1. TAMSAT African Rainfall Climatology And Time-series
TARCAT
Ross Maidment
Elena Tarnavsky
David Grimes
Richard Allan
TAMSAT Research Group,
Department of Meteorology, University of Reading

2. Need for rainfall time-series for Africa
Better understanding of rainfall climate trends and variability
Improving rainfall climate change prediction
Improving seasonal forecasting
Better calibration and validation of applications
crop yield forecasts
hydrological forecasts
health monitoring
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3. Need for rainfall time-series for Africa
Requirements of rainfall dataset:
full area coverage
unbiased
long time-series
temporally homogeneous (same algorithm, calibration and data input over time)
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4. Need for rainfall time-series for Africa
Global Precipitation Climatology Centre (GPCC)
- gauge based only
- various products, some dating back to early 20th Cen.
- spatially interpolated rain maps
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5. Current existing rainfall datasets
Satellite based datasets
Product
Spatial
Res
Temp Res
Start
End
Input
Comment
GPI
0.050
day
1979
---
TIR
Bias over land
GPCP
2.50
month, pentad
TIR+PMW+gge
Inhomogeneous
GPCP 1DD 10
1997
Inhomogeneous +
short
CMAP
month
2004
NWP
NOAA RFE2
0.10
2001
NOAA ARC
1995
TIR+gge
TRMM 3B42
0.250 3h
1998
TIR+PMW+PR
TRMM 3B43
3B42+gge
CMORPH
0.070
0.5h
2002
PMW+TIR
Short
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6. TARCAT project
Use Meteosat TIR imagery to identify cloud top temperature threshold Tt distinguishing between rain and no rain
Calculate Cold Cloud Duration (CCD = length of time cloud top is colder than Tt ) for each pixel
Estimate rain amount as rain = a0 + a1 CCD
a0, a1, Tt calibrated against local gauges using historic data
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7. TARCAT project method Overall plan
Acquire Meteosat TIR archive from EUMETSAT (1983 to present)
Extend spatial coverage of the operational calibration to all of Africa
Generate dekadal rainfall time series
Recover failed dekads where possible
Validate TARCAT using gauge data
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8. Meteosat TIR data Problems encountered: Missing data files
Corrupt data files (missing pixels, lines)
Missing calibration data in file headers
Using imagery from non-prime satellites
Inconsistent TIR absolute calibration over time due to change in operational satellite
Change in radiance definition in May 2008
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9. Meteosat TIR data availability
MISSING
DATA
< 180 min
< 360 min
> 360 min
(CCD failed)
Dekad
Failed
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10. TAMSAT Calibration zones - MARSOP project
September
Different zones shown by different shading
Zone map is different for each month but remains the same between years

11. TARCAT validation - Ethiopia
18 gauges with dekadal data over 5 years
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12. TARCAT validation - Ethiopia
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13. TARCAT validation - Ethiopia
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14. Climate trends in Ethiopian rainfall – Initial results
6 Month & Annual Running Mean – Mean Monthly Rainfall
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15. Climate trends in Ethiopian rainfall – Initial results
Linear regression 1983 – 2009
TARCAT
mm/dekad/
dekad
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16. Applications of TARCAT
Climatological research
investigating variability and trends
Improvements in NWP modelling
eg long-term rainfall trends
local climate comparisons and research
Seasonal forecasting
improved calibration of statistical forecasts
improved understanding of teleconnections
Other applications
Calibration, validation of hydrological, agricultural, health models
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17. MARSOP project JRC FOODSEC
Current contract with JRC to provide data for MARS bulletins on food security for countries in East Africa
TARCAT data set will allow rainfall estimates to be seen against background of change and variability over the last 28 years
Aim to work with NMSs to generate joint bulletins
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19. TARCAT workshops
Use gauge data from NMS to improve calibration for that region
Use improved calibration to generate 30 year time series for the NMS
So far workshops in Ethiopia and Uganda (including Sudan and SWALIM)
Currently seeking funding for further workshops for African NMSs
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20. Conclusions
For better understanding of African rainfall climate, we need a time series which is
temporally homogeneous,
full spatial coverage
The TAMSAT methodology can generate such a time series
Current situation: preliminary time series completed
Next steps
gap filling of missing dekads (ongoing)
validation against gauge data
improved calibration based on collaborative workshops with African NMSs
Future applications: climatology, hydrology, agriculture, health
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21. Any Questions?
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Photo courtesy NASA Earth Observatory

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23. TAMSAT methodology (1)
The TAMSAT approach to rainfall estimation is based entirely on Meteosat Thermal Infra-Red imagery to identify precipitating cumulonimbus clouds (deep convection)
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24. Using Meteosat TIR imagery, identify optimum cloud top
temperature threshold Tt distinguishing between rain and no rain
TAMSAT methodology (2)
Calculate Cold Cloud Duration (CCD) for each pixel (length of time
cloud top is colder than Tt )
Estimate rainfall total as rain = a0 + a1 CCD
a0, a1, Tt 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 – ° (sat pix)
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25. TAMSAT operational product
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26. Meteosat orbit
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27. EM spectrum – atmospheric transmission
Terrestrial radiation reaching top of atmosphere
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28. EM spectrum – atmospheric transmission
Terrestrial radiation reaching top of atmosphere
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29. Using satellite data
Satellite radiometer measure upwelling long-wave terrestrial radiation (thermal energy called radiance) and converts to an electrical signal and stores as counts
2. These radiometric counts are converted to actual radiances using vicarious or black-body calibration method
Using Planck’s Law, radiances are converted to brightness
temperature
SATOP (TAMSAT rainfall estimation software) carries out steps 2 and 3.
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30. TARCAT validation - Ethiopia
Why Ethiopia?
- TAMSAT involved in joint project (with IRI, Columbia University and NMA, Ethiopia) (Dinku et al 2011)
- Complex and interesting rainfall climate
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31. Climate trends in Ethiopian rainfall – Initial results
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32. Investigating effect of change in satellite
Select a warm scene that experiences little variation in surface temperature – tropical ocean
Region needs to typically cloud free to allow frequent measurements to be taken of the sea surface temperature (clear sky radiance)
2009 accumulated visible image from Meteosat VIS channel
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33. Investigating effect of change in satellite
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34. Investigating effect of change in satellite
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35. TARCAT validation - Ethiopia
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36. Climate trends in Ethiopian rainfall – Initial results
Period: 1989 – 2009
TAMSAT
ERA-Interim
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37. Investigating effect of change in satellite
Preliminary tests suggest that the changing biases in temperature retrieval DO NOT significantly affect rainfall
This is primarily because the TIR temperature is only used to define a threshold - it is not used directly to calculate rainfall amount
The effect on other algorithms that use TIR temperature directly to infer rainfall may be significant
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38. TARCAT project 1983 to present
Application of TAMSAT methodology creates a long term time series of rainfall estimates for sub-Saharan Africa with
full spatial coverage
temporally homogeneity
nominal resolution at °
1983 to present
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39. Meteosat prime satellite - period of operation
TAMSAT TIR archive
Meteosat prime satellite - period of operation
Meteosat 2 – 7
Met. First Generation (MFG)
Frequency: 30 minutes
Format: OpenMTP
Meteosat 8 – 9
Met. Second Generation (MSG)
Frequency: 15 minutes
Format: Native
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40. Overview Need for rainfall time-series for Africa
Current existing rainfall datasets
TAMSAT methodology
TARCAT project
Satellite data retrieval
7. TARCAT validation
8. Applications
9. Workshops
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