Characterizing the S-NPP/JPSS ATMS Science Data Long-Term Inter-Sensor Bias

As the new generation microwave radiometer, the Advanced Technology Microwave Sounder (ATMS) aboard Soumi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) of NOAA low earth orbit (LEO) satellites has provided high valuable data for weather forecasting and atmospheric studies since the launch of the first ATMS aboard S-NPP in 2011. After the launch of the latest ATMS aboard JPSS-2, renamed to NOAA-21 after reaching its operational altitude, in 2022, there are three ATMS on-orbit sensors now continuously support weather forecasts and the generation of environmental data records.

To make use of the ATMS science data in time series studies, the first step is to identify the inter-sensor observational bias among three ATMS sensors. Even though the design parameters are identical for all ATMS sensors, the observations obtained from different ATMS are different due to various causes, such as hardware build difference, operating temperature difference, and so on. It is important to characterize the science data systematic bias before applying the ATMS radiance data in time series research because of the high sensitivity of the climate change scale. Several ATMS science data quality evaluation and long-term inter-sensor bias analysis utilities have been developed in ATMS SDR and JPSS Integrated Cal/Val System Long-Term Monitoring (ICVS-LTM) teams to monitor the science data quality for all three on-orbit ATMS sensors. This study mainly focusses on the NOAA-21 ATMS inter-sensor bias feature after one-year operations against NOAA-20 and S-NPP. The inter comparison of different evaluation methods also help ATMS Cal/Val team to improve the accuracy of some data quality evaluation tools, such as the use of measured spectral response function (SRF) and boxcar in radiative transfer model simulation evaluations.