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
http://www-calipso.larc.nasa.gov/about/constellation.html
Hydrologic Cycle
http://www-calipso.larc.nasa.gov/about/constellation.html
Hydrologic Cycle
Precipitation
Source of Fresh Water
Precipitation
Source of Fresh Water
Severe Floods
Precipitation
Source of Fresh Water
Severe Droughts
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
Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
Mea
suri
ng
Pre
cip
itat
ion
WHYWater Resources Management
Disaster Preparedness
Short-Term Decision Making
Precipitation
Precipitation Measurement DevicesM
easu
rin
g P
reci
pit
atio
n
HOW - Rain Gauges
- Weather Radars
- Satellites
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
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
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
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
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)
Weather Radars
After J.C. Nam and G.H. Ryu 2001
Weather Radars
RADAR (NEXRAD: Next Generation Weather Radar system )
Weather Surveillance Radar-1988 Doppler (WSR-88D)
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)
Weather Radars
RADAR (NEXRAD: Next Generation Weather Radar system )
http://www.nws.noaa.gov/radar_tab.php
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)
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
SATELLITE-BASED
PRECIPITATION ESTIMATION
Remote Sensing
Satellites
http://www-calipso.larc.nasa.gov/about/constellation.html
Types of Satellites:
- Geosynchronous Earth Orbiting (GEO)
- Low Earth Orbiting (LEO)
Satellites
Source: www.comet.ucar.edu Source: www. history.nasa.gov
Geosynchronous Earth Orbiting (GEO) Low Earth Orbiting (LEO)
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 °)
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.
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
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
Satellites
http://hydis.eng.uci.edu/gwadi/
Satellites
http://hydis.eng.uci.edu/gwadi/
Why Satellites?
WSR-88D Radar Coverage Gauge Network
3 km Above Ground Level
Maddox, et. Al., 2002
Maddox, et. Al., 2002
WSR-88D Radar Coverage Gauge Network
2 km Above Ground Level
Why Satellites?
Maddox, et. Al., 2002
WSR-88D Radar Coverage Gauge Network
1 km Above Ground Level
Why Satellites?
Floods Among the worst Natural Disasters
Floods Among the worst Natural Disasters
Hydrologic Forecasting Needs: Flash Floods
Los Angeles (1955)
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
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
High Resolution Data from Satellites
Radar Observation (2 km AGL) PERSIANN-CCS Estimates
4km x 4km, 3-hour accumulated precipitation
Study Area
High Resolution Data from Satellites
Radar Observation (2 km AGL) PERSIANN-CCS Estimates
4km x 4km, 3-hour accumulated precipitation
Study Area
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
NASANOAA
NRLUC Irvine
Satellites
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
Quantile Probability of Detection (QPOD)
QPOD
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
Quantile False Alarm Ration (QFAR)
QFAR
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
Monthly Quantile Bias
Monthly Quantile Bias
Period of Analysis: 2005-2008
Reference data: Stage IV radar-based gauge adjusted data
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
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
Thank YouFor Your
Attention
Shundasht Fall, Shundasht, Iran
Top Related