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 radar products provide daily and monthly global statistics of the Level-2 Ku, Ka and DPR products on a latitude-longitude grid. In version 7 (V07), the organization of the products has changed with the highest-level categorization into FS (full swath), MS (matched or inner swath) and HS (high sensitivity) swath products. The next level of division is into high and low spatial resolution grids that are defined such that the low-resolution grid (G1) is 50×50 (lat×lon) while the high-resolution grid (G2) is 0.250× 0.250.
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).
