Ling Tang and Caitlin Moffitt CEE 6900. Introduction Flooding in Southern Texas Satellite Rainfall Data GPCP and TRMM Dartmouth Flood Observatory Objectives.

Ling Tang and Caitlin MoffittCEE 6900

Introduction◦ Flooding in Southern Texas◦ Satellite Rainfall Data

GPCP and TRMM◦ Dartmouth Flood Observatory

Objectives Methodology

◦ Study Region and Time Period◦ Datasets◦ Statistical Analysis◦ Qualitative Analysis

Results Conclusions

Torrential rains totaled as much as 2-3 feet

River levels reached record heights with crests as high as 30-40 feet above flood stage

9 fatalities 48,000 homes damaged-

5,000 people evacuated $1 billion in damage Extensive impact to livestock

and agriculture in the region

Flooding not just a localized issue- since1970 more than 7,000 major flooding and drought events have caused $2 trillion in damage and 2.5 million casualties world-wide. (World Water Assessment Programme, 2009)

TRMM◦ Sensor Packages:

TRMM Microwave Imager (TMI)

Precipitation Radar Visible Infrared Scanner

(VIRS)

GPCP ◦ Sensor Packages:

Special Sensor Microwave/Imager (SSMI)

GPCP Version 2.1 Satellite-Gauge (SG) combination

Atmospheric Infrared Sounder (AIRS)

low-orbit IR (leo-IR) GOES Precipitation Index (GPI) data from NOAA

Television Infrared Observation Satellite Program (TIROS) Operational Vertical Sounder (TOVS)

Satellite-based flood data could be solution to early flood warning and disaster management

Important component for flood analysis is rainfall Two satellite rainfall products considered in this study-

Uses satellite observations from MODIS to monitor flooding as it occurs◦ MODIS

Visible and infrared bands Used to determine properties of Earth’s surface and

atmosphere MODIS observations confirmed by flooding

reports

To understand the level of agreement of two satellite-based rainfall products

To understand which satellite-based rainfall and flood product would be more appropriate for early flood warning and disaster management

Study Region: TexasLatitude: 25.5N - 36.5NLongitude: 93.5W - 107.5W

Time period: one month June 09 - July 09, 2002

Three river gauge stations in southern Texas are selected for the flooding event

1. Frio River 28˚28'02“ N

98˚32'50"W

2. Nueces River 28˚18'31“N 98˚33'25“W

3. San Antonio River 28˚57'05“N 98˚03'50“W

Two Satellite Products: TRMM 3B42RT and Global Precipitation Climatology Project (GPCP) Ground Radar Data : Next Generation Radar (NEXRAD) Stage IV

Data Area Spatial Resolution

Temporal Resolution

TRMM 3B42RT

60˚N~60˚S 0.25˚ 3-hourly

GPCP global 1˚ daily

NEXRAD Stage IV

Mainly in US

0.04˚ hourly

-- All datasets were uniformed to 1˚ and daily resolution and cropped at the Texas region for statistical analysis.

1˚ and daily

1. Estimate the statistical properties of the datasets - Calculate the mean and standard deviation of the

datasets in each day in the study time period.

2. Compare the level of agreement between the two satellite datasets

- Estimate the correlation between the satellite products, and also with the ground data.

3. Estimate the uncertainty of satellite products based on the truth data (ground radar)

This includes the estimation of four error metrics: 1). Bias 2). Root Mean Square Error (RMSE) 3). Probability of Detection (POD) 4). False Alarm Ratio (FAR)

Error assessment 1. Bias the average of difference between the

study data and the truth of the days being estimated

2. RMSE the second moment of error, for an unbiased estimator, RMSE is the standard deviation

3. Probability of Detection (POD) RainThe fraction of observed events that were correctly

forecast

4. False Alarm Ratio (FAR)The fraction of forecast events that were observed

to be non-events

(source from : Ebert E. et. al 2007)

Compare hydrographs for point locations along satellite-claimed “flooded” rivers to determine if flooding occurred

Side-by-side comparison of DFO flood maps with 3B42RT and GPCP to determine which satellite product would be better for early flood detection and disaster management

3B42RT overestimates rainfall GPCP underestimates rainfall

3B42RT is more variable than GPCP

3B42RT NEXRAD GPCP

Mean

STD

Overall, 3B42RT hashigher bias than GPCP

• 3B42RT has higher RMSE than GPCP

POD is higher for 3B42RT

FAR is higher for 3B42RT

Flood indicated by large jump in hydrographs

Occurs immediately after rainfall begins

3B42RT shows stronger indication of high rainfall upstream from flood points

Overall, both satellite-based rainfall products indicated areas of high accumulation upstream of flooding points

3B42RT had higher probability of detecting rainfall and flooding, and a higher correlation with ground measurement

However, 3B42RT also has higher uncertainty compared to GPCP

GPCP is more appropriate for climatologic analysis or application

World Water Assessment Programme (2009). The United Nations World Water Development Report 3: Water in a Changing World. Paris:UNESCO, and London: Earthscan

Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, E. Nelkin 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4,1147-1167.

Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2, 36-50.

Kummerow, Christian et al. “The Tropical Rainfall Measurement Mission (TRMM) Sensor Package.” Journal of Atmospheric and Oceanic Technology. Volume 15 (June 1998). 809-817

http://trmm.gsfc.nasa.gov/ (TRMM) http://www.ncdc.noaa.gov/ (NEXRAD) http://precip.gsfc.nasa.gov/ (GPCP) http://www.dartmouth.edu/~floods/ (Dartmouth Flood Observatory)