Assessing seasonal variation of near surface air temperature lapse rate across India

Abstract

Precise characterization of surface air temperature at different spatial scales remains a challenge as it often requires a dense network of temperature sensors. The most common approach for estimating surface air temperature at a desired location in hydrological and terrestrial models is to interpolate surface air temperature observations obtained from a sparse set‐up of temperature sensors using the global standard uniform lapse rate. This interpolation technique, though easy to use, can lead to unreliable results, as the global standard uniform lapse rate may not be representative of seasonal and local variations in lapse rate. In this study, linear and nonlinear regression relationships are derived to estimate mean minimum, mean and mean maximum near surface air temperatures (at monthly and annual time scales) across India. The normal daily air temperature data from 439 stations and their corresponding latitude, longitude and elevation are used in the analysis. The computed temperature gradients exhibit seasonal variation. The effects of elevation and longitude are more pronounced in pre‐monsoon (March–May) and post‐monsoon (October–November) months, whereas the effects of latitude are more in winter months (December–February). Results indicate that the global standard uniform lapse rate (− 0.65 ° C 100 m− 1) is only applicable for monthly mean maximum temperature for April and May. The highest interpolation reliability is obtained for monthly mean minimum temperature. The regression equations though simple, hold promise for describing surface air temperature across India.

Identification of homogeneous regions of near surface air temperature lapse rates across India

“…The developed equations were used to predict monthly T̄ min , T̄ avg , and T̄ max at each station in the region and the results were compared with measured temperature. To allow easy comparison with the earlier analysis of Ojha (), the results are provided for only 4 months, that is, January, April, July, and October. The residual errors (difference of measured and predicted temperature) for monthly T̄ min are shown in Figure .…”

“…Ojha () considered latitude, longitude, and elevation for estimation of lapse rate across India, and concluded that the large residual errors in temperature prediction at different stations are mainly due to the negligence of various other factors that affect lapse rate variation. Therefore, for the CA, the monthly residual error for mean temperatures (maximum, mean, and minimum; RE – T̄ max , RE – T̄ avg , and RE – T̄ min ) obtained from the analysis of Ojha () were considered as the predictand. To obtain possible predictors for every month, the potential predictors (64) identified in Section 2 were standardized with respect to their monthly mean and variance.…”

“…In India, where great climate diversity is observed across its different regions, only limited number of studies (e.g., Bandyopadhyay et al, ; Ojha, ) have assessed variation in surface temperature lapse rates, and most of the modelling studies rely on Environmental lapse rate. As reliable prediction of hydrologic variables is of prime importance to the socio‐economic planning of India (Ojha et al, ), estimates of region‐specific surface lapse rates can be of great importance.…”

“…As reliable prediction of hydrologic variables is of prime importance to the socio‐economic planning of India (Ojha et al, ), estimates of region‐specific surface lapse rates can be of great importance. Ojha () estimated near surface lapse rate for entire India. However, the residual errors (measured–predicted) for monthly T̄ min , T̄ avg and T̄ max (mean minimum, mean, mean maximum temperatures) obtained from the analysis of Ojha () exhibited zonal bias.…”

“…Ojha () estimated near surface lapse rate for entire India. However, the residual errors (measured–predicted) for monthly T̄ min , T̄ avg and T̄ max (mean minimum, mean, mean maximum temperatures) obtained from the analysis of Ojha () exhibited zonal bias. For many stations in central India, high positive residual errors were observed during peak summer months.…”