Satellite Sea Surface Temperature Retrievals Research Articles

Cool skin effect and warm skin phenomenon observed by shipboard radiometer in the Northwest Pacific

Sea surface temperature (SST) is an important variable in the study of ocean boundary layers and heat exchange. The accurate simulation and measurement of skin effects are vital to air–sea model processing and satellite SST retrieval. Shipboard measurements from eleven cruises in the Northwest Pacific between August 2015 and October 2018 were used to estimate the cool skin effect and compare model results. The temperature difference ΔT between the sea surface skin temperature (SSTskin), as measured by an infrared radiometer, and the sea surface depth temperature (SSTdepth) at around 4 meters showed a mean difference and a standard deviation of the same 0.2 K, with a total of 5-min 39909 measurements. Both daytime and nighttime ΔT values were compared to physical model simulations and were found to have relatively larger mean values. A set of new coefficients for an exponential parameterization of the cool skin effect was derived in the research area, which performed well in comparison to previous empirical models. In nighttime observations from two summer cruises, the reverse process of heat flux transfer from the air to the sea in the form of a warm skin was distinguished. There were 667 positive ΔT values out of the 1917 nighttime observations, with magnitudes ranging from around 0 to 0.3 K. A high proportion of the cases of the warm skin phenomenon occurred when the air was very humid and much warmer than the sea surface.

A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Global, repeated, and accurate measurements of Sea Surface Temperature (SST) are critical for weather and climate projections. While thermometers on buoys measure SST relatively accurately, only sensors aboard satellites give global and repeated SST measurements necessary for many applications, including climate modeling. For satellite-based thermal infrared sensors, an atmospheric correction converts calibrated brightness temperatures measured at orbital height into an SST estimate, but imperfect assumptions in the correction algorithm coupled with variability in atmospheric conditions and viewing geometries can lead to a wide range of errors and uncertainties in the satellite-derived SST retrievals. Estimates of the resulting errors are imperative for satellite-derived SST assimilation into climate models. This paper evaluates the use of machine learning Decision Tree algorithms to predict the central tendency and dispersion of errors in satellite-derived SST retrievals. First, using records from the NASA R2014.1 MODIS Aqua SST Matchup Database, which includes matched-up satellite and in situ SST measurements, a set of seven variables was derived that addresses the assumptions and known issues in the satellite SST retrieval process. Then, both Random Forest and Cubist Decision Trees were used to predict the SST residual (satellite SST minus skin-corrected buoy SST) for each matchup. While both Decision Tree methods performed similarly well, the Cubist model is more easily interpreted and yields better predictions of errors for relatively infrequent conditions. Various characteristics of the groups of matchups identified by Cubist were explored, and uncertainty values for the error estimates were derived for each group. Overall, the Cubist model predicted the skin SST residual with a root-mean-squared-error of 0.380 °C across nighttime cloud-filtered domains, demonstrating that a Cubist model is viable for quantitatively and accurately predicting Single Sensor Error Statistics per pixel as required by the Group for High Resolution SST. In this paper, we present the training and testing of both Decision Tree models. Because of its interpretability, we explore in detail the characteristics of the Cubist-derived groups to gain new geophysical insight into the satellite-derived SST retrieval error across different measurement conditions.

Assessment of Extreme Diurnal Warming in Operational Geosynchronous Satellite Sea Surface Temperature Products

We evaluate the reliability and basic characteristics of observations of extreme DW events from current operational geostationary satellite sea surface temperature (SST) products through examination of three months of diurnal warming (DW) estimates derived by different methodologies from the Spinning Enhanced Visible and Infrared Imager on Meteosat-11, Advanced Himawari Imager on Himawari-8, and Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (GOES)-16. This work primarily focuses on the following research questions: (1) Can these operational SST products accurately characterize extreme DW events? (2) What are the amplitudes and frequencies of these events? To answer these, we compute distributions of DW and DW exceedance and compare them amongst the different methods and geostationary sensors. Examination of the DW estimates demonstrates several challenges in accurately deriving distributions of the DW amplitude, particularly associated with estimating the “foundation” temperature and uncertainties in cloud screening. Overall, the results suggest that current geostationary sensors can reliably assess extreme DW, but the estimates are sensitive to the computational methods applied. We thus suggest careful processing/screening of the SST retrievals. We find a value of 3 K, corresponding to the 99th percentile, provides a potential practical threshold for extreme warming, but events of at least 6 K were reliably observed. Warming in excess of 6 K occurred somewhere an average of 47% of the time, and its probability at a given location was of O(10−6).

The Copernicus Surface Velocity Platform drifter with Barometer and Reference Sensor for Temperature (SVP-BRST): genesis, design, and initial results

Abstract. To support calibration and validation of satellite sea surface temperature (SST) retrievals, over 60 high-resolution SST (HRSST) drifting buoys were deployed at sea between 2012 and 2017. Their data record is reviewed here. It is confirmed that sea state and immersion depth play an important role in understanding the data collected by such buoys and that the SST sensors need adequate insulation. In addition, calibration verification of three recovered drifters suggests that the sensor drift is low, albeit negative at around −0.01 K year−1. However, the statistical significance of these results is limited, and the calibration procedure could not be exactly reproduced, introducing additional uncertainties into this drift assessment. Based on lessons learnt from these initial buoys, a new sensor package for the Surface Velocity Platform with Barometer (SVP-B) was designed to serve calibration of SST retrievals by European Union's Copernicus satellites. The novel sensor package includes an HRSST sensor calibrated by a metrology laboratory. The sensor includes a pressure probe to monitor immersion depth in calm water and acquires SST data at 1 Hz over a 5 min interval every hour. This enables the derivation of mean SST as well as several percentiles of the SST distribution. The HRSST sensor is calibrated with an uncertainty better than 0.01 K. Analysis of the data collected by two prototypes deployed in the Mediterranean Sea shows that the buoys are able to capture small-scale SST variations. These variations are found to be smaller when the sea state is well mixed and when the buoys are located within eddy cores. This affects the drifter SST data representativeness, which is an aspect of importance for optimal use of these data.

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.

A multi-scale high-resolution analysis of global sea surface temperature

The Multi-scale Ultra-high Resolution (MUR) sea surface temperature (SST) analysis presents daily SST estimates on a global 0.01°×0.01° grid. The current version (Version 4.1, http://dx.doi.org/10.5067/GHGMR-4FJ04) features the 1-km resolution MODIS retrievals, which are fused with AVHRR GAC, microwave, and in-situ SST data by applying internal correction for relative biases among the data sets. Only the night-time (dusk to dawn locally) satellite SST retrievals are used to estimate the foundation SST. The MUR SST values agree with the GHRSST Multi-Product Ensemble (GMPE) SST field to 0.36°C on average, except in summer-time Arctic region where the existing SST analysis products are known to disagree with each other. The feature resolution of the MUR SST analysis is an order of magnitude higher than most existing analysis products.The Multi-Resolution Variational Analysis (MRVA) method allows the MUR analysis to use multiple synoptic time scales, including a 5-day data window used for reconstruction of mesoscale features and data windows of only few hours for the smaller scale features. Reconstruction of fast evolving small scale features and interpolation over persistent large data voids can be achieved simultaneously by the use of multiple synoptic windows in the multi-scale setting. The MRVA method is also a “mesh-less” interpolation procedure that avoids truncation of the geolocation data during gridding and binning of satellite samples. Future improvements of the MUR SST analysis will include ingestion of day-time MODIS retrievals as well as more recent high-resolution SST retrievals from VIIRS.

Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

Abstract In the infrared region, the quality of sea surface temperature (SST) retrievals critically depends on the cloud detection scheme. More than 5 million matchups, where the surface and top of atmosphere measurements are available, have been carefully analyzed to understand clouds related errors and to develop the advanced cloud detection scheme for improvement of satellite SST quality. The effectiveness of a Bayesian cloud detection (BCD) scheme, operationally implemented at the NOAA Office of Satellite Product Operations (OSPO) for the GOES-Imager, has been examined using an experimental filter and it is found that this scheme is not optimal. Thus, a new algorithm for cloud and error masking (CEM) scheme is proposed for physical SST retrievals. This is based on a quasi-deterministic approach combined with an approximated radiative transfer model and the functional spectral differences at pixel level. Although, traditionally the validation of cloud detection algorithms have often been reported qualitatively using visual inspection of imagery, we have made a quantitative validation of the cloud algorithm for its intended purpose by determining the quality of satellite SST retrievals against in situ data. Results show that CEM can reduce the root mean square error in SST by an average of 22% while increasing the data coverage by an average of 38% compared to the operationally implemented BCD at OSPO, as assessed over a period of fifty months.

The Hillarys Transect (2): Validation of satellite-derived sea surface temperature in the Indian Ocean off Perth, Western Australia

There has been a heightened interest in sea surface temperature (SST) measurements during the past two decades, particularly on a global scale, due largely to the advent of several El Nino episodes and increasing worldwide concern about global warming. Because of the continuous global measurements of SST that satellites can provide they play a fundamental role in acquiring the data sets necessary for studies of such climate processes. However, the satellite data still need to be validated against in situ measurements in order to assess the accuracy of satellite SST retrieval algorithms. Validation of such SST retrieval algorithms is the primary aim of the SST measurement program component of the Hillarys Transect. This paper describes a methodology for the validation of satellite-derived SST as well as the seasonal variation of SST off the coast of southern Western Australia. It discusses the factors which may affect the quality of in situ validation data and concludes that measurements of the bulk sea surface temperature (BSST) should be the preferred in situ data source for validation of satellite-based algorithms derived from floating buoy measurements. In this study BSST data possessed superior accuracy over the coincident radiometric sea surface skin temperature (SSST) data, and were found to be significantly better for validation purposes under wind speed conditions below 4 ms - 1 .

Impacts of in situ and additional satellite data on the accuracy of a sea‐surface temperature analysis for climate

AbstractAdditional in situ and satellite data improve the accuracy of a blended (in situ and satellite) sea‐surface temperature (SST) analysis using optimum interpolation (OI). Two studies were conducted to evaluate the impacts of in situ and additional satellite data. One study evaluated the adequacy of the recent in situ network. Because of the high coverage of satellite data, in situ data used in the analysis tends to be overwhelmed by satellite data. Thus, the most important role of the in situ data in the analysis is to correct large‐scale satellite biases. Simulations with different buoy densities showed the need for at least two buoys on a 10° spatial grid. This will ensure that satellite biases do not exceed 0.5 °C. Using this criterion, regions were identified where additional buoys are needed.A second study evaluated the impact of satellite SST retrievals from the tropical rainfall measuring mission microwave imager (TMI) on the OI analysis. The present version only uses infrared satellite data from the advanced very high resolution radiometer (AVHRR) instrument. The results of the intercomparisons showed that both AVHRR and TMI data have biases that must be corrected for climate studies. The addition of TMI data clearly improved the OI analysis accuracy without bias correction, but was less significant when bias correction was used. However, there are areas of the ocean with limited in situ data and restricted AVHRR coverage due to cloud cover, and the use of both TMI and AVHRR should improve the accuracy of the analysis in those areas. Copyright © 2005 Royal Meteorological Society

Operational Processing of Satellite Sea Surface Temperature Retrievals at the Naval Oceanographic Office

A complete overview of the national Shared Processing Program (SPP) satellite sea surface temperature (SST) retrieval product is presented. This paper summarizes the operational processing of digital Advanced Very High Resolution Radiometer (AVHRR) satellite data into a global SST retrieval product at the Naval Oceanographic Office (NAVOCEANO). Satellite SST generation is described, detailing data processing procedures, algorithms used, and quality control techniques. User interaction and data monitoring through the SPP algorithm research panel for SST is presented along with SST products and information available to users. The NAVOCEANO national SST product consists of more than 150 000 global retrievals per day and demonstrates monthly bias errors less than 0.1 °C and root-mean-square difference errors less than 0.6°C relative to global drifting-buoy measurements. The product is important to and operationally utilized within thermal structure analyses, civilian and military maritime activities, and nume...

A High-Resolution Global Sea Surface Temperature Climatology

Abstract In response to the development of a new higher-resolution sea surface temperature (SST) analysis at the National Meteorological Center (NMC), a new monthly 1° global sea surface temperature climatology was constructed from two intermediate climatologies: the 2° SST climatology presently used at NMC and a 1° SST climatology derived from the new analysis. The 2° SST climatology used a 30-yr 1950–1979 base period between roughly 40°S and 60°N based on in situ (ship and buoy) SST data supplemented by four years (1982–1985) of satellite SST retrievals. The 1° SST climatology was based on monthly analyses using in situ SST data, satellite SST retrievals, and sea-ice coverage data over a 12-yr period (1982–1993). The final climatology was combined from these two products so that a 1° resolution was maintained and the base period was adjusted to the 1950–1979 period wherever possible (approximately between 40°S and 60°N). Compared to the 2° climatology, the 1° climatology resolves equatorial upwelling an...

Sensitivity of satellite multichannel sea surface temperature retrievals to the air‐sea temperature difference

The advanced very high resolution radiometer multichannel sea surface temperature (MCSST) retrieval technique provides global algorithm accuracy statistics generally showing a bias of less than 0.1°C and an rms error of less than 0.7°C when compared to colocated drifting buoy in situ data in the absence of aerosols. This remaining error is not always random but is shown to be correlated to the air‐sea temperature difference. The MCSST technique is modeled and then compared to in situ data to show this dependency. Atmospheric radiative transfer calculations are used to provide a simulation of satellite retrieval sensitivity to air‐sea temperature differences. Buoy sea surface temperature (SST) and air temperature observations are then presented as experimental verification of the simulation results. Retrieval errors depend both on the mean air‐sea temperature difference conditions present in the data set used to empirically derive the algorithm and on the changes in air‐sea temperature difference conditions relative to the derivation data set mean conditions. Retrieval error is found to respond linearly with air‐sea temperature difference changes. MCSST retrieval errors of 1.0°C can occur for air‐sea temperature difference changes of 7°–10°C from mean conditions when the dual‐window (channels 3 and 4) or triple‐window (channels 3, 4, and 5) algorithms are used. The split‐window (channels 4 and 5) MCSST algorithm is shown to be less sensitive to air‐sea temperature differences. Cross‐product SST (CPSST) and nonlinear SST (NLSST) algorithms are also examined. These algorithms generate results similar to the MCSST algorithm for the dual‐ and triple‐window equations. However, the CPSST and NLSST split‐window algorithms demonstrate greater sensitivity to air‐sea temperature difference changes than do the MCSST split‐window algorithm. Retrieval errors of 1°C can occur for air‐sea temperature difference changes of 10°–12°C from mean conditions. Users of satellite SST retrievals in regions that experience large fluctuations in air‐sea temperature difference should be aware of this possible error source.