PERFORMANCE OF THE H-E ALGORITHMDURING THE CENTRAL AMERICAN RAINY

SEASON OF 2001

1.- Introduction

2.- Methodology:

2.1 Hydro-Estimator

2.2 Comparisons

3.- Data set

4.- Results and discussion

5. Future work

Introduction

• GOES digital data available since July 2001• Rain gauge network has been decreasing over the

years and radar data is not available over the area• Previous results for Keith showed the best results

for the H-E• The performance of the H-E was evaluated during

the CA rainy season to use the estimates for flash flood warnings in the near future.

Methodology: H-E

1.- variant of the A-E (Vicente et al. 1998)2.- cloud growth rate correction factor is not

used3.- uses a modified temperature gradient

correction4.- same temperature/rain rate relationship

and Z=(Mean-T)/SD5.- moisture correction factor: PW and RH

Comparisons

• Satellite estimates versus daily and 6-h rain gauge measurements

• Rain gauge image obtained from the rain gauges

• Each pixel of the rain gauge image is compared with the 9 pixels surrounding it from the corresponding satellite image (BEV comparisons)

BiasS G

NS Gi i

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CORRN G S G S

N G G N S S

i i i i

i i i i

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( ( ) ).( ( ) )2 2 2 2

RMSEN

S Gi ii

n

1 2

1

( )

PODq

q q

4

3 4

FARq

q q

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3 4

HSSq q q q

q q q q q q q q

2 1 4 2 3

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Statistical measures used in the comparisons

CSIq

q q q

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2 3 4

Data set• Orographic image at 10-km res.

• From 1515 UTC 20 August to 1445 UTC 22 August, from 1515 UTC 8 October to 1445 10 October, from 1315 UTC July 17 to 1245 UTC July 18, 2001 (Chantal, Iris, tropical wave)

• a.- 10.7 um TB

b.- corresponding grid files from the Eta model: u, v, PW and RH.

Data set (cont.)• H-E estimates for days between May and

Spetember 2001 (26 mm/day or more)

• Daily rain gauge data from Belize and the Yucatan Peninsula from their Met Services.

• Daily rain gauge data from the IMN

• Rain gauge data every 15 min from rain gauge networks over Guatemala, Honduras, El Salvador and Nicaragua.

Satellite estimates

Rain gauge values

CHANTAL

TROPICAL WAVE

Satellite estimates

Rain gauge values

Thresh

26.0 mm

Num Bias Corr RMSE POD FAR CSI HSS

Chantal 171 -21.02 0.58 49.71 0.96 0.20 0.77 0.71

Iris 451 -0.49 0.21 36.76 0.61 0.41 0.43 0.45

Tropical wave

119 -7.45 0.35 25.46 0.88 0.23 0.70 0.60

Thresh

52.0 mm

Num Bias Corr RMSE POD FAR CSI HSS

Chantal 146 -29.69 0.48 51.02 0.92 0.13 0.81 0.81

Iris 161 -1.78 0.18 52.54 0.54 0.48 0.36 0.48

Tropical wave

69 -12.04 0.14 31.75 0.64 0.29 0.51 0.54

Comments to the results

• Better results for Chantal compared with those obtained for Keith. A HSS of 0.71 and 0.81 for a threshold of 26 and 52 mm/day were obtained. Only stations over Belize and the Yucatan Peninsula were used.

• Due to the few raingauges available over Belize, in order to gain comparable points in the analysis, the comparisons for hurricane Iris included stations in the Yucatan peninsula, where warmer IR temperatures were observed. This fact could have an important influence on the results obtained since the center of hurricane Iris was over Belize

Comments to the results (cont.)

• For the tropical wave case, the results too were most likely influenced by the location of the gauges over Costa Rica; most stations are over the middle of the country where mainly light rain was observed.

• The 6-h analysis for tropical storm Chantal included stations over Guatemala, Honduras, El Salvador and Nicaragua. The 6-h totals were limited to fixed periods at 0, 6, 12, and 18 UTC. Besides, stations were not located close to the center of the storm which can have an importan influence on the poorly results obtained for these data.

Comments to the results (cont.)

• The rain gauge network used in each case seemed to limit and influence the results obtained in each case.

• Results from daily comparisons from May through September included stations over Costa Rica and Honduras mainly.

• The results obtained from rainy days with 26 mm/day or more, show poor results as compared with those obtained for Chantal and the tropical wave case. Further classification (as a funcion of cloud size/pattern/temperature) within tropical systems restrict the comparisons because of the sparsity of rain gauges. However, the use of TRMM data could solve this problem in the future.

Future work

• Include an orographic map with a higher resolution• Adapt the cloud top temperature-rain rate curve to

different type of cloud systems based on case studies over the region.

• Validation of the blended GOES/microwave (Turk et.al 1998) over CA is underway at the University of Costa Rica. This is an algorithm which give more direct precipitation measurements

• Validation for shorter periods of time in order to use the results for flash flood warnings.

Future work

• The use of precipitation algorithms is very new in Central America. The use of these products by the Central American countries can help to focus future work in problems detected on the daily use basis.

• Two versions of the H-E are running at this moment in the server at the IMN, the one from this study and a new one which has been used for Florida. Comparisons of the two versions are underway at the University of Costa Rica.