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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
Rômulo 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
üSurface Precipitation üSurface Type Index Ex.: 12- Standing Water and Rivers 13- Water/Land Coast Boundary
üNumber of Significant Profiles
Ø Ex.:
RESULTADOS PRELIMINARES DO IOP#1 Case Study: 20-March-2014 - 12:00UTC
Rômulo Oliveira – INPE PhD student
RESULTADOS PRELIMINARES DO IOP#2
Rômulo Oliveira – INPE PhD student
PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro-2014 - 20:30 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
PRELIMINARY RESULTS FROM THE IOP 2 Case Study: 24-Setembro-2014 - 20:30 UTC
Rômulo Oliveira – INPE PhD student
Case Study: 24-Setembro-2014 - 20:30 UTC
Rômulo 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