[1] We present in this study the results obtained when applying a physical algorithm based on a variational methodology to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership (SNPP) for a consistent retrieval of geophysical data in all weather conditions. The algorithm, which runs operationally at the U.S. National Oceanic and Atmospheric Administration, is applied routinely to a number of sounders from the Polar-Orbiting Operational Environmental Satellites, the Defense Meteorological Satellite Program, and the European Meteorological Operational satellite constellations. The one-dimension variational (1DVAR) methodology, which relies on a forward operator, the Community Radiative Transfer Model, allows for solving the inversion of the radiometric measurements into geophysical parameters which have a direct impact on the brightness temperatures. The parameters that are produced by this Microwave Integrated Retrieval System algorithm include the atmospheric temperature T(p), moisture Q(p), and vertically integrated total precipitable water; and the surface skin temperature and emissivity as well as the hydrometeor products of nonprecipitating cloud liquid water and rain-and ice-water paths. In this algorithm, a simple postprocessing is applied to the 1DVAR-generated emissivity to derive cryospheric products (snow water equivalent and sea-ice concentration) when the data are measured over these surfaces. The postprocessing is also applied to the hydrometeors products to generate a surface rainfall rate. This comprehensive set of sounding, surface, hydrometeor, and cryospheric products generated from SNPP/ATMS is therefore radiometrically consistent, meaning that when input to the forward operator, it will allow the simulation of the actual brightness temperatures measurements within noise levels. The geophysical consistency between the products, also critical, is satisfied due to the physical approach adopted and the geophysical constraints introduced through the correlation matrix used in the variational system. The results shown in this paper confirm that the performances of all products are within the expected accuracy and precision figures and comparable to performances usually obtained with single-parameter-dedicated algorithms, with the added value that the inverted products are both radiometrically and geophysically consistent.Citation: Boukabara, S. -A., et al. (2013), A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS,

Wideband Autocorrelation Radiometry (AR) offers a new approach to snowpack and freshwater ice sensing. AR snowpack sensing promises two-way microwave travel times, sp , attenuation, and sub-pixel sp variance in snowpacks. In combination with snowpack thermophysical models, AR offers a deterministic means of measuring snowpack Snow Water Equivalent (SWE), wetness, and sub-pixel SWE variance. AR sensing of freshwater ice promises two-way travel times in the ice layer and, thus, ice thickness. An AR sensor's frequency would lie below ~15 GHz for increased sensitivity to wetness and decreased sensitivity to scatter darkening. Preliminary computer simulations show that: (1) A 10 GHz radiometer with a bandwidth of 1 GHz will resolve two-way snowpack travel times, sp , to uncertainties of <10 cm equivalent thickness, and (2) a multiply reflected AR signal exhibiting an attenuation 37 dB is readily detected. Wide bandwidth AR sensors will be subject to greater Radio Frequency Interference (RFI) than traditional radiometers, but communications-type RFI with high duty cycles, while raising the noise floor, will not affect AR interpretability.

The WindSat polarimetric microwave radiometer measures top-of-atmosphere brightness temperature, useful for retrieving surface wind vector over the ocean. This procedure was previously documented in low to moderate wind and light precipitation [1,2]. An atmospheric clearing algorithm designed to remove the emissive and absorptive effects of stronger precipitation and extract the emissivity of the wind-driven ocean surface worked well in moderate rain but had limited success with strong rainfall and high winds [3]. This paper presents results using an improved forward model including Mie scattering from rain. We consider three 2005 WindSat hurricane overpasses for proper atmospheric conditions (Dennis, Katrina and Rita). The improved atmospheric clearing algorithm extracts ocean surface emissivity in and near the hurricane rain bands and eyewall. The emissivity is compared to NOAA H*Wind analysis of the near-surface wind field. Results show a monotonic dependence of emissivity on wind speed up to category 3 hurricane-force winds.

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