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).

We develop methods of using boreal, temperate, and tropical forests as vicarious calibration sites for spaceborne microwave radiometers. The extended sites and improved calibration techniques enable examining warm-scene performances as a complement to cold scenes over the ocean, providing information about scan-dependent biases, detecting and correcting possible calibration errors, and intercalibrating different radiometers. Specifically, this paper shows results as follows. Warm-scene calibration is expanded beyond previously utilized sites in the Amazon rainforest to include inland boreal and temperate forests, as well as forests in coastal and island regions. These additional sites increase the available warm-scene sample size by a factor of 30 compared with using Amazon rainforests alone, allowing more accurate and statistically robust calibration. In addition, their widespread near-global distribution enables near-continuous monitoring of radiometer performance, e.g., with a temporal resolution of almost once per day. Accurate warm-end vicarious calibration enables or enhances several new capabilities for radiometer characterization. Scan-dependent calibration biases that are present at the high end of a radiometer's dynamic range can, for the first time, be reliably detected and characterized. Using this method, we are able to detect the along-scan magnetic interference and edge-of-scan biases that are present in Global Precipitation Measurement Microwave Imager (GMI) observations at warm brightness temperatures (TBs). Our techniques are able to detect and characterize very small calibration anomalies, e.g., a scan-dependent error in GMI with a peak-to-peak amplitude of 0.1 K. In general, scan-dependent performance and antenna pattern correction can be improved with better constraints at both the high and low ends of on-Earth observed TBs. Regional dependence and seasonal changes in the brightness and predictability of the forested calibration sites are detected and characterized. Calibration exhibits a dependence on latitude, most significantly at water vapor channels, where tropical regions are different from middle and high latitudinal regions. Although the root cause is still under investigation, its presence suggests that using limited sites could result in undesirable latitude-dependent calibration biases. The distribution of calibration sites shifts seasonally, which results from green-up of the vegetation canopy in summer and defoliation in winter, and from snow contamination.

Passive microwave radiometer data over the ocean have been widely used, but data near coastlines or over lakes often cannot be used because of the large footprint with mixed signals from both land and water. For example, current standard Special Sensor Microwave Imager (SSM/I) products, including wind, water vapor, and precipitation, are typically unavailable within about 100 km of any coastline. This paper presents methods of correcting land-contaminated radiometer data in order to extract the coastal information. The land contamination signals are estimated, and then removed, using a representative antenna pattern convolved with a high-resolution land–water mask. This method is demonstrated using SSM/I data over the Great Lakes and validated with simulated data and buoy measurements. The land contamination is significantly reduced, and the wind speed retrieval is improved. This method is not restricted to SSM/I and wind retrievals alone; it can be applied more generally to microwave radiometer measurements in coastal regions for other retrieval purposes.