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REMOTE SENSING OF RAINFALL
http://www-calipso.larc.nasa.gov/about/constellation.html
Amir AghaKouchak & Soroosh SorooshianCenter for Hydrometeorology & Remote Sensing
University of California Irvine
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http://www-calipso.larc.nasa.gov/about/constellation.html
Hydrologic Cycle
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http://www-calipso.larc.nasa.gov/about/constellation.html
Hydrologic Cycle
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Precipitation
Source of Fresh Water
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Precipitation
Source of Fresh Water
Severe Floods
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Precipitation
Source of Fresh Water
Severe Droughts
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Precipitation
Mea
suri
ng
Pre
cip
itat
ion
WHYM
easu
rin
g P
reci
pit
atio
n
HOW
Mea
suri
ng
Pre
cip
itat
ion
HOWACCURATE
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Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
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Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
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Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
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Precipitation Measurement DevicesM
easu
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g P
reci
pit
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n
HOW - Rain Gauges
- Weather Radars
- Satellites
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Rain Gauges
Recording Gage
(a) Tipping bucket
0.01 inch or 0.2 mm / tipping
(b) Weighting type
It can measure other forms of precipitation, including hail and snow. It is more expensive and require more maintenance than tipping bucket gauges.
(c) Float Recording
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Rain Gauges
Wind-Induced Undercatch
Influencing Factors:
– Wind speed
– Temperature
– Gauge type
– Gauge height
– Windshield
– Exposure
Limitations: drops will stick to the sides or funnel of the collecting device.
Rain may fall on the funnel and freeze.
Snow can block the funnel
Nespor and Sevruk, 1999
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Gauges Networks
Rain Gauge Networks:
Advantages, Disadvantages and Limitations?
Dingman, 2002
U.S. National Weather Service operates:
≈ 3,500 recording gagesand ≈ 11,000 non-recording gages
Suggested minimum number of gages
1. Flat regions: tropical and relatively uniform 235 ~ 350 mi2 / gage2. Arid and Polar regions 600 ~ 4,000 mi2 / gage3. Mountainous region 40 ~ 100 mi2 / gage4. Small mountainous islands: irregular 10 mi2 / gage
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Gauges Networks
Rain Gauge Networks:
Advantages, Disadvantages and Limitations?
Dingman, 2002
U.S. National Weather Service operates:
≈ 3,500 recording gagesand ≈ 11,000 non-recording gages
Suggested minimum number of gages
1. Flat regions: tropical and relatively uniform 235 ~ 350 mi2 / gage2. Arid and Polar regions 600 ~ 4,000 mi2 / gage3. Mountainous region 40 ~ 100 mi2 / gage4. Small mountainous islands: irregular 10 mi2 / gage
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Gauges Networks
Rain Gauge Networks:
Advantages, Disadvantages and Limitations?
Advantages:
• Perhaps “True” measurement of rain
Disadvantages:
• No coverage over oceans or remote regions
• Point measurement not representative of area
• Wind and instrumental underestimates of rain
• Attempting to collect rain data during hurricane, forexample, can be nearly impossible or unreliable due toextreme winds (even if the equipments survive)
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Weather Radars
After J.C. Nam and G.H. Ryu 2001
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Weather Radars
RADAR (NEXRAD: Next Generation Weather Radar system )
Weather Surveillance Radar-1988 Doppler (WSR-88D)
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Weather Radars
http://weather.noaa.gov/radar/national.html
Los Angeles
Santa Ana
San Diego
RADAR (NEXRAD: Next Generation Weather Radar system )
Weather Surveillance Radar-1988 Doppler (WSR-88D)
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Weather Radars
RADAR (NEXRAD: Next Generation Weather Radar system )
http://www.nws.noaa.gov/radar_tab.php
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Weather Radars
RADAR (NEXRAD: Next Generation Weather Radar system )
Radar Coverage at 3km (msl) Radar Coverage at 5km (msl)
Source: www.cimms.ou.edu/~jzhang/radcov.html
Blockage (mountainous region)
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Weather Radars
Weather Radars:
Advantages, Disadvantages and Limitations?
Advantages:
• Excellent space and time resolution
• Rainfall estimation in near real-time
Disadvantages:
• Poor coverage over oceans or remote regions
• blockage (mountainous regions
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SATELLITE-BASED
PRECIPITATION ESTIMATION
Remote Sensing
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Satellites
http://www-calipso.larc.nasa.gov/about/constellation.html
Types of Satellites:
- Geosynchronous Earth Orbiting (GEO)
- Low Earth Orbiting (LEO)
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Satellites
Source: www.comet.ucar.edu Source: www. history.nasa.gov
Geosynchronous Earth Orbiting (GEO) Low Earth Orbiting (LEO)
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Satellite Precipitation Data
Meteosat 7 (EUMETSAT)
TRMM PR (NASA/NASDA)
SSMI 85GHz (DMSP)
Geostationary IRCloud top data15-30 minute temporal resolution
Passive Microwave (SSM/I)Some characterisation of rainfall~2 overpasses per day per spacecraft, moving to 3-hour return time (GPM)
TRMM precipitation RADAR3D imaging of rainfall 1-2 days between overpasses( S-35°N-35 °)
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PERSIANN Algorithm
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)
The algorithm utilizes a neural network classification and approximation approach to derive precipitation estimates based on IR data calibrated with microwave estimates.
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GEO (VIS/IR):- Less accurate estimates- Good global areal coverage with high temporal sampling
LEO (PMW): - More accurate and less frequent estimates- Areal Coverage 3 hour accumulation (Regional gaps)
PERSIANN Algorithm
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PERSIANN dataSpatial Resolution: 0.25o degreeTemporal Resolution: 3-hourSource: HyDIShttp://hydis8.eng.uci.edu/hydis-unesco/
PERSIANN-CCS dataSpatial Resolution: 0.04o degreeTemporal Resolution: 1-hourSource: GWADIhttp://hydis.eng.uci.edu/gwadi/
PERSIANN Algorithm
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Satellites
http://hydis.eng.uci.edu/gwadi/
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Satellites
http://hydis.eng.uci.edu/gwadi/
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Why Satellites?
WSR-88D Radar Coverage Gauge Network
3 km Above Ground Level
Maddox, et. Al., 2002
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Maddox, et. Al., 2002
WSR-88D Radar Coverage Gauge Network
2 km Above Ground Level
Why Satellites?
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Maddox, et. Al., 2002
WSR-88D Radar Coverage Gauge Network
1 km Above Ground Level
Why Satellites?
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Floods Among the worst Natural Disasters
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Floods Among the worst Natural Disasters
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Hydrologic Forecasting Needs: Flash Floods
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Los Angeles (1955)
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Application to Flood Forecasting
Hydrologic models are simplified, conceptualrepresentations of a part of the hydrologiccycle. They are primarily used for hydrologicprediction and for understanding hydrologicprocesses. An example of a conceptual modelthat represents a part of the natural system
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Application to Flood Forecasting
Hydrologic models are simplified, conceptualrepresentations of a part of the hydrologiccycle. They are primarily used for hydrologicprediction and for understanding hydrologicprocesses. An example of a conceptual modelthat represents a part of the natural system
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High Resolution Data from Satellites
Radar Observation (2 km AGL) PERSIANN-CCS Estimates
4km x 4km, 3-hour accumulated precipitation
Study Area
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High Resolution Data from Satellites
Radar Observation (2 km AGL) PERSIANN-CCS Estimates
4km x 4km, 3-hour accumulated precipitation
Study Area
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Satellites:
Advantages, Disadvantages and Limitations?
Advantages:
• Global Coverage
• Relatively high resolution in space and time
Disadvantages:
• Still needs research on development of precipitationretrieval algorithms
Satellites
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NASANOAA
NRLUC Irvine
Satellites
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http://www.bom.gov.au/bmrc/SatRainVal/validation-intercomparison.html
Validation and inter-comparison of daily satellite Precipitation estimates - An IPWG project
Validation of Satellite Retrieval Algorithms
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Quantile Probability of Detection (QPOD)
QPOD
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
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Quantile False Alarm Ration (QFAR)
QFAR
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
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Monthly Quantile Bias
Monthly Quantile Bias
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
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Bias Adjustment
InputAdjustment Reference
Output
PERSIANN (0.25ox0.25o)
GPCP Monthly (2.5ox2.5o)
PERSIANN-MBA (0.25ox0.25o) - hourly
PERSIANN (0.25ox0.25o)
GPCP Daily(1.0ox1.0o)
PERSIANN-DBA(0.25ox0.25o) - hourly
PERSIANN (0.25ox0.25o)
GPCP Pentad (2.5ox2.5o)
PERSIANN-PBA(0.25ox0.25o) – hourly
PERSIANN-CCS (0.04ox0.04o)
GPCP Daily(1.0ox1.0o)
PERSIANN-CCS-DBA(0.04ox0.04o) – 30 min
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Global IR
TRMM, DMSP, NOAA Satellites
ANN
ParameterAdjustment
Sate
llite
Data
High Temporal-Spatial Res.
Cloud Infrared Images
Fe
edback
Sampling
Instantaneous PMW Rain Estimates
PERSIANN Hourly Rainfall (0.25ox0.25o)
Downscaling
Adjusted Hourly Rainfall (0.25ox0.25o)
PMW-RRFill-in
PERSIANN-PMW filled Hourly (0.25o)
BiasAdjustment
Accumulation
PERSIANN Monthly Rainfall (2.5o)
Adjusted Monthly Rainfall (2.5o)
GPCP Monthly Precipitation (2.5ox2.5o)
Bias adjustment of PERSIANN rainfall:
Stage I: Fill-in missing PERSIANN rainfall by PMW-rainfall.
Stage II: Accumulation and Adjustment of PERSIANN rainfall based on GPCP monthly rainfall measurement.
Stage III: Spatial and temporal downscaling of PERSAINN bias estimates from monthly rainfall at 2.5 degree to hourly at 0.25 degree.
Downscaling of GPCP Rainfall to High Spatio-temporal Scale Using PERSIANN
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Shundasht Fall, Shundasht, Iran