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RTM/NWP-BASED SST ALGORITHMS

FOR VIIRS USING MODIS AS A PROXY

B. Petrenko1,2, A. Ignatov1, Y. Kihai1,3, J. Stroup1,4, X. Liang1,5

1NOAA/NESDIS/STAR , 2RTi , 3Dell Perot Systems, 4SSAI, 5CIRA 

Objectives

· Currently, all operational SST retrieval algorithms use regression (AVHRR, MODIS; baseline SST algorithm for VIIRS)

· Recent studies show that using RTM and NWP information can improve SST retrieval accuracy and cloud screening capabilities

· At NESDIS, the exploration of RTM/NWP – based SST algorithms began within preparations for GOES-R ABI mission

· Now the algorithms of this type are being tested for VIIRS. MODIS and AVHRR are used as proxy sensors. MODIS is focus of this presentation

· Objectives:

» Evaluate potential benefits of using RTM and NWP for SST retrieval and QC.

» Create a back-up SST capability for VIIRS

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Advanced Clear-Sky Processor for Oceans (ACSPO)

· ACSPO was originally developed at NESDIS for operational processing of AVHRR data at a pixel resolution

· Main ACSPO modules:

» Community Radiative Transfer Model (CRTM) - simulates clear-sky BTs using Reynolds Daily SST and GFS atmospheric profiles.

» SST module – incorporates SST algorithms

» Clear-Sky Mask (CSM) - performs cloud screening using simulated clear-sky BTs and analysis SST rather than cloud models

· The ACSPO infrastructure allows implementation and testing various SST algorithms

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AVHRR data processing with ACSPO

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• Operational SST retrieval uses Regression

• ACSPO produces quasi-Gaussian distributions of deviations of SST from Reynolds Daily SST for all AVHRRs flown on different satellites.

• Comparisons with other SST and cloud mask products (CLAVR-x; O&SI SAF) show that ACSPO performs comparably or better

August 1-7, 2008

ACSPO SST

O&SI SAF SST

SST algorithms for GOES-R ABI

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MSG2 SEVIRI - REYNOLDS SST • The ACSPO was used to

develop SST algorithms for GOES-R ABI using MSG2 SEVIRI as proxy

• Regression and Optimal Estimation (OE) algorithms were implemented and tested.

• New Incremental Regression (IncR) algorithm was developed.

• The IncR provided the highest and the most uniform SST accuracy and precision

Bias and SD of SEVIRI - In situ SST as functions of View Zenith Angle

Regression IncR OE

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Regression between TS and TB

Simulation of clear-sky BTs, TB0

Correction of bias in TB0

SST “increments”, ΔTS =TS-TS

0 ,

are retrieved from BT “increments”, ΔTB =TB-TB

0 ,with RTM inversion

Regression between ΔTS

and ΔTB

Regression

OE IncR

Implementation of SST Algorithms for SEVIRI

• The Incremental Regression is

• More accurate than Regression and OE

• Faster and simpler to implement than OE

• Correction of BT biases is implemented for SEVIRI as a standalone procedure

Optimal Estimation & Incremental Regression

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SST Algorithms for MODIS

Simulation of TB0

Regression between ΔTS and ΔTB with additional terms depending on NWP

Incremental Regression

NWP data

Regression between TS

and TB with additional terms depending on NWP

Extended Regression

NWP data

• Incremental Regression is simplified by correcting bias in retrieved SST: new NWP-dependent terms are added to the IncR equation

• Extended Regression (ExtR) eliminates RTM simulations: NWP-dependent terms are added to the conventional regression equation.

• Comparison of Conventional Regression with ExtR and IncR can reveal sequential improvements (if any) due to using NWP data and RTM

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Extended Regression for MODIS

NLSST (Day, 11 and 12 μm channels):

TS = ao+a1 TB11+a2 TS0

(TB11-TB12 )+a3 (TB11-TB12 )(sec-1) +

+ a4(sec-1) + a5W+ a6W2 + a7W3

MCSST (Night, 3.7, 11 and 12 μm channels):

TS = ao+a1T4+a2T11+a3T12+a4 (T4-T12 )(sec-1)+

+ a5(sec-1) + a6W+ a7W2

TS is SST

TS0 is first guess SST

TBλ is observed BT

Θ is view zenith angle

W is total precipitable columnwater vapor content

The terms in white represent Conventional Regression;

New NWP-dependent terms are shown in yellow

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Incremental Regression for MODIS

NLSST (Day, 11 and 12 μm channels):

TS = TS0

+ bo+b1 ΔTB11+b2 TS0

(ΔTB11- ΔTB12 ) +

+b3 (ΔBT11- ΔTB12 )(sec-1) + b4(sec-1) + b5W+ b6W2 + b7W3

MCSST (Night, 3.7, 11 and 12 μm channels):

TS = TS0 + bo+b1 ΔTB4+b2 ΔTB11+b3 ΔTB12+

+ b4 (ΔTB4- ΔTB12 )(sec-1)+ b5(sec-1) + b6W+ b7W2

ΔTBλ =TBλ - TBλ 0

Incremental regression equation replaces observed BTs with their deviations

from the first guess, and first guess SST is added to the right-hand side of

equation

SD of Retrieved SST wrt In situ(September 2011)

Satellite Conventional Regression

Extended Regression

Incremental Regression

NLSST (2 channels, day)

AQUA 0.44 K 0.40 K 0.39 K

TERRA 0.46 K 0.44 K 0.43 K

MCSST (3 channels, night)

AQUA 0.42 K 0.35 K 0.41 K

TERRA 0.47 K 0.36 K 0.41 K

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• IncR is more precise for two-channels split-window SST retrieval

• ExtR is more precise for three channels SST retrieval

• Monitoring of long-term trends in SST accuracy and precision is a subject of the future work

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Terra MODIS: Images of BT – CRTM BT at 3.7μm

and IncR SST – Reynolds

• “Striping” in Terra-MODIS channels affects SST and Clear-Sky Mask

BT – CRTM at 3.7 μm IncR SST – Reynolds

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Aqua MODIS: Images of BT – CRTM BT at 3.75μm

and ER SST – Reynolds

• SST retrieval and Clear-Sky Mask for Aqua-MODIS are also affected by striping

BT – CRTM at 3.7 μm ER SST – Reynolds

Daily Composite Maps of IncR SST – Reynolds SST

(October, 15 2011)

13• Aqua – MODIS SST is warmer than Terra – MODIS SST in the daytime and colder

in the nighttime

Terra- MODIS, NIGHT Aqua-MODIS, NIGHT

Terra-MODIS, DAY Aqua-MODIS, DAY

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Histograms of IncR SST – Reynolds SST

(October 15, 2011)

Mean Day/Night difference in SST - Reynolds SST:

Equator crossing times:

Terra- MODIS Aqua- MODIS

Satellite Night Day

Terra 10:30 pm 10:30 am

Aqua 1:30 am 1:30 am

Satellite ΔTS

Terra ~0.14 K

Aqua ~0.37 K

Future work

MODIS:

· Results shown here are preliminary

· Long-term monitoring of stability, accuracy and precision of SST

· Further enhancement of SST algorithms (including SST equations, bias correction and Clear-Sky Mask)

VIIRS:

· Implement RTM/NWP-based SST algorithms

· Process VIIRS data quasi-operationally

· Compare with the baseline VIIRS SST algorithm and Cloud Mask

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MODIS Aqua NLSST: Bias and SD wrt In situ as Functions

of TPW and VZA

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• Bias of SST – In situ is within 0.1 K for both ER and IncR within the entire ranges of TPW and VZA

• IncR slightly outperforms ER in terms of SD of SST – In situ

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MODIS Aqua MCSST:Bias and SD wrt In situ as Functions

of TPW and VZA

• Bias of SST – In situ is well within 0.1 K for ER and IncR within the entire ranges of TPW and VZA

• ER outperforms IncR in terms of SD of SST – In situ, due to insufficient correction of CRTM inaccuracy