GoAMAZON – CHUVA: GPM Ground Validation ipwg/meetings/tsukuba-2014/pres/6-4_Vila.pdf · GoAMAZON…

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  • GoAMAZON CHUVA: GPM Ground Validation Activities

    Daniel Vila - CPTEC/INPE and collaborators

    7th International Precipitation Working Group Workshop Tsukuba, 17-21 November, 2014

  • 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.

  • CHUVA PROJECT THE GOAMAZON CAMPAIGN

  • http://chuvaproject.cptec.inpe.br/

  • CHUVA PROJECT THE GOAMAZON CAMPAIGN

  • INTERNATIONAL COLLABORATIONS ON GPM GV

    CHUVA (2010-14)

    MC3E (2011)

    NASA-EC Snowfall (2012) LPVEx (2010)

    15 Active International Projects

    Joint field campaigns National networks and

    other ground assets (radar, gauges, etc.)

    Hydrological validation sites (streamflow gauges, etc.)

  • 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

    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

  • http://sigma.cptec.inpe.br/prec_sat/validacao.lista.logic?i=br

  • DAILY GROUND VALIDATION

  • PRECIPITATION COMPARISON OVER BOLIVIA

    Luis Blacutt - INPE PhD student

  • PRECIPITATION COMPARISON OVER BOLIVIA

    Luis Blacutt - INPE PhD student

  • Salio et al. , submitted Atm Res

    SATELLITE PRECIPITATION ESTIMATES OVER SOUTHERN SOUTH AMERICA

    Mean 24-hour precipitation rate (mm day-1). Period: 2008-2010

  • Salio et al. , submitted Atm Res

    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).

  • 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

    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

  • Rmulo Oliveira INPE PhD student

    PRELIMINARY RESULTS FIRST IOP

  • GPROF2014v1-4

    2014.03.17 - 1605 UTC 2014.03.30 - 1221 UTC

    PRELIMINARY RESULTS FIRST IOP

  • GPROF2014v1-4 (GMI) 2014.03.04 1952 UTC

    Surface Precipitation Surface Type Index Ex.: 12- Standing Water and Rivers 13- Water/Land Coast Boundary

    Number of Significant Profiles

    Ex.:

  • RESULTADOS PRELIMINARES DO IOP#1 Case Study: 20-March-2014 - 12:00UTC

    Rmulo Oliveira INPE PhD student

  • RESULTADOS PRELIMINARES DO IOP#2

    Rmulo Oliveira INPE PhD student

  • PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro-2014 - 20:30 UTC

    Radar banda X dupla polarizao Resoluo: 250 metros CAPPI 2 km

    Radar banda Ku (maior atenuao) Resoluo: 5 km Refletividade perto da superfcie

    Rmulo Oliveira INPE PhD student

  • PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro-2014 - 20:30 UTC

    Rmulo Oliveira INPE PhD student

  • Case Study: 24-Setembro-2014 - 20:30 UTC

    Rmulo Oliveira INPE PhD student

  • AEROSOL EFFECTS IN DIFFERENT TYPES OF PRECIPITATING CLOUDS IN THE AMAZON

    TRMM RAINFALL RATE

  • DAILY CYCLE FOR CONVECTIVE CLOUDS AT CLEAN AND POLLUTED ATMOSPHERE

    AEROSOL EFFECTS IN DIFFERENT TYPES OF PRECIPITATING CLOUDS IN THE AMAZON

    Ramon Braga INPE PhD student

  • 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

    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

  • Localization of Tocantins river basin Area: ~800,000 km2

    Bacia do rio Tocantins-Araguaia

    Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin

    Aline Falck - Aluna de doutorado INPE

  • Tocantins river basin Data Rain gauges: daily Satellite estimates of rainfall: daily/0.25o CMORPH 3B42RT HYDROE GSMAP Period Rainy Season 2008-2009 Dry Season 2009 Rainy Season 2009-2010 Dry Season 2010 Rainy Season 2010-2011 Dry Season 2011

    Climatology: Average Annual precipitation: 1600 mm

    Average Monthly precipitation

    Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin

    Aline Falck - Aluna de doutorado INPE

  • A Two Dimensional Satellite Rainfall Error Model SREM2D

    Satellite-based rainfall time series

    Truth reference rainfall time series (gauges)

    Rainfall as an Intermittent Process 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? Temporal Correlation of Systematic Error in Retrieval (9)

    (source: Hossain and Anagnostou)

    SREM2D: A two-dimensional satellite rainfall error model

  • Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin

    Aline Falck - Aluna de doutorado INPE

  • Evaluation of satellite rainfall estimates on hydrological modeling of Tocantins-Araguaia basin

    Aline Falck - Aluna de doutorado INPE

  • RELAMPAGO

  • RELAMPAGO

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

  • THANK YOU

    7th International Precipitation Working Group Workshop Tsukuba, 17-21 November, 2014

    1 2 3 4 5 6 7 8 9Role of GPM Ground Validation 11daily Ground ValidationPrecipitation Comparison over BoliviaPrecipitation Comparison over BoliviaSatellite Precipitation Estimates over Southern South AmericaSatellite Precipitation Estimates over Southern South AmericaRole of GPM Ground Validation 18 19 20 21 22 23 24 25Aerosol effects in different types of precipitating clouds in the Amazon 27Role of GPM Ground Validation 29 30 31 32 33 34 35 36 37 38 39 40 41

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