The Global Precipitation Measurement (GPM) mission is an international satellite mission that uses measurements from an advanced radar/radiometer system on a core observatory as reference standards to unify and advance precipitation estimates made by a constellation of research and operational microwave sensors. The GPM core observatory was launched on February 27, 2014 at 18:37 UT in a 65 • inclination nonsun-synchronous orbit. GPM focuses on precipitation as a key component of the Earth's water and energy cycle, and has the capability to provide near-real-time observations for tracking severe weather events, monitoring freshwater resources, and other societal applications. The GPM microwave imager (GMI) on the core observatory provides the direct link to the constellation radiometer sensors, which fly mainly in polar orbits. The GMI sensitivity, accuracy, and stability play a crucial role in unifying the measurements from the GPM constellation of satellites. The instrument has exhibited highly stable operations through the duration of the calibration/validation period. This paper provides an overview of the GMI instrument and a report of early on-orbit commissioning activities. It discusses the on-orbit radiometric sensitivity, absolute calibration accuracy, and stability for each radiometric channel.Index Terms-Calibration accuracy, passive microwave remote sensing, radiometric sensitivity.

[1] A simple wind/rain backscatter model is used with co-located precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) satellite to evaluate the effect of rain on SeaWinds on QuikSCAT s°observations. The model incorporates wind-induced surface scattering, the surface rain perturbation, and atmospheric rain attenuation and scattering. The co-located PR measurements afford direct computation of SeaWinds-scale averaged rain rate and atmospheric rain attenuation and scattering. An estimate of the wind-induced surface backscatter is computed via numerical weather prediction (NWP) winds. By synergistically combining the SeaWinds, NWP, and PR data, estimates of surface rain perturbation and combined surface/atmospheric scattering are made as a function of PR-derived rain rate. The scattering from rain is dominated mainly by the surface perturbation low rain rates, and by atmospheric scattering at high rain rates. The backscatter model estimates 94% of the observed rain-contaminated SeaWinds on QuikSCAT s°values to within 3 dB. Using the model, the conditions are determined for which it is possible to estimate rain from scatterometer measurements and where wind retrieval is not possible.

The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).

The Global Precipitation Measurement (GPM) Core Observatory was launched on 27 February 2014. One of the principal instruments on the spacecraft is the GPM Microwave Imager (GMI). This paper describes the absolute calibration of the GMI antenna temperature (TA) and the earth brightness temperature (TB). The deep-space observations taken on 20 May 2014, supplemented by nadir-viewing data, are used for the TA calibration. Data from two backlobe maneuvers are used to determine the primary reflector’s cold-space spillover, which is required to convert the TA into TB. The calibrated GMI observations are compared to predictions from an ocean radiative transfer model (RTM) using collocated WindSat ocean retrievals as input. The mean difference when averaged globally over 13 months does not exceed 0.1 K for any of the nine channels from 11 to 89 GHz. The RTM comparisons also show that there are no significant solar intrusion errors in the GMI hot load. The absolute accuracy of the GMI instrument is defined as the average ocean-viewing error of the measured TA or TB relative to the true TA or TB. Based on the analyses herein, the GMI absolute accuracy for TA is estimated to be about 0.1 K rms over all channels and 0.25 K rms over all channels for TB.

Abstract-The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.