Advanced Clear-Sky Processor For Oceans Research Articles

NOAA MODIS SST Reanalysis Version 1

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2020–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed.

JPSS VIIRS SST Reanalysis Version 3

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and ‘depth’ (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons.

AVHRR GAC Sea Surface Temperature Reanalysis Version 2

The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: ‘subskin’ (highly sensitive to true skin SST, but debiased with respect to in situ SST) and ‘depth’ (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs.

Metop First Generation AVHRR FRAC SST Reanalysis Version 1

The first full-mission global AVHRR FRAC sea surface temperature (SST) dataset with a nominal 1.1 km resolution at nadir was produced from three Metop First Generation (FG) satellites: Metop-A (2006-on), -B (2012-on) and -C (2018-on), using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) SST enterprise system. Historical reprocessing (‘Reanalysis-1’, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). ACSPO generates two SST products, one with global regression (GR; highly sensitive to skin SST), and another one with piecewise regression (PWR; proxy for depth SST) algorithms. Small residual effects of orbital and sensor instabilities on SST retrievals are mitigated by retraining the regression coefficients daily, using matchups with drifting and tropical moored buoys within moving time windows. In RAN, the training windows are centered at the processed day. In NRT, the same size windows are employed but delayed in time, ending four to ten days prior to the processed day. Delayed-mode RAN reprocessing follows the NRT with a two-month lag, resulting in a higher quality and a more consistent SST record. In addition to its completeness, the newly created Metop-FG RAN1 SST dataset shows very close agreement with in situ data (including the fully independent Argo floats), well within the NOAA specifications for accuracy (global mean bias; ±0.2 K) and precision (global standard deviation; 0.6 K) in a ~20% clear-sky domain (percent of clear-sky SST pixels to the total of ice-free ocean). All performance statistics are stable in time, and consistent across the three platforms. The Metop-FG RAN1 data set is archived at the NASA JPL PO.DAAC and NOAA NCEI. This paper documents the newly created dataset and evaluates its performance.

Optimization of Sensitivity of GOES-16 ABI Sea Surface Temperature by Matching Satellite Observations with L4 Analysis

Monitoring of the diurnal warming cycle in sea surface temperature (SST) is one of the key tasks of the new generation geostationary sensors, the Geostationary Operational Environmental Satellite (GOES)-16/17 Advanced Baseline Imager (ABI), and the Himawari-8/9 Advanced Himawari Imager (AHI). However, such monitoring requires modifications of the conventional SST retrieval algorithms. In order to closely reproduce temporal and spatial variations in SST, the sensitivity of retrieved SST to SSTskin should be as close to 1 as possible. Regression algorithms trained by matching satellite observations with in situ SST from drifting and moored buoys do not meet this requirement. Since the geostationary sensors observe tropical regions over larger domains and under more favorable conditions than mid-to-high latitudes, the matchups are predominantly concentrated within a narrow range of in situ SSTs >2 85 K. As a result, the algorithms trained against in situ SST provide the sensitivity to SSTskin as low as ~0.7 on average. An alternative training method, employed in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans, matches nighttime satellite clear-sky observations with the analysis L4 SST, interpolated to the sensor’s pixels. The method takes advantage of the total number of clear-sky pixels being large even at high latitudes. The operational use of this training method for ABI and AHI has increased the mean sensitivity of the global regression SST to ~0.9 without increasing regional biases. As a further development towards improved SSTskin retrieval, the piecewise regression SST algorithm was developed, which provides optimal sensitivity in every SST pixel. The paper describes the global and the piecewise regression algorithms trained against analysis SST and illustrates their performance with SST retrievals from the GOES-16 ABI.

Exploring MERRA-2 global meteorological and aerosol reanalyses for improved SST retrieval

This study explores the use of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) global meteorological and aerosol reanalysis for improving the simulation of satellite sensor infrared brightness temperatures (BTs), and the retrieval of sea surface temperature (SST) in the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) system, with a particular focus on ACSPO long-term reprocessing efforts. Using MERRA-2 upper-air pressure, temperature, and humidity profiles, rather than the currently used NCEP Global Forecast System (GFS) real-time data, as input into the Community Radiative Transfer Model (CRTM), reduces the global clear-sky observation-minus-model (O–M) BT biases in the infrared bands centered at 3.7, 8.6, 11, and 12 μm of the Visible Infrared Imaging Radiometer Suite onboard Suomi-NPP. The improvements are largely due to more accurate water vapor (total amount and/or vertical distribution) at low latitudes in MERRA-2, which brings the modeled BTs closer to observations. Additional stand-alone simulations, performed using RTTOV model and MERRA-2 aerosol profiles, further reduce the ACSPO global O–M BT biases and the dependence of O–M BT biases on the dust aerosol optical depth. The potential skill of MERRA-2 aerosol reanalysis for reducing dust-caused regional biases in the ACSPO global regression SST product is also demonstrated. Preliminary results suggest that MERRA-2 is a viable alternative to NCEP GFS for ACSPO reprocessing efforts.

Initial Evaluation of the Sensor-Specific Error Statistics in the NOAA Advanced Clear-Sky Processor for Oceans SST System: Diurnal Variation Signals Captured

The newly designed sensor-specific error statistics (SSES) for sea surface temperature (SST) in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans (ACSPO) system is evaluated using six months (September 15, 2017–March 14, 2018) of ACSPO SST derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) flown onboard the Suomi National Polar-orbiting Partnership satellite, SSTVIIRS. SSES bias field is shown to generally capture the temperature differences between the surface SSTVIIRS and the in situ SSTs (0.2–1 m) in both daytime and nighttime, especially at low-mid latitudes. Daytime SSES can capture, in addition to several other physical factors, the diurnal variation differences between the SSTVIIRS and SST in situ over most of the global ocean. The SSES biases also accurately pick up the cooling effect on satellite SST retrievals caused by the Saharan dust over the eastern tropical–subtropical Atlantic Ocean. However, at high latitudes, especially over the Southern Ocean where in situ SST measurements are sparse, and strong winds are persistent, the current ACSPO SSES biases may be suboptimal.

JPSS VIIRS level 3 uncollated sea surface temperature product at NOAA

Following the launch of the Suomi National Polar-orbiting Partnership satellite in October 2011 with the Visible Infrared Imager Radiometer Suite (VIIRS) sensor onboard, National Oceanic and Atmospheric Administration (NOAA) started generating a global level 2 preprocessed (L2P) sea surface temperature (SST) product. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) L2P data are organized into 144 10-min granules per day, with a total volume of ∼27 GB. The L2P product has been successfully assimilated in several level 4 (L4) analyses. At the same time, some other users requested a gridded level 3 (L3) product with a reduced data volume. An L3U “uncollated” product (in which multiple passes over the same grid are independently saved) was produced by mapping the L2P product into equal 0.02° grids. Similar to the L2P, the L3U data are also reported in 10-min granules, but with a daily volume <1 GB. Currently, the NOAA VIIRS L3U SST product is operationally used or tested in several major international numerical weather prediction centers. The L3U shows comparable performance with L2P, suggesting that both products can be used interchangeably as input into L4 analyses. The original L2P pixel-level swath data continue to be produced and available to interested users from NOAA (NCEI) and JPL (Physical Oceanography) data archives.

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.

Preliminary Inter-Comparison between AHI, VIIRS and MODIS Clear-Sky Ocean Radiances for Accurate SST Retrievals

Clear-sky brightness temperatures (BT) in five bands of the Advanced Himawari Imager (AHI; flown onboard Himawari-8 satellite) centered at 3.9, 8.6, 10.4, 11.2, and 12.3 µm (denoted by IR37, IR86, IR10, IR11, and IR12, respectively) are used in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) sea surface temperature (SST) retrieval system. Here, AHI BTs are preliminarily evaluated for stability and consistency with the corresponding VIIRS and MODIS BTs, using the sensor observation minus model simulation (O-M) biases and corresponding double differences. The objective is to ensure accurate and consistent SST products from the polar and geo sensors, and to prepare for the launch of the GOES-R satellite in 2016. All five AHI SST bands are found to be largely in-family with their polar counterparts, but biased low relative to the VIIRS and MODIS (which, in turn, were found to be stable and consistent, except for Terra IR86, which is biased high by 1.5 K). The negative biases are larger in IR37 and IR12 (up to ~−0.5 K), followed by the three remaining longwave IR bands IR86, IR10, and IR11 (from −0.3 to −0.4 K). These negative biases may be in part due to the uncertainties in AHI calibration and characterization, although uncertainties in the coefficients of the Community Radiative Transfer Model (CRTM, used to generate the “M” term) may also contribute. Work is underway to add AHI analyses in the NOAA Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) system and improve AHI BTs by collaborating with the sensor calibration and CRTM teams. The Advanced Baseline Imager (ABI) analyses will be also added in MICROS when GOES-R is launched in late 2016 and the ABI IR data become available.

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 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.

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.

SST Pattern Test in ACSPO clear-sky mask for VIIRS

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 SST Pattern Test (SPT) is formulated. Analyses are performed with SSTs retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard S-NPP using the NOAA operational Advanced Clear-Sky Processor for Oceans (ACSPO) system. Based on the analyses of global data, we have identified spatial features potentially useful for discriminating cloud from clear-ocean. The SPT 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 a decision based on the statistics of the whole region, rather than on a per pixel basis. The initial objective of the SPT was to automatically identify clear sky regions misclassified by ACSPO clear sky mask as cloudy, and improve coverage in dynamic areas of the ocean and in the coastal zones. Future work will be directed towards extending the SPT to also minimize cloud leakages, and redesigning the current ACSPO clear-sky mask making full use of pattern recognition approach.

Evaluation and selection of SST regression algorithms for JPSS VIIRS

AbstractTwo global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar‐Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear‐Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI‐SAF) for implementation in IDPS and ACSPO. Testing the OSI‐SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.

Adaptive Reduction of Striping for Improved Sea Surface Temperature Imagery from Suomi National Polar-Orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS)

Abstract The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)’s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%.

Selecting a first‐guess sea surface temperature field as input to forward radiative transfer models

Advanced Clear‐Sky Processor for Oceans (ACSPO), developed at National Environmental Satellite, Data, and Information Service, reports observed top‐of‐atmosphere clear‐sky brightness temperatures (BT) in AVHRR channels 3B (3.7), 4 (11), and 5 (12μm) along with the sea surface temperatures (SST) retrieved from these BTs as a level 2 (L2) product. Additionally, ACSPO provides the corresponding BTs simulated with Community Radiative Transfer Model (CRTM), using Reynolds daily Level 4 (L4) optimum interpolation SST (OISST) and NCEP‐GFS profiles as inputs. Accuracy of simulated BTs is critical for ACSPO functionalities, including detecting clouds, retrieving physical SSTs, monitoring sensor performance, and validating CRTM. This paper tests 11 gap‐free gridded L4 SSTs for their potential use as first‐guess fields in ACSPO to improve accuracy of simulated BTs. As a first step toward the objective, this study checks for consistency between various L4 products and ACSPO L2 SSTs. This SST consistency was shown earlier to be representative of, and equivalent to, the consistency between measured and simulated BTs, thus avoiding expensive CRTM calculations. The metrics employed in L4 comparisons include the global spatial L4‐L2 SST biases and variances and their temporal stability. Also, the effect of L4 fields on the corresponding satellite‐to‐satellite consistency (calculated as L2‐L2 double differences) is examined. Several L4 products, including the GHRSST Multi‐Product Ensemble and Canadian Meteorological Centre analysis (CMC‐0.2°), show better consistency with ACSPO L2 SST and will be explored in the future versions of ACSPO.

Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS)

Abstract Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) is a Web-based tool to monitor “model minus observation” (M − O) biases in clear-sky brightness temperatures (BTs) and sea surface temperatures (SSTs) produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). Currently, MICROS monitors M − O biases in three Advanced Very High Resolution Radiometer (AVHRR) bands centered at 3.7, 11, and 12 μm for five satellites, NOAA-16, -17, -18, -19 and Meteorological Operational (MetOp)-A. The fast Community Radiative Transfer Model (CRTM) is employed to simulate clear-sky BTs, using Reynolds SST and National Centers for Environmental Prediction Global Forecast System profiles as input. Simulated BTs are used in ACSPO for improving cloud screening, physical SST inversions, and monitoring and validating satellite BTs. The key MICROS objectives are to fully understand and reconcile CRTM and AVHRR BTs, and to minimize cross-platform biases through improvements to ACSPO algorithms, CRTM and its inputs, satellite radiances, and skin-bulk and diurnal SST modeling. Initially, MICROS was intended for internal use within the National Environmental Satellite, Data, and Information Service (NESDIS) SST team for testing and improving ACSPO products. However, it has quickly outgrown this initial objective and is now used by several research and applications groups. In particular, inclusion of double differences in MICROS has contributed to sensor-to-sensor monitoring within the Global Space-Based Intercalibration System, which is customarily performed using the well-established simultaneous nadir overpass technique. Also, CRTM scientists have made a number of critical improvements to CRTM using MICROS results. They now routinely use MICROS to continuously monitor M − O biases and validate and improve CRTM performance. MICROS is also instrumental in evaluating the accuracy of the first-guess SST and upper-air fields used as input to CRTM. This paper gives examples of these applications and discusses ongoing work and future plans.

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.

Implementation of the Community Radiative Transfer Model in Advanced Clear‐Sky Processor for Oceans and validation against nighttime AVHRR radiances

The fast Community Radiative Transfer Model (CRTM) has been integrated into National Environmental Satellite Data and Information Service's newly developed Advanced Clear‐Sky Processor for Oceans (ACSPO). CRTM is used in conjunction with the National Centers for Environmental Prediction (NCEP) Global Forecast System atmospheric profiles and Reynolds weekly version 2 sea surface temperatures (SST) to simulate clear‐sky brightness temperatures (BT). Model BTs are used to improve the ACSPO clear‐sky mask, monitor quality of advanced very high resolution radiometer (AVHRR) BTs, and explore physical SST retrievals. This paper documents CRTM implementation in ACSPO version 1 and evaluates nighttime “model minus observation” (M‐O) BT biases in three bands (3.7, 11, and 12 μm) of four AVHRR/3 instruments onboard NOAA‐16, NOAA‐17, NOAA‐18, and MetOp‐A. With careful treatment of input atmospheric and SST data, the agreement is generally good, showing only weak dependencies of M‐O biases on view zenith angle, column water vapor, and wind speed. The agreement improves if Reynolds weekly SST is used instead of NCEP SST. Including surface reflection also reduces the M‐O bias. After all optimizations, the M‐O biases are within several tenths of a Kelvin. Consistency between different platforms is ∼0.1K, except for NOAA‐16 channel 3B, which is biased low compared to other platforms by ∼0.4K. Our future plans include extending the analyses to daytime data and exploring physical SST retrievals. A web‐based tool is being established to continuously monitor the M‐O biases and physical SSTs. The validation methodology employed in this paper will be used to quantitatively measure the effect of each improvement on the M‐O bias and physical SST.