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Ground Validation of Satellite Precipitation Estimates over Spain

Ground Validation of Satellite Precipitation Estimates over Spain. Francisco J. Tapiador Institute of Environmental Sciences (ICAM) University of Castilla-La Mancha, UCLM Toledo, Spain francisco.tapiador@uclm.es

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Ground Validation of Satellite Precipitation Estimates over Spain

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  1. Ground Validation of Satellite Precipitation Estimates over Spain Francisco J. Tapiador Institute of Environmental Sciences (ICAM) University of Castilla-La Mancha, UCLM Toledo, Spain francisco.tapiador@uclm.es With inputs from Antonio Rodriguez and Miguel A.Martínez, Spanish Nal. Meteorological Institute (INM), Madrid, Spain

  2. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Introduction • A. The UCLM’s Environmental Modeling Group • GCM and NWP– The PROMES model • Remote Sensing – Satellite Precipitation • Algorithm development • Some Validation • B. Some examples of our validation work over Spain • Andalusia case study (METEOSAT+SSM/I) • IPWG satellite estimates over Spain (CICS, University of Maryland data) • EUMETSAT Convective Rain Rate product (INM, Spain) • C. Some notes on Spain as validation site

  3. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Merged Satellite Rainfall Algorithms • - EURAINSAT/A algorithm (Tapiador et al. 2004, IJRS) • - PMW-calibrated IR • Neural Networks(Tapiador et al. 2004, Met App) • PMW+IR IR spatial and temporal resolution + PMW directness • 4km/30 minutes resolution • Used by some farmers for irrigation planning – advised on shortcomings and limitations • Cloud motion winds PMW+IR estimate

  4. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions SSM/I only Neural Networks Product Neural Net (Meteosat+SSM/I) Histogram Matching (Meteosat+SSM/I)

  5. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Cloud Motion Winds (CMW) Scheme • Similar to CPC Morphing • Difference: CMW are directly modeled using Navier-Stokes equations instead of spatial correlation windows: more physically-direct and more realistic fields • Reference: Tapiador, 2004. 2nd IPWG meeting, Monterey, CA • Used for data assimilation into GCM

  6. 02:30 03:00 03:30 04:30 05:00 05:30 ACTUAL RAIN MEASUREMENT RAIN ESTIMATE CMW Diffusion ACTUAL RAIN MEASUREMENT CMW Diffusion RAIN ESTIMATE IndependentValidation

  7. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Comparison between CMW estimate and (independent) reference rainfall for 02:30 TUC (2 hour step, forward propagation)

  8. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions What if we use the 02:30 measure instead of the 04:30 CMW-scheme estimate when comparing @ 04:30? So, the CMW scheme is actually transporting rainfall

  9. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Time degradation: Average for 31/OCT/2003 Using the CMW, we can maintain correlations > 0.80 for up to 2.5 hours The performances of the method when compared with ground rainfall at instantaneous scale will be linked with the performances of the rainfall to be transported: relevant perhaps for GPM

  10. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions http://hermes.uclm.es

  11. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions

  12. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Validation activities • Opportunity: we needed data for algorithm pre-calibration • Validation has a geographical component: validation results are different in different places, and we need the algorithms tuned for Spain. • Validation against gauge, GR; comparison with models

  13. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Andalusia case study

  14. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Half-hourly raingauge data availability • Neural network IR+PMW fusion • Algorithm characteristics: • High temporal resolution • High spatial resolution • High accuracy • Tapiador, F.J., Kidd, C., Levizzani, V., Marzano, F.S., 2004. A Neural Networks-Based Fusion Technique to Estimate Half Hourly Rainfall Estimates at 0.1º Resolution from Satellite Passive Microwave and Infrared Data. Journal of Applied Meteorology, 43, 576-594.

  15. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Interpolation I – Kriging Rain Gauges in Andalusia • Interpolation II – Inverse distance SSM/I data

  16. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Correlations at 0.1º (monthly)

  17. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Correlations at 0.5º (monthly)

  18. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Validation of IPWG Products on Spain

  19. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions NOGAPS Geo • (CICS, University of Maryland archive) • 00Z-00Z products • NOGAPS • NRL GEO • NRL PWM • CPC Morphing • 3B42RT NRL PMW CPC

  20. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Rain Gauges Location

  21. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions

  22. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Geolocation error surface analysis • Data from 01/JAN/2005 to 01/SEP/2005 • Satellite vs gauge • Assuming 5km interval error in the nominal satellite data geolocation

  23. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions CPC Morphing 3B42RT NRL GEO NRL PMW NOGAPS

  24. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions EUMETSAT’s Convective Rain Rate Product (CRR) Nowcasting Satellite Application Facility (SAF)

  25. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions

  26. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions

  27. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Validation – Comparison data sources

  28. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions GR Visual comparison

  29. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Spain as validation site

  30. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Pros and Cons • Many examples of frontal, convective and orographic precipitation – and mixed cases. • Three rainfall regimes in 500,000 sq km (Texas= 696,000 sq Km) • High N-S gradient. Well-calibrated, reliable validation net • Rain gauges nets (INM, river authorities, etc.) • Ground Radar • TRMM coverage (South), MSG, SSM/I, AMSU, AVHRR, etc. • Limited area • Limited public availability of validation data – but this could be solved for GPM

  31. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Available Validation Data • INM gauges network • GR • River authorities networks • Agrarian Meteo Nets • Specifically-tailored nets and instrumentation

  32. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions Geography 37N

  33. Intro Algorithms Val example 1 Val example 2 Val example 3 Conclusions • Conclusions • Suitability for validation site in Catalonia (Daniel Sempere, GRAHI): • Experience in satellite rainfall estimates algorithms • Interface with NWP modelers (NWP+Sat+Merged algorithms) • Data availability and support from agencies • Geography of Spain: very different from other validation places

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