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contributor authorJung, Byoung-Joo
contributor authorKim, Hyun Mee
contributor authorAuligné, Thomas
contributor authorZhang, Xin
contributor authorZhang, Xiaoyan
contributor authorHuang, Xiang-Yu
date accessioned2017-06-09T17:30:31Z
date available2017-06-09T17:30:31Z
date copyright2013/11/01
date issued2013
identifier issn0027-0644
identifier otherams-86445.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230004
description abstractn increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%?70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.
publisherAmerican Meteorological Society
titleAdjoint-Derived Observation Impact Using WRF in the Western North Pacific
typeJournal Paper
journal volume141
journal issue11
journal titleMonthly Weather Review
identifier doi10.1175/MWR-D-12-00197.1
journal fristpage4080
journal lastpage4097
treeMonthly Weather Review:;2013:;volume( 141 ):;issue: 011
contenttypeFulltext


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