RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXY
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RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXY B. Petrenko 1,2 , A. Ignatov 1 , Y. Kihai 1,3 , J. Stroup 1,4 , X. Liang 1,5 1 NOAA/NESDIS/STAR , 2 RTi , 3 Dell Perot Systems, 4 SSAI, 5 CIRA . Objectives.

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RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXYB. Petrenko1,2, A. Ignatov1, Y. Kihai1,3, J. Stroup1,4, X. Liang1,51NOAA/NESDIS/STAR , 2RTi , 3Dell Perot Systems, 4SSAI, 5CIRA 


Objectives
Objectives PROXY

  • 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


Advanced clear sky processor for oceans acspo
Advanced Clear-Sky Processor for Oceans (ACSPO) PROXY

  • 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


Avhrr data processing with acspo
AVHRR data processing with ACSPO PROXY

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

  • 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.

ACSPO SST

August 1-7,

2008

O&SI SAF SST


Sst algorithms for goes r abi
SST algorithms for GOES-R ABI PROXY

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

Regression

IncR

OE

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


Implementation of SST Algorithms for SEVIRI PROXY

Optimal Estimation & Incremental Regression

Regression

Regression between TS and TB

Simulation of clear-sky BTs, TB0

Correction of bias in TB0

IncR

Regression between ΔTSand ΔTB

OE

SST “increments”,

ΔTS=TS-TS0,

are retrieved from BT “increments”,

ΔTB =TB-TB0 ,

with RTM inversion

  • 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


SST Algorithms for MODIS PROXY

Incremental Regression

Extended Regression

Simulation of TB0

Regression between TSand TB with additional terms depending on NWP

NWP data

NWP data

Regression between ΔTSand ΔTB with additional terms depending on NWP

  • 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


Extended regression for modis
Extended Regression for MODIS PROXY

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

Θ is view zenith angle

W is total precipitable column

water vapor content

TSis SST

TS0is first guess SST

TBλis observed BT

The terms in white represent Conventional Regression;

New NWP-dependent terms are shown in yellow


Incremental regression for modis
Incremental Regression for MODIS PROXY

NLSST (Day, 11 and 12 μm channels):

TS = TS0+ bo+b1 ΔTB11+b2TS0(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

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Sd of retrieved sst wrt in situ september 2011
SD of Retrieved SST wrt In situ PROXY(September 2011)

  • 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


Terra modis images of bt crtm bt at 3 7 m and incr sst reynolds
Terra MODIS: PROXYImages of BT – CRTM BT at 3.7μm and IncR SST – Reynolds

BT – CRTM at 3.7 μm

IncR SST – Reynolds

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


Aqua modis images of bt crtm bt at 3 75 m and er sst reynolds
Aqua MODIS: PROXYImages of BT – CRTM BT at 3.75μm and ER SST – Reynolds

ER SST – Reynolds

BT – CRTM at 3.7 μm

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

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Daily composite maps of incr sst reynolds sst october 15 2011
Daily Composite Maps PROXYof IncR SST – Reynolds SST (October, 15 2011)

Terra- MODIS, NIGHT

Aqua-MODIS, NIGHT

Terra-MODIS, DAY

Aqua-MODIS, DAY

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


Histograms of incr sst reynolds sst october 15 2011
Histograms of IncR SST – Reynolds SST PROXY(October 15, 2011)

  • Mean Day/Night difference in SST - Reynolds SST:

  • Equator crossing times:

Terra- MODIS

Aqua- MODIS


Future work
Future work PROXY

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


Modis aqua nlsst bias and sd wrt in situ as functions of tpw and vza
MODIS Aqua NLSST: PROXYBias and SD wrt In situ as Functions of TPW and VZA

  • 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
MODIS Aqua MCSST: PROXYBias 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

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