Comparison of satellite rainfall estimates with raingauge data for Africa

Transcription

1 Comparison of satellite rainfall estimates with raingauge data for Africa David Grimes TAMSAT Acknowledgements Ross Maidment Teo Chee Kiat Gulilat Tefera Diro TAMSAT = Tropical Applications of Meteorology using SATellite data 23/04/2008 1

2 Use of raingauge data to evaluate satellite-based rainfall estimates for Africa David Grimes TAMSAT Acknowledgements Ross Maidment Teo Chee Kiat Gulilat Tefera Diro TAMSAT = Tropical Applications of Meteorology using SATellite data 23/04/2008 2

3 Use of raingauge data to evaluate satellite-based rainfall estimates for Africa David Grimes TAMSAT Acknowledgements Ross Maidment Teo Chee Kiat Gulilat Tefera Diro TAMSAT = Tropical Applications of Meteorology using SATellite data 23/04/2008 3

4 Overview of talk The importance of rainfall monitoring for Africa Ground based observations Alternative methods The validation problem Case studies Ensemble approach Conclusion 23/04/2008 4

5 improved short range rainfall forecast How can meteorology help Africa? improved seasonal forecast start end dry spells total improved crop yields timely flood warning famine mitigation better understanding of rainfall mechanisms improved knowledge of current climate improved climate forecast better crop selection improved nutrition better risk management better long term economic planning 23/04/2008 5

6 improved short range rainfall forecast better understanding of rainfall mechanisms How can meteorology help Africa? improved seasonal forecast improved knowledge of current climate better rainfall measurements start end dry spells total improved climate forecast improved crop yields better crop selection timely flood warning famine mitigation improved nutrition better risk management better long term economic planning 23/04/2008 6

7 Current state of African rainfall measurement Africa raingauge station density = 1:26,000 km 2 8x lower than WMO recommended minimum 23/04/2008 7

8 African rainfall variability Spatial variability of rainfall events, Ibecetene Niger mean = 15.7mm mean = 49.8mm 23/04/2008 8

9 Alternatives to raingauge data Radar Satellite NWP model - virtually non-existent in Africa for technical + economic reasons - cheap, full area coverage but no perfect algorithm - cheap, full area coverage but problems with representation of African rainfall 23/04/2008 9

10 Satellite rainfall estimates Example: TAMSAT operational product Seasonal total Oct Mar 2008 Seasonal anomaly Oct Mar /04/

11 How can we evaluate the rainfall estimates? Problems lack of independent data for validation high spatial variability of rainfall g Useful protocol Convert satellite and gauge data to same spatial scale Focus on areas with sufficient gauges Assess uncertainty on raingauge areal estimates mean = 49.8mm 23/04/

12 Comparison of satellite and gauge data conversion to pixel scale by kriging z 1 r 1 z A r 3 r 2 A z 3 z t i i i= 1 n i= 1 = n λ = 1 i λ z ; z 2 Rainfall z A for block A is computed as a weighted sum of surrounding gauge observations using block kriging Weights λ i depend on distance from pixel r i and strength of correlation at that distance Kriging provides an uncertainty estimate for each gauge-pixel value 23/04/

13 Comparison of pixel average v. raw gauge data for Zambia y = 0.53x + 3.3x R 2 = 0.87 Zambian rainfall daily data pixel/mm gge/mm 23/04/

14 Daily calibrations using pixel and gauge data for Zambia, March (10 gauges) Calibration Zambia March Daily mean 10 gauges (all data) y = x R 2 = R raw gauge v RFE gge/mm CCD/h Calibration Zambia March Daily mean 10 gauge pixels (all data) kriged gauge-pixel v RFE pixgge/mm R 2 = 0.59 y = 0.566x R 2 = /04/ CCD/h

15 Evaluation of TAMSAT approach over Sahel Dekadal rain gauge data interpolated to grid using o data from Agrhymet (AMMA project ~ 600 gauges) o data from GPCC (60 gauges) AMMA 1.0 deg grid points latitude/deg longitude/deg 23/04/

16 TAMSAT dekadal 1 0 estimates v gauge data (July 2004) TAMSAT RFE/mm TAMSAT (mm) y = x R 2 = GPCC/mm GPCC (mm) 700 TAMSAT RFE/mm TAMSAT (mm) y = x R 2 = AMMA/mm Krig (mm) (mm). GPCC/mm y = x R 2 = /04/ Krig (mm) AMMA/mm

17 Validation of ERA40/NCEP rainfall climatology for Ethiopia I II NCEP ERA40 Gauge III IVa IVb V 23/04/

18 Ensemble approach to rainfall estimate uncertainty Plot gauge-pixel values against rainfall estimates (rfe) Subtract proportion of scatter caused by uncertainty on gauge pixel values Use statistical spread for each rfe to generate an ensemble of rainfall fields taking into account expected spatial correlation gge-pixel/mm rfe/mm 23/04/

19 Ensemble approach to rainfall estimate uncertainty Mean rainfall Frequency histogram for North Bank Division (400 simulations) Mean rainfall and 8 ensemble members, 7th August, /04/

20 Conclusions There is no such thing as rainfall ground truth For meaningful evaluation, rainfall estimates from different sources must be compared at the same spatial scale Uncertainty on validation data should be taken into account With care, even sparse data sets can be used to evaluate satellite or NWP estimates Use of an ensemble method facilitates area averaging and allows uncertainty to be propagated into downstream models (eg crop yield) 23/04/

21 Crop yield forecasting for Ethiopia Current project with Ethiopian National Met Agency (NMA) to predict maize yield in Oromiya province NMA has provided daily data from 200 gauges + crop yield data 23/04/

22 Validation general points Problem with using gauge data mismatch of spatial scale + gauge Target pixel Signal from this area Usual approach: interpolate gauge data to give area averages at pixel (or coarser) scale A geostatistical approach (kriging) is usually best because o uses information on spatial correlation of rainfall o gives error estimate for each interpolated value Only use gridboxes with a minimum number of gauges (eg 1) 23/04/

23 TAMSAT operational algorithm Use Meteosat TIR imagery Identify cloud top temperature threshold T t distinguishing between rain and no rain Calculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than T t ) Estimate rain amount as rain = a 0 + a 1 CCD a 0, a 1, T t are calibrated against local gauges using historic data Calibration parameters vary in space and time 23/04/

24 Thermal Infra-red (TIR) image from Meteosat 9 Images every 15 minutes 3 km resolution 23/04/

25 Country Examples of additional data in African Met Services GTS weather GTS rain Other real-time weather Other real time rain Algeria Ethiopia >50 >50 The Gambia Libya Nigeria Zambia Other stations (African Climate Report, DFID DEFRA, December 2004) 23/04/

26 Ethiopian gauge locations 14 gd mk 37 gauges (synoptic and reliable secondary) 12 bd cm ml Latitude ( degree) nj gr nk bl bg dm wl jm sk ho sd am aa zq as dl yc hm mj sn ku ng aw at gs ro gi dd ji go kd 4 mo /04/ Longitude ( degree)

27 Validation Study 2 Comparison of algorithms over Ethiopia June - Sept 2000 to 2004 by Tufa Dinku at IRI Dekadal totals evaluated for 1 0, 0.5 0, gridboxes Validation data comprises 120 gauges interpolated geostatistically to gridbox averages Minimum gauge density as in table Box size/ 0 Min gauges /04/

28 Validation Study 2 - map 23/04/

29 Validation Study 2 - details Comparison between TAMSAT, CPC, CMORPH, GPCP, TRMM-3B42 GPCP merges PMW, TIR and gauge data TRMM-3B42 merges PMW, TIR and gauge data calibrated with TRMM radar CMORPH uses available PMW images and interpolates between them using Meteosat TIR 23/04/

30 Validation Study 2 - results CPC-RFE GPCP N=1020 CPC- RFE GPCP 3B42 TAM CC TRMM-3B42 TAMSAT Efficiency Bias ME RMSE resolution, dekadal total, /04/

31 GPCC grid squares containing gauges, August 2004 No of gauges = 57 23/04/

32 AMMA grid squares containing gauges, August 2004 No of gauges = 800 Interpolation by kriging 23/04/

33 Monthly anomaly Validation Study 1 Geographical variations Normalised monthly anomaly (normalised with respect to the kriging std error) June July Aug Sept 2σ 1σ K > 2σ <1s.e. K K 1 to 2s.e. >2s.e. 23/04/

34 Current state of African rainfall measurement Spatial distribution of monthly in situ precipitation stations with at least 10 years of data in GPCC Data base 23/04/

35 TAMSAT dekadal 1 0 estimates v gauge data (2004) 600/mm July 0 600/mm Aug Sep TAMSAT RFE/mm GPCC (all squares) GPCC (only squares with gauges) AMMA 23/04/

36 TAMSAT dekadal 1 0 estimates v gauge data (July 2004) July Aug TAMSAT (mm) TAMSAT RFE/mm TAMSAT (mm) 600 y = x R 2 = GPCC (mm) y = x R 2 = GPCC (mm) TAMSAT (mm) TAMSAT (mm) RFE/mm y = x R 2 = Krig (mm) Krig (mm) y = x R 2 = GPCC (mm). 600 y = x R 2 = Krig (mm) Krig (mm) 600 (mm). GPCC/mm y = x R 2 = Sep TAMSAT (mm) y = x R 2 = TAMSAT (mm) y = x R 2 = GPCC (mm) y = x R 2 = GPCC (mm) 23/04/ Krig (mm) Krig (mm) GPCC/mm AMMA/mm AMMA/mm