Abstract. Satellite observations using microwave radiometers operating near the window regions are strongly affected by surface emissivity. Presently, the measurements obtained over land are not directly utilized in numerical weather prediction models because of uncertainties in estimating the emissivity. This study develops a new model to quantify the land emissivity over various surface conditions. For surfaces such as snow, deserts, and vegetation, volumetric scattering was calculated using a two-stream radiative transfer approximation. The reflection and transmission at the surface-air interface and lower boundary were derived by modifying the Fresnel equations to account for crosspolarization and surface roughness effects. Several techniques were utilized to compute the optical parameters for the medium, which is used in the radiative transfer solution. In the case of vegetation, geometrical optics is used because the leaf size is typically larger than the wavelength. For snow and deserts, a dense medium theory was adopted to take into account the coherent scattering of closely spaced particles. The emissivity spectra at frequencies between4.9 and94 GHz was simulated and compared with the ground-based radiometer measurements for bare soil, grass land, and snow conditions. It is shown that the features including the spectra, variability, and polarization agree well with the measurements. The simulated global distribution of land surface emissivity is also compared with the satellite retrievals from the Advanced Microwave Sounding Unit (AMSU). It is found that the largest discrepancies primarily occur over high latitudes where the snow properties are complex and least understood.

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Satellite data assimilation in numerical weather prediction systems requires information on microwave snow surface emissivity in a wide wavelength range. However, the existing models perform poorly for stratified snow or aged snow especially at high frequencies such that they are inapplicable for various snow types. The brightness temperatures at the window channels of the advanced microwave sounding unit (AMSU) are characterized strongly by surface emissivity and are thus used in this study to retrieve snow surface emissivity from 23.8 to 150 GHz under both clear and cloudy conditions. This algorithm uses an iteration scheme associated with a two‐stream radiative transfer model. The accuracy of the AMSU‐retrieved snow emissivity using this algorithm is first assessed against a set of satellite‐observed emissivity under clear skies and a set of simulated emissivity under cloudy conditions. The algorithm is then assessed by its application to seven consecutive snow events observed at Hagerstown, Maryland, in February 2003 and to a set of mountainous snowpacks observed at the Local Scale Observation Site of the Cold Land Processes Field Experiment in northern Colorado in February and March of 2002 and 2003. Results show that the AMSU‐retrieved snow emissivity spectra are consistent with the snow emissivity model simulations of the snow events in both Maryland and Colorado. Furthermore, the impact of the AMSU‐retrieved snow emissivity on global satellite data assimilation systems is investigated by applying the algorithm to the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) system. Compared to the existing analytic land emissivity model used in the GSI system, the retrieved emissivity significantly improves the use of the AMSU sounding data in the NCEP GSI system. Therefore, the AMSU‐based snow emissivity retrieval algorithm has demonstrated its potential use in the global satellite data assimilation systems.

A new algorithm has been developed for simultaneous retrieval of aerosol optical properties and chlorophyll concentrations in case I waters. This algorithm is based on an improved complete model for the inherent optical properties and accurate simulations of the radiative transfer process in the coupled atmosphere-ocean system. It has been tested against synthetic radiances generated for the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) channels and has been shown to be robust and accurate. A unique feature of this algorithm is that it uses the measured radiances in both near-IR and visible channels to find that combination of chlorophyll concentration and aerosol optical properties that minimizes the error across the spectrum. Thus the error in the retrieved quantities can be quantified.

Snowfall rate retrieval from spaceborne passive microwave (PMW) radiometers has gained momentum in recent years. PMW can be so utilized because of its ability to sense in‐cloud precipitation. A physically based, overland snowfall rate (SFR) algorithm has been developed using measurements from the Advanced Microwave Sounding Unit‐A/Microwave Humidity Sounder sensor pair and the Advanced Technology Microwave Sounder. Currently, these instruments are aboard five polar‐orbiting satellites, namely, NOAA‐18, NOAA‐19, Metop‐A, Metop‐B, and Suomi‐NPP. The SFR algorithm relies on a separate snowfall detection algorithm that is composed of a satellite‐based statistical model and a set of numerical weather prediction model‐based filters. There are four components in the SFR algorithm itself: cloud properties retrieval, computation of ice particle terminal velocity, ice water content adjustment, and the determination of snowfall rate. The retrieval of cloud properties is the foundation of the algorithm and is accomplished using a one‐dimensional variational (1DVAR) model. An existing model is adopted to derive ice particle terminal velocity. Since no measurement of cloud ice distribution is available when SFR is retrieved in near real time, such distribution is implicitly assumed by deriving an empirical function that adjusts retrieved SFR toward radar snowfall estimates. Finally, SFR is determined numerically from a complex integral. The algorithm has been validated against both radar and ground observations of snowfall events from the contiguous United States with satisfactory results. Currently, the SFR product is operationally generated at the National Oceanic and Atmospheric Administration and can be obtained from that organization.