Comparative Seasonal Variability of the Atmospheric Boundary Layer over Gangetic Plain (Varanasi) and Coastal Region (Goa), India

Abstract

Seasonal variations in the atmospheric boundary layer over different geographical regions have distinct features due to variations in topography, land cover, and geographical position. This study focuses on Atmospheric boundary layer height (ABLH) characteristics seasonally and diurnally over the Indo-Gangetic Plain (Varanasi) and Coastal region (Goa). The study also emphasizes the relationship between ABLH and associated variables such as surface sensible heat flux (SSHF), surface latent heat flux (SLHF), convective available potential energy (CAPE), and Relative humidity (RH) for pre-monsoon and monsoon season. ECMWF-ERA-5 reanalysis data for the period of 2015 to 2017 were used for the study. ECMWF-ERA-5 reanalysis data was validated with MERRA-2 data using correlation coefficient (r) and Factor of Two (FAC2) value. Validation suggested the reliability of ECMWF-ERA-5 data which showed an r-value of 0.8 and FAC2 value in ≥ 0.5 ≤ 2.0 range with MERRA-2 data. The results showed 1400–1800 m and 1000–1400 m ABLH during pre-monsoon and monsoon respectively over Varanasi while ABLH of 1000–1400 m and 500–700 m was seen over Goa during pre-monsoon and monsoon respectively. The analysis indicated that deeper ABLH was observed over Varanasi than over Goa during both seasons. Further, regression correlation analysis detailed the parameters affecting the ABL height characteristics. Surface heat fluxes showed a stronger correlation with ABLH over Varanasi than over Goa. Regression coefficients for ABLH and surface sensible heat flux (SSHF) were found to be 0.88 and 0.82 during pre-monsoon and monsoon respectively over Varanasi and 0.76 and 0.66 during pre-monsoon and monsoon respectively over Goa. Surface latent heat flux (SLHF) also showed significant regression coefficients of 0.83 and 0.62 over Varanasi and 0.76 and 0.66 over Goa during premonsoon and monsoon respectively. Multivariate regression analysis quantified the effect of independent variables (SSHF, SLHF, CAPE, RH) on the dependent variable (ABLH) by estimating β coefficient. The variables which affected ABLH at a significant level (α = 0.05) were heat fluxes (SSHF and SLHF) and CAPE during pre-monsoon over both regions. Significant contributor to ABL height found to be SLHF over Goa during monsoon. South westerly moist winds and buoyant thermals might have impacted the ABLH over Goa because latent heat flux is highly affected by surface winds and specific humidity gradient at surface and atmosphere. Also, uninterrupted moisture supply along with strong onshore winds strengthen the SLHF. Furthermore, the geographical location of Goa might have amplified the variable effects shaping ABLH during the monsoon. On the other hand, significant contributors were SSHF, SLHF and CAPE over Varanasi during the same season i.e. monsoon.