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

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 23/11/2018

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) 23/11/2018

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 23/11/2018

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 23/11/2018

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 23/11/2018

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 23/11/2018

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 23/11/2018

Meteosat TIR data availability MISSING DATA < 180 min < 360 min > 360 min (CCD failed) Dekad Failed 23/11/2018

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

TARCAT validation - Ethiopia 18 gauges with dekadal data over 5 years 23/11/2018

TARCAT validation - Ethiopia 23/11/2018

TARCAT validation - Ethiopia 23/11/2018

Climate trends in Ethiopian rainfall – Initial results 6 Month & Annual Running Mean – Mean Monthly Rainfall 23/11/2018

Climate trends in Ethiopian rainfall – Initial results Linear regression 1983 – 2009 TARCAT mm/dekad/ dekad 23/11/2018

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 23/11/2018

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 23/11/2018

23/11/2018

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 23/11/2018

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 23/11/2018

Any Questions? 23/11/2018 Photo courtesy NASA Earth Observatory

23/11/2018

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) 23/11/2018

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 – 0.0375° (sat pix) 23/11/2018

TAMSAT operational product 23/11/2018

Meteosat orbit 23/11/2018

EM spectrum – atmospheric transmission Terrestrial radiation reaching top of atmosphere 23/11/2018

EM spectrum – atmospheric transmission Terrestrial radiation reaching top of atmosphere 23/11/2018

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. 23/11/2018

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 23/11/2018

Climate trends in Ethiopian rainfall – Initial results 23/11/2018

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 23/11/2018

Investigating effect of change in satellite 23/11/2018

Investigating effect of change in satellite 23/11/2018

TARCAT validation - Ethiopia 23/11/2018

Climate trends in Ethiopian rainfall – Initial results Period: 1989 – 2009 TAMSAT ERA-Interim 23/11/2018

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 23/11/2018

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 0.0375 ° 1983 to present 23/11/2018

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 23/11/2018

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 23/11/2018