Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

Abstract

The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of ''clear'' pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

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“…The ACSPO system is the most recent NESDIS system for generating SST from the AVHRR sensors onboard POES and EUMETSAT's Meteorological Operational (MetOp) satellites, Moderate Resolution Imaging Spectroradiometers (MODIS) onboard NASA's Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard NOAA-NASA's Suomi-National Polar-orbiting Partnership (Suomi-NPP) satellite. SST retrieval algorithms are regression-based, whereas ACSPO also incorporates a community radiative transfer model and employs an internal Clear-Sky Mask module [45]. Version 1.0 of the ACSPO SST processing system, released in May 2008, has since been updated to the current Version 2.3.…”

Improved VIIRS and MODIS SST Imagery

“…The impact of the proposed resampling on the performance of the current ACSPO clear-sky mask (ACSM; [3]) was initially evaluated and reported here. Recall that two ACSM tests-the "SST filter" and the "spatial uniformity check"-employ information in spatial windows surrounding the pixel [3].…”

“…Recall that two ACSM tests-the "SST filter" and the "spatial uniformity check"-employ information in spatial windows surrounding the pixel [3]. In the resampled data, the performance of these two tests is expected to improve, i.e., to reduce the number of the corresponding "false alarms", and the number of clear-sky ocean pixels, CN, and the corresponding clear-sky fraction, CF (defined as a ratio of clear-sky to the total number of ocean pixels), to increase.…”

“…However, some L2 algorithms do use spatial information. For instance, the ACSPO clear-sky mask employs spatial filters in which local statistics such as mean, range and standard deviation are computed over a sliding spatial window surrounding the pixel [3]. The bow-tie effects are currently ignored in ACSPO, because the re-projection is computationally expensive and there were no fast off-the-shelf algorithms easily available that preserve both the array dimensions and the corresponding geo-information.…”

Suomi satellite brings to light a unique frontier of nighttime environmental sensing capabilities

“…Sensing based on DIM light sources could improve the nocturnal low cloud climatology. In turn, it could improve the fidelity of the sea surface temperature (SST) climate data record that is fundamental to our assessments of climate change, because SSTs are derived from nighttime observations and require a cloud screening that is inherently problematic for low clouds (36). As shown in Fig.…”