Investigations of vertical wind variations at a mountain top in the Himalaya using Doppler Lidar observations and model simulations

Hight-resolved observations of vertical winds remain nearly non-existing over the Himalayas, despite of anticipated crucial role of vertical motions in transporting pollution across the Himalayan hills. The present study analyze the vertical wind observations from surface to 1 km above ground level...
Ausführliche Beschreibung

Hight-resolved observations of vertical winds remain nearly non-existing over the Himalayas, despite of anticipated crucial role of vertical motions in transporting pollution across the Himalayan hills. The present study analyze the vertical wind observations from surface to 1 km above ground level over Manora Peak (29.4° N; 79.5° E; 1958 m amsl) in the Himalaya performed using a Doppler Lidar during the Ganges Valley Aerosol Experiment (GVAX). Vertical wind exhibited a pronounced diurnal variability at Manora Peak comprising of upward motions during the daytime (05–10 UT) and downward motions during nighttime typical of a mountain-valley system. Mean vertical wind speeds are observed to be varying from −0.8 to +0.8 ms−1 during the study period with a variance of 0.1–1.5 m2s-2, which is attributed to the thermally driven turbulence. Mean vertical winds are observed to be stronger in the Doppler Lidar profiles above Manora Peak (−0.8 to 0.8 ms−1) as compared to near surface measurements at this station using an ultrasonic anemometer (−0.4 to 0.4 ms−1), and low altitude stations in India. Daytime vertical wind speeds are observed to be higher during pre-monsoon (0.81 ms−1), as compared to post-monsoon (0.24 ms−1) and winter (0.33 ms−1). Average Black Carbon (BC) concentrations are significantly higher during strong upward vertical winds, which indicates efficient transport of polluted air mass from low-altitude regions to the Himalaya. Weather Research and Forecasting (WRF) model reproduces the observed diurnal pattern in the vertical wind at the observation site however the model underestimates the variability. Ausführliche Beschreibung