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

2. Objectives of today’s presentation 1.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 2.Assess the quality of these estimates: Intercomparison / Validation: HM method Down-top approach 3.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 OutlineHighlightsNeural NetsCase StudyProductsFuture work Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002

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 Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

4. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK Neural Networks 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

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. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

6. Input selection Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

7. Correlations for some simple models Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

8. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 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)) 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

9. Model selection: Results Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

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

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

12. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK Histogram Matching 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

13. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

14. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK Validation (case study) 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

16. 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 Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

17. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture work Methodology Highlights

18. What means “field truth” in satellite estimates validation? –Point estimates: more close to the truth AGL –Areal interpolations: encompassing errors and odd effects Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

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

20. Point measures (average)Maximum Entropy Interpolation Inverse Distance Weighted Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK Small intercomparison of interpolation methods (Niger 2000 and Andalusia 2001) IDW Bilinear Kriging MEM 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

21. Instantaneous Intercomparison SSM/INN HM NN* Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

22. Small area, short-duration events Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

23. Instantaneous estimates Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

24. Results: Skill Scores Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

25. Coincident data histogram comparison (October 2001) Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

26. 0.1º Accumulated results Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

27. 0.5º / 3 month accumulated data Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

28. 0.5º accumulated results Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

29. Grid size, averaging periods and correlations (Turk et. al 2002) Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

30. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK Global Coverage (Reseach Products) 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

31. Global-IR coverage (HM) Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

32. Meteosat coverage (NN) Product to be validated using land- GPCC or other dataset Oriented to MSG: we are ready to apply this methodology Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

33. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights GOES-E 14:32GOES-E 15:45Trajectories SSM/I F14 14:30SSM/I F15 15:44IR temperature along trajectory 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 IR/PMW Advection Scheme

34. Subscenes: - Guinea Gulf - GIS integration Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

35. 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 Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

36. 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 Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

37. Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights

38. 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 Nal. Council of Research, Italy University of L’Aquila, Italy University of Birmingham, UK 1 st INTERNATIONAL PRECIPITATION WORKING GROUP WORKSHOP Madrid, 23 – 27 September 2002 OutlineNeural NetsCase StudyProductsFuture workHighlights