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AMESD eStation users’ training N 08. Precipitations products from RS Marco Clerici, JRC/IES/GEM

AMESD eStation users’ training N 08. Precipitations products from RS Marco Clerici, JRC/IES/GEM. TAMSAT product (UK) FEWSNET product (USA) MPE product (EUMETSAT). Contents. TAMSAT Operational Rainfall Monitoring for Africa. David Grimes TAMSAT* Dept of Meteorology University of Reading

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AMESD eStation users’ training N 08. Precipitations products from RS Marco Clerici, JRC/IES/GEM

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  1. AMESD eStation users’ trainingN 08. Precipitations products from RSMarco Clerici, JRC/IES/GEM

  2. TAMSAT product (UK) FEWSNET product (USA) MPE product (EUMETSAT) Contents

  3. TAMSAT Operational Rainfall Monitoring for Africa David Grimes TAMSAT* Dept of Meteorology University of Reading U.K. TAMSAT = Tropical Applications of Meteorology using SATellite data

  4. Use Meteosat TIR imagery Identify cloud top temperature threshold Tt distinguishing between rain and no rain Calculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than Tt ) Estimate rain amount as rain = a0 + a1 CCD a0, a1, Tt are calibrated against local gauges using historic data Calibration parameters vary in space and time TAMSAT algorithm

  5. TIR image 18:00, 23/07/08

  6. Comparison of Satellite rainfall estimates and gauge data over the Sahel July dekad 2 2004 Met office – Raingauge anomaly TAMSAT – Raingauge anomaly Met office – TAMSAT anomaly 23/08/2014

  7. TAMSAT Calibration zones August Calibration zones vary slightly from month to month Differently shaded areas show the different zones for August

  8. TAMSAT operational estimates No of gauges for calibration = 4569 Format: idrisi or geotiff Projection = lat long Nominal resolution = 0.03750

  9. Dekad 2 August, 2009 9

  10. Validation of rainfall estimates Question: How do we know if rainfall estimates are any good? Answer: compare against independent data set. For Africa, this usually means comparison against raingauge data

  11. Use of gauge measurements for validation Validation in Africa is problematic because of the lack of ground-based observations JRC in collaboration with IPWG have produced a set of guidelines for African validation Main recommendations: use geostatistical methods to convert gauge data to pixel (or larger) scale specify minimum number of gauges per grid square take account of uncertainty of gauge data as areal estimator + gauges = + + + +

  12. Calibration currently being updated and extended as part of the MARSOP3 project Additional raingauge data provided by Meteoconsult New calibration Old calibration Improved calibration

  13. Continental product

  14. TAMSAT algorithm provides good quality rainfall estimates for most of Africa The approach is successful because of careful calibration against local gauge data Current calibrations are being extended to cover all Africa + Arabian peninsula for all months Operational products being used by JRC, Agrhymet, Uganda, Sudan, Ethiopia Current research includes 30 year time climatology and time series improved algorithm using all MSG channels ensemble estimation of uncertainty applications to crop yield and hydrology TAMSAT: Conclusions

  15. The Climate Prediction CenterRainfall Estimation Algorithm Version 2Tim Love -- RSIS/CPC

  16. Run daily at NOAA CPC for Africa, southern Asia, Afghanistan area domains The Overall schema is: Use satellite IR temperature data (MSG) for the GOES Precipitation Estimate (GPI) gauge fields (via GTS) Use microwave precip estimates (SSM/I & AMSU-B), Combine the above products into RFE 2.0 RFE 2.0 Overview

  17. Resultant field = cold cloud duration (CCD) @ 0.1° resolution (about 10 km) CCD used for GOES Precipitation Index (GPI) calculation Meteosat Data • GPI tends to overestimate spatial distribution but underestimates convective precipitation

  18. GPI Estimate CPC RFE 2.0

  19. 2534 stations available daily Only 400-800 report daily Few reports from Nigeria, none from Liberia, Sierra Leone Data ingested from GTS line, Quality Controlled, fed to operational machine, then gridded to 0.1° resolution file Other station data may be readily used as input to algorithm via changing 2 tables in base code Requirements for RFE processing: GPI and GTS inputs GTS Data GTS = Global Telecommunication System

  20. GTS Inputs

  21. GTS vs GPI

  22. Special Sensor Microwave/Imager a seven-channel, four-frequency, linearly polarized passive microwave radiometric system The instrument is flown onboard the US Air Force DMSP spacecraft SSM/I: definition

  23. 2 instruments estimate precip twice daily ~6 hourly data frequency Fails to catch other rainfall in temporal gaps Data needs only small conversion in preparation for input to algorithm SSM/I Inputs

  24. Advanced Microwave Sounding Unit 15-channel microwave radiometer installed on NOAA polar orbiting satellites. AMSU-B definition

  25. As with SSM/I, data is available 4 times daily, staggered temporally Tends to overestimate most precip, but does well with highly convective systems Data sent in HDF format, thus needs to be deciphered before input to RFE algorithm Preprocessing straightforward AMSU-B Data

  26. AMSU-B Estimate

  27. TAMSAT – FEWSNET2nd dekad Nov 2009

  28. Multi-sensor Precipitation Estimate (MPE): An operational real-time rain-rate product Thomas Heinemann Meteorological Operations Division EUMETSAT thomas.heinemann@eumetsat.int

  29. MPE Algorithm : used data Geostationary satellite data: METEOSAT IR - data from the operational METEOSAT satellites: 0° (currently MET-9) and 57° East (currently MET-7) and RSS (currently MET-8) Polar orbiting satellite data: SSM/I and SSMIS passive microwave data from currently 2-3 of the American DMSP satellites on a sun-synchronous orbit: DMSP13: (SSM/I) DMSP15: (SSM/I), when undisturbed DMSP16: (SSMIS), pre-processed with SSMISPP

  30. LUTs build Create LUT 3 SSM/I data METEOSAT data Rain Rate (mm/h) IR Brightness Temperature Rainrate (mm/h) Latitude IR Brightness Temperature(METEOSAT) Longitude Algorithm overview Temporal and spatial co-location 2 Store in the corresponding geographical box during a certain period of accumulation 1 (RR,TIR) 4 Co-located data Derive the product on IR- pixel level

  31. MPE: a real-time precipitation algorithm

  32. Validation against radar

  33. Precipitation intensity & soil erosion / degradation

  34. It is estimated that the erosion impact on the global scale is between 15 to 30 t/ha/yr, which equals 1 to 2 mm/yr soil loss. As a reference: Introduction à la gestion conservatoire de l'eau, de la biomasse » (http://www.fao.org/docrep/T1765F/t1765f0d.htm) Impact of rain

  35. Relation between precipitation rates and Ek

  36. Influence of season, max intensity in 30 minutes, and rains during preceding dekad (h 10 jours = soil humidity index) on erosion and water flow due by similar rain amounts on bare and vegetation covered soils (Roose, 1973)

  37. Indonesia: I=86.517*D–0.408 I: intensity P (mm/day), D: rain duration (nb days) Problems if P > 80 mm/j for 3-5 days sustained rains Azores: I=144.06*D–0.5551 problems if 78 > P > 144 mm/j for 1-3 days Relations between Intensity and Duration

  38. 72 h 1.5h Rainfall erosion power index Disaster Prevention System Department, Fukken Co, Japan

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