Alerta

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SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION
OVER SOUTH AMERICA.   Daniel Vila1, Inés
Velasco2     1 Sistema de Alerta Hidrológico
- Instituto Nacional del Agua y de
Ambiente Autopista Ezeiza - Cañuelas km 1.60 -
(1402) Ezeiza - Buenos Aires - Argentina TE/FAX
54 -11 - 4480 - 9174 2 Universidad de Buenos
Aires PhotoIguazu Falls
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OVERVIEW

• Results of the study of the South American
  version of NOAA/NESDIS Hydro-Estimator
  satellite rainfall estimation technique in
  selected regions of the Del Plata River basin.
• Brief algorithm description and correction
  methodologies constant rate integration and
  local bias correction.
• Verification methods.
• Case studies Salado River Basin (Pcia de
  Buenos Aires, Argentina) and Uruguay River
  subcatchment (Argentina, Brazil and Uruguay.
• Conclusions.
• Some results and research activities in
  progress.

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ALGORITHM DESCRIPTION

• This is a fully automated method using an
  empirical power-law function that generates
  rainfall rates (mm/h) based on GOES-8 channel 4
  brightness temperature
• Moisture correction factor (PWRH) defined as
  the product of precipitable water (PW)
  (integrated over the layer from surface to 500
  hPa) times the relative humidity (RH) (mean value
  between surface and 500 hPa., in percentage) is
  applied to decrease rainfall rates in dry
  environments and increases them in the moist
  ones.
• New screening method This technique assumes
  that raining pixels are colder than the mean of
  the surrounding pixels.
• Standardized temperature is defined as

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ALGORITHM DESCRIPTION

• To 0
• Stratiform precipitation whose maximum value
  cannot exceed 12mmh-1 and must be less than the
  fifth part of the convective rainfall for a given
  pixel

• To lt -1.5
• Convective precipitation defined essentially
  by the empirical power-law function corrected by
  PWRH.

• 1.5 lt To lt 0

• To gt 0 ? pp 0

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CORRECTION METHODOLOGIES
GOES 8 - Ch4 - Image availability for southern
hemisphere sector from 20 May - 12 Z to 22 June
12 Z (open circles). The time difference (in
hours) between consecutive images are plotted in
blue (left axis).
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CORRECTION METHODOLOGIES
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CONSTANT RATE INTEGRATION

• Rain rate remains constant between images

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CONSTANT RATE INTEGRATION

• but something better may be made

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LOCAL BIAS CORRECTION

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

Schematic procedure of the best adjusted value
(MVE). Rainfall data is compared with a nine
pixels kernel centered in the rain gauge location
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LOCAL BIAS CORRECTION
BASIN LIMITS
ARGENTINA
BRAZIL
URUGUAY
ATLANTIC OCEAN
24-hour estimated rainfall 21 Aug -2002
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CASE STUDY SALADO RIVER

• LOCAL BIAS CORRECTION

The 10º x 10º box used to evaluate the technique.
Dashed area belongs to the Salado River
catchment. Solid triangles show the location of
rain gauges used for the local bias correction.
Right Geographical distribution of rain gauges
used to validate the technique.
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CASE STUDY SALADO RIVER
Observed vs. estimated values for the 23-24
September 2001 event. Straight line represents
the ideal estimation
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CASE STUDY SALADO RIVER
VALIDATION STATISTICAL PARAMETERS

• Overestimation are present in all intervals.
• Weighted averaged bias of 5.8 mm represents a
  positive difference of around 27 between
  estimated and observed values.
• While for the first rows POD and FAR appear
  close to ideal, for the higher intervals (26 and
  52 mm) high values of FAR and lower of POD are
  present

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CASE STUDY URUGUAY RIVER

• CONSTANT RAIN RATE

Geographical position of rain gauges used for
evaluation purposes. Dashed area belongs to the
Salto Grande Dam Immediate catchment
Satellite rainfall estimation for Salto Grande
Dam region - 31 May/ 1 June 2001
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CASE STUDY SALADO RIVER
Observed vs. estimated values for the 31 May 1
June, 2001 event. Straight line represents the
ideal estimation
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CASE STUDY URUGUAY RIVER
VALIDATION STATISTICAL PARAMETERS

• Underestimation are present in all intervals.
• Weighted bias represents only 15 of
  underestimation and the RMSE is around 30.
• The probability of detection (POD) and False
  alarm ratio (FAR) exhibit very good values near 1
  and 0 respectively.

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CONCLUSIONS

• The main purpose of this work is to present the
  recent improvements of the Auto-Estimator
  Algorithm and the application of this technique
  in two flash flood events in Del Plata basin in
  South America.
• The main difference between the South American
  model and the one for North America is the image
  availability. Gaps up to three hours in South
  America imagery may be a very important factor in
  the accuracy of the estimations.
• The errors involved in these kind of techniques
  were evaluated in the cases study presented.
• Future efforts should include a detailed
  validation and statistical analysis of a
  reasonable number of cases

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OPERATIVE RESEARCH

• Areal rainfall estimation
• 15-Feb / 15 Mar2002 24 hours rainfall
  estimation and mean river level at Paso Mariano
  Pinto.
• Local bias correction applied

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OPERATIVE RESEARCH
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OPERATIVE RESEARCH