Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction
Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction
复制标题
DOI：
10.3402/tellusa.v64i0.18408
复制
发表时间：
2012-09
期刊：
影响因子：
--
通讯作者：
T. Miyoshi;Masaru Kunii
中科院分区：
文献类型：
--
作者：
T. Miyoshi;Masaru Kunii
摘要除常规观测数据外，利用局地集合变换卡尔曼滤波(LETKF)将来自大气红外探测仪(AIRS)版本5的大气温度和湿度廓线资料同化到天气研究和预报(WRF)模式中。虽然对所有现有的质量控制的AIRS资料进行天真的同化会产生较差的分析，但自适应膨胀和水平数据稀疏的额外增强导致了由于AIRS资料的数值天气预报技能的普遍提高。特别是，改进了自适应膨胀方法，使其不再假定观测网络的时间均匀，并允许更好地处理时间上不均匀的AIR数据。结果表明，AIRS数据带来的改善在较长期的预报中更为显著。2008年9月台风森拉库和江米的预报显示，由于AIRS数据，情况有所改善。
ABSTRACT In addition to conventional observations, atmospheric temperature and humidity profile data from the Atmospheric Infrared Sounder (AIRS) Version 5 retrieval products are assimilated into the Weather Research and Forecasting (WRF) model, using the local ensemble transform Kalman filter (LETKF). Although a naive assimilation of all available quality-controlled AIRS retrieval data yields an inferior analysis, the additional enhancements of adaptive inflation and horizontal data thinning result in a general improvement of numerical weather prediction skill due to AIRS data. In particular, the adaptive inflation method is enhanced so that it no longer assumes temporal homogeneity of the observing network and allows for a better treatment of the temporally inhomogeneous AIRS data. Results indicate that the improvements due to AIRS data are more significant in longer-lead forecasts. Forecasts of Typhoons Sinlaku and Jangmi in September 2008 show improvements due to AIRS data.