%0 Journal Article
%T The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks
%A Chien-Ben Chou
%A Huei-Ping Huang
%J Advances in Meteorology
%D 2011
%I Hindawi Publishing Corporation
%R 10.1155/2011/803593
%X This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction. 1. Introduction The atmospheric infrared sounder (AIRS) is a state-of-the-art hyperspectral infrared sensor that has provided critical observational data for weather and climate analysis since its launch in 2002 (McNally et al. [1], Chahine et al. [2]). The AIRS spectrum consists of 2378 channels from 3.7£¿¦Ìm¨C15.4£¿¦Ìm with a spectral resolution of . The cross-track swath width is 1650£¿km and spatial resolution is 13.5£¿km at the nadir field of view (Aumann et al. [3], Chahine et al. [2]). Due to its high resolution and accuracy (errors are within 1£¿K for the temperature of a 1£¿km vertical layer and 20% for the lower tropospheric moisture of a 2£¿km vertical layer, http://airs.jpl.nasa.gov/), AIRS can potentially provide high-quality temperature and humidity data for applications that would otherwise rely on conventional sounding. Given its uniformly high spatial resolution, the strategy for assimilating the AIRS retrieval data into a weather forecast system may differ from that for assimilating conventional soundings. With the increasing usage of AIRS retrieval products in data assimilation for regional weather prediction (e.g., [4¨C7]), it remains an outstanding problem to determine the ideal weight given to the AIRS retrieval profiles (and satellite observation in general) in order to optimize its impact on the forecast. As a contribution to this topic, this work will first examine the usefulness of assimilating the AIRS retrieval data for the prediction of typhoon tracks then explore the sensitivity of the forecast error on the adjustable
%U http://www.hindawi.com/journals/amete/2011/803593/