Effects of spatial resolution on WRF v3.8.1 simulated meteorology over the central Himalaya

Abstract. The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation Himalaya in models. However, accuracy models remains limited this region due to highly complex mountainous topography. This article delineates effects spatial resolution on modeled meteorology dynamics over CH by utilizing Weather Research Forecasting (WRF) model extensively evaluated against Ganges Valley Aerosol Experiment (GVAX) during summer monsoon. WRF simulation performed a domain (d01) encompassing northern India at 15 km × two nests (d02 5 d03 1 km) centered CH, with boundary conditions respective parent domains. simulations reveal higher variability meteorology, e.g., relative humidity (RH = 70.3 %–96.1 %) wind speed (WS 1.1–4.2 m s?1), compared ERA-Interim reanalysis 80.0 %–85.0 %, WS 1.2–2.3 s?1) owing resolution. WRF-simulated temporal evolution meteorological variables found agree balloon-borne measurements, stronger correlations aloft (r 0.44–0.92) than those lower troposphere 0.18–0.48). overestimates temperature (warm bias 2.8 ?C) underestimates RH (dry 6.4 surface d01. Model results show significant improvement (P 827.6 hPa, T 19.8 ?C, 92.3 %), closer GVAX 801.4 19.5 94.7 %). Interpolating output coarser domains (d01, d02) altitude station reduces biases pressure temperature; however, it suppresses diurnal variations, highlighting importance well-resolved terrain (d03). Temporal variations near-surface P, T, are also reproduced extent (r>0.5). A sensitivity incorporating feedback nested demonstrates simulated CH. Our study shows that setup finer can significantly reduce such be adopted through into regional-scale simulations. Interestingly, simulates dominant easterly component (d03), missing coarse simulations; frequency southeasterlies underestimated. implementing high-resolution (3 s) topography input (SRTM) prediction directions; nevertheless, further improvements required better reproduce observed local-scale