Variability and uncertainty in measuring sea surface temperature

Sea Surface Temperature (SST) measurement is one of the most easily obtainable climate variables. However, it is challenging to meet the required absolute accuracy and long term stability whether the data are derived by in situ or satellite measurements. This study explores the quality of SST measurements, in particular those derived by the Advanced Along Track Scanning Radiometer (AATSR) and in situ measurements recorded by the shipborne Infrared Sea surface temperature Autonomous Radiometer (ISAR), which are used for validating AATSR data. Its broad objective is to improve understanding of measurement uncertainties in order to quantify the quality of satellite derived SST used for climate records.The uncertainties of in situ measurement by ISAR have been analysed and modelled in order to estimate an independent measurement uncertainty for every SST data point in the ISAR records. In a complementary study the separate uncertainties of the SST as observed by AATSR, ISAR and ship-based hull-mounted thermometry (SSTdepth), when observing the same track, have been resolved by means of three way uncertainty analysis. This not only serves to verify the ISAR uncertainty model but also demonstrates the effectiveness of using shipborne radiometry in preference to in water thermometry from ships or buoys for validating satellite SST products. A third area of study concerns the errors and uncertainties when comparing satellite and in situ observations, which result from failure to properly match the in situ observations to what the satellite sees". A new method has been developed for classifying the match-up quality" of each data pair. Its use is demonstrated to show that the quality of AATSR data may be better than classical validation match

Task team Shipborne Radiometry report 2023 (GHRSST24)

<p>Task team report to the GHRSST Science Team. This presentation was delivered at the GHRSST24 meeting, 16-20 October 2023.</p&gt

The ISAR Instrument Uncertainty Model

Measurements of sea surface temperature at the skin interface () made by an Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) have been used for a number of years to validate satellite sea surface temperature (SST), especially high-accuracy observations such as made by the Advanced Along-Track Scanning Radiometer (AATSR). The ISAR instrument accuracy for measuring is ±0.1 K (Donlon et al.), but to satisfy Quality Assurance Framework for Earth Observation (QA4EO) principles and metrological standards (Joint Committee for Guides in Metrology), an uncertainty model is required. To develop the ISAR uncertainty model, all sources of uncertainty in the instrument are analyzed and an uncertainty value is assigned to each component. Finally, the individual uncertainty components are propagated through the ISAR retrieval algorithm to estimate a total uncertainty for each measurement. The resulting ISAR uncertainty model applied to a 12-yr archive of measurements from the Bay of Biscay shows that 77.6% of the data are expected to be within ±0.1 K and a further 17.2% are within 0.2 K

Extreme wave heights in the North Atlantic from altimeter data

Extreme waves are an important ocean feature. We estimate return values of significant wave height from measurements by satellite altimeters over the North Atlantic. The data were divided into 2° latitude by 2° longitude grid squares and the median along the satellite track was taken in each. Return values were estimated by fitting a Generalised Pareto Distribution to all values above a threshold, which was allowed to vary spatially. This method is objective, more statistically robust and thus theoretically preferable to fitting a distribution to all the data. The novel method gave return values that were up to 37% smaller than those estimated by fitting a Fisher-Tippet 1 distribution to all the data

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system

This paper describes a new Sea surface temperature (SST) analysis that is produced with global coverage on a daily basis at the Met Office called the Operational SST and Sea Ice Analysis (OSTIA) system. OSTIA uses satellite SST data provided by international agencies via the Group for High Resolution SST (GHRSST) Regional/Global Task Sharing (R/GTS) framework. GHRSST products include data from microwave and infrared satellite instruments with accompanying uncertainty estimates. The system also uses in situ SST data available over the Global Telecommunications System (GTS) and a sea-ice concentration product from the EUMETSAT Ocean and Sea Ice Satellite Applications Facility (OSI-SAF). The SST analysis is a multi-scale optimal interpolation that is designed for applications in numerical weather prediction and ocean forecasting systems. The background error covariance matrix is specified using ocean model data and the analysis uses correlation length scales of 10 km and 100 km. The OSTIA system produces a foundation SST estimate (SSTfnd, which is the SST free of diurnal variability) at an output grid resolution of 1/20° (~ 6 km) although the smallest analysis feature resolution is based on the correlation length scale of 10 km. All satellite SST data are adjusted for bias errors based on a combination of ENVISAT Advanced Along Track Scanning Radiometer (AATSR) SST data and in situ SST measurements from drifting buoys. Data are filtered (based on surface wind speed data) to remove diurnal variability and AATSR data are adjusted to represent the SST at the same depth as drifting buoy measurements (0.2–1 m) before bias adjustments are made. Global coverage outputs are provided each day in GHRSST L4 netCDF format. A variety of secondary products are also provided including weekly and monthly mean data sets. OSTIA products are continuously monitored and validation/verification activities demonstrate that SST products have zero mean bias and an accuracy of ~ 0.57 K compared to in situ measurements. OSTIA is now used operationally as a boundary condition for all weather forecast models at the Met Office and at European Centre for Medium-range Weather Forecasting (ECMWF). OSTIA is produced by the Met Office as part of the European Union Global Monitoring for Environment and Security (GMES) MyOcean project

Evaluation of sea state products from the Sentinel-3A and Sentinel-3B tandem phase

Sentinel-3A (S3A) was launched in February 2016 and routinely provides data on ocean wind and waves (significant wave height/SWH, Sigma0 and wind speed). In April 2018, S3A was joined in orbit by Sentinel-3B (S3B) and during the first few months the satellites operated in tandem. The operation of S3B in tandem with S3A during the early phase provides a unique opportunity to obtain data close in space and time to quantify instrument-related sources of discrepancies. During the tandem phase, S3B flies as close as 30 seconds ahead of S3A, which for most purposes can essentially be considered to be instantaneous. In the case of the Sentinel-3 Surface Topography Mission (STM) altimeter payload, the operation of the altimeter instruments on the two Sentinel satellites in different operating modes (Low Resolution Mode/LRM and Synthetic Aperture Radar Mode/SARM) brings additional benefits by providing the opportunity to directly compare the performance and dependencies of the retrieved measurements in the two operating modes. Evaluation of the inter-satellite consistency incorporates independent data such as in situ data, model output and other satellite data all of which, like the S3 data, include uncertainties. In this study, we present work concerned with the calibration and validation of sea state data from the two Sentinel-3 STM instruments. Using independent data, we examine the geographical distribution and uncertainty characteristics of SWH and wind speed from the Sentinel-3 satellites, as well as any global and regional offsets and discrepancies. Statistical methods are explored to formally quantify the errors of the STM sea state measurements, as well as the dependence of errors in SWH and wind speed on various sea state parameters in LRM and SARM operating modes

A printable device for measuring clarity and colour in lake and nearshore waters

Two expanding areas of science and technology are citizen science and three-dimensional (3D) printing. Citizen science has a proven capability to generate reliable data and contribute to unexpected scientific discovery. It can put science into the hands of the citizens, increasing understanding, promoting environmental stewardship, and leading to the production of large databases for use in environmental monitoring. 3D printing has the potential to create cheap, bespoke scientific instruments that have formerly required dedicated facilities to assemble. It can put instrument manufacturing into the hands of any citizen who has access to a 3D printer. In this paper, we present a simple hand-held device designed to measure the Secchi depth and water colour (Forel Ule scale) of lake, estuarine and nearshore regions. The device is manufactured with marine resistant materials (mostly biodegradable) using a 3D printer and basic workshop tools. It is inexpensive to manufacture, lightweight, easy to use, and accessible to a wide range of users. It builds on a long tradition in optical limnology and oceanography, but is modified for ease of operation in smaller water bodies, and from small watercraft and platforms. We provide detailed instructions on how to build the device and highlight examples of its use for scientific education, citizen science, satellite validation of ocean colour data, and low-cost monitoring of water clarity, colour and temperature.</p