MODIS AND GOES DATA TO DETECT WARM …MODIS AND GOES DATA TO DETECT WARM RAINING CLOUDS IN PUERTO...
Transcript of MODIS AND GOES DATA TO DETECT WARM …MODIS AND GOES DATA TO DETECT WARM RAINING CLOUDS IN PUERTO...
MODIS AND GOES DATA TO
DETECT WARM RAINING CLOUDS
IN PUERTO RICO AND CARIBBEAN BASIN
Joan M. Castro-SánchezDepartment of Civil Engineering
Advisor: Dr. Nazario D. Ramírez-BeltránDepartment of Industrial Engineering
University of Puerto Rico at Mayaguez
October 9, 2015
PRYSIG 2015
Agenda
• Rain Identification
• SCaMPR
• Hot Cloud Detection Problems
• Cloud Products Potential Indicator
• Preliminary Results
• Future Work
Introduction
• A cloud rainfall event is the result of a complex thermodynamic process that starts with nucleation of cloud drops, continues with drop growth, and finishes with water drop precipitation.
The Hydrologic Cycle: Thermodynamic processes
which govern cloud microphysics. Source: National
Weather Service (2010)
Rainfall Cloud
Identification• Caribbean Rainfall Causes:
• Low Pressure Systems
• Tropical Systems (waves, storms, and hurricanes) summer and autumn season
• Cold Fronts: winter and spring season
• Troughs during all year
• Orographic Effects (water vapor, mountains and winds integrations)
SCaMPR
• Self-Calibrating Multivariate PrecipitationRetrieval.
• Developed by Robert Kuligowski (NOAA-NESDIS).
• SCaMPR is an algorithm that combines therelative strengths of infrared (IR)- based andmicrowave (MW)-based estimates ofprecipitation.
• Detection and estimation process is separatedby two steps: (1) rain/no rain classificationusing discriminant analysis, (2) andprecipitation rate calibration using regression.
SCaMPR
• SCaMPR uses GOES bands 3 (6.7
microns) and 4 (10.7 microns)
brightness temperatures.
• Spatial Resolution: 4 km
• Temporal Resolution: 15 minutes
• Output: Rainrate (mm/hr) and
Accumulate Precipitation (mm, 1, 6,
and 24 hours)
SCaMPR: Domain
Latitude: (70 N, 60 S)
Longitude: (165 E, 15 W)
September 29, 2008 at 1745 UTC
SCaMPR: Rainrate (mm/hr)
Rainy Cloud
Detection Problems
Colder Clouds: Vertical Convection
Highest Vapor elevation and lower top
cloud temperature. BT Band 4 < 235 K
Hot Clouds: Horizontal Convection
Lower vapor elevation and higher top cloud
temperature. (BT Band 4 > 235 K)
Potential Rainfall
Indicators• Cloud Product combines infrared and visible techniques to determine
physical and radiative cloud properties.
• GOES: Visible and IR Bands (0.65, 3.9, 6.7, 10.7 um) – Passive Sensor –Geostationary
• Visible Reflectance (Visible Band)
• Effective Radius: (IR Bands 2 and 4)
• Albedo (Bands 2)
• Bands Ratio (Bands 2,3 and 6)
• Band Differences (Bands 2,3 and 6)
• MODIS: Microwave Bands(1.6, 2.1, 3.7 um) – Active Sensor – Orbital
• Liquid Water Path (g/m^2)
• Optical Thickness (Cloud depth)
• Effective Radius (Dropsize Distribution)
Channel 1 Channel 3 Channel 4
Potential Rainfall IndicatorsGOES Bands
Potential Rainfall IndicatorsMODIS Clouds Products
SCaMPR: Colder Cloud
Event
July 18, 2013
NEXRAD (dBz) GOES Visible Reflectance
MODIS Liquid Water Path g/m^2 SCaMPR (mm/hr)
SCaMPR: Hotter Cloud
Event
December 1, 2012
NEXRAD (dBz)GOES Visible Reflectance
MODIS Liquid Water Path g/m^2 SCaMPR (mm/hr)
Dispersion Analysis
• Identify potential interaction byNEXRAD Rainrate and MODIS CloudProducts.
• Find potential colder and hotter cloudinteraction between MODIS and GOESCloud Products.
• 6 Rainfall Events are selected : 3 coldercloud and 3 hotter clouds events.
• Evaluation Period: 2008 - 2015
Preliminary Results: Colder
Clouds
0 10 20 30 40 50 600
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Effective Radius (um)
MODIS Water Path Cold Clouds
0 5 10 150
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Albedo (%)
MODIS Water Path Cold Clouds
99 99.5 100 100.5 1010
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Visible Reflectance (%)
MODIS Water Path Cold Clouds
180 200 220 240 2600
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Brigthness Temperature 3 (K)
MODIS Water Path Cold Clouds
240 260 280 300 3200
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Brigthness Temperature T2 (K)
MODIS Water Path Cold Clouds
180 200 220 240 260 2800
5
10
15
20
25
30
MO
DIS
Wat
er P
ath
(g/m
2 )
GOES Brigthness Temperature T6 (K)
MODIS Water Path Cold Clouds
0 10 20 30 40 50 600
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)
GOES Effective Radius (um)
MODIS Optical Thickness Cold Clouds
0 5 10 150
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)
GOES Albedo (%)
MODIS Optical Thickness Cold Clouds
99 99.5 100 100.5 1010
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)
GOES Visible Reflectance (%)
MODIS Optical Thickness Cold Clouds
180 200 220 240 2600
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)
GOES Brigthness Temperature 3 (K)
MODIS Optical Thickness Cold Clouds
240 260 280 300 3200
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)
GOES Brigthness Temperature T2 (K)
MODIS Optical Thickness Cold Clouds
180 200 220 240 260 2800
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Opt
ical
Thi
ckne
ss (
)GOES Brigthness Temperature T6 (K)
MODIS Optical Thickness Cold Clouds
-20 0 20 40 600
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T43 (K)
MODIS Water Path Cold Clouds
-60 -40 -20 0 200
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T42 (K)
MODIS Water Path Cold Clouds
-20 0 20 40 600
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T46 (K)
MODIS Water Path Cold Clouds
-70 -60 -50 -40 -30 -200
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T32 (K)
MODIS Water Path Cold Clouds
-30 -20 -10 0 10 200
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T36 (K)
MODIS Water Path Cold Clouds
0 10 20 30 40 50 600
5
10
15
20
25
30
MO
DIS
Wate
r P
ath
(g/m
2)
GOES Brigthness Temperature T26 (K)
MODIS Water Path Cold Clouds
Inverse interaction
between MODIS
Water Path and
Optical Thickness
with GOES Albedo
Product.
Positive interaction
between MODIS
Water Path and
Cloud Top Bands 3
and 6 Differences.
Preliminary Results:
Hotter Clouds
5 10 15 20 25 30 35 40 45 5010
1
102
103
104
MODIS Effective Radius (um)
NE
XR
AD
Reflectivity (
mm
6/m
3)
Dispersion Analysis: MODIS Effective Radius
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.610
1
102
103
104
MODIS Optical Thickness
NE
XR
AD
Reflectivity (
in/h
r)
Dispersion Analysis: MODIS Optical Thickness
0 5 10 15 20 2510
1
102
103
104
MODIS Water Path
NE
XR
AD
Reflectivity (
mm
6/m
3)
Dispersion Analysis: MODIS Water Path
Potential Logarithm interaction
between NEXRAD Rainrate and
MODIS Optical Thickness and Liquid
Water Path.
Preliminary Results:
Hotter Clouds
4 5 6 7 8
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T4T3 ( K2 )
MODIS Optical Thickness Cold Clouds
5 6 7 8 9
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T4T2 ( K2 )
MODIS Optical Thickness Cold Clouds
4 5 6 7 8 9
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T4T6 ( K2 )
MODIS Optical Thickness Cold Clouds
5 5.5 6 6.5 7 7.5 8
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T3T2 ( K2 )
MODIS Optical Thickness Cold Clouds
4 4.5 5 5.5 6 6.5 7
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T3T6 ( K2 )
MODIS Optical Thickness Cold Clouds
5 6 7 8 9
x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MO
DIS
Optical T
hic
kness (
)
GOES T2T6 ( K2 )
MODIS Optical Thickness Cold Clouds
Positive interaction between Optical Thickness and GOES Bands
Preliminary Results:
Hotter Clouds
4 5 6 7 8
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T4T3 ( K2 )
MODIS Water Path Cold Clouds
5 6 7 8 9
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T4T2 ( K2 )
MODIS Water Path Cold Clouds
4 5 6 7 8 9
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T4T6 ( K2 )
MODIS Water Path Cold Clouds
5 5.5 6 6.5 7 7.5 8
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T3T2 ( K2 )
MODIS Water Path Cold Clouds
4 4.5 5 5.5 6 6.5 7
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T3T6 ( K2 )
MODIS Water Path Cold Clouds
5 6 7 8 9
x 104
0
5
10
15
20
25
MO
DIS
Wate
r P
ath
(g/m
2)
GOES T2T6 ( K2 )
MODIS Water Path Cold Clouds
Positive interaction between Liquid Water Path and GOES Bands
Future Work
• Develop new formulas to estimate Liquid
Water Path and Optical Thickness using
GOES Bands (Top Cloud Differences).
• Improve hotter cloud detection for
SCaMPR (Top Cloud Combinations).
• Generate new empirical equations to
estimate SCaMPR rainrate based on
GOES Products for daytime and
nighttime.
ACKNOWLEDGEMENTS
• This work is supported primarily by
NOAA-CREST (Cooperative Agreement
No. NA11SEC4810004). Any opinions,
findings, conclusions, or
recommendations expressed in this
material are those of the authors and
do not necessarily reflect those of the
NOAA.
References
• Ramirez-Beltran, N.D., J. M. Castro, and J.Gonzalez. An algorithm for predicting the spatialand temporal distribution of rainfall rate, AnInternational Journal of Water. IJW 2015 Vol. 9 No4.
• Kuligowski, R. J., 2002: A self-calibrating real-timeGOES rainfall algorithm for short-term rainfallestimates. J. Hydrometeor., 3, 112-130.
• Scofield, R. A., and R. J. Kuligowski, 2003: Statusand outlook of operational satellite precipitationalgorithms for extreme-precipitation events. Mon.Wea. Rev., 18, 1037-1051.