Land surface temperature from INSAT‐3D imager data: Retrieval and assimilation in NWP model

Abstract

A new algorithm is developed for retrieving the land surface temperature (LST) from the imager radiance observations on board geostationary operational Indian National Satellite (INSAT‐3D). The algorithm is developed using the two thermal infrared channels (TIR1 10.3–11.3 µm and TIR2 11.5–12.5 µm) via genetic algorithm (GA). The transfer function that relates LST and thermal radiances is developed using radiative transfer model simulated database. The developed algorithm has been applied on the INSAT‐3D observed radiances, and LST retrieved from the developed algorithm has been validated with Moderate Resolution Imaging Spectroradiometer land surface temperature (LST) product. The developed algorithm demonstrates a good accuracy, without significant bias and standard deviations of 1.78 K and 1.41 K during daytime and nighttime, respectively. The newly proposed algorithm performs better than the operational algorithm used for LST retrieval from INSAT‐3D satellite. Further, a set of data assimilation experiments is conducted with the Weather Research and Forecasting (WRF) model to assess the impact of INSAT‐3D LST on model forecast skill over the Indian region. The assimilation experiments demonstrated a positive impact of the assimilated INSAT‐3D LST, particularly on the lower tropospheric temperature and moisture forecasts. The temperature and moisture forecast errors are reduced (as large as 8–10%) with the assimilation of INSAT‐3D LST, when compared to forecasts that were obtained without the assimilation of INSAT‐3D LST. Results of the additional experiments of comparative performance of two LST products, retrieved from operational and newly proposed algorithms, indicate that the impact of INSAT‐3D LST retrieved using newly proposed algorithm is significantly larger compared to the impact of INSAT‐3D LST retrieved using operational algorithm.

A spatiotemporal analysis of the relationship between near‐surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

“…Applications for satellite‐derived LST are now widely reported in the literature. Examples include using LST to evaluate land surface models [ Koch et al ., ], improve the performance of numerical weather prediction models [ Singh et al ., ], diagnose surface response to dry spells [ Folwell et al ., ], assess drought [ Karnieli et al ., ; Mühlbauer et al ., ], characterize urban heat islands [ Azevedo et al ., ; De Ridder et al ., ; Dousset et al ., ; Hu and Brunsell , ], and to estimate near‐surface air temperature in the absence of meteorological stations [ Chen et al ., ; Good , ; Kilibarda et al ., ]. Operational LST data sets are now available from several sensors, some of which extend for more than a decade in length.…”

“…LST observations are most readily available from spaceborne radiometers that operate at infrared (IR) or microwave (MW) wavelengths offering complete global coverage at spatial scales ranging from a few tens of meters (IR) to a few tens of kilometers (MW), with a temporal frequency of around twice per month to once every 15 min. models [Singh et al, 2016], diagnose surface response to dry spells [Folwell et al, 2016], assess drought [Karnieli et al, 2010;Mühlbauer et al, 2016], characterize urban heat islands [Azevedo et al, 2016;De Ridder et al, 2012;Dousset et al, 2011;Hu and Brunsell, 2013], and to estimate near-surface air temperature in the absence of meteorological stations [Chen et al, 2014;Good, 2015;Kilibarda et al, 2014]. Operational LST data sets are now available from several sensors, some of which extend for more than a decade in length.…”

Physical retrieval of sea-surface temperature from INSAT-3D imager observations

“…Since the objective of the present study was the development of the SST algorithm, the above conservative cloud-detection checks were chosen to avoid any possibility of error due to cloud leakage. Our recent studies (Singh et al, 2016a(Singh et al, , 2016b show that the INSAT-3D radiances have significant biases and they need to be corrected prior to their use. It was further verified by comparing the INSAT-3D observations (TIR1 and TIR2) with the corresponding SEVIRI (on-board Meteosat second generation-1 satellite, MSG-1) observations.…”

“…The angular dependency is not incorporated in the OPR algorithm directly, rather the regression coefficients are generated for four sets of satellite viewing angles. The INSAT-3D radiances are reported to have large biases (Singh et al 2016a(Singh et al , 2016b. In this study, we have analysed the OPR SST in terms of its accuracy as compared with in-situ observations.…”

Land surface temperature variability across India: a remote sensing satellite perspective

“…Ground-based observations of LST are rather sparse and limited at the global scale, and remote sensing satellites provide a cost-effective alternative to measure global and regional LST comprehensively. LST measurements from the polar-orbiting satellites are generally used as reference for validating LST derived from the geostationary satellites over in-situ data sparse regions (Singh et al, 2016). LST estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the polar-orbiting Aqua and Terra satellites have been widely used for various applications at global and regional scales (Li et al, 2013;Prakash et al, 2016Prakash et al, , 2018Deilami et al, 2018;Phan and Kappas, 2018;Sharifnezhadazizi et al, 2019).…”