Advances in Meteorology

This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen’s slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months.

In this study, solar ultraviolet (UV) radiation data collected in Riyadh, Saudi Arabia, between 2015 and 2022 were analyzed to explore quasi-periodicities in the UV time series. The power spectrum density analysis revealed several local peaks that exceeded the 95% confidence interval. These peaks included periodicities of 483–490 days, 272 days, 157−162 days, 103−110 days, 64–72 days, 27 days, and 13 days. To investigate the potential influence of space weather parameters on UV radiation, data on cosmic rays, solar radio flux at 10.7 cm (F10.7 cm), the Kp index, and solar wind speed for the same time period were examined. The aim was to identify periodicities in these variables that aligned with those found in the UV radiation data. The analysis reveals that several periodicities observed in the UV radiation spectrum are also present in the spectra of the considered parameters. Prominent periodicities include a 270-day cycle in UV radiation and cosmic rays, as well as periodicities of 72 days, 27 days, and 13 days in all considered variables. Furthermore, 110-day peaks are observed in spectrum of the UV radiation, the Kp index, solar radio flux F10.7, and solar wind speed. Notably, consistent peaks at 157-day periodicity are identified in the UV spectrum, also present in the spectra of all the considered variables (cosmic rays ∼162 days, Kp index ∼162 days, solar radio flux ∼156 days, and solar wind speed ∼163 days). The identification of common periodicities between UV radiation and space weather parameters in this study provides compelling evidence of a potential direct or indirect influence of solar variations on UV radiation. This finding significantly enhances our understanding of the impact of extraterrestrial factors, particularly solar activity, on the Earth’s environment.

Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufficient recorded rainfall data. Common indices like the standard precipitation index, coefficients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. This research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination () that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. The average spatial distribution of drought in the Nile basin was classified as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the significance of understanding and managing these phenomena for the sustainable development of the region.

On May 11, 2022, despite the favorable upper and lower-level circulation patterns of the high-altitude trough, shear line, and southwest jet stream, the urban cluster of the Guangdong-Hong Kong-Macao Greater Bay Area experienced light to moderate rainfall, deviating significantly from the forecasted heavy rain and local heavy rainstorm. This study explores the reasons for false alarms and predictability using ground observation data, radar data, ECMWF-ERA5 reanalysis field data, and ECMWF and CMA-TRAMS forecast data. The results indicate that the warm and moist airflow transported by the low-level jet stream was intercepted by the upstream MCS (mesoscale convective system) along the coastal area of western Guangdong, and inadequate conditions of negative vorticity dynamics led to insufficient moisture, thermodynamic, and dynamic conditions over the urban cluster, preventing the triggering of heavy precipitation. In addition, the 700 hPa westerly flow guiding the airflow and the stable low-level shear line, coupled with surface convergence lines, influenced the northward or southward movement of MCSs along the coastal and inland regions of western Guangdong. The weak and discontinuous intensity of echoes in the upstream Zhaoqing region further hindered the influence of surrounding echoes on the urban cluster. Numerical forecast models ECMWF and CMA-TRAMS overestimated the 850 hPa windspeed and 925 hPa meridional windspeed, resulting in the forecasted urban cluster experiencing heavy rain. Sensitivity tests of wind fields indicate that the 850 hPa wind field information is more sensitive to precipitation in the urban cluster. In this process, weak signal correction can be achieved in strong precipitation forecasts using the distinct signal of lower 850 hPa water vapor flux divergence compared to 925 hPa. Therefore, in the future, when the Guangdong-Hong Kong-Macao Greater Bay Area encounters similar warm-sector heavy rainfall events, adjustments to model forecasts can be made using specific 850 hPa elements such as wind speed, water vapor flux divergence, or specific humidity to enhance predictive accuracy.

This article uses data from the second-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites (wetPf2) to study the temperature and humidity properties of the air masses over Paracel and Spratly Islands in the Vietnam East Sea (South China Sea). The satellite observational data were validated with the radiosonde data from three stations in Vietnam: Hanoi, Danang, and Ho Chi Minh City. Subsequently, the wetPf2 data are used to analyze the characteristics of temperature and relative humidity variations of the air masses over the Paracel and Spratly regions. Results show that the mean error of the satellite observational data for temperature ranges from −0.06°C to −0.02°C, with standard deviations ranging from 0.73°C to 1.04°C. The mean error of relative humidity fluctuates between 11.6% and 12.5%, with standard deviations ranging from 15.1% to 19.1%. The values are reasonable and comparable to those in previous studies. Seasonal variations of temperature and humidity show that the air mass over the Paracel Islands exhibits a larger annual temperature with an annual variation of approximately 5.0°C, significantly higher than the value of 2.2°C in the air mass over the Spratly Islands. The difference may be due to the greater influence of continental and seasonal wind systems in the northern region. Within both air masses, the annual temperature variation in the boundary layer is much larger than that in the free atmosphere. Annual relative humidity variation is higher in summer and autumn than in winter and spring. The significant changes in the relative humidity with height during winter and no significant change of the relative humidity with height during summer may be related to the important role of strong convective activity carrying moist air upward to higher atmospheric levels during the summer time.

Accurate forecasting of reference crop evapotranspiration (ET₀) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET₀ values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET₀ forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d⁻¹ and 0.61 mm·d⁻¹ or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET₀ forecasting performance in the North China Plain.