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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.


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


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).









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


RESULTADOS PRELIMINARES DO IOP#2


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


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


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


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









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



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





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