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 (%)


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 (%)


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)


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)


180 200 220 240 260 2800

5

10

15

20

25

30

MO

DIS

Wat

er P

ath

(g/m

2 )



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 (%)


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 (%)


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)


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 (

)



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)


-20 0 20 40 600

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



-60 -40 -20 0 200

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



-20 0 20 40 600

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



-70 -60 -50 -40 -30 -200

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



-30 -20 -10 0 10 200

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



0 10 20 30 40 50 600

5

10

15

20

25

30

MO

DIS

Wate

r P

ath

(g/m

2)



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.


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 )


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 )


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 )


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 )


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 )


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 )


Positive interaction between Optical Thickness and GOES Bands


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 )


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 )


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 )


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 )


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 )


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 )


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.

modis and goes data to detect warm …modis and goes data to detect warm raining clouds in puerto...

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