Satellite Rainfall Retrieval Applications and Validation Results over South America. Dr. Daniel Alejandro Vila DSA/CPTEC/INPE

Transcription

1 Satellite Rainfall Retrieval Applications and Validation Results over South America Dr. Daniel Alejandro Vila DSA/CPTEC/INPE

2 SUMMARY 1 Rainfall Retrievals Applications 2 Data Fusion and CoSch Technique 3 Hydrological Modeling Examples

3 The Problem MAIN GOAL: QUANTIFY PRECIPITATION OVER LAND Pluviometer Satellite retrieval MERGING APPROACH: THE BEST OF BOTH WORLDS?

4 The Problem Hydroestimator Pluviometer

5 SALDAS Atmospheric Forcing Datasets The definition and derivation of a 0.125º, 3 hourly atmospheric forcing dataset for the South America continent is described which is appropriate for use in a Land Data Assimilation System and which, because of the limited surface observational networks available in this region, uses remotely sensed data merged with surface observations as the basis for the precipitation and downward shortwave radiation fields; while other variables are model calculated values from South American Regional Reanalysis (SARR). (i.e., air temperature, wind speed and specific humidity at 2m, surface pressure, etc). REMOTELY SENSED DATASETS & SURFACE OBSERVATIONS DOWNWARD SHORTWAVE RADIATION PRECIPITATION MODEL BASED DATASETS (SARR) AIR TEMPERATURE WIND SPEED SPECIFIC HUMIDITY, etc.

6 SALDAS rainfall retrieval methodology: Motivation MAIN GOAL: QUANTIFY PRECIPITATION OVER LAND MULTI-SATELLITE APPROACH GAUGE OBSERVATIONS PROS Great spatial and temporal coverage IR/MW blended techniques: a good approach to take advantage of the physically based MW estimates and IR high resolution CONS The performance is highly dependant on the regime and the scale PROS Unique source of direct measurement Largest available records for climate studies CONS Unevenly distributed around the world Sampling and instrumental errors (wind, evaporation, etc.) MERGING APPROACH: THE BEST OF BOTH WORLDS

7 SALDAS rainfall retrieval methodology Daily Gauges* Multi-satellite estimate: 3B42RT Mask analysis ADD Bias Removal RAT Bias Removal ADD Selected ADD<RAT RAT Selected Weighting factor: = #ADD/ #Total (3x3 boxes) Block diagram for CoSch satellite gauge merge process. * ~ rain gauges daily over South America 3B42RT retained Final Merge Analysis RR = *ADD + (1- )*RAT

8 SALDAS rainfall retrieval methodology

9 SALDAS rainfall validation methodology bias 3B42RT COSCH ADDITIVE RATIO RMSE Corr bias RMSE Corr bias RMSE Corr bias RMSE Corr bias 2B42V6 January Aprii Juiy October RMSE Corr..and a degraded network experiment: 10% of the available gauges are used into the correction scheme bias 3B42RT COSCH ADDITIVE RATIO RMSE Corr bias RMSE Corr bias RMSE Corr bias RMSE Corr bias 2B42V6 January April July October RMSE Corr

10 SALDAS rainfall validation methodology Annual Mean of Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Threat Score (ETS) and Bias Score for different daily rainfall thresholds.

11 Validation Results Oct 2008 até Dez 2010 María Paula Hobouchian, Paola Salio, Daniel Vila y Yanina García Skabar

12 Validation Results Oct 2008 até Dez 2010 María Paula Hobouchian, Paola Salio, Daniel Vila y Yanina García Skabar

13 María Paula Hobouchian, Paola Salio, Daniel Vila y Yanina García Skabar

14

15 + gauges? 15 minutes 4km of resolution mm por hora

16 Application of a Combined Daily Rain Gauges and Rainfall Satellite Estimates Scheme for Basin Management Daniel Vila (daniel.vila@cptec.inpe.br) Cesar Luis Garcia (cesarnon@gmail.com) Centro de Relevamiento y Evaluación de Recursos Agrícolas y Naturales

17 Relevance and interest Hydrological information is needed for:

18 Objectives and Target Provide a tool and a source of hydro meteorological data to: Scientists, Resource managers, Public and private decision makers (a) Develop a blended product based on Hydroestimator and daily rain gauge values using the Combined Scheme (CoSch) technique (Vila et al, 2009) (b) Calculate daily catchment information like: mean areal rainfall, maximum precipitation, percentage of the catchment with more than 1 mm, conditional precipitation, etc. (c) Catchment level or at a region of interest (ROI).

19 NOAA: CPC Unified Gauge Based Analysis of Global Daily Precipitation Combined Scheme (CoSch) technique

20 Principais bacias da América do Sul Estatística do CoSch para o período 01 Jan 2011 até 31 Jan 2011

21 SUMMARY 1 Rainfall Retrievals Applications 2 Data Fusion and CoSch Technique 3 Hydrological Modeling Examples

22 The Problem A realistic modeling of flooding in large and medium basins requires large amount of rainfall data at hydrologically relevant scales ranging from 1 5 km. However, the data of precipitation estimated by satellites has historically been found in spatial resolutions that can be considered somewhat rough to provide the flooding phenomenon dynamics (~25 100km). As a natural response to this limitation that has persisted for a long time, hydrologists created numerous statistics and spatial downscaling schemes.

23 Action Plan?

24 Action Plan? (from The University of Mississippi Geoinformatic Center)

25 Action Plan (from The University of Mississippi Geoinformatic Center)

26 Action Plan (from The University of Mississippi Geoinformatic Center)

27 SREM2D (A Two Dimensional Satellite Rainfall Error Model) Recognizing that it is the intermittency of the rainfall process in space and time that dictates the variability of a hydrologic process overland, the SREM2D conceptualizes that the error metrics in three general dimensions. (1) temporal dimension (How does the error vary in time?); (2) spatial dimension (How does the error vary in space?), and (3) retrieval dimension (How off is the rainfall estimate from the true value over rainy areas?). (from :A Practical Guide to a Space Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data)

28 Rainfall as an Intermittent Process Satellite based rainfall time series Truth reference rainfall time series (radar, gauges) RAINY AREAS NON-RAINY AREAS How well does satellite data delineate the rainy/non-rainy areas? HIT? MISS? HIT? MISS? Probability of Detection Probability of Detection False Alarm of Rain of No-Rain (Probability Distribution (As a function of magnitude of (Fixed marginal value) parameters) reference or satellite rainfall) (2) (3) (1) How does the error vary in space? Correlation Length Correlation Length of Successful Detection of Successful Detection of Rain (4) of No-Rain (5) How off is rainfall estimate from true value over rainy areas? Systematic and Random Errors in Retrieval (6) and (7) Correlation Length of Retrieval (8) How does the error vary in time? (from :A Practical Guide to a Space Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data) Temporal Correlation of Systematic Error in Retrieval (9)

29 SREM2D (1) Model Calibration Alpine region of Northern Italy. Shaded grey boxes represent the actual location of the 0.25o satellite pixels for 3B41RT and 3B41V6 data used in the calibration of SREM2D error metrics. Black circles represent the location of tipping bucket gages that comprised reference rainfall data Period: June November 2002 (from :A Practical Guide to a Space Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data)

30 SREM2D (2) Checking Reproducibility of Error Metrics Cumulative rainfall hyetographs over Northern Italy. Blue line represents the mean of 100 SREM2D realizations. Solid black line represents the actual satellite hyeotograph. Upper panel 3B41RT; Lower panel 3B42V6 The cumulative hyetographs generated from 100 SREM2D realizations (mean and ±σ) and actual satellite rainfall data for 3B41RT and 3B42V6 appear reasonably realistic for the domain of interest in Northern Italy. (from :A Practical Guide to a Space Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data)

31 Upper Cumberland River Basin Owsley Knott Pike Laurel Clay Leslie Perry Letcher Wise Dickenson Norton Knox Harlan KENTUCKY Bell Whitley Lee VIRGINIA Scott - Union Claiborne Hancock TENNESSEE Hawkins Grainger Miles Legend Sullivan Rivers State Boundaries County Boundaries Subbasins Washington GreeneSatellite Pixels (from The University of Mississippi Geoinformatic Center)

32 Event Selection Flow, cfs Rainfall, inches Hours from March 16, 2002, 0:00 5 (from The University of Mississippi Geoinformatic Center)

33 Native Scale (0.25 ) 3B41 Precipitation Data versus Stream flow 3B42 Precipitation Data versus Stream flow (from The University of Mississippi Geoinformatic Center)

34 Uncertainty of Simulated Stream flow Using SREM2D at Native Scale (0.25 ) 3B41 Precipitation Data versus Stream flow (from The University of Mississippi Geoinformatic Center)

35 Uncertainty of Simulated Streamflow Using SREM2D at Native Scale (0.25 ) 3B42 Precipitation Data versus Stream flow (from The University of Mississippi Geoinformatic Center)

36 Pearl River Basin (from The University of Mississippi Geoinformatic Center)

37 3B41 Precipitation Data 3B42 Precipitation Data (from The University of Mississippi Geoinformatic Center)

38 References: CoSch: Vila D., L.G. de Goncalves, D. Toll and J. Rozante, 2009, Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimations over Continental South America, J. Hidrometeor. Vol. 10, No. 2, Hobouchian M.P., P. Salio, D. Vila, Y. García Skabar, 2012, Validación de estimaciones de precipitación por satélite sobre Sudamérica utilizando una red de observaciones de alta resolución espacial, Primer Encuentro de Investigadores en Formación en Recursos Hídricos. ISBN Garcia C., Vila D., 2011, Estimativa de precipitação a uma região de interesse: Aplicando CoSch às imagens de satélite e dados de precipitação. ITC Holanda (disponível em português) SREM2D : Hossain, F.. (2008). Error Metrics and error models for characterizing high resolution satellite rainfall data: A surface hydrologic perspective. Book Chapter in Satellite Applications for Surface Hydrology, Eds. M. Gebremichael and F. Hossain. Springer, New York. Hossain, F. and E. N. Anagnostou. (2006). A two dimensional satellite rainfall error model. IEEE Transactions on Geosciences and Remote Sensing, vol. 44, pp , DOI: /TGRS Rahman, S., A.C. Bagtzoglou, L.D. Yarbrough, R. Adler, G. Huffman, and F. Hossain, Investigating Satellite Rainfall Based Flood Modeling in Anticipation of GPM: Understanding the Worth of Spatial Downscaling and Satellite Rainfall Uncertainty. Eos Trans. AGU, Fall Meeting Supplement, Abstract IN43B Harris, A., S. Rahman, F. Hossain, L. D. Yarbrough, A. C. Bagtzolgou and G. Easson, Satellite based Flood Modeling using TRMM based rainfall products and Statistical Downscaling, Sensors, 7, pp Hossain, F. et al. (2009). A Practical Guide to a Space Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data. Satellite Rainfall Applications for Surface Hydrology Springer Science+Business Media B.V., New York

39 Obrigado!! Daniel Vila