# Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer
## 2 Methods  
To understand the impact of CIMR on the future observability of SSTs, we have undertaken a simulation study that provided synthetic observations to the OSTIA system. By providing input data with and without CIMR observations and with and without realistic observational errors, we could test the effect of the increased coverage provided by adding CIMR data to the IR constellation and whether this effect remained when allowance was made for the increased uncertainties associated with PMW-derived SSTs. To achieve this, a \"truth\" dataset was required, from which, simulated observations for CIMR, SLSTR and AVHRR could subsequently be derived, and to which, analysis fields could be compared. To act as an SST truth, we obtained data generated as part of the North Atlantic Climate System Integrated Study (ACSIS) [15], consisting of the output of a free-running coupled general-circulation simulation using the Nucleus for European Modelling of the Ocean (NEMO) model [16]. These data were provided on the tripolar, curvilinear, ORCA12 grid [17; 18], with an approximate 1/12\({}^{\circ}\) resolution, and regridded here to an equiangular 0.05\({}^{\circ}\) grid for input to OSTIA. As the ORCA grid is slightly coarser than the target OSTIA grid, there is the potential for the loss of feature resolution at the smallest scales. Consequently, sharpening was applied using a 2-D Laplacian filter to increase the power at shorter length scales to match the -11/3 power law expected for sea surface height and SST in the open ocean [14; 19; 20]. In order to simulate IR observations of the \"truth\" dataset with an appropriate spatial coverage, it was necessary to apply realistic masking to the data. The largest single cause of invalid IR-based SST values over the ocean, within the satellite swath, is the presence of clouds. Suitable masking was achieved using level 3 uncollated (L3U) data from the two IR instruments in 2018. This was generated with the Generalised Bayesian Cloud Screening (GBCS) processor used for the Sea Surface Temperature Climate Change Initiative project [7; 21; 22; 23] and the Copernicus Climate Change Service Climate Data Store [24; 25]. The processor is named for historical reasons but the modern software is capable of retrieving SSTs from level 1 (L1) data as well as generating an accompanying cloud mask, uncertainties and other products for a variety of instruments. L3U data were generated at a 0.05\({}^{\circ}\) resolution with multiple files a day, each corresponding to an input L1 file. The SST in each L3U file was replaced with the value from the \"truth\" dataset for that day of the year at all locations with a valid SST value and with a quality level of 4 or higher. In January, this resulted in 14.0 \(\pm\) 0.6% of global ocean cells having at least one AVHRR observation on a given day and 12.4 \(\pm\) 0.5% having at least one SLSTR observation. Being based on a free-running model, the choice of year for the \"truth\" data was arbitrary rather than being specifically representative of 2018. Model cells with an SST less than -1.7\({}^{\circ}\)C were interpreted as the locations of sea ice. The resulting dataset constituted the SSTs that would be derived in the case of a perfect observing and retrieval system for the two infrared instruments given the cloud distribution of the target days. To provide a more realistic observational dataset, synthetic noise was added to the SST in the \"perfect\" files based on the uncertainty terms provided for each cell in the L3U files. These were (with typical values for AVHRR) large-scale correlated uncertainty (\(\approx\)0.1 K), adjustment uncertainty (0.05-0.1 K), synoptically correlated uncertainty (0.1-1.0 K) and uncorrelated uncertainty (0.05-0.2 K). Large-scale correlated uncertainties are those that arise from effects that can be assumed to be correlated everywhere and over long time scales (such as calibration of the satellite sensor). The adjustment uncertainty arises from the process of adjusting SSTs to a standard time and depth.