Intercalibrated, Multi-Satellite Precipitation Data Near Real-Time
Available for Licensing
At A Glance
Researchers at Colorado State University have developed an algorithm capable of near real-time high-resolution precipitation data for Africa and northern Latin America (Mexico through Panama) and is available for other global regions for use in environmental studies such as infectious disease, food security, and for driving GIS tools that would utilize downscaled precipitation estimates from the satellite information. This data package is produced using a multisensory combination of several passive microwave precipitations algorithms available near real-time from NOAA.
What is unique about the product is the near-real time intercalibration capabilities of the Africa sector data, its enhanced resolution capabilities, and its ability to be ingested easily into various data systems. Furthermore, the system is applicable for any regional domain of data that the current system is able to produce, with the normal caveat that some regions will require special data handling to account for snow and ice conditions, but for tropical regions the system should not need further adjustment.
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Precipitation data is important and has many uses outside of agriculture. Having accurate rain rate data is critical for many research operations and environmental studies such as infection disease and food security. The National Oceanic and Atmospheric Administration (NOAA) has multiple satellites that produce this data via a multitude of sensors. This data is then released to the public for research related use.
The blended rain rate (xRR) product is a new development off-shoot of the NOAA operational blended precipitation rain rate product, that is now operationally produced at NOAA/NESDIS. The new improved product is higher resolution, and more timely in its production. Like it’s NOAA operational counter-part it is produced hourly by blending together recent rain rate retrievals from passive microwave instruments on six polar-orbiting satellites, including POES NOAA-18, NOAA-19 and Metop-A, and also DMSP F16, F17 and F18 (Kidder et al., 2012). The blended RR eliminates the bias between those data sets and provides a unified, meteorologically significant rain rate field for satellite analysts and weather forecasters. The product is generated with the latest 12 hours’ worth of rain rate retrievals from multi-satellites/algorithms and output with a Mercator projection. New code and script files were created to generate the product at 4 times the resolution of the original product (2x resolution in each spatial dimension), in addition the sectorization and remapping operations were confined to regional sectors for more accurate product generation and efficient transfer of the data sets (Jones et al., 2013 and Jones et al., 2014).
While the algorithms used for recalibrating and remapping the many satellite data sets are necessarily complex, the CSU Data Processing and Error Processing System (DPEAS) (also developed here) allows us to document the data processing succinctly, by noting the particular data processing scripts that are used to drive the algorithm product generation cycles. Each hour this process is repeated at CSU. Satellite data inputs are shared with our NOAA satellite experimental data processing projects, and all value-added product data generation is accomplished on the CSU premises through the CSU DPEAS processing system.
After core satellite swath orbital data processing and quality control checks are performed, a regional sector-dependent DPEAS script is run each hour, which:
- Generates the rain fall intercalibration data coefficient files;
- Performs a full reanalysis of the rain fall data;
- Remaps the data to the sector at the higher data resolution (5 minute – Lat/Long degree spatial resolution); and
- Composites the remapped data into an hourly product using the most recent recalibrated rain fall estimate.
These hourly high-resolution rainfall estimates are then transferred for further processing into daily aggregate totals using data analysis software – generating CSU’s hourly product, at the 5-minute resolution (an approximate 8-9 km grid spacing that varies with Earth latitude). Each time a new global sector is defined the DPEAS remapping and data processing algorithms process a data projection file which manipulates the data into the desired output projection space and creates further adjustments as designed into the software algorithms.
- Higher resolution than any current solution
- Near real time data is provided
- Data is easily exported into various data systems
- Applicable to any regional domain
- Infectious diseases migration research/tracking
- Biomedical research
- Satellite precipitation data products/research
- Food security research/tracking
- Environmental studies
Kidder, S.Q. and A.S. Jones 2007; A blended satellite total precipitable water product for operational forecasting. J. Atmos Oceanic technol., 24,74-81
The Use of a Portable Parallel Data Processing and Error Analysis System (DPEAS) for Technology Transition of Complex Multi-Satellite Data Fusion Algorithms into Operations Andrew S. Jones, CIRA/Colorado State Univ., Fort Collins, CO; and S. Q. Kidder, J. Forsythe, J. Zhao, P. Keehn, J. C. Davenport, and L. Zhao
Last updated: January 2021