Sea Surface Temperature Retrieval Algorithms Research Articles

Comparison of FY-4A/AGRI SST with Himawari-8/AHI and In Situ SST

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted.

Sea surface temperature retrieval based on simulated space-borne one-dimensional multifrequency synthetic aperture microwave radiometry

The space-borne one-dimensional multifrequency synthetic aperture microwave radiometer (1D-MSAMR) offers new possibilities for detecting high spatial resolution sea surface temperature (SST). To achieve higher SST retrieval accuracy, an SST retrieval algorithm, the two-step retrieval algorithm (TSSR), is proposed based on the multiple linear regression (MLR) algorithm. In this study, we investigated the SST retrieval accuracy of 1D-MSAMR based on simulation experiments. For the study, we assumed that the frequencies of the 1D-MSAMR were 6.9, 10.65, 18.7, 23.8, and 36.5 GHz, and that all frequencies worked in a dual polarization (vertical and horizontal) manner. We used an ocean-atmosphere microwave radiation brightness temperature model and the 1D-MSAMR simulator to simulate the measured brightness temperature based on WindSat data provided by the Remote Sensing Systems (RSS). An MLR algorithm and the TSSR were then developed to retrieve the SST within the incidence angle range of 0°–65°. The results show that the SST retrieval errors of the two SST retrieval algorithms decreased with the increase of incidence angle. The TSSR had higher retrieval accuracy, especially at low incidence angle. The average retrieval accuracy of the TSSR was about 0.3 K higher than that of the MLR algorithm. The retrieval error of the TSSR was also less sensitive to the measurement error of the 6.9 GHz frequency than the MLR algorithm.

Retrieval of Sea Surface Temperature From HY-1B COCTS

The Chinese Ocean Color and Temperature Scanner (COCTS) on board HY-1 series satellites has two thermal infrared channels with the spectrum range of 10.30-11.40 μm and 11.40-12.50 μm for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in this study. The Bayesian cloud detection algorithm that has been developed is based on the Bayes’ theorem and uses simulation of COCTS observations. The MODerate resolution atmospheric TRANsmission (MODTRAN) model was used for simulation of COCTS brightness temperatures. SSTs were retrieved from COCTS by OE from 2009 to 2011 in the northwest Pacific. Comparison of COCTS OE SST with in situ SST showed that the COCTS SSTs are cooler than buoy measurements by –0.23 °C on average, and the standard deviation (SD) of differences was 0.51 °C. A large component of the mean difference is attributable to the cool skin effect at the ocean surface (typically –0.15 to –0.2 °C), the remainder being attributable to simulation and calibration biases. The mean difference of COCTS OE SST with matched skin temperatures from the Advanced Along Track Scanning Radiometer (AATSR) is closer to zero, being –0.09 °C, with a SD of 0.49 °C. These validation results of COCTS OE SST demonstrate that Bayesian cloud detection and OE SST retrieval algorithm work well for improving COCTS SST accuracy, and show the potential of these methods to help develop SST products for operational HY-1 satellites, HY-1C and HY-1D.

Discrepancies between satellite-derived and in situ SST data in the Cape Frio Upwelling System, Southeastern Brazil (23˚S)

ABSTRACTSatellite-derived sea surface temperature (SST) presents discrepancies in both magnitude and temporal variability in the wind-driven coastal upwelling region off the Cape Frio (23°S) in Southeastern Brazil. Level 2 (L2) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, and Level 4 (L4) SST products are compared with in situ measurements. Strong positive biases up to 4°C are systematically observed in L4 products during upwelling events and decrease to less than 1°C in non-upwelling days. Lower biases were found in L2 MODIS SST, and the statistical performance of L4 SST products did not present significant improvements in days when high-resolution clear-sky observations from MODIS were available. This indicates that spatio-temporal interpolation and ingestion of microwave-based data sets in L4 SST analyses are likely the main cause of observed discrepancies during upwelling events. Improved L2 SST retrieval algorithms and L4 SST analyses that take into account upwelling-related regional characteristics are required for more accurate satellite SST products over coastal regions.

Development of Sea Surface Temperature Retrieval Algorithms for Geostationary Satellite Data (Himawari-8/AHI)

This study provides an overview of the development of sea surface temperature retrieval algorithms by using Himawari-8/AHI data as a proxy data of GK-2A with quite similar spectral bands except for 2.26-μm and 1.38-μm bands. For contingency preparation, several potential algorithms, such as Multi-channel SST (MCSST), Non-linear SST (NLSST), Hybrid SST, and Multi-band SST, were developed over the full disk region. The accuracy of each algorithm was assessed by determining the root mean square error (RMSE) and bias errors from the regression procedure of the matchup database between satellite data and quality controlled drifter temperature in-situ data for a year, from August 2016 to July 2017. Comparison of the four algorithms revealed that the Multi-band algorithm performed markedly well, with the smallest RMSE of ~0.4 °C. Time-varying validation of the estimated SST accuracy highlighted consistently low RMSE as well as the stability of the Multi-band algorithm. In addition, it is suggested that SSTs with a satellite zenith angle exceeding 60° tended to have relatively large errors which degraded the quality of the estimated SSTs. It is concluded that the SST coefficients should be updated each day, based on the previous one-month matchup database, contributing to the expected SST accuracy in the future with the degradation of the sensor or other aging effects. Further, this work discusses the importance of cloudy or cloud-contaminated pixels for the better performance of SST retrieval procedures and their real-time operational use.

On the use of NLSST and MCSST for the study of spatio-temporal trends in SST gradients

ABSTRACTOngoing efforts are dedicated by meteorological and oceanographic agencies to improve the accuracy of Sea Surface Temperature (SST) estimates from satellite observations via improved retrieval algorithms and validation data. An important application of satellite-based SST observations is the analysis of the spatio-temporal characteristics of ocean fronts, which depend on several parameters including the SST retrieval scheme from Top-of-Atmosphere Brightness Temperatures. In this study, we focus on two widely used SST retrieval algorithms, namely the Multichannel SST (MCSST) and the Nonlinear SST (NLSST). Using night-time Level 2 SST derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua from 2005 to 2015 over the Southwestern Atlantic Ocean (SAO), we show that 1) the spatial distribution and temporal variability of SST gradient magnitudes derived from these two SST retrieval schemes are different despite statistical consistency of SST fields 2) the widely used NLSST formulation introduces a correlation between SST gradient magnitudes and SST values. This correlation, likely due to the use of a first guess SST in the NLSST formulation, is not observed in the MCSST data and may affect the study of long-term changes in ocean dynamics.

Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so.

Using Artificial Neural Network Ensembles With Crogging Resampling Technique to Retrieve Sea Surface Temperature From HY-2A Scanning Microwave Radiometer Data

The brightness temperature data acquired during 2012-2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite- Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) 0.25° × 0.25° gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs.

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.

FEASIBILITY STUDY OF LANDSAT-8 IMAGERY FOR RETRIEVING SEA SURFACE TEMPERATURE (CASE STUDY PERSIAN GULF)

Abstract. Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R2 = 0.95 and RMSE = 0.24.

FEASIBILITY STUDY OF LANDSAT-8 IMAGERY FOR RETRIEVING SEA SURFACE TEMPERATURE (CASE STUDY PERSIAN GULF)

Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30 m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R<sup>2</sup> = 0.95 and RMSE = 0.24.

Validation of S-NPP VIIRS Sea Surface Temperature Retrieved from NAVO

The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, the magnitude and characteristics of uncertainties in S-NPP VIIRS SST produced by the Naval Oceanographic Office (NAVO) are investigated. The NAVO S-NPP VIIRS SST and eight types of quality-controlled in situ SST from the National Oceanic and Atmospheric Administration in situ Quality Monitor (iQuam) are condensed into a Taylor diagram. Considering these comparisons and their spatial coverage, the NAVO S-NPP VIIRS SST is then validated using collocated drifters measured SST via a three-way error analysis which also includes SST derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard AQUA. The analysis shows that the NAVO S-NPP VIIRS SST is of high accuracy, which lies between the drifters measured SST and AQUA MODIS SST. The histogram of NAVO S-NPP VIIRS SST root-mean-square error (RMSE) shows normality in the range of 0–0.6 °C with a median of ~0.31 °C. Global distribution of NAVO VIIRS SST shows pronounced warm biases up to 0.5 °C in the Southern Hemisphere at high latitudes with respect to the drifters measured SST, while near-zero biases are observed in AQUA MODIS. It means that these biases may be caused by the NAVO S-NPP VIIRS SST retrieval algorithm rather than the nature of the SST. The reasons and correction for this bias need to be further studied.

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.

The effects of anomalous atmospheres on the accuracy of infrared sea-surface temperature retrievals: Dry air layer intrusions over the tropical ocean

The effects of layers in the atmosphere with anomalously low moisture content on the accuracy of the sea-surface temperature (SST) derived from measurements of infrared radiometers on earth observation satellites are quantified using measurements taken from research cruises and numerical simulations. Radiosonde data from areas of the oceans that are seasonally affected by intrusions of dry air masses of continental origin were used with the Line-By-Line Radiative Transfer Model (LBLRTM) to simulate brightness temperatures measured in MODIS bands 31 and 32 (at wavelengths of ~11μm and ~12μm). The radiosonde datasets contained profiles with and without a dry layer, representing the baseline (no dry layer aloft) and the anomalous conditions (dry layer present). MODIS SST retrieval algorithm versions 5 and 6 were applied to the simulated brightness temperatures to obtain the SST and the retrieval errors were examined. Whereas the average errors for the baseline ‘no dry layer’ conditions range between 0.29 and 0.72K, in the case of very deep dry layers the errors can be >1K. Simulations were also performed for atmospheric profiles that were created from the measured profiles by ‘drying’ layers at various altitudes. It was found that the retrieval errors 1) depend on the amount of water vapor in the atmosphere, 2) change in a systematic way dependent on the presence and characteristics of a dry layer, 3) dry layers in the lower troposphere make the SST retrieval errors more positive, and dry layers in the upper troposphere tend to introduce SST retrieval errors that are more negative.

ATSR infrared radiometric calibration and in-orbit performance

Three Along Track Scanning Radiometers (ATSRs) form a series of space-borne instruments specifically optimised to provide accurate remotely sensed measurements of Sea Surface Temperature (SST), which is a key geophysical parameter required to inform the debate on climate change and global warming. These sensors' well-calibrated, high quality data have wide applicability and are being used in a much wider range of earth observation studies and applications, in addition to the planned SST mission. Each successive instrument has been an incremental improvement over its predecessor. Since early 1991 the ATSR sensors have provided global observations from the European Space Agency's Earth Observation satellites; namely ATSR-1 on ERS-1 (European Remote-sensing Satellite), then ATSR-2 on ERS-2 and then AATSR (Advanced ATSR) on ENVISAT. The missions have been operated with good overlaps between successive sensors; AATSR is currently the operational instrument. The fundamental requirement for each ATSR instrument is a design that is capable of delivering absolutely calibrated infrared data; therefore this paper concentrates on how the sensors provide the calibrated radiometric observations required for the SST retrieval algorithms to work. It does not discuss the validation of the algorithms to produce SST derived from these basic observations. Described are the rigorous pre-launch measurements over a range of simulated flight environments which verify that this aim has been achieved, a calibration which exercises the same brightness temperature algorithm that is then used when calibrating in-flight measurements. Crucially, the ATSRs measure calibration source radiances in-flight without interrupting Earth-viewing which permits continuous gain and offset monitoring and calibration. For clarity where it is thought necessary for a data user's better understanding of the calibration for flight data, some detail is provided concerning the differences between the three different ATSRs.

Atmospheric correction for sea surface temperature retrieval from single thermal channel radiometer data onboard Kalpana satellite

An atmospheric correction method has been applied on sea surface temperature (SST) retrieval algorithm using Very High Resolution Radiometer (VHRR) single window channel radiance data onboard Kalpana satellite (K-SAT). The technique makes use of concurrent water vapour fields available from Microwave Imager onboard Tropical Rainfall Measuring Mission (TRMM/TMI) satellite. Total water vapour content and satellite zenith angle dependent SST retrieval algorithm has been developed using Radiative Transfer Model [MODTRAN ver3.0] simulations for Kalpana 10.5–12.5 μm thermal window channel. Retrieval of Kalpana SST (K-SST) has been carried out for every half-hourly acquisition of Kalpana data for the year 2008 to cover whole annual cycle of SST over Indian Ocean (IO). Validation of the retrieved corrected SST has been carried out using near-simultaneous observations of ship and buoys datasets covering Arabian Sea, Bay of Bengal and IO regions. A significant improvement in Root Mean Square Deviation (RMSD) of K-SST with respect to buoy (1.50–1.02 K) and to ship datasets (1.41–1.19 K) is seen with the use of near real-time water vapour fields of TMI. Furthermore, comparison of the retrieved SST has also been carried out using near simultaneous observations of TRMM/TMI SST over IO regions. The analysis shows that K-SST has overall cold bias of 1.17 K and an RMSD of 1.09 K after bias correction.

Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

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 .

Definition of a radiosounding database for sea surface brightness temperature simulations: Application to sea surface temperature retrieval algorithm determination

The Ocean and Sea Ice Satellite Application Facility (O&SI SAF) project includes the development of Sea Surface Temperature (SST) retrieval algorithms for infrared radiometers. The algorithm definition stage presented in this paper includes three steps: building of representative simulated brightness temperature databases, definition and analysis of formalisms on simulated brightness temperatures, and tests of these formalisms on a Matchup Database (MDB). A cloud-free, latitude equally distributed simulation database has been built to determine SST algorithms, which were further checked on a NOAA-14 latitude equally distributed validation database build from the Pathfinder MDBs. The tests showed that the presence of clouds in the training database increases the bias, while an underrepresentation of low-latitude profiles in the training database increases the standard deviation. The use of a cloud-free, latitude equally distributed training database reduced both the standard deviation in the algorithms and the bias consistently over the whole range of latitudes. The effect of the instrumental noise has been assessed and algorithms using noisy simulated data have been tested: they appear much more useful at daytime than at nighttime. Indications suggest that such noise algorithms should be more adaptive in atypical situations and that they should perform better than noise-free algorithms in the presence of cloud contamination or aerosols. The systematic procedure allowed us to test a large number of different algorithms. We determined that the best possible theoretical accuracy that may be obtained with NOAA-14 derived SSTs against buoy measurements on a global scale is 0.4 K. The successive steps that were performed in this study (cloud-free training database, algorithm determination, and noise scheme) allowed us to obtain retrievals with a 0.57 K standard deviation at both daytime and nighttime. These performances appears to be comparable or better than those obtained with algorithms directly derived by regression against in situ measurements. This proves that simulation-based SST retrievals are feasible and accurate when sufficient care is taken in the constitution of the training database. The effect of maritime aerosols has also been assessed and was found to be generally of little consequences for SST retrievals: a positive bias found to be lower than 0.08 K and no effect on the standard deviation. This bias is overestimated when maritime aerosols are used in the training of the algorithms using radiative transfer models. Current SST products (North Atlantic Regional and high latitudes) based on NOAA-14 and NOAA-16 data and routine validations against drifting buoys are available at 〈 http://www.meteorologie.eu.org/safo〉.

Methodology to obtain accurate sea surface temperature from locally received NOAA-14 data in the Canary-Azores-Gibraltar area

A methodology to obtain the sea surface temperature (SST) in the Canary-Azores-Gibraltar area has been developed. The final accuracy of the AVHRR-based SST retrievals depends both on the accuracy of the measured radiance and the accuracy of the SST retrieval algorithm that converts the measured radiance into sea surface temperature. The procedure made in this framework consists of: (i) a radiance-based non-linearity correction method; (ii) an automatic procedure for the geometric correction, achieving accuracy to the order of the pixel; (iii) an improved algorithm for cloud detection, based on the method of the multiband thresholds; (iv) a new split-window function (SWF) to perform atmospheric correction in CANIGO area. This task has been accomplished by using the matchup data set of AVHRR brightness temperature (channel 4 and 5) and in situ SST data. The matchups were carefully screened from the HRPT scenes of all seasons in 1996-1998. The temporal and spatial coincidences are within 30 minutes and one pixel resolution, respectively. Coefficients of that algorithm are estimated from regression analysis using 60 co-located in situ and satellite measurements (matchups). Error analysis has shown that SST can theoretically be retrieved to within 0.38oC RMS.