High-resolution satellite-based precipitation data in near real-time

#precipitation data #semi-arid areas #water management #geostationary satellites #NASA GPM mission
The figure on the left shows an example of the results of the developed rain retrieval method for a scene over Iran from March 24, 2017 03:00 UTC. The unit is millimetres per hour. On the right, the results of the retrieval are compared with independent validation data from the GPM IMERG rainfall product over Iran in a scatter plot for the period from February 2017 to February 2018.
(a) Example of the satellite-retrieved rain rate for a scene from March 24, 2017 03:00 UTC. (b) Validation results for the developed method for satellite-based rainfall retrieval in Iran for the period February 2017 to February 2018. © Laboratory for Climatology and Remote Sensing, Philipps-Universität Marburg

High-resolution precipitation information in near real time is crucial for water management in semi-arid regions. Second-generation GEO systems are combined with the GPM IMERG precipitation product to generate regional precipitation information in high spatio-temporal resolution.

Precise high-resolution precipitation information in near real time is crucial for sustainable water management in semi-arid regions. This demand, however, contrasts with a decline of in-situ measurement equipment in meteorological networks worldwide. In this context, satellite-based precipitation products provide comprehensive precipitation information to overcome the limitations of the sparse observation network. Here, the created product combines the advantages of the second-generation GEO systems and the new GPM IMERG precipitation product using machine learning algorithms in order to generate regionally adapted precipitation information with a high spatio-temporal resolution.

The algorithm is based on the infrared bands of the GEO satellites. Random forest models were created with microwave-based GPM IMERG precipitation data as a reference in order to (i) delimit the precipitation area and (ii) estimate the rainfall rate. The method was validated with independent, microwave-based GPM IMERG precipitation data that was not used for model training. The validation results show the promising potential of the new satellite-based precipitation estimate. The algorithm was adapted for the GEO systems over the respective research regions in the SaWaM project (Iran, Brazil, Ecuador, Sudan and West Africa) and provides input for hydrological models in the research areas.

Water resource: Drinking water, Rainwater, Surface water
Type of product:
  • Management concepts & assessments
  • Modelling & software tools
Application sector: Agriculture, Natural water environment, Water resource management
Funding measure: GRoW
Project: SaWaM

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Logo Laboratory for Climatology and Remote Sensing - Philipps-Universität Marburg, Fachbereich Geographie
  • Laboratory for Climatology and Remote Sensing - Philipps-Universität Marburg, Fachbereich Geographie,
  • Deutschhausstrasse 12,
  • 35037 Marburg
www.lcrs.de

Laboratory for Climatology and Remote Sensing - Philipps-Universität Marburg, Fachbereich Geographie,
Marburg

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