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.
Download File:GPM Microwave Imager (GMI) Level 1B (L1B) Algorithm Theoretical Basis Document (ATBD) (Version 4)Keywords:Publication Date:02/01/2016
Download File:GPM Geolocation Toolkit Algorithm Theoretical Basis Document (ATBD)Publication Date:07/01/2012
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. The details of this software structure, inputs and outputs, are documented in the PPS Geolocation Toolkit Architecture and Design Specification Document (Bilanow, 2012). In addition, this document provides a description of how the sensor alignment angles for input to the algorithm are defined, and how these angles may be adjusted based on misalignments observed after launch.
Download File:CloudSat-GPM Coincidence Dataset Version 1CAuthor(s):Publication Date:07/25/2016
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. Examples include algorithm evaluation and
identification of deficiencies, snow and light rain sensitivity studies, cloud process studies, radiative transfer simulations, and studying land surface effects on the radar, radiometer, or combined-sensor precipitation retrieval algorithms.
Download File:Goddard Convective-Stratiform Heating (CSH) AlgorithmPublication Date:
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).
The CSH algorithm only requires information on surface precipitation rates, amount of stratiform rain, and the location of the observed cloud systems (i.e., land or ocean).
Download File:GPM/DPR Level-3 Algorithm Theoretical Basis Document (ATBD)Publication Date:08/01/2015
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.
The level 3 code is written so that the 15 or 16 orbits of level 2 DPR data produced daily can optionally be processed in two runs, where one output file contains statistics from the ascending orbital passes while the other file contains statistics from the descending passes. Since all orbits for the day are processed in each run, there is no need for intermediate files. What is produced are two daily level 3 HDF files. Nominally, the standard level 3 product will be obtained by processing the twice-daily HDF files over a calendar month; however, this is not required. In particular, output products can be generated from any set of daily HDF files. It should be noted that the daily files will contain a mean square statistic rather than the standard deviation. For the monthly (or multi-day) file, however, the mean square statistic will be replaced with the standard deviation.
Download File:GPM Combined Radar-Radiometer Precipitation Algorithm Theoretical Basis Document (ATBD) (Version 4)Publication Date:03/28/2016
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. Second, a global, representative collection of combined algorithm estimates will yield a single common reference dataset that can be used to “cross-calibrate” rain rate estimates from all of the passive microwave radiometers in the GPM constellation. The cross-calibration of radiometer estimates is crucial for developing a consistent, high time-resolution precipitation record for climate science and prediction model validation applications. Because of the Combined Algorithm’s essential roles as accurate reference and calibrator, the GPM Project is supporting a Combined Algorithm Team to implement and test the algorithm prior to launch. In the pre-launch phase, GPM-funded science investigations led to significant improvements in algorithm function, and the basic algorithm architecture was formulated. This algorithm architecture is largely consistent with the successful TRMM Combined Algorithm design, but it has been updated and modularized to take advantage of improvements in the representation of physics, new climatological background information, and model-based analyses that may become available at any stage of the mission. Post-launch, algorithm physical parameterizations for effects such as the non-uniform beamfilling of the radar footprint by rain and multiple scattering of radar pulses by ice-phase precipitation have been improved. This document presents a description of the GPM Combined Algorithm architecture, scientific basis, supporting ancillary datasets, inputs/outputs, and testing plan.
Download File:TMPA - IMERG ComparisonAuthor(s):Publication Date:08/31/2016
File Naming Convention for Precipitation Products For the Global Precipitation Measurement (GPM) MissionAuthor(s):Publication Date:10/14/2015
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 file naming conventions described in this document are applicable to all regular files intended for distribution to the public and routinely produced by the PPS. These file naming conventions are also intended to apply to files produced or reprocessed from the Tropical Rainfall Measuring Mission (TRMM) satellite during the period of GPM operations.
These file naming conventions are not required to be used by any other GPM partner. Each partner may adopt the file naming convention most appropriate for their needs and systems. PPS does not rename files provided by partners.
Download File:Validation Network Data Product User’s Guide - Volume 2 (GPM)Author(s):Document Type:Publication Date:11/16/2015
This document provides a basic set of documentation for the data products available from the GPM Ground Validation System (GVS) Validation Network (VN). In the GPM era the VN performs a direct match-up of GPM’s space-based Dual-frequency Precipitation Radar (DPR) data with ground radar data from the U.S. network of NOAA Weather Surveillance Radar-1988 Doppler (WSR-88D, or “NEXRAD”). Ground radar networks from international partners are also part of the VN. The VN match-up will help evaluate the reflectance attenuation correction algorithms of the DPR and will identify biases between ground observations and satellite retrievals as they occur in different meteorological regimes. Volume 2 of the Validation Network Data User’s Guide describes the GPM core and constellation VN data set.
An earlier version of the VN capability performed a match-up of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data with ground-based radar (GR) measurements from a subset of the WSR-88D sites now included in the GPM-era VN operational radar data network. Legacy TRMM data and their matching GR observations will continue to be part of the VN operations in the GPM era. Refer to Volume 1 of the Validation Network Data User’s Guide for a description of the legacy TRMM-specific VN data set.
Download File:2014 PMM Science Team Meeting Summary from the Earth Observer, November 2014Author(s):Document Type:Publication Date:11/01/2014
This excerpt from the November 2014 edition of The Earth Observer provides a summary of the activities at the PMM Science Team Meeting which took place from August 4 - 7, 2014. The PMM program supports scientific research, algorithm development, and ground-based validation activities for the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory that launched on February 27, 2014. The meeting opened with a special memorial session dedicated to Arthur Hou, the former GPM Project Scientist, who passed away November 20, 2013. Hou’s friends and colleagues remembered him as an exceptional scientist and leader who was able to build and navigate the international relationships that got the GPM mission off the ground.