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Microwave Integrated Retrieval System (MIRS) - Background

The Microwave Integrated Retrieval System (MIRS) is a physically-based microwave retrieval system designed to treat both atmospheric and surface parameters which affect passive microwave measurements. Some of the objectives of MIRS are (1) to perform the retrieval in all-weather conditions and (2) over all-surface types, with a major benefit being the extension of the spatial coverage to critically important active regions.

MiRS is a One-Dimensional Variational inversion algorithm (1DVAR) (Boukabara et al. 2011, 2013) that employs the Community Radiative Transfer Model (CRTM) as the forward and adjoint operators. It simultaneously solves for surface (Tskin, emissivity), and atmospheric parameters (temperature, water vapor, non- precipitating cloud and hydrometeor profiles).

In addition, once the core parameters of the state vector are retrieved in the 1DVAR minimization step, an additional post-processing is done in which a number of derived parameters are retrieved, based on inputs from the core 1DVAR retrieval. These include: surface rain rate, as well as cryospheric parameters such as snow pack properties, sea ice concentration and sea ice age.

MiRS is currently being run operationally at NOAA for Suomi-NPP/ATMS, POES N18/N19, Metop-A, Metop-B, DMSP-F17/F18, and Megha-Tropiques/SAPHIR. In August 2016, an updated version (v11.2) was delivered to NOAA operations, extending processing capability to GPM/GMI measurements. MiRS is currently being extended to process data from JPSS-1/ATMS in preparation for launch in 2017.

More details may be found under the 'Algorithm' section. Due to its flexible design, it is capable of performing retrievals using different instrumental configurations. It is also envisioned that it can serve as a tool for performing sensor design studies and performance estimation for future satellites and concepts such as a potential geostationary-based microwave sensor.

Because the same algorithm is used consistently across the platforms (different channels spectra, polar and geostationary orbits), the time series of these retrievals will be more self consistent, making the resulting climate data records free of jumps due to changes in the algorithms.