1 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  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 2

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 3

AVHRR data processing with ACSPO 4 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 5 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 RegressionIncROE

6 Regression between T S and T B Simulation of clear-sky BTs, T B 0 Correction of bias in T B 0 SST “increments”, ΔT S =T S -T S 0, are retrieved from BT “increments”, ΔT B =T B -T B 0, with RTM inversion Regression between ΔT S and ΔT B 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

7 SST Algorithms for MODIS Simulation of T B 0 Regression between ΔT S and ΔT B with additional terms depending on NWP Incremental Regression NWP data Regression between T S and T B 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

8 Extended Regression for MODIS NLSST (Day, 11 and 12 μm channels): T S = a o +a 1 T B11 +a 2 T S 0 (T B11 -T B12 )+a 3 (T B11 -T B12 )(sec  -1) + + a 4 (sec  -1) + a 5 W+ a 6 W 2 + a 7 W 3 MCSST (Night, 3.7, 11 and 12 μm channels): T S = a o +a 1 T 4 +a 2 T 11 +a 3 T 12 +a 4 (T 4 -T 12 )(sec  -1)+ + a 5 (sec  -1) + a 6 W+ a 7 W 2 T S is SST T S 0 is first guess SST T Bλ is observed BT Θ is view zenith angle W is total precipitable column water vapor content The terms in white represent Conventional Regression; New NWP-dependent terms are shown in yellow

9 99 Incremental Regression for MODIS NLSST (Day, 11 and 12 μm channels): T S = T S 0 + b o +b 1 ΔT B11 +b 2 T S 0 (ΔT B11 - ΔT B12 ) + +b 3 (Δ B T 11 - ΔT B12 )(sec  -1) + b 4 (sec  -1) + b 5 W+ b 6 W 2 + b 7 W 3 MCSST (Night, 3.7, 11 and 12 μm channels): T S = T S 0 + b o +b 1 ΔT B4 +b 2 ΔT B11 +b 3 ΔT B b 4 (ΔT B4 - ΔT B12 )(sec  -1)+ b 5 (sec  -1) + b 6 W+ b 7 W 2 ΔT Bλ =T Bλ - T Bλ 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) SatelliteConventional Regression Extended Regression Incremental Regression NLSST (2 channels, day) AQUA0.44 K0.40 K0.39 K TERRA0.46 K0.44 K0.43 K MCSST (3 channels, night) AQUA0.42 K0.35 K0.41 K TERRA0.47 K0.36 K0.41 K 10 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

11 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 μmIncR SST – Reynolds

12 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, ) 13 Aqua – MODIS SST is warmer than Terra – MODIS SST in the daytime and colder in the nighttime Terra- MODIS, NIGHTAqua-MODIS, NIGHT Terra-MODIS, DAYAqua-MODIS, DAY

14 Histograms of IncR SST – Reynolds SST (October 15, 2011) Mean Day/Night difference in SST - Reynolds SST: Equator crossing times: Terra- MODIS Aqua- MODIS SatelliteNightDay Terra10:30 pm10:30 am Aqua1:30 am SatelliteΔTSΔTS 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 15

16 MODIS Aqua NLSST: Bias and SD wrt In situ as Functions of TPW and VZA 16 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

17 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