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Ground Validation of Satellite Precipitation Estimates over Spain

This study validates satellite precipitation estimates over Spain using ground measurements and various algorithms. Examples of validation work over different regions in Spain are presented, along with comparisons to other rainfall products and models. The study also discusses the use of cloud motion winds and the time degradation of precipitation estimates.

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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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