Abstract
Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.
Highlights
Sea Surface Temperature (SST) is an essential climate variable (ECV), which is very important for understanding the Earth’s climate variability (e.g., [1])
Here we evaluate the thermal gradients observed in the SST retrieved from INSAT-3D/3DR using both the 1-Dimensional Variational (1DVAR) and the non-linear regression-based algorithms (NLSST) algorithms
The motivation of the present study comes from the utility of high spatial and temporal resolution GEO satellite observations for estimating one of the most important boundary parameters over the ocean, i.e., SST
Summary
Sea Surface Temperature (SST) is an essential climate variable (ECV), which is very important for understanding the Earth’s climate variability (e.g., [1]). It is a critical boundary condition in the Numerical Weather Prediction (NWP) models, Ocean and Coupled models to predict the weather and ocean state [2,3,4,5,6]. This is mainly because SST as a parameter plays an important role in determining the exchange of heat, moisture, and momentum fluxes at the interface of the ocean and the atmosphere [7]. SST provides an insight into various physical processes that are responsible for several oceanographic as well as meteorological phenomena, thereby directly affecting large and small-scale weather and climate patterns [8,9,10,11]
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