SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH AMERICA. Daniel Vila 1, Inés Velasco 2 1 Sistema de Alerta Hidrológico - Instituto Nacional del Agua y de Ambiente Autopista Ezeiza - Cañuelas km (1402) Ezeiza - Buenos Aires - Argentina TE/FAX: Universidad de Buenos Aires Photo:Iguazu Falls

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. Brief algorithm description and correction methodologies: constant rate integration and local bias correction. Verification methods. Verification methods. Case studies: Salado River Basin (Pcia de Buenos Aires, Argentina) and Uruguay River subcatchment (Argentina, Brazil and Uruguay. Case studies: Salado River Basin (Pcia de Buenos Aires, Argentina) and Uruguay River subcatchment (Argentina, Brazil and Uruguay. Conclusions. Conclusions. Some results and research activities in progress. Some results and research activities in progress.

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. 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. New screening method: This technique assumes that raining pixels are colder than the mean of the surrounding pixels. Standardized temperature is defined as: Standardized temperature is defined as:

ALGORITHM DESCRIPTION Tơ < -1.5 Convective precipitation: defined essentially by the empirical power-law function corrected by PWRH. Tơ = 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 –1.5 < Tơ < 0 Tơ > 0  pp = 0

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).

CORRECTION METHODOLOGIES

·CONSTANT RATE INTEGRATION Rain rate remains constant between images …

·CONSTANT RATE INTEGRATION but something better may be made …

·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

·LOCAL BIAS CORRECTION 24-hour estimated rainfall: 21 – Aug ATLANTIC OCEAN BASIN LIMITS BRAZIL URUGUAY ARGENTINA

·CASE STUDY : SALADO RIVER LOCAL BIAS CORRECTION 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.

·CASE STUDY : SALADO RIVER Observed vs. estimated values for the September 2001 event. Straight line represents the ideal estimation

·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

·CASE STUDY : URUGUAY RIVER CONSTANT RAIN RATE 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

·CASE STUDY : SALADO RIVER Observed vs. estimated values for the 31 May –1 June, 2001 event. Straight line represents the ideal estimation

·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.

·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 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. 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 Future efforts should include a detailed validation and statistical analysis of a reasonable number of cases

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

·OPERATIVE RESEARCH