Owing to the Global Precipitation Measurement (GPM) core satellite’s unique asynchronous orbit, its orbital ground tracks intersect the orbital tracks of many other sun-synchronous satellites. Of particular interest are the intersections (coincidences) between the GPM core satellite and the 94-GHz (W-band) CloudSat profiling radar (CPR), within small enough time differences, such that the combination of the resulting “pseudo three-frequency” radar profiles (W-band from CPR, and Ku/Ka-band from GPM), and the 13-channel (10-183 GHz) GMI radiometer are useful for many scientific purposes.
This document describes the algorithms for the Geolocation Toolkit (GeoTK) for the Global Precipitation Measurement (GPM) Mission. The core part of the algorithm uses input orbit ephemeris, spacecraft attitude, and instrument pointing data to compute each pixel latitude and longitude viewed, along with ancillary data such as zenith/incidence and Sun angle data. These calculations are implemented in the GeoTK software subroutines, which will be used for Level 1B (L1B) algorithms for GPM.
This document describes the GMI Level 1B algorithm developed by PPS. It consists of physical bases and mathematical equations for GMI calibration, as well as after-launch activities. The document also presents high-level software design. Parts of this document are from the Remote Sensing Systems (RSS) GMI Calibration ATBD and the BATC Calibration Data Book as contributed by the BATC GMI manufactory contract. The GMI L1B geolocation algorithm is described in a separate Geolocation Toolkit ATBD.
The GPM Combined Radar-Radiometer Algorithm performs two basic functions: first, it provides, in principle, the most accurate, high resolution estimates of surface rainfall rate and precipitation vertical distributions that can be achieved from a spaceborne platform, and it is therefore valuable for applications where information regarding instantaneous storm structure are vital.
The Level 3 DPR product provides space-time statistics of the level 2 DPR results. High and low spatial resolution grids are defined such that the high-resolution grid is 0.250 × 0.250 (lat×lon) while the lowresolution grid is 50 ×50. For the variables defined on the low-resolution grid, the statistics include mean, standard deviation, counts and histogram. For variables defined on the high-resolution grid, the same
statistics are computed with the exception of a histogram, which is omitted.
Latent heating (LH) cannot be measured directly with current techniques, including current remote sensing or in situ instruments, which explains why nearly all satellite retrieval schemes depend heavily on some type of cloud-resolving model or CRM (Tao et al. 2006, 2016). This is true for the current CSH algorithm (Tao et al. 2010).
Input: Combined 2BCMB (DPR + GMI) rainfall products
A table comparing the older TRMM Multi-satellite Preciptiation Analysis (TMPA) datasets with the new Integrated Multi-satellitE Retrievals for GPM (IMERG) datasets.
This document describes the file naming conventions that will be used to name data products produced by the Precipitation Processing System (PPS) for the Global Precipitation Measurement (GPM) Mission.
The transition from the Tropical Rainfall Measuring Mission (TRMM) data products to the Global Precipitation Measurement (GPM) mission products is well underway. This document specifically addresses the multi-satellite products, the TRMM Multi-satellite Precipitation Analysis (TMPA), the real-time TMPA (TMPA-RT), and the Integrated Multi-satellitE Retrievals for GPM (IMERG).
This document describes the algorithm and processing sequence for the Integrated Multi-satellitE Retrievals for GPM (IMERG). This algorithm is intended to intercalibrate, merge, and interpolate “all” satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. The system is run several times for each observation time, first giving a quick estimate and successively providin