Center for Hydrometeorology and Remote Sensing - University of California, Irvine Precipitation Detection and Estimation Using Multi-Spectral Remotely

Center for Hydrometeorology and Remote Sensing - University of California, Irvine

Precipitation Detection and Estimation Using Multi-Spectral Remotely Sensed Data

1 Center For Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine2 NASA/GSFC Code 613.1

3 NOAA/NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD

Ali Behrangi1

Kuo-lin Hsu1

Bisher Imam1

Soroosh Sorooshian1

George Huffman2

Robert J. Kuligowski3


LEO (PMW): LEO (PMW): More accurate estimateMore accurate estimateEven after 3 hour accumulation still we have gapsEven after 3 hour accumulation still we have gaps

GEO (VIS/IR):GEO (VIS/IR):Less accurate estimateLess accurate estimateGlobal coverage is available frequentlyGlobal coverage is available frequently

Introduction: Problem Statement


Introduction: Solution

1) Interpolating the precipitation intensity obtained from LEO (PMW) Satellites

2) GEO (VIS/IR) satellites provide high-resolution (time and space) images

(Joyce et al., 2004)


Question:

Can Multi–spectral images help us to improve GEO-based precipitation estimation ?


MULTI-SPECTRAL imagesSpinning Enhanced Visible and Infra-red Imager (SEVIRI). 12 different wavelengths once every 15 minutes,


The ABI (Advanced Baseline Imager) on

Future GOES-R

Figure courtesy of ITT Industries

(Advanced Baseline Imager )

Multi- Spectral Precipitating Estimation



Kurino (1997) 6.7, 11 and 12 µm

Rosenfeld and Gutman (1997) 0.65, 3.7, 10.8 and 12 µm

Inoue and Aonashi ( 2000) 0.6, 1.6, 3.8, 11 and 12 µm

Ba and Gruber (2001) 0.65, 3.9, 6.7, 11 and 12 µm

Capacci and Conway (2005)9 MODIS and corresponding SEVIRI

channels

- IR 11µm & 12 µm: => removal of thin cirrus cloud

- IR 11µm & WV 6.7 µm: => sign of deep convective

- NIR 3.7 µm : => sensitive to cloud drop size distribution - VIS : => cloud optical thickness.


Relative-frequency distributions of different channels under rain and no-rain conditions

No RainRain

(0.65 μm) (3.9 μm) (6.7 μm)

(10.8 μm) (13.3 μm)



Algorithm Development:

1- Grid-box based :

2- Cloud Patch based :


Grid-Box Based Approach


Grid-box Approach

Algorithm Development:

Unsupervised Classification

PCA

A : Thick-Cold cloud (i.e., Convective)B : Thin-Cold cloud (i.e., Cirrus)C : Clear Sky

Rain Probability/Intensity

Multi-spectral Images

Textural information

Clusters (MRR)



Case Study 1: Florida : August 30 2006

Hit Under Estimation Over Estimation

Ch 1 : 0.6 µm Ch2 : 3.9 µm Ch3 : 6.5 µm Ch4:10.7µm Ch5 : 13.3µm

f) Ch3+Ch5

ETS=25POD=74FAR=45

ETS=29POD=77FAR=42

ETS=27POD=78FAR=44

ETS=36POD=76FAR=35

ETS=30POD=80FAR=42

ETS=30POD=72FAR=39

ETS=35POD=79FAR=37

ETS=37POD=78FAR=35

ETS=37POD=80FAR=36

ETS=48POD=75FAR=22

ETS=49POD=79FAR=24

g) Ch4+Ch5

i) Ch3+Ch4+Ch5

d) Ch5

f) Ch3+Ch5

VIS

IR (10.8 µm)


Case Study 2:

Over Estimation

d) Ch5

g) Ch4+Ch5

i) Ch3+Ch4+Ch5

f) Ch3+Ch5

Ch 1 : 0.6 µm Ch2 : 3.9 µm Ch3 : 6.5 µm Ch4:10.7µm Ch5 : 13.3µm


SEVIRI(MSG)

VIS (0.65 µm) IR (10.8 µm)

Case Study 3: Precipitation Estimation (using SEVIRI)




Under EstimateUnder Estimate

Over EstimateOver Estimate


Overall Results

ETS

POD/FAR

BIAS(area)

Scenario

Rain/No-rain Detection

RMSE

BIAS(volume)

CC

Scenario

Rain Rate Estimation


Multi-spectral & Diurnal Cycle of Precipitation

New York

Florida

Texas


BT 10.8 µm

Day:BT (0.65 &10.8) µm

Night:BT (6.7 & 10.8 )µm

Day & Night:BT (6.7 & 10.8 )µm

NEXRAD

Diurnal Cycle over Florida, USA (Summer 2006)


Patch Based Approach



IR-Based Patching

VIS - Based Patching

Multi-spectral - Patch based


IR-Based Patching

VIS - Based Patching

Warm thick cloud

Cold Thin cloud

Multi-spectral - Patch based


Results ofResults of Multi-spectral Cloud ClassificationMulti-spectral Cloud Classification experiment: experiment:

In general results are encouraging !

Detail Statistics will be provided in near future


ConclusionsConclusions::- Multi-spectral data are promising for precipitation retrieval,

Particularly for delineation of areal extent of precipitation.

- In addition to 10.8 μm band, VIS channel for day time and WV channel for night time seems to be good candidates.

Future Work- Developing a combined algorithm using multi-

spectral data and PMW estimate, …. is ongoing.


Thank You !


Documents

Center for Hydrometeorology and Remote Sensing - University of California, Irvine Precipitation Detection and Estimation Using Multi-Spectral Remotely