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Satellite radiance data assimilation for binary tropical cyclone cases over the western N orth P acific
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2017
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Source: Journal of Advances in Modeling Earth Systems, 9(2), 832-853
[PDF-10.35 MB]
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Journal Title:Journal of Advances in Modeling Earth Systems
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Description:A total of three binary tropical cyclone (TC) cases over the Western North Pacific are selected to investigate the effects of satellite radiance data assimilation on analyses and forecasts of binary TCs. Twoparallel cycling experiments with a 6 h interval are performed for each binary TC case, and the differencebetween the two experiments is whether satellite radiance observations are assimilated. Satellite radianceobservations are assimilated using the Weather Research and Forecasting Data Assimilation (WRFDA)’sthree-dimensional variational (3D-Var) system, which includes the observation operator, quality controlprocedures, and bias correction algorithm for radiance observations. On average, radiance assimilationresults in slight improvements of environmental fields and track forecasts of binary TC cases, but thedetailed effects vary with the case. When there is no direct interaction between binary TCs, radiance assimi-lation leads to better depictions of environmental fields, and finally it results in improved track forecasts.However, positive effects of radiance assimilation on track forecasts can be reduced when there exists adirect interaction between binary TCs and intensities/structures of binary TCs are not represented well. Aninitialization method (e.g., dynamic initialization) combined with radiance assimilation and/or moreadvanced DA techniques (e.g., hybrid method) can be considered to overcome these limitations. 1. Introduction Tropical cyclone (TC) forecasts have improved in the past two decades. Errors in TC track forecasts are signif- icantly reduced, and TC intensity forecasts also show notable improvements [ Rappaport et al ., 2009]. This substantial progress can be attributed to advances in numerical weather prediction (NWP) models (e.g.,numerical and physics schemes), data assimilation (DA) and initialization methods (e.g., hybrid DA anddynamic initialization methods), and observing platforms (e.g., satellite and radar observations). However, despite these advancements, numerical forecast of TCs is still a challenging issue due to complex nature of
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Source:Journal of Advances in Modeling Earth Systems, 9(2), 832-853
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ISSN:1942-2466;1942-2466;
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Rights Information:CC BY-NC-ND
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