NOAA's new operational analysis combines polar-orbiting and geostationary data to provide daily global fields of sea surface temperature on a 0.05 degrees (similar to 5 km) grid for a range of applications in climate, ecosystems, weather, and mesoscale oceanography.

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.

After many years of development and experimental production, the United States National Oceanic and Atmospheric Administration (NOAA) transitioned the generation of winds derived from satellite synthetic aperture radar (SAR) data to operational status on May 1, 2013, employing SAR data from the Canadian RADARSAT-2 satellite. Winds of 500 m resolution are being produced from SAR data being purchased for the U.S. National Ice Center. Products are distributed internally within NOAA and via public websites. Currently the production of winds from Sentinel-1A data are being added to this system along with the ability to produce new product output formats (e.g., CoastWatch) and a wind archive is being developed by the NOAA National Centers for Environmental Information.

The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW’s heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA’s next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013–2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time.

There are a growing number of level 4 (L4; gap-free gridded) sea surface temperature (SST) products generated by blending SST data from various sources which are available for use in a wide variety of operational and scientific applications. In most cases, each product has been developed for a specific user community with specific requirements guiding the design of the product. Consequently differences between products are implicit. In addition, anomalous atmospheric conditions, satellite operations and production anomalies may occur which can introduce additional differences. This paper describes a new web-based system called the L4 SST Quality Monitor (L4-SQUAM) developed to monitor the quality of L4 SST products.L4-SQUAM intercompares thirteen L4 products with 1-day latency in an operational environment serving the needs of both L4 SST product users and producers. Relative differences between products are computed and visualized using maps, histograms, time series plots and Hovmoller diagrams, for all combinations of products. In addition, products are compared to quality controlled in situ SST data (available from the in situ SST Quality Monitor, iQUAM, companion system) in a consistent manner. A full history of products statistics is retained in L4-SQUAM for time series analysis. L4-SQUAM complements the two other Group for High Resolution SST (GHRSST) tools, the GHRSST Multi Product Ensemble (GMPE) and the High Resolution Diagnostic Data Set (HRDDS) systems, documented in part 1 of this paper and elsewhere, respectively.Our results reveal significant differences between SST products in coastal and open ocean areas. Differences of > 2 degrees C are often observed at high latitudes partly due to different treatment of the sea-ice transition zone. Thus when an ice flag is available, the intercomparisons are performed in two ways: including and excluding ice-flagged grid points. Such differences are significant and call for a community effort to understand their root cause and ensure consistency between SST products. Future work focuses on including the remaining daily L4 SST products, accommodating for newer L4 SSTs which resolve the diurnal variability and evaluating retrospectively regenerated L4 SSTs to support satellite data reprocessing efforts aimed at generating improved SST Climate Data Records. (c) 2012 Elsevier Ltd. All rights reserved.

SAR-derived wind measurements are in the process of being implemented for operational production within NOAA's National Environmental Satellite, Data, and Information Service. For C-band ENVISAT and RADARSAT-1/2 data, the CMOD5 algorithm is being used; for ALOS data, a special L-band wind algorithm is employed. Comparisons of both C-band and L-band winds with ASCAT scatterometer wind measurements show biases of 0.58 m/s or less and standard deviations of 1.31 m/s or less. SAR wind vectors will be stored in a NetCDF4-formatted file and made available in a number of product formats via the NOAA CoastWatch program.

The National Environmental Satellite, Data, and Information Service (NESDIS) has been operationally generating sea surface temperature (SST) products (T-S) from the Advanced Very High Resolution Radiometers (AVHRR) onboard NOAA and MetOp-A satellites since the early 1980s. Customarily, T-S 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 (T-R) and performs statistical analyses of the differences Delta T-S = T-S - T-R. The results are posted online in NRT. The T-S 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 T-R fields include daily Reynolds, real-time global (RTG), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Ocean Data Analysis System for Marine Environment and Security for the European Area (MERSEA) (ODYSSEA) analyses. Using multiple fields facilitates the distinguishing of artifacts in satellite SSTs from those in the L4 products. Global distributions of Delta T-S are mapped and their histograms are analyzed for proximity to Gaussian shape. Outliers are handled using robust statistics, and the Gaussian parameters are trended in time to monitor SST products for stability and consistency. Additional T-S checks are performed to identify retrieval artifacts by plotting Delta T-S versus observational parameters. Cross-platform T-S biases are evaluated using double differences, and cross-L4 T-R differences are assessed using Hovmoller diagrams. SQUAM results compare well with the customary in situ validation. All satellite products show a high degree of self- and cross-platform consistency, except for NOAA-16, which has flown close to the terminator in recent years and whose AVHRR is unstable.

NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) has generated sea surface temperature (SST) products from Geostationary Operational Environmental Satellite (GOES)-East (E) and GOES-West (W) on an operational basis since December 2000. Since that time, a process of continual development has produced steady improvements in product accuracy Recent improvements extended the capability to permit generation of operational SST retrievals from the Japanese Multifunction Transport Satellite (MTSAT)-IR and the European Meteosat Second Generation (MSG) satellite, thereby extending spatial coverage. The four geostationary satellites (at longitudes of 75°W, 135°W, 140°E, and 0°) provide high temporal SST retrievals for most of the tropics and midlatitudes, with the exception of a region between ~60° and ~80°E. Because of ongoing development, the quality of these retrievals now approaches that of SST products from the polar-orbiting Advanced Very High Resolution Radiometer (AVHRR). These products from GOES provide hourly regional imagery, 3-hourly hemispheric imagery, 24-h merged composites, a GOES SST level 2 preprocessed product every ½ h for each hemisphere, and a match-up data file for each product. The MTSAT and the MSG products include hourly, 3-hourly, and 24-h merged composites. These products provide the user community with a reliable source of SST observations, with improved accuracy and increased coverage in important oceanographic, meteorological, and climatic regions.

National Oceanic and Atmospheric Administration (NOAA) operational sea surface temperature (SST) products are customarily calibrated and validated (Cal/Val) against in situ SSTs from buoy data. However, the match-ups are sparse, geographically biased, and non-uniform in space and time, which complicates their use for continuous, long-term quality control and quality assurance (QC/QA) of the near-real time global satellite-derived SST products. This is best achieved by statistical analysis of anomalies with respect to a global reference SST state. In this study, Bauer-Robinson (1985) SST climatology derived from about 20 years of ground-based observations is used as a reference state. This work describes the development and initial results of a global near-real time QC/QA processor based on statistical self- and cross-consistency checks. The Global QC/QA Tool (GQT) relies on the analyses of SST anomalies with respect to the reference SST. Anomaly is expected to be distributed normally, although the SST global distribution is highly skewed. The diagnostics are based on the analyses of global histograms of anomalies within a specified time interval (here, 8 days), their four statistical moments (mean, standard deviation, skewness, and kurtosis), and plots of long-term time- series of these statistical parameters. In addition, "artificial trend plots" are used to detect any unrealistic dependencies of SST retrievals upon observational (e.g., latitude, view, or sun angles) or geophysical parameters (e.g., integral water vapor or wind speed). An effort is also made towards identifying extreme outliers (unrealistic retrievals) and their removal from the data. Initial results using several years of NOAA-16 through 18 and a few months of MetOp-A AVHRR SST products were preliminarily analyzed and inter-compared. Typically, anomaly histograms from different platforms are consistent, with a mean bias of ca. 0.45 K and an RMSD of ca. 1.0 K with respect to Bauer-Robinson 1985 climatological SST. More detailed analyses are underway and their results will be reported elsewhere. In the future, the GQT will also be tested to operationally monitor the quality of SST products from NPOESS/VIIRS and GOES-R/ABI. We emphasize that the GQT is not a substitute for the customary Cal/Val against in situ data, rather it is a complementary, near-real time, diagnostic tool for timely detection, diagnosis, and correction of problems in the SST products.

Since 1988, the National Oceanic and Atmospheric Administration (NOAA) has provided operational aerosol observations (AEROBS) from the Advanced Very High Resolution Radiometer (AVHRR/2) on board the afternoon NOAA satellites [nominal equator crossing time, (EXT)similar to1330]. Aerosol optical depth (AOD) has been retrieved over oceans from channel 1 of AVHRR/2 on board NOAA-11 (1988-94) and -14 (1995-2000) using the first- and second-generation algorithms, respectively. With the launch of the NOAA-KLM series of satellites, in particular NOAA-16 (L) in September 2000 (EXTsimilar to1400), and NOAA-17 (M) in June 2002 (EXTsimilar to1000), an extended and improved third-generation algorithm was enabled. Like its predecessors, this algorithm continues to employ a single-channel methodology, by which all parameters in the retrieval algorithm (excluding AOD) are set globally as nonvariables. But now, in addition to AOD from channel 1, tau(1) (lambda(1)=0.63 mum), the algorithm also retrieves tau(2) and tau(3) in AVHRR/3 channels 2 (lambda(2)=0.83 mum) and 3A (lambda(3)=1.61 mum). The retrievals are made with more accurate and flexible, satellite- and channel-specific lookup tables generated with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. From pairs of tau(i) and tau(j), the Angstrom exponent (AE) parameters can then be determined as alpha(ij)=-ln(tau(i)/tau(j))/ln(tau(i)/tau(j)).This paper describes the AEROBS processing and gives examples of aerosol products, along with a preliminary diagnostics of their quality using some of the previously developed self-consistency checks. Interconsistency between the NOAA-16 and -17 aerosol retrievals is also checked. The AODs are largely coherent but distorted by the AVHRR calibration uncertainties, and subject to noise and outliers. These tau errors, unavoidable in real-time AVHRR processing, severely impact the derived AE, demonstrating a fundamental instability in estimating the aerosol model under typical maritime conditions from AVHRR. Consequently, it is concluded that the robust single-channel retrievals should be continued in the AEROBS operations in the KLM era. The more sophisticated multichannel techniques may be tested while reprocessing historical AVHRR data, only after the data quality issues have been resolved (viz., calibration uncertainties constrained, outliers removed, and noise suppressed by spatial averaging).