1. T ropical A pplications of M eteorology using SAT ellite data Ross Maidment r.i.maidment@pgr.reading.ac.uk TAMSAT Research Group, University of Reading Supervisors: Dr David Grimes and Dr Richard Allan

2. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

3. T ropical A pplications of M eteorology using SAT ellite data 1. Introduction Most of the African continent is economically dependent on rain fed agriculture, therefore changes in expected rainfall patterns can have serious consequences, both economically and from a humanitarian point of view However, future predictions must be based on a secure knowledge of the present rainfall climate The current African rainfall climatology is poorly understood, mainly due to inadequate ground based observations GPCC: Gauge locations – 2007, March - May

4. T ropical A pplications of M eteorology using SAT ellite data Introduction Cont... Satellite based rainfall data sets exist but many, such as the TRMM based algorithms only cover short time periods Those covering longer time periods, such as the widely used GPCP product tend to be a blend of different satellite sensors and gauge data, the proportions of which vary from year to year This makes these data sets unsuitable for climate studies as it is impossible to extract meaningful trends and statistics when biases vary interanually

5. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

6. T ropical A pplications of M eteorology using SAT ellite data 2. Aims of my research 1. To create TARCAT (TAMSAT African Rainfall Climatology And Time-series), a temporally homogeneous 30 year rainfall time-series and climatology for all of sub- Saharan Africa using the TAMSAT approach to rainfall estimation 2. Analyse this data set to better understand temporal trends and statistics that describe the rainfall climate 3. Evaluate model data to increase confidence in future predictions

7. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

8. T ropical A pplications of M eteorology using SAT ellite data 3. TAMSAT Methodology The TAMSAT approach to rainfall estimation is based entirely on Meteosat Thermal Infra-Red imagery to identify precipitating cumulonimbus clouds (deep convection)

9. T ropical A pplications of M eteorology using SAT ellite data TAMSAT Methodology Cont... Calculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than T t ) Estimate rainfall total as rain = a 0 + a 1 CCD a 0, a 1, T t are calibrated using local gauges from historic data Calibration parameters vary in space and time (i.e. local calibration) Resolution: Temporal – 10 days (1 dekad), Spatial – 0.05° (sat. pixel) Using Meteosat TIR imagery, identify optimum cloud top temperature threshold T t distinguishing between rain and no rain

10. T ropical A pplications of M eteorology using SAT ellite data TAMSAT Operational Product

11. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

12. T ropical A pplications of M eteorology using SAT ellite data 4. Rainfall Validation in Uganda Validating rainfall products against spatially interpolated gauge data (res: 0.5° x 0.5°) Period of study: 2001 to 2005 for first rainy season (Feb – June) Figure: spatially averaged dekadal rainfall ERA-40ERA-Interim TAMSATRFE 2.0 GPCP

13. T ropical A pplications of M eteorology using SAT ellite data Rainfall Validation in Uganda Cont... Temporal variability using 2003 and 2004 data

14. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

15. T ropical A pplications of M eteorology using SAT ellite data 5. Meteosat TIR data Meteosat archive spans 30 years, 1980 to present, giving complete African coverage every 30 minutes (First Generation) and every 15 minutes post June 2006 (Second Generation) Problems with the TIR archive: 1. Archive isn’t complete, with some gaps from one slot up to several days (these gaps will be interpolated)

16. T ropical A pplications of M eteorology using SAT ellite data Meteosat TIR data - Problems OK< 6 hours< 36 hours> 36 hours

17. T ropical A pplications of M eteorology using SAT ellite data Meteosat TIR data - Problems 2. Change in satellite sensor introducing artificial temporal discontinuities in time-series - Timeline of warmest/brightest pixel over a ‘cloud free’ region of the Equatorial Atlantic to detect sudden changes:

18. T ropical A pplications of M eteorology using SAT ellite data Meteosat TIR data - Problems Satellite (Meteosat 2 – 9) Daily warmest pixel – warm ocean scene

19. T ropical A pplications of M eteorology using SAT ellite data Meteosat TIR data - Problems MET-7MET-8/9MET-6MET-7 Satellite (Meteosat 2 – 9) Daily warmest pixel – warm ocean scene

20. T ropical A pplications of M eteorology using SAT ellite data Meteosat TIR data - Problems Other problems: 3. Changes in data format from MFG (OpenMTP) to MSG (Native) 4. Corrupt images (missing pixels, lines and whole images) 5. Failure in reading the header of some OpenMTP files 6. Change in radiance definition from ‘spectral’ to ‘effective’ radiance in May 2008 7. Using data obtained from the EUMETSAT archive that is from the non-prime satellite

21. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

22. T ropical A pplications of M eteorology using SAT ellite data 6. Initial results - Ethiopia Why Ethiopia? - TAMSAT involved in joint project (with IRI, Columbia University and NMA, Ethiopia) based on Ethiopia and funded by Google (Dinku et al 2011) - Complex and interesting rainfall climate Have compared (so far): TAMSAT GPCP ERA-Interim

23. T ropical A pplications of M eteorology using SAT ellite data Initial Results – Ethiopia Cont...

24. T ropical A pplications of M eteorology using SAT ellite data Initial Results – Ethiopia Cont... Mean dekadal rainfall

25. T ropical A pplications of M eteorology using SAT ellite data Initial Results – Ethiopia Cont... Period: 1989 – 2009

26. T ropical A pplications of M eteorology using SAT ellite data Initial Results – Ethiopia Cont... 6 Month & Annual Running Mean – Mean Monthly Rainfall

27. T ropical A pplications of M eteorology using SAT ellite data Initial Results – Ethiopia Cont... TARCAT – Trend during 1983-2009 period

28. T ropical A pplications of M eteorology using SAT ellite data Overview 1.Introduction 2.Aims of my research 3.The TAMSAT methodology to estimate rainfall 4.Validation of rainfall estimates in Uganda 5.Meteosat satellite data 6. Initial results – Ethiopia 7. Future work and Conclusions

29. T ropical A pplications of M eteorology using SAT ellite data 7. Future work and Conclusions Complete TARCAT and extend study to all of Africa Main interests: trends over the last 30 years changes in length and start of wet season changes in wet/dry regimes changes in frequency of extreme events Evaluate model data The TAMSAT methodology and the Meteosat archive provides the opportunity to create a unique and consistent data set that can enhance our current understanding of the African rainfall climate and hopefully increase our confidence in future predictions

30. T ropical A pplications of M eteorology using SAT ellite data Thank you for your time Photo courtesy NASA Earth Observatory