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Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Remote Sensing Precipitation Using
GEO Satellite Information
Kuolin Hsu Center for Hydrometeorology and Remote Sensing,
University of California, Irvine
The IPWG7 Training Course Program 17-20 November 2014
Tsukuba International Congress Center, Tsukuba, Japan
New and emerging remote-sensing technologies for precipitation data sets and their applications and validation
Session 1: Precipitation Remote Sensing and retrieval algorithms I: Infrared Algorithm
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Outline
• Precipitation Measurement
• GEO Satellite Information for Precipitation Retrieval
• Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks (PERSIANN)
• PERSIANN-Climate Data Record (PERSIANN-CDR)
• Summary
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Extreme Precipitation & Flash Flooding
Floods caused by extreme precipitation are the most widespread nature disasters
High spatial and temporal resolution of precipitation measurement is needed for operational hydrology
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Precipitation Observation
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Coverage of the WSR-88D and gauge networks
3 km AGL 2 km AGL 1 km AGL
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Satellite Precipitation Monitoring
Geostationary IR Cloud top heights only 15-30 minute data
Meteosat 7 (EUMETSAT)
TRMM precipitation RADAR 3D imaging of rainfall 1-2 days between overpasses (35°N-35°S only)
TRMM PR (NSA/JAXA)
Passive Microwave (SSM/I) Some characterisation of rainfall ~2 overpasses per day per spacecraft, moving to 3-hour return time (GPM)
SSMI 85GHz (DMSP)
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
TRMM Rain Rate
“Instantaneous” rain rate from TRMM
http://trmm.gsfc.nasa.gov/
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Typical Microwave Coverage in 3 Hr
TMI – white AMSR-E – medium grey SSM/I – light grey AMSU-B – dark grey
http://trmm.gsfc.nasa.gov/
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
+
-
- + +
Interpolation of 3-hour Precipitation
T T+3hr T+6hr t-hr t+3hr
Rain started between 3-hr period
Missed the peak
Rain ended between 3-hr period
Short-life event
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Rain Estimation from Geostationary satellite (VIS/IR)
Advantages: • Good space and time resolution (half-hour, 4 km) • Observations in near real time • Near global coverage
Disadvantages: • Measures cloud-top properties instead of rain • May mistake cirrus for rain clouds • May not capture rain from warm clouds
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
VIS/IR Rainfall Estimates
• IR brightness temperature Deeper cloudsà colder à heavier rainfall Low clouds à Warm à no rain
• VIS reflectivity Thicker cloudsà brighter: heavier rainfall light clouds à Dark: no rain
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
VIS-IR Image vs. Rainfall
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
VIS-IR Image vs. Rainfall
Cold-Thin clouds
Warm-Thick clouds
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Infrared image
IR (K)
Visible image
Albedo (%)
Hit Under Estimation Over Estimation
10
20
3
0
40
5
0
Latit
ude
Longitude-130 -120 -110 -100 -90 -80 -70
0 10 20 30 40 50 60 70 80
Before albedo adjustment After albedo adjustment
10
20
3
0
40
5
0
Latit
ude
Longitude-130 -120 -110 -100 -90 -80 -70
0 10 20 30 40 50 60 70 80
Before albedo adjustment After albedo adjustment
Albedo (%)
Multi-spectral Image for Rain Classification
Ali et al., J. Hydrometeorology, 2009
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Florida : Hurricane Ernesto August 30 2006
Ch 1 : 0.6 µm Ch2 : 3.9 µm Ch3 : 6.5 µm Ch4:10.7µm Ch5 : 13.3µm
f) Ch3+Ch5
Hit Under Estimation Over Estimation
ETS=25 POD=74 FAR=45
ETS=29 POD=77 FAR=42
ETS=27 POD=78 FAR=44
ETS=36 POD=76 FAR=35
ETS=30 POD=80 FAR=42
ETS=30 POD=72 FAR=39
ETS=35 POD=79 FAR=37
ETS=37 POD=78 FAR=35
ETS=37 POD=80 FAR=36
ETS=48 POD=75 FAR=22
ETS=49 POD=79 FAR=24
g) Ch4+Ch5
i) Ch3+Ch4+Ch5
d) Ch5
f) Ch3+Ch5
Ali et al., J. Hydrometeorology, 2009
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
f(.) Nonlinear
multivariate function
GO
ES
IR VIS …
Location Topography Wind flow
Water Vapor . . .
Rainfall Intensity
High quality Rainfall sampling data
Rain gauge, radar, SSM/I, and TRMM
error
A Strategy for Satellite Precipitation Estimation
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Some IR based Algorithms
• Global Precipitation Index (GPI): Arkin and Meisner, 1987
• Negri-Adler-Wetzel (NAW) technique: Negri et al., 1984
• Convective Stratiform Technique (CST): Adler & Negri, 1988
• AutoEstimator: Vicente et al., 1998
• Hydro-Estimator: Scofield and Kuligowski, 2003
• Tropical Applications of Meteorology using SATellite data (TAMSAT): Grimes et al., 1999
• PERSIANN/PERSIANN-CCS.PERSIANN-MSA: Hsu et al., 1997; Sorooshian et al., 2000; Hong et al., 2004; Behrangi et al., 2009
• and many more …
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
PERSIANN System Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
PERSIANN System “Estimation” Global IR
MW-RR (TRMM, NOAA, DMSP Satellites)
HyDIS WEB
ANN
Error Detection
Quality Control
Merging
Sate
llite
Dat
a G
roun
d O
bser
vatio
ns
Products
High Temporal-Spatial Res. Cloud Infrared Images
Radar Coverage
Feed
back
Hourly Rain Estimate Sampling
MW-PR Hourly Rain Rates
Hourly Global Precipitation Estimates
Gauges Coverage
Sorooshian et al., BAM, 2000
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Designing the PERSIANN System
Input feature Classification
Rain Rate Estimation
Error Detection-Correction
Switch
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Input Variables
Surface Type: Land, Coast Ocean
Tb at the central pixel
Tb and Tb -SD in the 3 x 3 window
Tb and Tb-SD in the 5 x 5 window
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
SOFM Classification Map (After Training) Comparing the rain rate distribution on the output layer with the weight distributions of input variables on the SOFM layer
VTbj VTbk
SOFM Layer
j k
Output Layer
Weight Vectors
Each Neuron for An Input Class
Weight Map of Surface Type
Weight Map of Tb
Weight Map of Tb -SD
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
IPGW Validation of Precipitation Measurement (Australia)
http://www.bom.gov.au/bmrc/SatRainVal Daily Rainfall: January 23, 2005
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
IPWG Validation of Precipitation (US) http://cics.umd.edu/ipwg/us_web.html
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Rainfall Estimation Using Satellite-Based Cloud Classification Maps
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
· Tb: IR temperature of calculation pixel
· m3x3: Mean temperature of 3x3 pixels
· s3x3: Standard deviation temperature of 3x3 pixels
· m5x5: Mean temperature of 5x5 pixels
· s5x5: Standard deviation temperature of 5x5 pixels
Satellite Image Feature Extraction
200 225 250 275 300 325 oK
Cloud information
Pixel information
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
200 225 250 275 300 325 oK
Cloud Top Temperature Tb (ok)
Rai
nfal
l Rat
e (m
m/h
r)
Cirrus Cloud
Convective Cloud
Cloud Type Classification
Tb–R relationship
Cloud Types and Rainfall Distribution
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Tb=220K
Tb=235K
Tb=253K
t=t0 t=t1 t=t2 t=tk
c1
c2
ck
Tb (K)
R (mm/h)
200 300 0
80
],,[)( texturepatchgeometrypatchcoldnesspatchVvectorFeaature Îv
T220K
T235K
T253K
KV220
v
KV253
v
KV235
vT253K, t=tk
Patch Classification Patch Feature Extraction
Image Segmentation
Rainfall Estimation
Patch-based Approach (PERSIANN-CCS)
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
400 Tb-R curves from PERSIANN-CCS
model
(1) simple threshold (2) Linear: single line (3) Nonlinear: single
curve
Multiple vs. Single Curve Fitting Models
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Tb=220K
Tb=235K Tb=253K
Hei
ght
],,[ texturepatchgeometrypatchcoldnesspatchV Îv
T220K
T235K
T253K
KV220
v
KV253
v
KV235
v
T253K, t=tk
Patch Feature Extraction
Cloud IR Image
Image Segmentation T220K
T235K
T253K
KV220
v
KV253
v
KV235
v
Patch index j Patch index k
Patch index j
Patch index k
Features Extraction
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
1 10 20 1
10
20
G0 G1
G2
G3
G4 G5
G6
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Near Real Time Global Precipitation Data http://hydis.eng.uci.edu/gwadi/
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Global PERSAINN-CCS Hourly Estimates
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Six-Hour PERSIANN-CCS Rainfall
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Continue Development
• Adjust PERSIANN-CCS precipitation estimates using passive microwave rainfall
• Improve rain estimation from warm clouds
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
PERSIANN-CCS Hourly Estimates: Jan 4, 2012
CCS Estimation
CCS (PMW Adjusted) Estimation
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Change Threshold from 253K to 280K
K
mm/hr
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
PERSIANN Precipitation Climate Data Record Reconstruction of 30-year+ Daily Precipitation Data
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
PERSIANN Precipitation Climate Data Record
• Daily Precipitation Data • Data Period: 1983~2014 • Coverage: 60oS ~ 60oN • Spatial Resolution: 0.25ox0.25o
http://www.ncdc.noaa.gov/cdr/operationalcdrs.html
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
LEO Satellites for Precipitation Estimation Limited PMW Samples
Before year 2000
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Source: NOAA NCDC
• International Satellite Cloud Climatology Project (ISCCP) 1979 to present
10-km and 3-hour intervals
GOES-11 (135°West) GOES-12 (75°West)
MET-9 (0°East) MET-7(57.5°East)
FY2-C(105°East) MTSAT-1R(140°East)
1. U.S. Geostationary Operational Environmental Satellite (GOES)
2. European Meteorological satellite (Meteosat) series
3. Japanese Geostationary Meteorological Satellite (GMS)
4. The Chinese Fen-yung 2C (FY2) series.
Historical GEO Satellite Data
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
PERSIANN structure in a simple scheme
Global IR
TRMM, DMSP, NOAA Satellites
Parameter Adjustment
Sate
llite
Dat
a
High Temporal-Spatial Res. Cloud Infrared Images
Feed
back
Sampling
Instantaneous PMW Rain Estimates
PERSIANN Hourly Rainfall (0.25ox0.25o)
Tempo-Spatial Accumulation
PERSIANN Monthly Rainfall (2.5ox2.5o)
GPCP Monthly Precipitation (2.5ox2.5o) Bias
Adjustment
Adjusted PERSIANN Hourly Rainfall (0.25ox0.25o)
Products
PERSIANN Adjusted (Monthly Scale)
Bias Adjustment of PERSIANN Estimates
Center for Hydrometeorology & Remote Sensing, University of California, Irvine Center for Hydrometeorology and Remote Sensing (CHRS)
PERSIANN-CDR daily rainfall During Katrina 2005
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Rain rate (mm/day)
Center for Hydrometeorology and Remote Sensing (CHRS)
Daily Precipitation: Hurricane Katrina, 2005
PERSIANN w/o GPCP adjustment PERSIANN w/o GPCP adjustment
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
App
licat
ions
Drought Management Flood Forecasting Water Resources
Satellite Precipitation Data for Hydrologic Applications A
lgor
ithm
Web
Ser
vice
s
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Summary Advantages of GEO-based precipitation retrieval: • Good space and time resolution • Observations in near real time • Near global coverage
Improve IR-based estimation by: • Extending from pixel to texture based classification • Extending from single IR band to multi-spectral bands • Integrating information with LEO satellite PMW
measurements • Merging estimation with ground measurements • Applying advanced machine learning methods to learn
cloud-rain system
Center for Hydrometeorology & Remote Sensing, University of California, Irvine
Thanks !!