Performance of AIRS ozone retrieval over the central Himalayas: use of ozonesonde and other satellite datasets

Abstract

Abstract. Data from 242 ozonesondes launched from ARIES, Nainital (29.40∘ N, 79.50∘ E; 1793 m elevation), are used to evaluate the Atmospheric Infrared Sounder (AIRS) version 6 ozone profiles and total column ozone during the period 2011–2017 over the central Himalayas. The AIRS ozone products are analysed in terms of retrieval sensitivity, retrieval biases/errors, and ability to retrieve the natural variability in columnar ozone, which has not been done so far from the Himalayan region, having complex topography. For a direct comparison, averaging kernel information is used to account for the sensitivity difference between the AIRS and ozonesonde data. We show that AIRS has more minor differences from ozonesondes in the lower and middle troposphere and stratosphere with nominal underestimations of less than 20 %. However, in the upper troposphere and lower stratosphere (UTLS), we observe a considerable overestimation of the magnitude, as high as 102 %. The weighted statistical error analysis of AIRS ozone shows a higher positive bias and standard deviation in the upper troposphere of about 65 % and 25 %, respectively. Similarly to AIRS, the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) are also able to produce ozone peak altitudes and gradients successfully. However, the statistical errors are again higher in the UTLS region, which are likely related to larger variability in ozone, lower ozone partial pressure, and inadequate retrieval information on the surface parameters. Furthermore, AIRS fails to capture the monthly variation in the total column ozone, with a strong bimodal variation, unlike unimodal variation seen in ozonesondes and the Ozone Monitoring Instrument (OMI). In contrast, the UTLS and the tropospheric ozone columns are in reasonable agreement. Increases in the ozone values of 5 %–20 % after biomass burning and during events of downward transport are captured well by AIRS. Ozone radiative forcing (RF) derived from total column ozone using ozonesonde data (4.86 mW m−2) matches well with OMI (4.04 mW m−2), while significant RF underestimation is seen in AIRS (2.96 mW m−2). The fragile and complex landscapes of the Himalayas are more sensitive to global climate change, and establishing such biases and error analysis of space-borne sensors will help us study the long-term trends and estimate accurate radiative budgets.

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

“…Though suitable collocation criteria are used while comparing the data sets, the error due to different vertical resolutions among the data sets are minimized utilizing the averaging kernel smoothing or Gaussian smoothing (Figure S1 in Supporting Information S1) (Boynard et al, 2016(Boynard et al, , 2018Verstraeten et al, 2013;Zhang et al, 2010). Further details on the averaging kernel used in the INSAT-3D/3DR retrieval is discussed in Rawat et al (2023). Since this work focuses on the quality of the retrieved data at standard pressure levels, we have used the data from 14 pressure levels (1,000,950,920,850,780,700,620,500,400,300,250,200,150,and 100 hPa) and are interpolated to the spatial resolution of the reference data sets.…”

“…Further details on the averaging kernel used in the INSAT‐3D/3DR retrieval is discussed in Rawat et al. (2023). Since this work focuses on the quality of the retrieved data at standard pressure levels, we have used the data from 14 pressure levels (1,000, 950, 920, 850, 780, 700, 620, 500, 400, 300, 250, 200, 150, and 100 hPa) and are interpolated to the spatial resolution of the reference data sets.…”