A RAIN ACCUMULATION PRODUCT TO INVESTIGATE RAIN EFFECTS ON AQUARIUS SEA SURFACE SALINITY MEASUREMENTS

Andrea Santos-Garcia1, Maria Marta Jacob2, Linwood Jones1, and Shadi Aslebagh3

1 Central Florida Remote Sensing Lab., University of Central Florida, Orlando, FL2 Comisión Nacional de Actividades Espaciales, Argentina

3 Applied Physics Laboratory, University of Washington, Seattle, WA

Rain accumulation can produce a fresh water surface layer that reduces SSS in the top few cm of the ocean

Transient processes of diffusion and mechanical mixing by ocean waves occurs • After several hours, the ocean becomes slightly

lower salinity (assimilation of fresh water)• SSS vertical profile in the upper 5 m depth is

restored to a predictable value

Since AQ samples each ocean pixel only once in 7-days, it is important to identify these transient effects of rain, which may NOT represent the steady state SSS value

AQ Rain Accumulation (RA) Product provides a time-history of rainfall averaged over an AQ IFOV• Rain accumulation estimates derived from both

- TRMM 3B42 near-global rain product

- NOAA Climate Prediction Center Morphing Technique (CMOPPH)

AQ RAIN ACCUMULATION – SOURCE DATAINTRODUCTION

0 no observation1 AMSU2 TMI3 AMSR4 SSMI5 SSMI/S6 MHS7 TCI30 AMSU/MHS avg31 Conical scanner avg50 IR

3B42/CMORPH Rain Products

Sat Rain Meas Temporal Sampling

3B42

Every 3 Hours

CMORPH

Every 30 minutes

AQ RAIN ACCUMULATION ALGORITHMEARTH GRIDDED AQ IFOV

(0.25°resolution)RAIN DATA PRODUCT

(ORIGINALLY @ 0.25°resolution)

DATA SETS ARE COLLOCATED

TIME INTERPOLATION TO THE AQ OBSERVATION TIME

COLLOCATED DATASET

LINEAR TIME INTERPOLATION IN 0.25 HR TIME STEPS FOR

LAST 24 HRS

INSTANTANEOUS RAIN RATE (RR) @ AQ OBSERVATION

TIME

RAIN ACCUMULATION RA

0 – 3 hrs 3 – 6 hrs 6 – 9 hrs 21 – 24 hrs

3B42IN TIME CLOSEST

MEASUREMENT TO THE AQ OBSERVATION TIME

USE OF DATASET FORLAST 24 HRS

CMORPH

OUTPUT

INPUT

AQ RAIN ACCUMULATION ALGORITHM

• Validation conducted using WindSat EDR’s (environmental data records)

• 3B42 & CMORPH data interpolated to WindSat time using AQ RA product methodology

• Rain events were evaluated using visual comparison and normalized cross correlation

-1

0

1

2

3

4

5

6

7

8

-1

0

1

2

3

4

INSTANTANEOUS 3B42 RR INTERPOLATED TO WINDSAT TIME

INSTANTANEOUS WINDSAT RR

NORMALIZED CROSS CORRELATION MATRIX

Normalized cross-correlation reflects :• Shifting of 2 pixels (50

Kms) in horizontal direction.

• Maximum correlation equals to 0.6026

0

5

0

5

0

5

0

5

0

INSTANTANEOUS RR

0

1

2

3

4

5

RA03RA06

RA15RA12

RA09

RA24RA21

RA18

[mm

/hr]

INSTANTANEOUS RAIN RATE OVER AQ FOOT PRINT

AQ IFOV REPRESENTATION

RAIN ACCUMULATION PRODUCT OVER PREVIOUS 24 HOURS TO AQ TIME

TIME DIFFERENCE TO THE CENTER OF THE TIME WINDOW

DATA SOURCE

INSTANTANEOUS RAIN RATE FROM 3B42 @ 3 HOURS

Time difference

Source Flag

INSTANTANEOUS RAIN RATE FROM 3B42 Tim

e difference Source Flag

0 5 10 15 20 25 30 35 40 45-5

0

5

10

15

20

25

30

35INSTANTANEOUS RAIN RATES – January 10th 2012

3B42 IRR INTERPOLATED EVERY 15 min

CMORPH in 30 min time resolution

IRR

[mm

/hr]

Time [hours]

CONCLUSIONS

The AQ RA product is a unique source ofancillary rain accumulation history thatenables a comprehensive analysis of raineffects on the AQ SSS measurements

A beta version of AQ RA product will bereleased during Summer 2014• Preliminary findings are that time

interpolated CMORPH performsbetter than 3B42 when comparedwith independent WindSat rain ratemeasurements

REFERENCESSoloviev, A. and R. Lukas, 1996: Observation ofspatial variability of diurnal thermocline and rain-formed halocline in the western Pacific warm pool.J. Phys. Oceanogr., 26, pp. 2529-2538

Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie,2004: CMORPH: A method that produces globalprecipitation estimates from passive microwave andinfrared data at high spatial and temporalresolution.. J. Hydromet., 5, 487-503.