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The joint National Aeronautics and Space Administration and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) mission will provide considerably more observations over complex and dynamically changing land backgrounds. A physically based precipitation retrieval using GPM's satellite constellation of passive microwave (PMW) observations has to accommodate the spatially and temporally varying radiometric signature of the land surface to constrain the set of candidate rainfall solutions. The challenge for retrieval algorithms is to identify and isolate precipitation profiles whose simulated observations agree with the satellite observations and are also representative of the surface conditions. Microwave emissivity modeling results are presented from a physically based land algorithm that retrieves soil moisture, vegetation water content, and surface temperature, along with the emissivity using polarized 10, 18, and 37 GHz channel measurements from the WindSat sensor onboard the Coriolis satellite, and results from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The emissivity mean, coefficient of variation, covariance, and correlation slope are examined for the range of clear-scene surface properties observed by WindSat and TRMM between 2003-2012 and 2002-2011, respectively, under a range of seasons, time of day, rain events, etc. These joint data provide a means to examine the extent to which the surface geophysical properties control the microwave land surface emissivity covariability, to better utilize these lower frequency observations in overland PMW-based precipitation retrievals. [ABSTRACT FROM PUBLISHER]

The sea surface temperature (SST) is a crucial parameter system in climate monitoring. Satellite remote sensing is currently the most common approach for measuring long-term and large-area sea surface temperatures. The SST data measured by the satellite radiometer include the sea surface skin temperature (SSTskin) at a depth of approximately 10 μm. Satellite remote sensing measurement data must be compared and validated with on-site measured data. There are various solutions for on-site measuring instruments; the essential components are usually infrared radiation sensors with radiation output. This paper uses an ordinary integrated infrared thermometer without a radiation output function to remotely measure the sea surface temperature to achieve a high-precision measurement. The scheme of integrating infrared thermometers to measure the sea surface temperature is investigated in this paper. Based on Planck's formula, the bidirectional conversion relationship between temperature and radiation in a certain band is established. The experimental system introduced in this paper uses an integrated infrared thermometer to measure the small blackbody and the target in a cyclic measurement system. We combine it with the sea surface emissivity characteristics and eliminate the influence of sky background radiation on the sea surface to obtain the actual amount of radiation on the sea surface, from which we obtain the actual radiation amount on the sea surface. Accurate SST can be calculated from the actual amount of radiation at the sea surface. The temperature measurement accuracy can reach 0.1 K, allowing it to meet on-site temperature measurement requirements, as well as the comparison measurement requirements confirmed by satellite remote sensing on-site data. There are relatively few products available for sensors with a temperature measurement accuracy of 0.1 K on the market, and temperature measurement equipment with a temperature measurement accuracy of 0.1 K is relatively expensive. Cost is one of the important factors to consider when using in bulk, especially as global warming increases the need for ocean monitoring. The scheme proposed in this paper is beneficial to reduce the volume and weight of measuring instruments, reduce the cost, and promote the large-scale combined application of sea surface temperature change monitoring. [ABSTRACT FROM AUTHOR]

Knowledge about the surface soil water content is essential because it controls the surface water dynamics and land-atmosphere interaction. In high mountain areas in particular, soil surface water content controls infiltration and flood events. Although satellite-derived surface soil moisture data from passive microwave sensors are readily available for most regions globally, mountainous areas are often excluded from these data (or at least flagged as biased) due to the strong topographic influence on the retrieved signal. Even though a substantial volume of literature is available dealing with topographic effects on spaceborne brightness temperature, no systematic analysis has been reported. Therefore, we present a comprehensive analysis of topographic effects on brightness temperature at C-band using a two-step approach. First, a well-controlled field experiment is carried out using a mobile truck-mounted C-band radiometer to analyze the impact of geometric and adjacent effects on the radiometer signal. Additionally, a comprehensive radiative transfer model is developed accounting for both effects and tested on the ground-based data. Second, recorded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data over the Tibetan Plateau were used to analyze the error due to the impact of topography using the developed model. The results of the field experiment clearly show that the geometric effect of a single hill has a much larger impact on brightness temperature compared to the adjacent effect of multiple hills, whereby, due to the geometric effect, the bias is up to +20 K for horizontal and -13 K for vertical polarization. For the adjacent effect, the bias is less than 3 K for both polarizations. Additionally, the developed radio transfer model was able to reproduce both effects with high accuracy. For the AMSR-E data, the model shows that the brightness temperature recorded is biased in the same way as the ground-based measurements and that uncertainties induced by the wide existence of atypical mountain regions in the Tibetan Plateau will have a great impact on the retrieving error (maximum 30%). The largest impact on the retrieval error, on the other hand, is calculated for the soil moisture with a maximum relative error of 44%. The negligible impact can be attributed to false parameterization of the soil texture, soil surface temperature, and sky temperature. Finally, the overall absolute error in the estimated water content is quantified on average with 4%, whereby single pixels indicate a maximum absolute error of up to 16%. In conclusion, we show that recorded spaceborne brightness temperatures are highly biased by topographic effects in mountainous regions using a comprehensive radiative transfer model. Additionally, we suggest using this model to invert the effective surface emissivity of mountain areas for standard processing of higher level data products such as surface soil water content. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects. [ABSTRACT FROM AUTHOR]