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 sensitiv ity, retrieval biases/errors, and ability to retrieve the natu ral variability in columnar ozone, which has not been done
so far from the Himalayan region, having complex topog raphy. For a direct comparison, averaging kernel informa tion 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 underes timations of less than 20 %. However, in the upper tropo sphere and lower stratosphere (UTLS), we observe a con siderable 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 success fully. 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 re trieval information on the surface parameters. Furthermore,
AIRS fails to capture the monthly variation in the total col umn ozone, with a strong bimodal variation, unlike unimodal
variation seen in ozonesondes and the Ozone Monitoring In strument (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 col umn ozone using ozonesonde data (4.86 mW m−2
) matches
well with OMI (4.04 mW m−2
), while significant RF under estimation 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.
