NOAA/MiRS: A 1-Dimensional Variational Retrieval Approach for the GPM Microwave Imager

Monday, 15 December 2014: 9:30 AM
Tanvir Islam1,2, Christopher Grassotti1,3, Xiwu Zhan1, Sid Ahmed Boukabara1, Kevin Garrett1,4, Craig K Smith1,3, Pan Liang1,3, Wanchun Chen1,5, Ralph R Ferraro1, Limin Zhao6 and Fuzhong Weng1, (1)NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, United States, (2)Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United States, (3)Atmospheric and Environmental Research, Lexington, MA, United States, (4)Riverside Technology Inc., Fort Collins, CO, United States, (5)ERT, Laurel, MD, United States, (6)NOAA/NESDIS Office of Satellite and Product Operations, College Park, MD, United States
The Microwave Integrated Retrieval System (MiRS), which is the official NOAA microwave retrieval algorithm for processing passive microwave observations from more than 7 different operational satellites, has been extended to data from the recently launched GPM Microwave Imager (GMI). Using a variational approach, the system simultaneously retrieves hydrometeor profiles (e.g. cloud, rain, and ice), as well as sounding and surface state parameters, which are all parts of the multidimensional state vector. MiRS is a physically-based optimal estimation algorithm, that finds a solution by minimizing a two-term cost function: 1) the departure of the forward model simulated radiances from the satellite measurements, and 2) the departure of the retrieved parameters from their a priori information. The community radiative transfer model (CRTM) is used as both the forward and Jacobian operator in simulating the radiances, with the goal of fitting the multichannel measurements within the assumed radiometric noise levels.

Once, the state vector is retrieved, further post-processing is carried out to derive important hydrometeor products such as the cloud water path, ice water path, and rain water path, which are then converted to surface rain rate using a physical relationship between water path amount and surface rain rate derived from offline simulations of a mesoscale model. Additional derived geophysical products include total precipitable water from vertical moisture profiles, and the sea ice concentration and the snow water equivalent from emissivity spectra. Since, the surface emissivity is included as part of the state vector, an all-surface retrieval has been possible without compromising a smooth transition of the geophysical products across different surfaces.

In this presentation, several examples of retrieved geophysical parameters using the MiRS 1DVAR algorithm applied to measurements from the GMI sensor on-board the GPM core satellite will be shown. When available, comparisons with collocated independent data will be included. Moreover, the algorithm is readily applicable to both cross-track sounders and conical imagers without needing any cross-calibration of radiometric measurements between the sensors in the GPM constellation.