A Machine Learning-Based SST Retrieval from Thermal Infrared Observations of INSAT-3D Imager: Improvement Over Regression-Based NLSST Algorithm

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Rishi Kumar Gangwar, M. Jishad, P. K. Thapliyal
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Abstract

Sea surface temperature (SST) is one of the key Essential Climate Variables for studying and monitoring Earth’s climate, besides playing an important role in physical oceanographic processes and as a boundary condition in the numerical prediction models. Understanding these processes requires the availability of accurate and consistent SST products over the global ocean, which can be fulfilled by retrieving them from satellite-based observations. Therefore, the present study exploits a supervised machine learning technique, Deep Neural Network (DNN), for the retrieval of SST using thermal infrared (TIR) split-window observations from Imager onboard India’s geostationary satellite, INSAT-3D, which was launched in 2013. A matchup dataset is prepared to train and test the DNN, comprising the collocated brightness temperatures of TIR channels of INSAT-3D Imager with the in-situ SST measurements for 2017–2020. The DNN-based algorithm exhibits a similar statistics with reference to the in-situ SST for both training and testing datasets. It is further assessed on independent INSAT-3D observations for May 2021- February 2022 to demonstrate its robustness. Moreover, the performance of the DNN is also compared to the widely used regression-based non-linear SST (NLSST) algorithm, which is presently operational for INSAT-3D. Validation against the in-situ SST shows an improvement of about 37.5% in the accuracy of SST retrieved using DNN (RMSE = 0.5 K) over the NLSST (RMSE = 0.8 K) algorithms for INSAT-3D Imager.

Abstract Image

基于机器学习的INSAT-3D成像仪热红外观测海温检索:对基于回归的NLSST算法的改进
海温(SST)是研究和监测地球气候的关键气候变量之一,在海洋物理过程中起着重要作用,在数值预报模式中也是一个边界条件。要了解这些过程,就需要获得准确和一致的全球海洋海温产品,这可以通过从卫星观测中检索它们来实现。因此,本研究利用有监督的机器学习技术——深度神经网络(DNN),利用2013年发射的印度地球同步卫星INSAT-3D上的成像仪上的热红外(TIR)分窗观测数据检索海温。将2017-2020年INSAT-3D成像仪TIR通道的亮度温度与现场海温测量数据进行匹配,准备了一个匹配数据集来训练和测试DNN。基于dnn的算法在训练和测试数据集上都显示出与原位海表温度相似的统计数据。通过2021年5月至2022年2月的独立INSAT-3D观测进一步评估,以证明其稳健性。此外,还将深度神经网络的性能与广泛使用的基于回归的非线性海表温度(NLSST)算法进行了比较,该算法目前在INSAT-3D中运行。对INSAT-3D Imager的原位海表温度验证表明,使用DNN (RMSE = 0.5 K)检索海表温度的精度比使用NLSST (RMSE = 0.8 K)算法提高了约37.5%。
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来源期刊
pure and applied geophysics
pure and applied geophysics 地学-地球化学与地球物理
CiteScore
4.20
自引率
5.00%
发文量
240
审稿时长
9.8 months
期刊介绍: pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys. Long running journal, founded in 1939 as Geofisica pura e applicata Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research Coverage extends to research topics in oceanic sciences See Instructions for Authors on the right hand side.
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