323 Improved CRTM Simulations for SST Retrievals Using ECMWF vs. GFS profiles

Wednesday, 9 July 2014
Xingming Liang, NOAA, College Park, MD; and A. Ignatov

NOAA SST system, Advanced Clear-Sky Processor for Ocean, simulates top-of-atmosphere clear-sky brightness temperatures at 3.7 (IR37), 11 (IR11), and 12µm (IR12). NOAA Community Radiative Transfer Model (CRTM) is used in conjunction with first-guess SST and atmospheric profiles. Based on earlier sensitivity analysis (Saha et al., 2012), initial first-guess SST implementation (Reynolds) was replaced by the Canadian Met Centre (CMC) product. This study additionally checks sensitivity to atmospheric profiles, by testing the European Center for Medium range Weather Forecasting (ECMWF, 0.25° lat-lon resolution, 97 levels) as an alternative to the current NOAA National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS, 1°, 26 levels) implementation. The comparisons are performed by analyzing nighttime Model minus Observation (M-O) biases in the NOAA Monitoring of IR Clear-sky Radiances over Ocean for SST (MICROS; www.star.nesdis.noaa.gov/sod/sst/micros/) system.

Based on one-month of global data for 5 AVHRRs (onboard NOAA-16, -18, -19, and Metop-A, -B), 2 MODISs (onboard Terra and Aqua) and 1 VIIRS (onboard the Suomi National Polar-orbiting Partnership, S-NPP), the number of clear sky pixels increases by ~3%, suggesting a small yet consistent positive effect on the ACSPO cloud mask. In IR37, the global mean M-O biases (~0.2K) and standard deviations (STD<0.5K) remain largely unchanged. In IR11 & IR12, the ECMWF mean biases are reduced by ~0.2K (from the GFS 0.5-0.6K), and become closer to those in IR37. This suggests that ECMWF has more moisture than GFS, which mainly affects the longwave bands. The large warm spots in M-O biases in the tropics seen with GFS implementation are reduced and even reverted to small negative M-O biases, suggesting that ECMWF may slightly overestimate water vapor in the tropics. Additionally, using ECMWF instead of GFS also improves stability of M-O biases, and corresponding Double-Differences, in all three AVHRR bands and derived SSTs.

We plan to include ERA-Interim and MERRA profiles in these analyses, which are critically important for long-term reprocessing of AVHRR data using NOAA ACSPO system.

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