MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System

作者：Sid‐Ahmed Boukabara, Kevin Garrett, Wanchun Chen, Flavio Iturbide‐Sánchez, Christopher Grassotti, Cezar Kongoli, Ruiyue Chen, Quanhua Liu, Banghua Yan, Fuzhong Weng, Ralph Ferraro, Thomas J. Kleespies, Huan Meng · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2011 · DOI：10.1109/tgrs.2011.2158438 · 被引用次数：280 · 研究领域：Meteorological Phenomena and Simulations、Precipitation Measurement and Analysis、Atmospheric aerosols and clouds

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental pro...