Boundary layer evolution over the central Himalayas from radio wind profiler and model simulations

Abstract

Abstract. We investigate the time evolution of the Local Boundary Layer (LBL) for the first time over a mountain ridge at Nainital (79.5° E, 29.4° N, 1958 m a.m.s.l.) in the central Himalayan region, using a radar wind profiler (RWP) during November 2011 to March 2012, as a part of the Ganges Valley Aerosol Experiment (GVAX). We restrict our analysis to clear–sunny days, resulting in a total of 78 days of observations. The standard criterion of the peak in the signal-to-noise ratio (S ∕ N) profile was found to be inadequate in the characterization of mixed layer (ML) top at this site. Therefore, we implemented a criterion of S ∕ N > 6 dB for the characterization of the ML and the resulting estimations are shown to be in agreement with radiosonde measurements over this site. The daytime average (05:00–10:00 UTC) observed boundary layer height ranges from 440 ± 197 m in November (late autumn) to 766 ± 317 m above ground level (a.g.l.) in March (early spring). The observations revealed a pronounced impact of mountain topography on the LBL dynamics during March, when strong winds (> 5.6 m s−1) lead to LBL heights of 650 m during nighttime. The measurements are further utilized to evaluate simulations from the Weather Research and Forecasting (WRF) model. WRF simulations captured the day-to-day variations up to an extent (r2 = 0.5), as well as the mean diurnal variations (within 1σ variability). The mean biases in the daytime average LBL height vary from −7 % (January) to +30 % (February) between model and observations, except during March (+76 %). Sensitivity simulations using a mixed layer model (MXL/MESSy) indicated that the springtime overestimation of LBL would lead to a minor uncertainty in simulated surface ozone concentrations. However, it would lead to a significant overestimation of the dilution of black carbon aerosols at this site. Our work fills a gap in observations of local boundary layer over this complex terrain in the Himalayas, and highlights the need for year-long simultaneous measurements of boundary layer dynamics and air quality to better understand the role of lower tropospheric dynamics in pollution transport.

Review report

“…Printer-friendly version Discussion paper air pollution mixing and dispersion, which should be corroborated with relevant recent references (e.g. Singh et al, 2016;Mues et al, 2017).…”

On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter

“…Model is seen to reasonably capture the temporal variations and magnitudes, and stronger bias at one of the stations is also within the range of model bias (40-60%) reported in an earlier evaluation of this model over this region during winter 28 . The underestimations of aerosol loadings by model are suggested to be resulting from the uncertainties associated with the input emissions 28,39 and fine-scale dynamics 30,40 , besides some small-scale fires and local sources such as trash and wood burning unaccounted or underestimated in the inputs. Furthermore, models can have a dilution effect over grid boxes as compared to the observations sampling directly the urban and rural air masses at a location/point, which we have tried to minimize by performing simulations at higher spatial resolution (12 km × 12 km).…”

Mixing Layer Height Retrievals From MiniMPL Measurements in the Chiang Mai Valley: Implications for Particulate Matter Pollution

“…Table 2 summarizes the number of observed and screened-in datasets for each month, which has been analyzed for understanding ML variations. Considering the dynamic state of the ML over mountainous topography (Singh et al, 2016) the backscatter profiles were averaged for every 15 min for extracting the ML height. The AL height has been estimated based on semiobjective method (De Wekker et al, 2004) for every 15 min and compared with the ML height measurements on similar temporal scales.…”