150 likes | 253 Views
Learn about the importance of satellite rainfall estimates for crop monitoring and food security in the SADC region. Explore various algorithms used to estimate rainfall from meteorological satellites, microwave sensors, and CCD images. Discover the advancements in rainfall estimation technology for accurate predictions.
E N D
R. Tsheko, M. Tapela, N.J. Batisani Botswana College of Agriculture Crop and Rangeland Monitoring Agriculture Service
Agenda SADC THEMA Services Course Content • Rainfall Products
Rainfall products • What are RFEs?
Rainfall products • Most of the developing world and huge water bodies like the ocean do not have adequate rainfall gauge networks and weather radars coverage Global telecommunication stations
Rainfall products • Meteorological satellites are then only source of precipitation information to fill in the gaps. • Characterization of rainfall variation in time and space is very important for crop monitoring for food security in the SADC region • Satellite rainfall estimates could be fed into crop yield models
Rainfall products • remote sensing systems unlike rain gauges, do not directly measure rainfall but rather rely indirectly measure the precipitation based on cloud reflectance, cloud-top temperature, and on the presence of frozen precipitation to estimation rainfall intensity
Rainfall products • There are several different types of satellite rainfall estimation algorithms developed and these may use, only infrared (IR) data; passive microwave (PM) data; combination of IR and PM; assimilate available real-time gauge data and precipitation data onboard the Tropical Rainfall Measuring Mission (TRMM) satellite
Rainfall products • The GOES Precipitation Index (GPI) is based on the thermal infrared information of the clouds (temperature of clouds-top is below 235 K and is assigned a rain rate of 3 mmhr-1 ). • MeteoSat data used to composite a CCD image at -38oC, a rainfall estimate is generated from the CCD using the GOES Precipitation Index (GPI) where GPI = CCD x 3
Rainfall products • Microwave sensors are used to estimate rainfall based on the emmissivity of the cloud (ice particles and large rainfall drops reduces emmissivity hence reduces cloud brightness temperature) • The Tropical Applications of Meteorology using SATellite (TAMSAT) group at Reading produced daily CCD (Cold Cloud Duration) maps from raw Meteosat thermal infra-red (IR) images using a -38oC threshold temperature.
Rainfall products • The Tropical Applications of Meteorology using SATellite (TAMSAT) group at Reading produced daily CCD (Cold Cloud Duration) maps from raw Meteosat thermal infra-red (IR) images using a -38oC threshold temperature. CCD – cold cloud duration Created from Thermal &IR images (Met Sats)
Rainfall products • The NOAA Climate Prediction Center (CPC) used the Rainfall Estimation (RFE 1.0) algorithm from 1995 to 2000 • In 2001, the Rainfall Estimation Algorithm Version 2 (RFE 2.0) replaced REF 1.0 on operation status. • The REF 2.0 significantly reduced bias and random error hence increases the accuracy of the rainfall estimates.
Rainfall products • REF 2.0 ingests four (4) different data sets namely; • 1) Daily Global Telecommunications Station (GTS) rainfall gauge data for up to 1000 stations • 2) AM SU microwave precipitation estimates • 3) Special Sensor Microwave/Imager (SSM/I) satellite rainfall estimates and • 4) GPI cloud-top IR temperature estimates.
The END Thank You.