SOME EXPERIENCES ON SATELLITE SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH RAINFALL ESTIMATION OVER SOUTH

AMERICA.AMERICA. 

Daniel Vila1, Inés Velasco2

 

 

11 Sistema de Alerta Hidrológico - Instituto Nacional del Agua y de Sistema de Alerta Hidrológico - Instituto Nacional del Agua y de AmbienteAmbiente

Autopista Ezeiza - Cañuelas km 1.60 - (1402) Ezeiza - Buenos Aires - Autopista Ezeiza - Cañuelas km 1.60 - (1402) Ezeiza - Buenos Aires - ArgentinaArgentina

TE/FAX: +54 -11 - 4480 - 9174TE/FAX: +54 -11 - 4480 - 9174

22 Universidad de Buenos Aires Universidad de Buenos AiresPhoto:Iguazu Falls

OVERVIEWOVERVIEW

• Results of the study of the South American version of Results of the study of the South American version of NOAA/NESDIS “Hydro-Estimator” satellite rainfall NOAA/NESDIS “Hydro-Estimator” satellite rainfall estimation technique in selected regions of the Del Plata estimation technique in selected regions of the Del Plata River basin. River basin.

• Brief algorithm description and correction Brief algorithm description and correction methodologies: constant rate integration and local bias methodologies: constant rate integration and local bias correction. correction.

• Verification methods.Verification methods.

• Case studies: Salado River Basin (Pcia de Buenos Aires, Case studies: Salado River Basin (Pcia de Buenos Aires, Argentina) and Uruguay River subcatchment (Argentina, Argentina) and Uruguay River subcatchment (Argentina, Brazil and Uruguay.Brazil and Uruguay.

• Conclusions.Conclusions.

• Some results and research activities in progress.Some results and research activities in progress.

ALGORITHM ALGORITHM DESCRIPTIONDESCRIPTION

• This is a fully automated method using an empirical power-law This is a fully automated method using an empirical power-law function that generates rainfall rates (mm/h) based on GOES-8 channel function that generates rainfall rates (mm/h) based on GOES-8 channel 4 brightness temperature 4 brightness temperature

• Moisture correction factor (PWRH) defined as the product of Moisture correction factor (PWRH) defined as the product of precipitable water (PW) (integrated over the layer from surface to 500 precipitable water (PW) (integrated over the layer from surface to 500 hPa) times the relative humidity (RH) (mean value between surface hPa) times the relative humidity (RH) (mean value between surface and 500 hPa., in percentage) is applied to decrease rainfall rates in dry and 500 hPa., in percentage) is applied to decrease rainfall rates in dry environments and increases them in the moist ones.environments and increases them in the moist ones.

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

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

TT Tave

ALGORITHM ALGORITHM DESCRIPTIONDESCRIPTION

• 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

-1.5

-1.0 0.0

Entirely Convective

Entirely Stratiform

Standarized Temperature

CORRECTION CORRECTION METHODOLOGIESMETHODOLOGIES

GOES 8 - Ch4 - Image availability for southern hemisphere sector from 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 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).hours) between consecutive images are plotted in blue (left axis).

CORRECTION CORRECTION METHODOLOGIESMETHODOLOGIES

·· CONSTANT RATE CONSTANT RATE INTEGRATIONINTEGRATION

• Rain rate remains constant between images …Rain rate remains constant between images …

·· CONSTANT RATE CONSTANT RATE INTEGRATIONINTEGRATION

• but something better may be made …but something better may be made …

·· LOCAL BIAS CORRECTIONLOCAL BIAS CORRECTION

• This algorithm takes into account the difference This algorithm takes into account the difference between rain gauges and the HE estimation for a given between rain gauges and the HE estimation for a given rain gauge networkrain 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 Schematic procedure of the best adjusted value the best adjusted value (MVE). Rainfall data is (MVE). Rainfall data is compared with a nine compared with a nine pixels kernel centered in pixels kernel centered in the rain gauge locationthe rain gauge location

·· LOCAL BIAS CORRECTIONLOCAL BIAS CORRECTION

5 50 100 150 200 250

24-hour estimated rainfall: 21 – Aug -24-hour estimated rainfall: 21 – Aug -20022002

ATLANTIC OCEAN

BASIN LIMITS

BRAZIL

URUGUAY

ARGENTINA

·· CASE STUDY : SALADO RIVERCASE STUDY : SALADO RIVER

-65 -63 -61 -59 -57 -55

LO NG ITU DE

-40

-38

-36

-34

-32

-30

LAT

ITU

DE

URUGUAY

ARGENTINARio de la Plata

-61 -60 -59 -58 -57 -56

LO NG ITU D

-39

-38

-37

-36

-35

-34

LAT

ITU

D

Buenos Aires Province,Argentina

• LOCAL BIAS LOCAL BIAS CORRECTIONCORRECTION

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 RIVERCASE STUDY : SALADO RIVER

Observed vs. estimated values for the 23-24 September 2001 Observed vs. estimated values for the 23-24 September 2001 event. Straight line represents the ideal estimationevent. Straight line represents the ideal estimation

·· CASE STUDY : SALADO RIVERCASE STUDY : SALADO RIVER

Table I

THRESH NUM BIAS CORR RMSE POD FAR SKILL

0.0 216 3.98 0.72 15.76 0.98 0.01 0.61

7.0 186 4.63 0.67 16.93 0.87 0.12 0.67

26.0 104 7.97 0.46 21.21 0.78 0.63 0.72

52.0 34 5.65 0.10 24.28 0.65 0.70 0.79

WTAVG 1276 5.82 0.49 19.85 0.81 0.39 0.70

VALIDATION STATISTICAL VALIDATION STATISTICAL PARAMETERSPARAMETERS

• 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 CASE STUDY : URUGUAY RIVERRIVER

• CONSTANT RAIN RATECONSTANT RAIN RATE

Geographical position of rain gauges used for evaluation purposes. Dashed area belongs to the Salto Grande Dam Immediate catchment

-59 -58 -57 -56 -55

LO N G ITU D E

-32

-31

-30

-29

-28

LAT

ITU

D

ARGENTINA

BRAZIL

URUGUAY

Uruguay

R iver

Sa lto G rande D am

Sub-C atchm ent lim it

Quare im R iver

C ountry borders

Satellite rainfall estimation for Salto Grande Dam region - 31 May/ 1 June 2001

·· CASE STUDY : SALADO RIVERCASE STUDY : SALADO RIVER

Observed vs. estimated values for the 31 May –1 June, 2001 Observed vs. estimated values for the 31 May –1 June, 2001 event. Straight line represents the ideal estimationevent. Straight line represents the ideal estimation

·· CASE STUDY : URUGUAY CASE STUDY : URUGUAY RIVERRIVER

VALIDATION STATISTICAL VALIDATION STATISTICAL PARAMETERSPARAMETERS

• 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.

TABLE II

THRESH NUM BIAS CORR RMSE POD FAR SKILL

0.0 321 -9.41 0.93 24.10 1.00 0.03 0.84

7.0 313 -9.49 0.92 24.40 0.92 0.06 0.86

26.0 210 -14.38 0.87 29.64 0.89 0.06 0.86

52.0 145 -17.25 0.76 34.47 0.93 0.08 0.88

WTAVG 2947 -13.91 0.85 29.82 0.93 0.07 0.86

·· 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 and the improvements of the Auto-Estimator Algorithm and the application of this technique in two flash flood events in application of this technique in two flash flood events in Del Plata basin in South America.Del Plata basin in South America.

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

• The errors involved in these kind of techniques were The errors involved in these kind of techniques were evaluated in the cases study presented.evaluated in the cases study presented.

• Future efforts should include a detailed validation and Future efforts should include a detailed validation and statistical analysis of a reasonable number of casesstatistical analysis of a reasonable number of cases

··OPERATIVE RESEARCHOPERATIVE RESEARCH

-5 8 -5 7 -5 6 -5 5 -5 4 -5 3 -5 2 -5 1 -5 0 -4 9

-3 1

-3 0

-2 9

-2 8

-2 7

Rio Ib icuy BasinA proximated area : 46 .500 km 2

Areal rainfall estimationAreal rainfall estimation

• 15-Feb / 15 Mar2002 – 24 15-Feb / 15 Mar2002 – 24 hours rainfall estimation and hours rainfall estimation and mean river level at Paso mean river level at Paso Mariano Pinto. Mariano Pinto.

• Local bias correction Local bias correction appliedapplied

··OPERATIVE RESEARCHOPERATIVE RESEARCH

Paso Mariano Pinto (Brazil)

0

10

20

30

40

50

60

70

20/02/2002 25/02/2002 02/03/2002 07/03/2002 12/03/2002 17/03/2002

date

pp

(m

m)

0

50

100

150

200

250

300

hei

gh

t (c

m)

10 days

··OPERATIVE RESEARCHOPERATIVE RESEARCH

0

10

20

30

40

50

60

70

20/02/2002 25/02/2002 02/03/2002 07/03/2002 12/03/2002

0

5

10

15

20

25

30

35

40

Corrected Estimated Nr of Images

IBICUY RIVER BASIN: ESTIMATED & CORRECTED RAINFALL

Mean Estimated rainfall: 9,6 mm

Mean Corrected rainfall: 10,4 mm

Number of images (ave): 29 /48 (ideal)