Validation of INSAT‐3D and INSAT‐3DR Temperature Profile Retrievals Using Ground‐Based, Satellite, and Reanalysis Data

Abstract

Nowcasting and short‐range predictions of severe weather events require frequent observations of atmospheric thermodynamic state. To meet these requirements, India has launched a series of advanced weather satellites in geostationary platform, with Indian National Satellite System (INSAT)‐3D and INSAT‐3DR as first two satellites placed in this orbit. Both satellites are designed for meteorological observations, particularly the vertical profiles of temperature and humidity at hourly intervals. As these satellites are relatively new, there is a need to validate and make accuracy assessment of their retrievals prior to scientific analyses. These sounders have a spatial resolution of 10 km, and measurements are available from 2014 to date. This study makes use of collocated measurements of INSAT‐3D and INSAT‐3DR with those from 18 radiosondes, Atmospheric Infrared Sounder (AIRS), and reanalysis data within the troposphere (1,000–100 hPa) to validate INSAT data. We analyzed the data for India land regions, and found an average bias of about 2–3 K (1–1.5 K) in most regions, when INSAT‐3D (3DR) is compared to AIRS measurements. Compared to the reanalysis data, INSAT‐3D (3DR) shows a bias within 2.5–5 K (2–3 K) in lower troposphere, 1.5–2.5 K (1–2 K) in mid‐troposphere and 1.5–2.5 (<2 K) in upper troposphere. Similar biases were also estimated with radiosonde measurements. In general, the validation shows that temperature profiles are generally accurate within 1–1.5 K, and INSAT‐3DR retrievals are better than that of INSAT‐3D. Nevertheless, a better cloud filter together with an advanced radiative transfer model can further improve the algorithm used for retrieval.

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

“…To build a complete RS profile, they might have to take into account the comparatively slow balloon rise [145]. Moreover, to overcome RS data sparsity, more studies are now exploring the use of ERA5 data acquired from global analysis systems such as that at the European Center for Medium-Range Weather Forecasting (ECMWF) to calculate and reduce some collocation errors by explicitly considering the dynamics of the atmosphere [168,169].…”