Editorial Type:
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Article Type:
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Four-Dimensional Variational Assimilation of Water Vapor Differential Absorption Lidar Data: The First Case Study within IHOP_2002

Volker Wulfmeyer Institut für Physik und Meteorologie, Universität Hohenheim, Stuttgart, Germany

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Hans-Stefan Bauer Institut für Physik und Meteorologie, Universität Hohenheim, Stuttgart, Germany

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Matthias Grzeschik Institut für Physik und Meteorologie, Universität Hohenheim, Stuttgart, Germany

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Andreas Behrendt Institut für Physik und Meteorologie, Universität Hohenheim, Stuttgart, Germany

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Francois Vandenberghe Research Applications Program, National Center for Atmospheric Research, Boulder, Colorado

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Edward V. Browell Atmospheric Sciences Division, NASA Langley Research Center, Hampton, Virginia

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Syed Ismail Atmospheric Sciences Division, NASA Langley Research Center, Hampton, Virginia

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Richard A. Ferrare Atmospheric Sciences Division, NASA Langley Research Center, Hampton, Virginia

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Print Publication:
01 Jan 2006
DOI:
https://doi.org/10.1175/MWR3070.1
Page(s):
209–230
Restricted access

Abstract

Four-dimensional variational assimilation of water vapor differential absorption lidar (DIAL) data has been applied for investigating their impact on the initial water field for mesoscale weather forecasting. A case that was observed during the International H2O Project (IHOP_2002) has been selected. During 24 May 2002, data from the NASA Lidar Atmospheric Sensing Experiment were available upstream of a convective system that formed later along the dryline and a cold front. Tools were developed for routinely assimilating water vapor DIAL data into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The results demonstrate a large impact on the initial water vapor field. This is due to the high resolution and accuracy of DIAL data making the observation of the high spatial variability of humidity in the region of the dryline and of the cold front possible. The water vapor field is mainly adjusted by a modification of the atmospheric wind field changing the moisture transport. A positive impact of the improved initial fields on the spatial/temporal prediction of convective initiation is visible. The results demonstrate the high value of accurate, vertically resolved mesoscale water vapor observations and advanced data assimilation systems for short-range weather forecasting.

Corresponding author address: Volker Wulfmeyer, Institut für Physik und Meteorologie, Universität Hohenheim, Garbenstrasse 30, 70599 Stuttgart, Germany. Email: wulfmeye@uni-hohenheim.de

Abstract

Four-dimensional variational assimilation of water vapor differential absorption lidar (DIAL) data has been applied for investigating their impact on the initial water field for mesoscale weather forecasting. A case that was observed during the International H2O Project (IHOP_2002) has been selected. During 24 May 2002, data from the NASA Lidar Atmospheric Sensing Experiment were available upstream of a convective system that formed later along the dryline and a cold front. Tools were developed for routinely assimilating water vapor DIAL data into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The results demonstrate a large impact on the initial water vapor field. This is due to the high resolution and accuracy of DIAL data making the observation of the high spatial variability of humidity in the region of the dryline and of the cold front possible. The water vapor field is mainly adjusted by a modification of the atmospheric wind field changing the moisture transport. A positive impact of the improved initial fields on the spatial/temporal prediction of convective initiation is visible. The results demonstrate the high value of accurate, vertically resolved mesoscale water vapor observations and advanced data assimilation systems for short-range weather forecasting.

Corresponding author address: Volker Wulfmeyer, Institut für Physik und Meteorologie, Universität Hohenheim, Garbenstrasse 30, 70599 Stuttgart, Germany. Email: wulfmeye@uni-hohenheim.de

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