Vol. 1 No. 1 (2025)

The paper considers the temporal statistical structure of carbon monoxide (CO) and studies the dependence of the influence of meteorological variables (temperature, relative humidity, pressure, wind speed) and variations in solar activity (Wolf numbers) on the variability of its concentration in Tashkent as a function of the month of the year. The months of the year with a significant correlation between CO and meteorological values were identified.
The spectrum of variations in carbon monoxide concentrations was calculated for a 30-year sample (1991-1920). The main components of the spectrum with an oscillation period of 0.5, 1, 2.8, 4, 8 and 30 years were obtained. The causal relationship between CO and the meteorological values and Wolf numbers under consideration was investigated. The causal dependence of variations in CO concentration in Tashkent on meteorological factors and the absence of such dependence on variations in solar activity were obtained.

The article assesses the dynamics of dust haze for the period 2010-2021 based on a number of ground-based observations conducted at the Tashkent Observatory weather station (Uzhydromet). A comparative analysis of the obtained characteristics with the data presented in scientific publications was performed. The features of the temporal distribution of this phenomenon have been identified. During the period under review, the total number of days with dust haze was 110, and its total duration was 482.9 hours. It has been established that dust haze is observed more frequently and lasts longer than dust storms. The maximum activity of the phenomenon was recorded in 2021. Pronounced seasonal fluctuations were noted with peaks in the winter and autumn months. An analysis of the duration of episodes showed that most of them last less than three hours, but there is a tendency towards an increase in the share of long-term events.

This study examines the con-elation between observed meteorological data and ERA5 reanalysis data in the Akhangaran River Basin. The evaluation is conducted using five statistical metrics across three key categories: Error Magnitude (RMSE, MAE), Systematic Bias (PB1AS), and Performance or linear correlation (NSE, R2). Monthly data between 1970-2024 from six meteorological stations are compared with corresponding monthly ERA 5 datasets to assess the reliability of reanalysis data in representing local meteorological conditions. Additionally, line graphs are used to visualize temporal variations and discrepancies though different years. By integrating both statistical and visual analyses, this study provides a comprehensive assessment of ERA5 reanalysis data applicability for hydrometeorological research in Akhangaran River Basin.