http://dx.doi.org/10.1155/2010/797265
work_keys_str_mv AT qinxu a35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT liwei a35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT weigu a35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT jiandonggong a35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT qingyunzhao a35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT qinxu 35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT liwei 35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT weigu 35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT jiandonggong 35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations
AT qingyunzhao 35dimensionalvariationalmethodfordopplerradardataassimilationanditsapplicationtophasedarrayradarobservations