Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans

Abstract

AbstractThe formulation of the sensor-specific error statistics (SSES) has been redesigned in the latest implementation of the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) to enable efficient use of SSES for assimilation of the ACSPO baseline regression SST (BSST) into level 4 (L4) analyses. The SSES algorithm employs segmentation of the SST domain in the space of regressors and derives the segmentation parameter from the statistics of regressors within the global dataset of matchups. For each segment, local regression coefficients and standard deviations (SDs) of BSST minus in situ SST are calculated from the corresponding subset of matchups. The local regression coefficients are used to generate an auxiliary product—piecewise regression (PWR) SST—and SSES biases are estimated as differences between BSST and PWR SST. Correction of SSES biases, which transforms BSST back into PWR SST, reduces the effects of residual cloud; variations in view zenith angle; and, during the daytime, diurnal surface warming. This results in significant reduction in the global SD of fitting in situ SST, making it comparable with SD for the Canadian Meteorological Centre (CMC) L4 SST. Unlike the foundation CMC SST (which is consistent with in situ SST at night but biased cold during the daytime), the PWR SST is consistent with in situ data during both day and night and thus may be viewed as an estimate of “depth” in situ SST. The PWR SST is expected to be a useful input into L4 SST analyses, especially for foundation SST products, such as the CMC L4.