scholarly journals Skin Sea-Surface Temperature from VIIRS on Suomi-NPP—NASA Continuity Retrievals

2020 ◽  
Vol 12 (20) ◽  
pp. 3369 ◽  
Author(s):  
Peter J. Minnett ◽  
Katherine A. Kilpatrick ◽  
Guillermo P. Podestá ◽  
Robert H. Evans ◽  
Malgorzata D. Szczodrak ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Infrared Imaging ◽  
Sea Surface ◽  
Climate Data ◽  
Physical Processes ◽  
Radiometric Measurements ◽  
The Future ◽  
Accuracy And Stability

Retrievals of skin Sea-Surface Temperature (SSTskin) from the measurements of the Visible Infrared Imaging Radiometer Suite on the Suomi-National Polar-orbiting Partnership satellite are presented and discussed. The algorithms used to derive the SSTskin from the radiometric measurements are given in detail. A number of approaches to assess the accuracy and stability of the Visible Infrared Imaging Radiometer Suite (VIIRS) SSTskin retrievals are reported, and factors including latitude and season, and physical processes in the atmosphere and at the surface are discussed. We conclude that the Suomi National Polar-orbiting Partnership (S-NPP) VIIRS is capable of matching and improving upon the accuracies of SSTskin from the MODISs on Terra and Aqua, and that the VIIRS SSTskin fields have the potential to contribute to the extension of the satellite-derived Climate Data Records of SST into the future.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

Construction of a climate data record of sea surface temperature from passive microwave measurements

2020 ◽  
Vol 236 ◽  
pp. 111485 ◽  
Author(s):  
Emy Alerskans ◽  
Jacob L. Høyer ◽  
Chelle L. Gentemann ◽  
Leif Toudal Pedersen ◽  
Pia Nielsen-Englyst ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Passive Microwave ◽  
Sea Surface ◽  
Microwave Measurements ◽  
Climate Data ◽  
Data Record ◽  
Climate Data Record

scholarly journals Automatic Detection of Optical Signatures within and around Floating Tonga-Fiji Pumice Rafts Using MODIS, VIIRS, and OLCI Satellite Sensors

2021 ◽  
Vol 13 (3) ◽  
pp. 501
Author(s):  
Andra Whiteside ◽  
Cécile Dupouy ◽  
Awnesh Singh ◽  
Robert Frouin ◽  
Christophe Menkes ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Land Surface ◽  
Pacific Islands ◽  
Infrared Imaging ◽  
Far East ◽  
Automatic Detection ◽  
Sea Surface ◽  
Chlorophyll A Concentration

An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km2. The pumice’s far-reaching effects from its origin in the Tonga region to Fiji and the methods of automatic detection using satellite imagery are described, making it possible to track the westward drift of the pumice raft over 43 days. Level 2 Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Color Instrument (OLCI), and Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) imagery of sea surface temperature, chlorophyll-a concentration, quasi-surface (i.e., Rayleigh-corrected) reflectance, and remote sensing reflectance were used to distinguish consolidated and fragmented rafts as well as discolored and mesotrophic waters. The rafts were detected by a 1 to 3.5 °C enhancement in the MODIS-derived “sea surface temperature” due to the emissivity difference of the raft material. Large plumes of discolored waters, characterized by higher satellite reflectance/backscattering of particles in the blue than surrounding waters (and corresponding to either submersed pumice or associated white minerals), were associated with the rafts. The discolored waters had relatively lower chlorophyll-a concentration, but this was artificial, resulting from the higher blue/red reflectance ratio caused by the reflective pumice particles. Mesotrophic waters were scarce in the region of the pumice rafts, presumably due to the absence of phytoplanktonic response to a silicium-rich pumice environment in these tropical oligotrophic environments. As beach accumulations around Pacific islands surrounded by coral shoals are a recurrent phenomenon that finds its origin far east in the ocean along the Tongan trench, monitoring the events from space, as demonstrated for the 7 August 2019 eruption, might help mitigate their potential economic impacts.

scholarly journals Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

2020 ◽  
Vol 12 (6) ◽  
pp. 1048 ◽  
Author(s):  
Christopher J. Merchant ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
Owen Embury ◽  
Jonathan P. D. Mittaz ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Optimal Estimation ◽  
Sea Surface ◽  
Observation Error ◽  
Climate Data ◽  
Water Vapor Path ◽  
Along Track ◽  
Reference Sensor

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

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