GoAMAZON CHUVA: GPM Ground Validation Activities. Daniel Vila - CPTEC/INPE and collaborators

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1 GoAMAZON CHUVA: GPM Ground Validation Activities Daniel Vila - CPTEC/INPE and collaborators 7th International Precipitation Working Group Workshop Tsukuba, November, 2014

2 CHUVA PROJECT THE GOAMAZON CAMPAIGN CHUVA, meaning rain in Portuguese, is the acronym for the Cloud processes of the main precipitation systems in Brazil: A contribution to cloud resolving modeling and to the GPM (GlobAl Precipitation Measurement). The CHUVA project has conducted five field campaigns; the sixth and last campaign is inside the GoAmazon campaign. CHUVA's main scientific motivation is to contribute to the understanding of cloud processes, which represent one of the least understood components of the weather and climate system.

3 CHUVA PROJECT THE GOAMAZON CAMPAIGN

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8 CHUVA PROJECT THE GOAMAZON CAMPAIGN

9 Joint field campaigns INTERNATIONAL COLLABORATIONS ON GPM GV National networks and other ground assets (radar, gauges, etc.) Hydrological validation sites (streamflow gauges, etc.) 15 Active International Projects NASA-EC Snowfall (2012) LPVEx (2010) MC3E (2011) CHUVA ( )

10 ROLE OF GPM GROUND VALIDATION Pre-launch algorithm development & post-launch product evaluation - Refine algorithm assumptions & parameters - Characterize uncertainties in satellite retrievals & GV measurements Truth is estimated through the convergence of satellite and ground-based estimates Three complementary approaches: Direct statistical validation (surface): - Leveraging off operational networks to identify and resolve first-order discrepancies between satellite and ground-based precipitation estimates Physical process validation (vertical column): - Cloud system and microphysical studies geared toward testing and refinement of physically-based retrieval algorithms Integrated hydrologic validation/applications (4-dimensional): - Identify space-time scales at which satellite precipitation data are useful to water budget studies and hydrological applications; characterization of model and observation errors

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12 DAILY GROUND VALIDATION

13 PRECIPITATION COMPARISON OVER BOLIVIA Luis Blacutt - INPE PhD student

14 PRECIPITATION COMPARISON OVER BOLIVIA Luis Blacutt - INPE PhD student

15 SATELLITE PRECIPITATION ESTIMATES OVER SOUTHERN SOUTH AMERICA Mean 24-hour precipitation rate (mm day-1). Period: Salio et al., submitted Atm Res

16 SATELLITE PRECIPITATION ESTIMATES OVER SOUTHERN SOUTH AMERICA Northeastern Argentina (NE, top panel), Southern Brazil (SB, upper central panel), Central Andes (CA, lower central panel) and Central Argentina and Uruguay (CE, bottom panel). Salio et al., submitted Atm Res

17 ROLE OF GPM GROUND VALIDATION Pre-launch algorithm development & post-launch product evaluation - Refine algorithm assumptions & parameters - Characterize uncertainties in satellite retrievals & GV measurements Truth is estimated through the convergence of satellite and ground-based estimates Three complementary approaches: Direct statistical validation (surface): - Leveraging off operational networks to identify and resolve first-order discrepancies between satellite and ground-based precipitation estimates Physical process validation (vertical column): - Cloud system and microphysical studies geared toward testing and refinement of physically-based retrieval algorithms Integrated hydrologic validation/applications (4-dimensional): - Identify space-time scales at which satellite precipitation data are useful to water budget studies and hydrological applications; characterization of model and observation errors

18 PRELIMINARY RESULTS FIRST IOP Rômulo Oliveira INPE PhD student

19 PRELIMINARY RESULTS FIRST IOP GPROF2014v UTC UTC

20 GPROF2014v1-4 (GMI) UTC Ex.: Surface Precipitation Surface Type Index Ex.: 12- Standing Water and Rivers 13- Water/Land Coast Boundary Number of Significant Profiles

21 RESULTADOS PRELIMINARES DO IOP#1 Case Study: 20-March :00UTC Rômulo Oliveira INPE PhD student

22 RESULTADOS PRELIMINARES DO IOP#2 Rômulo Oliveira INPE PhD student

23 PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro :30 UTC Radar banda X dupla polarização Resolução: 250 metros CAPPI 2 km Radar banda Ku (maior atenuação) Resolução: 5 km Refletividade perto da superfície Rômulo Oliveira INPE PhD student

24 PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro :30 UTC Rômulo Oliveira INPE PhD student

25 Case Study: 24-Setembro :30 UTC Rômulo Oliveira INPE PhD student

26 TRMM RAINFALL RATE AEROSOL EFFECTS IN DIFFERENT TYPES OF PRECIPITATING CLOUDS IN THE AMAZON

27 AEROSOL EFFECTS IN DIFFERENT TYPES OF PRECIPITATING CLOUDS IN THE AMAZON DAILY CYCLE FOR CONVECTIVE CLOUDS AT CLEAN AND POLLUTED ATMOSPHERE Ramon Braga INPE PhD student

28 ROLE OF GPM GROUND VALIDATION Pre-launch algorithm development & post-launch product evaluation - Refine algorithm assumptions & parameters - Characterize uncertainties in satellite retrievals & GV measurements Truth is estimated through the convergence of satellite and ground-based estimates Three complementary approaches: Direct statistical validation (surface): - Leveraging off operational networks to identify and resolve first-order discrepancies between satellite and ground-based precipitation estimates Physical process validation (vertical column): - Cloud system and microphysical studies geared toward testing and refinement of physically-based retrieval algorithms Integrated hydrologic validation/applications (4-dimensional): - Identify space-time scales at which satellite precipitation data are useful to water budget studies and hydrological applications; characterization of model and observation errors

29 Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin Bacia do rio Tocantins-Araguaia Localization of Tocantins river basin Area: ~800,000 km 2 Aline Falck - Aluna de doutorado INPE

30 Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin Tocantins river basin Data Rain gauges: daily Satellite estimates of rainfall: daily/0.25 o CMORPH 3B42RT HYDROE GSMAP Climatology: Average Annual precipitation: 1600 mm Average Monthly precipitation Period Rainy Season Dry Season 2009 Rainy Season Dry Season 2010 Rainy Season Dry Season 2011 Aline Falck - Aluna de doutorado INPE

31 A Two Dimensional Satellite Rainfall SREM2D: A two-dimensional Error Model SREM2D RAINY AREAS satellite rainfall error model Rainfall as an Intermittent Process NON-RAINY AREAS Satellite-based rainfall time series Truth reference rainfall time series (gauges) 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? (source: Hossain and Anagnostou) Temporal Correlation of Systematic Error in Retrieval (9)

32 Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin Aline Falck - Aluna de doutorado INPE

33 Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin Aline Falck - Aluna de doutorado INPE

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36 RELAMPAGO

37 RELAMPAGO

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40 1 ST KEY WORD: LATENCY! 2 ND KEY WORD: CAN WE DO BETTER?

41 THANK YOU 7th International Precipitation Working Group Workshop Tsukuba, November, 2014