The Advanced Very High Resolution Radiometer (AVHRR) Clear-Sky Processor for Oceans (ACSPO) is currently being developed at NESDIS. The major ASCPO products are clear-sky radiances over oceans (CSR) in all AVHRR bands, and sea surface temperature (SST) and single-channel aerosols derived from the CSRs. A fast Community Radiative Transfer Model (CRTM) has been integrated into ACSPO. It is used in conjunction with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) upper air fields and Reynolds-Smith version 2 weekly SST to simulate AVHRR top-of-atmosphere (TOA) clear-sky brightness temperatures (BT). Model BTs are used to improve the ACSPO cloud mask, monitor quality of AVHRR BTs, and explore physical SST retrievals.

This paper documents CRTM/GFS implementation in ACSPO version 1 and evaluates the “Model minus Observation” (M-O) BT biases in three bands (3.7, 11 and 12 μm) of four AVHRR/3 instruments onboard NOAA-16, -17, -18 and MetOp-A platforms using one week of global data from 16–22 February 2007. We find that if the input atmospheric and SST data are treated carefully within CRTM, then the agreement is generally good and the M-O bias shows only weak dependence on the sensor view angle and environmental parameters (water vapor, SST, sea-air temperature difference, and wind speed). Also, the CRTM and AVHRR BTs agree better if Reynolds-Smith SST is used instead of NCEP SST available from GFS files. Including Fresnel's reflection from a flat surface also reduces the M-O biases, compared to black surface. Typically, the M-O bias is positive and within a few tenths of a Kelvin, leaving some margin for future improvements in CRTM and AVHRR BTs. In particular, inclusion of aerosols and using skin SST, instead of the current Reynolds-Smith bulk SST, are expected to reduce the CRTM BTs, and the ongoing improvements to ACSPO cloud mask may increase the AVHRR BTs. Cross-platform consistency of the M-O bias is typically within ~0.1K, except for NOAA-16 channel 3B which is biased low with respect to the other three platforms by ~0.4K, likely due to a possible shift in its spectral response.

Our next step will be establishing physical SST retrievals within the available CRTM/GFS infrastructure in ACSPO. To achieve this major objective, we plan a number of steps. A web-based tool will be established to monitor the M-O bias and physical SSTs in time and to estimate the long-term performance of the CRTM and AVHRR radiances during both day and night. Adding extraterrestrial solar radiation and atmospheric scattering in the CRTM, and including global aerosols, is needed to improve the forward and inverse modeling and achieve sufficiently accurate physical SSTs. Finally, the developed system will be applied to the MSG/SEVIRI radiances to get ready for the GOES-R/ABI. The methodology described in this paper will be employed to quantitatively measure these improvements.