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Abstract Review

Corresponding Author
Name
Dale Barker
Authors
NameAffiliation
Ying-Hwa Kuo UCAR
Abstract
Session3 (Approaches for modeling atmospheric optical turbulence.)
Title'High-Resolution Atmospheric Data Assimilation and Numerical Weather Prediction in Data Sparse Areas '
AbstractModern atmospheric data assimilation systems have access to observations from a wide
range of sensors. Traditional in-situ meteorological observations (e.g. weather
balloons, aircraft, surface stations, etc) are supplemented by an ever increasing
number of remotely-sensed observations including Doppler radars, radiances,
and atmospheric refractivity data.

Numerous major astronomical observatories (e.g. Hawaii, Chile, Canary Islands) are located in
traditionally data-sparse areas. The complex relationship between key remotely-sensed observations (e.g. microwave emissivity) and forecast meteorological fields (e.g. temperature, humidity) requires the use of sophisticated data assimilation systems, such as 3/4 dimensional variational and ensemble Kalman filter algorithms.

This talk will briefly summarize the current atmospheric observation network available to
real-time NWP, and also briefly describe the state-of-the-art atmospheric data assimilation algorithms. The talk will then focus on latest results from applications of the Weather Research and Forecasting (WRF) model in high-resolution applications. Initial results from the impact of the newly available COSMIC (launched April 2006) refractivity data will also be presented.


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Updated on: Wed, Dec 17 2014 - 1849 UTC
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