Title :
MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System
Author :
Boukabara, Sid-Ahmed ; Garrett, Kevin ; Chen, Wanchun ; Iturbide-Sanchez, Flavio ; Grassotti, Christopher ; Kongoli, Cezar ; Chen, Ruiyue ; Liu, Quanhua ; Yan, Banghua ; Weng, Fuzhong ; Ferraro, Ralph ; Kleespies, Thomas J. ; Meng, Huan
Author_Institution :
Center for Satellite Applic. & Res., Nat. Oceanic & Atmos. Adm., Camp Springs, MD, USA
Abstract :
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 products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been- - extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.
Keywords :
atmospheric techniques; clouds; covariance matrices; data assimilation; iterative methods; radiative transfer; radiometry; rain; sea ice; snow; weather forecasting; 1D variational system; DMSP-F16; F18SSMI/S; Metop-A; MiRS system; Microwave Integrated Retrieval System; NOAA-18 satellite; NOAA-19; U.S. National Oceanic and Atmospheric Administration; all-weather 1DVAR satellite data assimilation system; atmospheric product; cloudy condition; cloudy radiances; covariance matrix; geophysical solution; hydrometeor parameters; hydrometeor product; hydrometeors; ice water path; integrated cloud liquid amount; inverted state vector; iterative physical inversion system; liquid cloud; numerical weather prediction; passive microwave sensors; precipitating condition; radiative transfer equation; radiative transfer model; radiometric measurements; rainfall rate; rainy radiances; retrieval system; sea ice concentration; skin temperature; snow backgrounds; snow water equivalent; spaceborne measurements; surface emissivity spectrum; total precipitable water; Clouds; Covariance matrix; Geophysical measurements; Ice; Ocean temperature; Rain; Sea surface; Atmospheric sounding; cloudy and rainy data assimilation; microwave retrieval; surface sensing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2011.2158438