The uncertainty of the Advanced Clear-Sky Processor for Oceans (ACSPO) Sea Surface Temperature (SST) products from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite is examined using consecutive orbital overlaps in coastal waters of the Gulf of Mexico. The overlapping region on the left and right side of the VIIRS swath at 23-35 degree latitude covers approximately 500 pixels, which occur within 100 minutes and can provide a total of 4 SST products (2 day and 2 night) per day. By assuming the ocean SST should be similar on each side of the swath in this short time period, diel changes are examined and the uncertainty of SST retrieval is determined by comparing with buoy-derived SST. The VIIRS ACSPO product from NOAA STAR was used to determine the difference in SST within the overlapping regions. These SST changes are evaluated between consecutive orbits to validate the accuracy of SST algorithms on each side of the swath at high sensor angles. The SST product differences across the swath can result from surface glint, sensor angular impacts and sensor characteristics such as half angle mirror side (HAM) and calibration. The absolute diurnal SST changes that can occur within 100 minutes are evaluated with the buoy and VIIRS-derived SST. Sensitivity of the SST to water types is evaluated by measuring diurnal differences for open ocean, shelf and coastal waters. The 100 minute VIIRS SST overlap shows the capability to monitor the diurnal ocean heating and cooling which are associated with water mass optical absorption. The seasonal trends of the difference in SST at the overlaps for these water masses were tracked on a monthly basis. The unique capability of using the same VIIRS sensor for self-characterization can provide a method to define the uncertainty of ocean products and characterize the diurnal changes for different water types.
Full-swath Sea Surface Temperature (SST) derived from data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on-board the Suomi-National Polar-orbiting Partnership (S-NPP) satellite produces significant overlap between consecutive orbits at all latitudes. In this study, we use those overlap regions to evaluate VIIRS SST, as inconsistencies between SST values from consecutive orbits are indications of likely degraded quality. The studies investigate two sources of inconsistencies: those resulting from the response of the SST equations when observing a scene from differing view angles and those caused by undetected data contamination. This study will present results for two VIIRS SST products: one from the Naval Oceanographic Office (NAVOCEANO), which is assimilated in the Navy Ocean Models, and the Advanced Clear-Sky Processor for Oceans (ACSPO) product from the National Oceanic and Atmospheric Administration (NOAA) Center for Satellite Applications and Research (STAR). Global statistics based on drifting buoys for both NAVOCEANO and NOAA products complete the analysis.
Several groups produce Sea Surface Temperature (SST) retrievals derived from data acquired by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on-board the S-NPP satellite. Because of varying requirements or history, the groups often use differing SST equations to make their SST retrievals. Here we compare and discuss the equations through an examination of the SST fields. In most cases, the fields are created using the same program but differing equations, while in other cases, such as for the Interface Data Processing Segment (IDPS) Environmental Data Records (EDR), the SST fields are directly produced by other groups. Also discussed is the effect of the equation coefficients because independent groups may use the same equation but with different coefficients The focus of this study is on a region covering the Northern Gulf of Mexico and part of the Western North Atlantic. The comparison to buoys tries to minimize the effect of data contamination such as clouds on the results by matching the best satellite derived SST value in a neighborhood to the value from drifting or moored buoys. Finally we look at the overlap between consecutive passes to evaluate how the various equations perform at higher satellite zenith angles.
The Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (VCM) Intermediate Product (IP) has been developed for use with Suomi National Polar-orbiting Partnership (NPP) VIIRS Environmental Data Record (EDR) products. In particular, the VIIRS Sea Surface Temperature (SST) EDR relies on VCM to identify cloud contaminated observations. Unfortunately, VCM does not appear to perform as well as cloud detection algorithms for SST. This may be due to similar but different goals of the two algorithms. VCM is concerned with detecting clouds while SST is interested in identifying clear observations. The result is that in undetermined cases VCM defaults to “clear,” while the SST cloud detection defaults to “cloud.” This problem is further compounded because classic SST cloud detection often flags as “cloud” all types of corrupted data, thus making a comparison with VCM difficult. The Naval Oceanographic Office (NAVOCEANO), which operationally produces a VIIRS SST product, relies on cloud detection from the NAVOCEANO Cloud Mask (NCM), adapted from cloud detection schemes designed for SST processing. To analyze VCM, the NAVOCEANO SST process was modified to attach the VCM flags to all SST retrievals. Global statistics are computed for both day and night data. The cases where NCM and/or VCM tag data as cloud-contaminated or clear can then be investigated. By analyzing the VCM individual test flags in conjunction with the status of NCM, areas where VCM can complement NCM are identified.
The Naval Oceanographic Office (NAVOCEANO) produces Sea Surface Temperature (SST) retrievals from satellite data. NAVOCEANO also obtains satellite-derived SST data sets from other groups. To provide consistency for assimilation into analyses and models, all the SST data sets are evaluated for their accuracy with the same methodology. In this paper, the focus is SST derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board the Suomi National Polar-orbiting Partnership (NPP) satellite. Of particular interest is the evaluation of NAVOCEANO produced SST with its NAVOCEANO Cloud mask, the VIIRS cloud mask, and VIIRS Environmental Data Record SST. The evaluation results show that these products are in some ways comparable, with similar strengths and weaknesses, although they target different customers. For comparison, the reliability results for the Meteorological Operational (METOP-A) satellite-derived SST, which is a NAVOCEANO operational product, are presented. As a by-product of the NAVOCEANO VIIRS SST evaluation, the non-linear SST (NLSST) equations used to derive the SST values were found to be less than optimal, depending on the unit of the field temperature term. NAVOCEANO VIIRS SST employs an expanded NLSST equation, which in effect refines the approximation of the gamma term by adding an offset. In view of the evaluation results, NAVOCEANO VIIRS SST became operational at the end of January 2013.
The ultimate goal of the prediction of Sea Surface Temperature (SST) from satellite data is to attain an accuracy of 0.3°K or better when compared to floating or drifting buoys located around the globe. Current daytime SST algorithms are able to routinely achieve an accuracy of 0.5°K for satellite zenith angles up to 53°. The full scan swath of VIIRS (Visible Infrared Imaging Radiometer Suite) results in satellite zenith angles up to 70°, so that successful retrieval of SST from VIIRS at these higher angles would greatly increase global coverage. However, the accuracy of present SST algorithms steadily degrades to nearly 0.7°K as the satellite zenith angle reaches 70°, due mostly to the effects of increased atmospheric path length. We investigated the use of Tfield, a gap-free first guess temperature field used in NLSST, as a separate predictor to the MCSST algorithm in order to clearly evaluate its effects. Results of this new algorithm, TfieldSST, showed how its rms error is heavily dependent on the aggressiveness of the pre-filtering of buoy matchup data with respect to Tfield. It also illustrated the importance of fully exploiting the a priori satellite-only information contained in Tfield, presently tamed in the NLSST algorithm due to the fact that it shows up as a multiplier to another predictor. Preliminary results show that SST retrievals using TfieldSST could be obtained using the full satellite swath with a 30% improvement in accuracy at large satellite zenith angles and that a fairly aggressive pre-filtering scheme could help attain the desired accuracy of 0.3°K or better using over 75% of the buoy matchup data.
The Suomi National Polar-orbiting Partnership (NPP) satellite was placed in orbit October 28, 2011, and began
providing advanced imaging and radiometric data from the Visible Infrared Imager Radiometer Suite (VIIRS) in
December 2011. The Naval Oceanographic Office (NAVOCEANO) is processing the VIIRS data as part of the
generation of sea surface temperature (SST) retrievals for ingest by Navy meteorological and oceanographic
analyses and models. This new sensor has an increased number of channels, higher resolution, and larger volume
than previous operational polar-orbiting environmental satellites. In order to prepare for processing this new data, a
proxy datastream was generated by the Government Resource for Algorithm Verification, Independent Testing, and
Evaluation (GRAVITE) from Moderate-resolution Imaging Spectroradiometer (MODIS) data and provided in near
real-time. This allowed for NAVOCEANO to write software to ingest, process, and deliver SST products before the
actual data began flowing. A discussion of these preparatory activities and the initial results of processing VIIRS
SSTs will be presented, including global drifting buoy matchup statistics.
The wide availability of workstations has made the creation of sophisticated image processing algorithms economically possible. Here the latest version of an algorithm designed to detect fronts automatically in satellite-derived Sea Surface Temperature (SST) fields, is presented. The Algorithm operates at three levels: picture level, window level, and local/pixel level, much as humans seem to. Following input of the data, the most obvious clouds (based on temperature and shape) are identified and tagged so that data which do not represent sea surface temperature are not used in the subsequent modules. These steps operate at the picture and then at the window level. The procedure continues at the window level with the formal portion of the edge detection. Using techniques for unsupervised learning, the temperature distribution (histogram) in each window is analyzed to determine the statistical relevance of each possible front. To remedy the weakness related to the fact that clouds and water masses do not always form compact populations, the algorithm also includes a study of the spatial properties instead of relying entirely on temperatures. In this way, temperature fronts are unequivocally defined. Finally, local operators are introduced to complete the contours found by the region based algorithm. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context, thus making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of other algorithms commonly used to locate edges in satellite-derived SST images.
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