Yury Kihai

The SST Quality Monitor (SQUAM)

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (TS) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, TS are validated against in situ SSTs. However, in situ data are sparse and are not available globally in near–real time (NRT). This study describes a complementary SST Quality Monitor (SQUAM), which employs global level 4 (L4) SST fields as a reference standard (TR) and performs statistical analyses of the differences ΔTS = TS − TR. The results are posted online in NRT. The TS data that are analyzed are the heritage National Environmental Satellite, Data, and Information Service (NESDIS) SST products from NOAA-16, -17, -18, and -19 and MetOp-A from 2001 to the present. The TR fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analy...

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

TheAdvancedClearSkyProcessorforOceans(ACSPO)generatesclear-skyproducts,suchasSST,clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer(AVHRR)-likemeasurements. 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 enabledwithinACSPOwiththeCommunityRadiativeTransferModel(CRTM)inconjunctionwithnumerical 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 temperatureswithCRTM, checkretrievedSSTfor consistencywithReynolds SST,andidentifyambientcloudiness 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. ACSMincreasestheamountof‘‘clear’’pixelsby30%to40%andimprovesstatisticsofretrievedSSTcompared 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.

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

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

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 w...

Improved VIIRS and MODIS SST Imagery

Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites, are capable of providing superior sea surface temperature (SST) imagery. However, the swath data of these multi-detector sensors are subject to several artifacts including bow-tie distortions and striping, and require special pre-processing steps. VIIRS additionally does two irreversible data reduction steps onboard: pixel aggregation (to reduce resolution changes across the swath) and pixel deletion, which complicate both bow-tie correction and destriping. While destriping was addressed elsewhere, this paper describes an algorithm, adopted in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans (ACSPO) SST system, to minimize the bow-tie artifacts in the SST imagery and facilitate application of the pattern recognition algorithms for improved separation of ocean from cloud and mapping fine SST structure, especially in the dynamic, coastal and high-latitude regions of the ocean. The algorithm is based on a computationally fast re-sampling procedure that ensures a continuity of corresponding latitude and longitude arrays. Potentially, Level 1.5 products may be generated to benefit a wide range of MODIS and VIIRS users in land, ocean, cryosphere, and atmosphere remote sensing.

A fast and robust implementation of the adaptive destriping algorithm for SNPP VIIRS and Terra/Aqua MODIS SST

Radiometric performance of MODIS and VIIRS sensors is superior to that of the AVHRR, thanks to improved design and implementation of stringent pre-launch sensor characterization efforts and in-flight monitoring practices. Nevertheless, the imagery of the measured brightness temperatures (BT) and derived sea surface temperatures (SST) from multi-detector MODIS and VIIRS instruments is subject to striping artifacts. A robust adaptive destriping algorithm recently introduced by Bouali and Ignatov1 was optimized and operationally implemented at NOAA to remove striping artifacts in the VIIRS BT data. Destriped BTs are used as input into the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) SST system. The algorithm is also run with MODIS data onboard Terra/Aqua, in an experimental mode. We demonstrate improved image quality of VIIRS and MODIS BTs in bands centered at 3.7, 11 and 12 μm, and significant improvements in the derived SST imagery. The algorithm proves capable of removing the striping noise, while preserving the fine natural contrasts of the original satellite imagery. It was also tested to remove striping artifacts from the VIIRS and MODIS “optional SST” bands, centered at 4 and 8.5 μm. Destriping is critically important for several SST applications relying on accurate BT or SST gradient data, including ocean front detection and pattern recognition improvements to ACSPO cloud mask. We present the results of statistical characterization of striping artifacts in the VIIRS and MODIS thermal IR bands under various observational conditions. Our implementation of destriping is computationally efficient, adding only a fraction of time to the SST data processing flow. It is currently used at NOAA with VIIRS operations and reprocessing efforts.

SST algorithms in ACSPO reanalysis of AVHRR GAC data from 2002-2013

In response to a request from the NOAA Coral Reef Watch Program, NOAA SST Team initiated reprocessing of 4 km resolution GAC data from AVHRRs flown onboard NOAA and MetOp satellites. The objective is to create a longterm Level 2 Advanced Clear-Sky Processor for Oceans (ACSPO) SST product, consistent with NOAA operations. ACSPO-Reanalysis (RAN) is used as input in the NOAA geo-polar blended Level 4 SST and potentially other Level 4 SST products. In the first stage of reprocessing (reanalysis 1, or RAN1), data from NOAA-15, -16, -17, -18, -19, and Metop-A and -B, from 2002-present have been processed with ACSPO v2.20, and matched up with quality controlled in situ data from in situ Quality Monitor (iQuam) version 1. The ~12 years time series of matchups were used to develop and explore the SST retrieval algorithms, with emphasis on minimizing spatial biases in retrieved SSTs, close reproduction of the magnitudes of true SST variations, and maximizing temporal, spatial and inter-platform stability of retrieval metrics. Two types of SST algorithms were considered: conventional SST regressions, and recently developed incremental regressions. The conventional equations were adopted in the EUMETSAT OSI-SAF formulation, which, according to our previous analyses, provide relatively small regional biases and well-balanced combination of precision and sensitivity, in its class. Incremental regression equations were specifically elaborated to automatically correct for model minus observation biases, always present when RTM simulations are employed. Improved temporal stability was achieved by recalculation of SST coefficients from matchups on a daily basis, with a ±45 day window around the current date. This presentation describes the candidate SST algorithms considered for the next round of ACSPO reanalysis, RAN2.

Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands

Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth’s shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs.

Suppressing the noise in SST retrieved from satellite infrared measurements by smoothing the differential terms in regression equations

Multichannel regression algorithms are widely used in retrievals of sea surface temperature (SST) from infrared brightness temperatures (BTs) observed from satellites. The SST equations typically include terms dependent on the difference between BTs observed in spectral bands with different atmospheric absorption. Such terms do account for variations in the variable atmospheric attenuation, but may introduce additional noise in the retrieved SST due to amplification of the radiometric noise. Some processing systems (e.g., the EUMETSAT OSI-SAF) incorporate noise suppression algorithms, based on spatial smoothing of the differential terms in the SST equations. A similar algorithm is being tested for the potential use in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO). The ACSPO smoothing algorithm aims to preserve natural variations in SST field, while minimizing distortions in the original SST imagery, at a minimal processing time. This presentation describes the ACSPO smoothing algorithm and results of its evaluation with the SST imagery, and with the in situ matchups for NOAA and Metop AVHRRs, Terra and Aqua MODISs, and SNPP/JPSS VIIRS.

Exploring pattern recognition enhancements to ACSPO clear-sky mask for VIIRS: potential and limitations

Discriminating clear-ocean from cloud in the thermal IR imagery is challenging, especially at night. Thresholds in automated cloud detection algorithms are often set conservatively leading to underestimation of the Sea Surface Temperature (SST) domain. Yet an expert user can visually distinguish the cloud patterns from SST. In this study, available pattern recognition methodologies are discussed and an automated algorithm formulated. Analyses are performed with the SSTs retrieved from the VIIRS sensor onboard S-NPP using the NOAA ACSPO system. Based on the analyses of global data, we have identified low-level spectral and spatial features potentially useful for discriminating cloud from clear-ocean. The algorithm attempts to mimic the visual perception by a human operator such as gradient information, spatial connectivity, and high/low frequency discrimination. It first identifies contiguous areas with similar features, and then makes decision based on the statistics of the whole region, rather than on a per pixel basis. Our initial objective was to automatically identify clear sky regions misclassified by ACSPO as cloud, and improve coverage of dynamic areas of the ocean and coastal zones.