SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH AMERICA: HYDROESTIMATOR TECHNIQUE

SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH AMERICA:

HYDROESTIMATOR TECHNIQUE

Daniel Vila ([email protected])Daniel Vila ([email protected])

ESSIC/CICS – Computer & Space Science Building - Bldg #224

College Park, MD 20742

OutlinesOutlines

• Something about satellite rainfall estimation. Something about satellite rainfall estimation.

• Brief algorithm description of the South American Brief algorithm description of the South American version of NOAA/NESDIS “Hydro-Estimator” satellite version of NOAA/NESDIS “Hydro-Estimator” satellite rainfall estimation and correction methodologies.rainfall estimation and correction methodologies.

• Image availability and convection activity. Image availability and convection activity.

• Verification methods and some preliminary results Verification methods and some preliminary results over Del Plata basin.over Del Plata basin.

• Conclusions.Conclusions.

Satellite Quantitative Precipitation Estimation (QPE)

1970s1970s ------ Manual Procedures

1980s - 1990s1980s - 1990s ---- Interactive Methods / some use of microwave

20002000 ------ Single channel (10.7 mm) automatic algorithm

20052005 ------ GOES multi-channel and microwave

20082008 ------ Global Precipitation Measurement (GPM) Mission and GOES (postponed 2012?)

20??20?? ------ GPM and Hyperspectral (GIFTS) and GOES Microwave?


Why Use Satellite QPE?Why Use Satellite QPE?

Superior spatial coverageSuperior spatial coverage

Offshore coverage (tropical systems)Offshore coverage (tropical systems)

No beam block problemsNo beam block problems

ConsistencyConsistency

Differences in calibration from radar to radarDifferences in calibration from radar to radar

Radar range effectsRadar range effects

Beam overshoot (especially stratiform precip)Beam overshoot (especially stratiform precip)

Not a Not a replacementreplacement, but a , but a companioncompanion to radar to radar


GOES-Based QPE: TheoryGOES-Based QPE: Theory

BasisBasis

• Assumes that cloud-top temperature ~ cloud-top height Assumes that cloud-top temperature ~ cloud-top height cloud cloud evolution evolution cloud-top thickness cloud-top thickness rainfall rate rainfall rate

StrengthsStrengths

• 24/7 coverage every 15 minutes throughout North America (this 24/7 coverage every 15 minutes throughout North America (this is not true in South America!!)is not true in South America!!)

• High spatial resolution (~4 km)High spatial resolution (~4 km)

WeaknessesWeaknesses

• Relationship between cloud-top properties and rain rate often Relationship between cloud-top properties and rain rate often does not hold, esp. for non-convective precipitation.does not hold, esp. for non-convective precipitation.

• Cold cirrus can be mistaken for cumulonimbus.Cold cirrus can be mistaken for cumulonimbus.


Microwave QPE AlgorithmsMicrowave QPE Algorithms

Special Sensor Microwave/Imager (SSM/I)—available since Special Sensor Microwave/Imager (SSM/I)—available since 19871987

Advanced Microwave Sounding Unit-A (AMSU-A)—available Advanced Microwave Sounding Unit-A (AMSU-A)—available since 1999since 1999

Advanced Microwave Sounding Unit-B (AMSU-B)—available Advanced Microwave Sounding Unit-B (AMSU-B)—available since 2000since 2000

Tropical Rainfall Measuring Mission (TRMM) --- available since Tropical Rainfall Measuring Mission (TRMM) --- available since 19981998


Microwave QPE Algorithms TheoryMicrowave QPE Algorithms Theory



Microwave-IR Blended AlgorithmMicrowave-IR Blended Algorithm

• Relationship between 10.7- mm TRelationship between 10.7- mm Tbb and rain rate and rain rate calibrated using SSM/I rain rate estimatescalibrated using SSM/I rain rate estimates

““Best of both worlds”—combine robustness of MW Best of both worlds”—combine robustness of MW estimates with availability of GOES dataestimates with availability of GOES data

• Calibration updated every few hours for a 5x5-degree Calibration updated every few hours for a 5x5-degree regionregion

• Uses all operational adjustments (moisture, orography, Uses all operational adjustments (moisture, orography, etc.)etc.)

• Developed by F. J. Turk of NRLDeveloped by F. J. Turk of NRL

Program for the Evaluation of High Resolution Precipitation Products (PEHRPP)

International Precipitation Working Group (Working Group of CGMS) found this International Precipitation Working Group (Working Group of CGMS) found this an interesting and relevant problem, and agreed to sponsor and advise the an interesting and relevant problem, and agreed to sponsor and advise the effort effort ((http://www.isac.cnr.it/~ipwg/)

Hypotheses:

• HRPP errors can be characterized by comparing them to independent observations from rain gauges and radars. • Errors of and differences between HRPP are meaningful, in that they can be systematically related to precipitation characteristics and/or algorithm methodology.• Improved HRPP can be derived by combining products or methods based on the observed errors and differences.• HRPP spatial and temporal variability is realistic on scales appropriate for scientific studies (e.g., hydrology).• Numerical weather prediction forecasts of precipitation can be used to improve HRPP in some locations and times (e.g., high latitudes).



http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml

• Rain rate estimationRain rate estimation: fully automated method using an empirical : fully automated method using an empirical power-law function that generates rainfall rates (mm/h) based on GOES-power-law function that generates rainfall rates (mm/h) based on GOES-8 channel 4 brightness temperature and precipitable water (ETA model).8 channel 4 brightness temperature and precipitable water (ETA model).

• Moisture correctionMoisture correction: precipitable water (PW) (integrated over the : precipitable water (PW) (integrated over the layer from surface to 500 hPa) and relative humidity (RH) (mean value layer from surface to 500 hPa) and relative humidity (RH) (mean value between surface and 500 hPa., in percentage) obtained by ETA model between surface and 500 hPa., in percentage) obtained by ETA model are applied to decrease rainfall rates in dry environments and increases are applied to decrease rainfall rates in dry environments and increases them in the moist ones.them in the moist ones.

• Orographic correctionOrographic correction: a Digital Elevation Model of South America : a Digital Elevation Model of South America at the GOES scale combined with low-level winds to produce an at the GOES scale combined with low-level winds to produce an orographic correction to the satellite rainfall rate distributionorographic correction to the satellite rainfall rate distribution.

Hydroestimator Hydroestimator algorithm descriptionalgorithm description

• Screening methodScreening method: This technique assumes that raining pixels are : This technique assumes that raining pixels are colder than the mean of the surrounding pixels.colder than the mean of the surrounding pixels.

• ClusterizationClusterization: Statistical parameters : Statistical parameters (average temperature and (average temperature and standard deviation) of each system is used to produce adjustments standard deviation) of each system is used to produce adjustments resulting in more realistic patterns of rain, especially for small-scale resulting in more realistic patterns of rain, especially for small-scale convection (CPTEC algoritm).convection (CPTEC algoritm).

RAIN RATE ESTIMATIONRAIN RATE ESTIMATION

• Standardized temperature is defined as:Standardized temperature is defined as:

TT Tave


• Tơ < -1.5

Conventive core: defined essentially by the empirical power-law function corrected by PW.

• Tơ = 0

“Non-core” precipitation: whose maximum value cannot exceed 12mmh-1 and must be less than the fifth part of the convective rainfall for a given pixel

• –1.5 < Tơ < 0

• Tơ > 0 pp = 0

-1.5

-1.0

0.0

Entirely Convective

Entirely Stratiform

Standarized Temperature


Brigthness temperature - rainfall rate relationship for various values of Brigthness temperature - rainfall rate relationship for various values of precipitable water (mm) for convective core and for “non-core” precipitable water (mm) for convective core and for “non-core”

precipitationprecipitation


Ororaphic correction: east-southest wind flow produces enhaced Ororaphic correction: east-southest wind flow produces enhaced precipitation upstream and less rainfall downstreamprecipitation upstream and less rainfall downstream


Image availabilityImage availability

Image availability for southern hemisphere sector from 13 January 2003 to Image availability for southern hemisphere sector from 13 January 2003 to 28 February. Green boxes represent available images28 February. Green boxes represent available images

date 15 45 115 145 215 245 315 345 415 445 515 545 615 645 715 745 815 845 915 945 1015 1045 1115 1145 1215 1245 1315 1345 1415 1445 1515 1545 1615 1645 1715 1745 1815 1845 1915 1945 2015 2045 2115 2145

1201 -1 -1 0 5 0 0 -1 0 0 27 0 0 0 0 0 0 0 0 9 -1 -1 0 0 -1 -1 -1 -1 -1 0 0 7 13 0 27 0 0 -1 0 -1 0 0 0 -1 0

1301 -1 -1 0 0 5 0 -1 0 0 27 0 0 -1 0 0 0 0 0 17 0 0 17 -1 -1 -1 0 0 -1 0 0 -1 -1 0 29 0 1672 -1 -1 0 0 0 0 -1 -1

1401 -1 -1 5 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 0 1672 0 -1 0 0 1672 -1 -1 -1 1672 1672 1672 0 1672 -1 0 -1 0 -1 1672 0 1672

1501 -1 -1 1672 1672 1672 1672 -1 1672 0 27 1672 0 -1 0 0 144 1672 1672 -1 1672 0 401 -1 0 -1 1672 -1 0 1672 1672 -1 1672 1672 1175 169 0 -1 1672 484 200 -1 1672 415 0

1601 -1 -1 -1 0 1672 0 -1 -1 -1 27 1297 0 -1 0 -1 0 1672 0 -1 -1 -1 0 0 0 -1 -1 -1 -1 -1 0 -1 -1 685 -1 0 0 -1 0 0 -1 0 0 -1 0

1701 -1 -1 0 0 9 0 -1 -1 0 27 0 0 -1 0 0 0 313 0 -1 -1 0 0 0 0 -1 0 0 5 0 0 -1 0 -1 27 0 0 -1 1672 7 0 0 0 -1 -1

1801 -1 -1 7 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 1672 -1 0 0 9 0 0 -1 0 0 101 0 0 -1 0 -1 0 0 0 -1 0

1901 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 7 0 0 0 -1 0 0 0 0 0 -1 -1 -1 0 7 0 -1 0

2001 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 -1 0 0 -1 -1 -1 0 0 0 -1 215 0 27 0 1672 -1 0 5 0 0 0 -1 0

2101 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 -1 0 0 0 0 0 0 0 0 -1 0 -1 -1 0 0 0 0 -1 -1 0 27 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

2201 -1 -1 -1 -1 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 -1 0 0 -1 0 0 5 0 0 -1 0 17 27 0 0 -1 0 0 5 0 0 -1 27

2301 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 9 -1 -1 -1 -1 -1 0 0 0 0 -1 0 -1 -1 0 0 -1 0 0 0 0 0 27 0

2401 -1 -1 5 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 0 1672 0 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 0 7 0 0 0 -1 0

2501 -1 -1 0 0 -1 0 -1 0 0 0 0 0 -1 -1 0 0 0 0 0 0 -1 5 -1 1672 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 789 -1 -1 -1 -1 0 7

2601 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 0 0 0 -1 0 -1 0 11 0 0 1672 -1 0 0 0 0 0 -1 0 0 27 0 0 -1 0 15 0 0 1672 0 0

2701 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 9 0 0 0 0 -1 0 0 7 0 0 -1 0 0 27 0 0 -1 0 7 5 0 0 323 0

2801 -1 -1 7 5 27 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 0 -1 0 -1 0 837 0 -1 -1 0 1078 0 1672 -1 -1 -1 -1 0 0 0 0 -1 0

2901 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 0 -1 -1 -1 0 0 -1 0 0 0 0 -1 0 0 0 0 1672 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0

3001 -1 -1 17 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 -1 0 0 0 0 1672 -1 0 0 0 0 0 -1 0 0 27 7 7 -1 0 7 0 0 0 0 0

3101 -1 -1 0 0 0 0 -1 -1 -1 27 0 0 -1 0 0 0 0 0 -1 0 -1 -1 567 0 -1 1672 5 -1 -1 -1 -1 0 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

102 -1 -1 -1 -1 -1 -1 -1 0 1672 27 0 1672 -1 -1 -1 0 1672 1672 -1 0 1672 0 -1 0 -1 -1 -1 -1 -1 0 685 0 -1 26 0 -1 -1 -1 -1 -1 -1 -1 -1 -1

202 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 -1 -1 -1 -1 0 0 0 27 0 0 -1 0 0 -1 0 0 -1 0

302 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 0 -1 0 0 0 -1 9 0 1243 -1 -1 -1 0 0 7 0 0 -1 0 0 27 0 0 -1 0 7 0 0 0 -1 0

402 -1 -1 0 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 -1 -1 0 0 0 0 -1 11 0 7 0 0 -1 0 0 27 0 0 -1 0 -1 0 0 0 -1 0

502 -1 -1 23 0 0 0 -1 -1 -1 -1 0 0 -1 -1 0 27 0 0 -1 0 0 -1 -1 0 -1 0 0 0 0 0 -1 0 -1 27 0 0 -1 -1 0 0 0 1672 0 0

602 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 -1 0 0 27 0 0 -1 0 0 7 0 -1 0 0 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 0 0

702 -1 -1 0 5 0 0 -1 0 0 27 0 0 -1 0 0 27 0 0 27 -1 0 -1 0 0 -1 0 0 0 0 0 0 1672 0 27 0 0 -1 0 -1 -1 0 -1 -1 -1

802 -1 -1 0 0 0 0 0 0 7 27 0 0 -1 -1 0 0 0 0 5 -1 5 0 0 0 -1 0 0 0 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 0 0

902 -1 -1 0 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 27 -1 0 -1 0 0 0 0 0 259 0

1002 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 7 5 0 27 0 0 -1 0 0 0 0 0 0 0

1102 -1 -1 15 0 0 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 -1 0 0 -1 0 0 -1 0 0 -1 0 0 -1 0 0 27 0 0 -1 0 0 0 0 0 0 -1

1202 -1 -1 9 0 5 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 27 0 0 -1 0 0 0 7 0 0 0

1302 -1 -1 0 0 0 0 -1 0 0 0 0 0 -1 -1 -1 -1 0 0 0 0 9 0 0 851 -1 5 9 5 0 0 -1 0 8 27 18 14 -1 -1 0 18 0 0 0 0

1402 -1 -1 9 11 0 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 -1 0 0 0 0 1087 -1 -1 0 18 0 0 9 11 0 27 11 0 -1 0 0 27 0 0 -1 -1

1502 -1 -1 -1 -1 -1 0 -1 -1 -1 -1 -1 0 -1 -1 -1 -1 -1 0 -1 0 1085 0 0 0 -1 0 0 0 0 0 -1 26 24 29 0 0 -1 0 0 0 0 0 0 10

1602 -1 -1 0 0 0 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 7 11 7 10 0 1381 -1 -1 -1 25 0 0 7 0 0 29 0 0 -1 0 0 7 0 0 0 0

1702 -1 -1 15 7 5 0 -1 0 0 27 0 0 -1 -1 0 0 0 0 5 7 0 0 0 1672 -1 0 0 0 0 1672 0 0 0 27 1672 1672 -1 0 0 0 0 1672 275 0

1802 -1 -1 17 9 0 0 -1 0 -1 -1 -1 0 13 -1 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 -1 0 -1 0 0 26 0 1672 -1 0 27 -1 0 1649 -1 0

1902 -1 -1 -1 0 11 0 -1 5 -1 -1 -1 0 -1 -1 0 0 0 0 6 17 0 0 0 0 -1 -1 -1 -1 0 0 0 0 0 27 0 0 -1 0 27 0 27 0 0 0

2002 -1 -1 0 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 5 0 0 7 0 1672 -1 -1 -1 0 0 1672 0 0 0 27 0 1672 -1 0 0 0 27 0 27 -1

2102 -1 -1 0 0 0 0 -1 0 -1 -1 -1 0 -1 -1 0 0 0 0 9 5 7 0 0 1672 -1 0 -1 -1 -1 0 -1 -1 -1 -1 -1 0 -1 -1 -1 -1 -1 0 -1 -1

2202 -1 -1 0 0 0 0 -1 0 -1 -1 -1 0 -1 -1 0 0 0 0 0 0 0 0 5 0 -1 0 7 0 -1 0 -1 -1 -1 -1 -1 0 -1 -1 -1 -1 -1 0 -1 -1

2302 -1 -1 9 0 0 0 -1 0 -1 -1 -1 0 -1 -1 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 -1 27 0 0 -1 -1 0 0 0 0 15 0

2402 -1 -1 0 0 591 0 -1 0 -1 -1 -1 0 0 -1 0 0 0 0 0 5 0 0 0 0 -1 0 0 0 0 0 0 11 -1 163 0 0 -1 0 0 0 0 0 489 0

2502 -1 -1 0 0 0 0 -1 -1 -1 -1 -1 0 -1 -1 0 0 0 0 0 0


NOAA PLANS SHIFT IN GEOSTATIONARY SATELLITE ORBIT TO IMPROVE WEATHER FORECAST COVERAGE OVER SOUTH AMERICA

Continent Will Benefit from Emerging Global Earth Observation Network

Jan. 18, 2006 — In Buenos Aires on Tuesday, NOAA, the Comisión Nacional de Activades Espaciales, and the World Meteorological Organization announced news of the repositioning of GOES-10. Shifting the spacecraft from its current position above the equator in the West to a new spot in orbit will greatly improve environmental satellite coverage of the Western Hemisphere, especially over South America. The repositioning is planned for October 2006 pending the successful launch of GOES-N, NOAA's new geostationary satellite, and the continued operation of GOES-12.

(extracted from NOAA News Online - http://www.noaanews.noaa.gov/stories2006/s2561.htm


Verification methodsVerification methods

Verification methodsVerification methods

Preliminary resultsPreliminary results

EXPERIMENT DESIGN

• Period: January - February 2003

• Region: Del Plata basin (40°S - 20 °S, 60°W - 35 °W)

• Image availability: more than 28 images in 24 hours. 27 cases were selected

• Ground truth: Raingauges (INMET meteorological stations, PCDs and other networks)

• Integration method: cubic splines time interpolation.

BIAS: mean value= -4.02 mm

13/01/200318/01/2003

23/01/200328/01/2003

02/02/200307/02/2003

12/02/200317/02/2003

22/02/2003 S1

-14

-12

-10

-8

-6

-4

-2

0

2

mil

lim

ete

rs


RMSE: mean value = 12.7 mm

13/01/200318/01/2003

23/01/200328/01/2003

02/02/200307/02/2003

12/02/200317/02/2003

22/02/2003

S1

0

5

10

15

20

25

30

35


POD & FAR: POD mean value = 0.83 FAR mean value = 0.05

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

09/01/2003 14/01/2003 19/01/2003 24/01/2003 29/01/2003 03/02/2003 08/02/2003 13/02/2003 18/02/2003 23/02/2003 28/02/2003


Case study (1)Case study (1)

21 January 2003

21 January 2003 (CPTEC algorithm)

Overall statistics for 20-21 January 2003. Underestimation is present in Overall statistics for 20-21 January 2003. Underestimation is present in all intervals for both algorithms.all intervals for both algorithms.

NESDIS algorithm (available in INTERNET)THRESH (mm) NUM BIAS CORR RMSE POD FAR

0 7 5554 -5.95 0.77 15.2 0.91 0.017 26 3281 -9.93 0.65 19.76 0.78 026 52 1241 -22.15 0.5 31.5 0.32 052 + 254 -52.13 0.48 60.05 0.28 0WTAVG 19112 -16.43 0.63 25.77 0.65 0

THRESH (mm) NUM BIAS CORR RMSE POD FAR0 7 5575 -0.54 0.98 4.62 0.92 0.017 26 3300 -0.92 0.97 5.95 0.96 0.0326 52 1267 -2.72 0.94 9.26 0.93 0.0352 + 256 -11.87 0.89 19.57 0.74 0.01WTAVG 19291 -2.45 0.95 7.88 0.92 0.02


21 January 2003

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100 120 140 160

Observed value (mm)

MVE

val

ue (m

m)

21/01/2003


10-11 February 2003


12 February 2003 (Diario El Pais - Uruguay)


10-11 February 2003


Cuareim River BasinCuareim River Basin

• 24-hours rainfall estimation using HE technique (NESDIS version)

• Period: March 1st 2002 – July 31st 2003

• Calibration data: GTS available raingauges

• Validation data: CTMSG raingauge network

Scatterplot of Observed vs. Estimated mean areal rainfalll


0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Estimated (mm)

ob

serv

ed (

mm

)

• This correction algorithm takes into account the difference between rain gauges and the HE estimation for a given rain gauge network

Sa te llite E stim at ion

Ra in Data G aug e(g rou nd tru th )

N in e pi xe l co m p ar is o n k e rn e l

Schematic procedure of the best adjusted value (MVE). Rainfall data is compared with a nine pixels kernel centered in the rain gauge location


0

20

40

60

80

100

120

140

160

0 20 40 60 80 100 120 140 160

Estimated (mm)

ob

se

rve

d (

mm

)Cuareim River BasinCuareim River Basin

Scatterplot of Observed vs. Estimated mean areal rainfall corrected with rain gauges values.

Statistical Statistical SummarySummary

• The results of the corrected spatial precipitation estimates improve noticeably with respect to those without correction.

Bias RMSENo Correction -6.4 19.3Corrected (precip. < 10 mm) 0.6 2.5Corrected (precip. > 10 mm) -2.4 12.6

Cuareim River Watershed



ConclusionsConclusions

• The main purpose of this work is to present the recent The main purpose of this work is to present the recent improvements of the Auto-Estimator Algorithm improvements of the Auto-Estimator Algorithm (Hydroestimator) and the application of this technique in (Hydroestimator) and the application of this technique in Del Plata basin (South America).Del Plata basin (South America).

• The main difference between the South American model The main difference between the South American model and the one for North America is the image availability. and the one for North America is the image availability. Gaps up to three hours in South America imagery may be Gaps up to three hours in South America imagery may be a very important factor in the accuracy of the estimations.a very important factor in the accuracy of the estimations.

• The errors involved in these kind of techniques were The errors involved in these kind of techniques were evaluated during January-February 2003 (summer) and evaluated during January-February 2003 (summer) and the case study presented.the case study presented.

• More detailed statistics (i.e. warm and cold tops More detailed statistics (i.e. warm and cold tops discrimination and seasonal behavior) will be carried out discrimination and seasonal behavior) will be carried out in the future.in the future.

MUCHAS GRACIAS !!!MUCHAS GRACIAS !!!

THANK YOU !!!THANK YOU !!!

MUCHAS GRACIAS !!!MUCHAS GRACIAS !!!

THANK YOU !!!THANK YOU !!!

Documents

SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH AMERICA: HYDROESTIMATOR TECHNIQUE