ИСТИНА

Traditional meteorological observations are mainly focused on adequate representation of large-scale structures in the atmosphere such as cyclones, planetary waves, or cloud systems. Mesoscale models are designed to obtain a detailed description of terrain orography, surface properties, and heat and moisture fluxes, both natural and anthropogenic. Differences between local observations and large-scale model simulated fields follow from this mesoscale variability. The classical near-surface observations at the meteorological stations or radiosonde data which are quite sparse in time and space domain, do not allow us to analyze the discrepancies in the modelled profiles of temperature and wind speed. Therefore, comparisons between properties of atmospheric boundary layer and turbulent fluxes of heat, momentum and moisture in the ABL require special attention. A proper description of lapse rates and wind speed profiles in the boundary layer presents an important and complex numerical simulation problem. Though the average temperature is a slowly varying field with a regularly changing stability parameter, the "average" wind speed can change rapidly and randomly. This variation is related to the intensity of the momentum flux, which, in turn, is related to the turbulent heat and moisture flux from the surface. Long-term comparisons help to understand how well the mesoscale models describe the kinetic energy of turbulence or, for example, the Richardson number. However, such comparisons are really different from the traditional ones and do not satisfy meteorological regulations because comparisons between measured turbulence parameters and their estimation in weather models started to gain widespread acceptance just recently. In particular, mesoscale model variations don’t need to be “exact” or correlate with the measured ones; they only have to agree with statistical properties of observed variations. In this work we examine spatial and temporal variabilities of mesoscale temperature and wind speed variations using long-term remote measurements of temperature and wind speed profiles in the ABL at a few sites located in the urban environment and in countryside. We analyze the empirical distribution functions of spatial and temporal differences (lapse rates and trends) and study diurnal and seasonal variations of these distributions with altitude. Simultaneously, we show that the individual devices and different principles of measurements may cause a significant lapse rate bias due to comparison misinterpretations, and that the spectral properties of differences between model and measured fields can play a key role in separating from each other large-scale errors, mesoscale fluctuations, and instrumental noise.