Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds

Abstract

Passive longwave infrared radiometric satellite-based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) # 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (308S-308N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical qualityassured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Coldbiased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.338 to 0.558C for the three sensors, with an absolute (bulk mean) bias between 0.098 and 0.148C. Triple-window retrievals are more resilient, ranging from 0.088 to 0.148C relative and from 0.028 to 0.048C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.

Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC4RS

“…Consistent with Marquis et al (), we see that the MxD35‐like cloud mask is in strong agreement with the CPL for clear skies. On the other hand, when CPL detects a cloud, the MxD35‐like cloud mask misses about 25%, with 17% denoted to be entirely clear of clouds.…”

“…In other words, the high AOD bias, when compared to AERONET is not explained by low‐cloud contamination (unmasked low cloud) in the eMAS retrieval. It may also be possible that both the cloud products and DT cloud mask may be missing optically thin cirrus cloud (e.g., Holz et al, ; Marquis et al, ). We note that AERONET is also susceptible to unscreened thin cirrus (e.g., Chew et al, ).…”

“…Side scattering into the lidar's field of view from adjacent objects provides negligible enhancement as compared with the active return from the target. Therefore, a lidar can help to validate imager cloud masks, as demonstrated by the many studies that have used lidar data from CALIPSO to validate cloud identification schemes from different imagers (e.g., Hutchison et al, ; Kim et al, ; Kopp et al, ; Marquis et al, ; Wang et al, ).…”

Daytime Sea Surface Temperature Retrieval Incorporating Mid-Wave Imager Measurements: Algorithm Development and Validation

“…Currently, most satellite-derived SST producers are using the TWA/SST4 algorithm or at least one of the MWIR channels (3.7, 3.9, and 4 μm) for nighttime retrieval in their operational chain. However, MWIR channel/s are rarely used in operational daytime SST retrieval; at the same time, two-channel SWT is still applied for daytime and nighttime to retain a consistent record [14].…”

“…Retrieval errors of PDSST in terms of SD (dashed line) and RMSD (solid) with respect to the percentage of total matches using EXF new and different channel combinations. Sixteen channels of MODIS in the thermal infrared band are indicated as 1-16 (e.g., [3.7, 3.9, 4, 4.5, 8.7, 11, 12, 13.4, 13.6] μm -> [1,3,4,6,9,11,12,13,14]). AE stands for analytical error values.…”