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A Neural Network PMW/IR Combined Procedure for Short Term/Small Area Rainfall Estimates

University of Birmingham, UK. Nal. Council of Research, Italy. University of L’Aquila, Italy. 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002. A Neural Network PMW/IR Combined Procedure for Short Term/Small Area Rainfall Estimates.

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A Neural Network PMW/IR Combined Procedure for Short Term/Small Area Rainfall Estimates

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  1. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 A Neural Network PMW/IR Combined Procedure for Short Term/Small Area Rainfall Estimates Francisco J. Tapiador & Chris Kidd University of Birmingham, UK Vincenzo Levizzani National Council of Research, Italy Frank S. Marzano University of L’Aquila, Italy

  2. Objectives of today’s presentation Present a methodology of data fusion of IR and PMW data at global scale: Short term, large coverage and high resolution rainfall estimates Methodology to be applied to MSG (soon) and GPM products Assess the quality of these estimates: Intercomparison / Validation: HM method Down-top approach Present further research and operative products schedule Scheme: Some comments on Neural Nets Histogram matching Validation / Intercomparison case study: Andalusia, Spain: 3 months of 30 minutes rain gauge data for validation Global research products Global IR – derived estimates METEOSAT-derived estimates Further work in this line University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  3. Highlights Why fuse PMW and IR? Direct response vs indirect relationship “Bad” spatial and temporal resolutions vs geostationary capabilities Re-inforce the strengths and avoid the weaknesses Inputs processing IR data from the Global IR database (Janowiak et al 2001) and EUMETSAT archive PMW Rainfall retrieval based upon Kidd&Barrett SSM/I algorithm: V19-V85 or H19-H85 combination over ocean and over land Polarization Corrected Temperatures (PCT) over coast Gauge processing: point to area estimates using maximum entropy interpolation Histogram matching and GPI calculation for inter-comparison Neural nets Inputs selection Model selection Inversion procedures University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  4. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Neural Networks

  5. Neural Networks NN works fairy well in rainfall estimation Operative system: PERSIANN (Sooroshian et al 2000) Bellerby et al. 2000, etc. Neural Nets are not black-boxes It is possible to make an objective NN selection (Murata et al 1994) There are inversion procedures to investigate inside They allow both deterministic and probabilistic approach Some advantages over other methods Any function (Dirichlet’s, not pathological function) can be approximate with an arbitrary degree of accuracy with a NN: Universal Aproximator. An easy method to simulate complex physical models in a quick (operative) way. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  6. Input selection University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  7. Correlations for some simple models University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  8. Several NN architectures Hopfield nets SOM (cloud characterization)+(GOES data) Multilayer Perceptron (MLP) Adaptative Resonance Theory Nets (Grossberg 1969, Carpenter et al 1997) ART1 and ART2 ARTMAP Distributed ARTMAP Fuzzy ARTMAP (including a voting procedure (ref)) University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  9. Model selection: Results University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  10. Model selection into MLP Calculate (not guess) the number of neurons in the hidden layer Network information criterion (NIC) (Murata et al. 1994) • n Number of observations • Set of parameters • Gradient 2 Hessian log L Estimated maximum log likelihood University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work • This allow a conscious design of the net based on Information Theory results

  11. Research after training: model inversion University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work • What kind of inputs generate an output?: insight into precipitation processes at IR-focus

  12. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Histogram Matching

  13. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  14. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Validation (case study)

  15. Case study data: Global IR (Meteosat 5) DMSP SSM/I 30 min gauge validation data Resolutions: Spatial: 4 Km Temporal: 30 min Coverage: Andalusia (Spain) Oct-Dec 2001 University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  16. Methodology University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  17. What means “field truth” in satellite estimates validation? Point estimates: more close to the truth AGL Areal interpolations: encompassing errors and odd effects University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  18. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Maximum Entropy Interpolation The (theoretically) less-biased interpolation method available: an appropriate base to compare 1) Maximize the entropy function (using variational methods) Constraints 2) Solving… 2) Which means that we can solve the computational problems using a simple spherical kriging

  19. Point measures (average) University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work • Maximum Entropy Interpolation • Inverse Distance Weighted • Small intercomparison of interpolation methods • (Niger 2000 and Andalusia 2001) • IDW • Bilinear • Kriging • MEM

  20. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work SSM/I NN Instantaneous Intercomparison NN* HM

  21. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Small area, short-duration events

  22. Instantaneous estimates University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  23. Results: Skill Scores University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  24. Coincident data histogram comparison (October 2001) University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  25. 0.1º Accumulated results University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  26. 0.5º / 3 month accumulated data University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  27. 0.5º accumulated results University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  28. Grid size, averaging periods and correlations (Turk et. al 2002) University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  29. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work Global Coverage (Reseach Products)

  30. Global-IR coverage (HM) University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  31. Meteosat coverage (NN) Product to be validated using land-GPCC or other dataset Oriented to MSG: we are ready to apply this methodology University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  32. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work • IR/PMW Advection Scheme GOES-E 14:32 Trajectories GOES-E 15:45 SSM/I F14 14:30 IRtemperature along trajectory SSM/I F15 15:44 • Wind (CMW?) trajectories found by 19x19 correlation matching over 19x19 region. • SSM/I rain then advected along trajectories and adjusted by dIR and tied at end points

  33. Subscenes: - Guinea Gulf - GIS integration University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  34. Future operational applications QPE / QPF: SSM/I estimates improve the forecasting (Hou et al 2002) We can simulate SSM/I Agriculture Hydrology Natural Hazards But only when the product become operative and better results will be obtained University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  35. Future research work: MSG and GPM Radar data for validation/calibration Operativity of the global coverage products: intercomparison Integration in forecasting models: RAMS Use of MSG channels: More information means more discrimination capabilities Bidirectional reflectance model GPM and EGPM addressing University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  36. University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

  37. Conclusions Accumulated areal estimates at 0.1º and 0.5º at monthly scale are similar to other works, but the down-top approach allow to know about small scale and short term estimates. There is an almost-operative product to analyse and to improve with further research. There are many reseach directions in NN data fusion to follow: Inversion New methods (probabilistic nets) Integration of other models Other physical models can be integrated into the NN methodology. Any meteorological information can be integrated without major modifications Complex models can be speed up simulating the result using NN University of Birmingham, UK Nal. Council of Research, Italy University of L’Aquila, Italy 1st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 Outline Highlights Neural Nets Case Study Products Future work

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