Historical Rainfall and Evapotranspiration Changes over Mpologoma Catchment in UgandaRead the full article
Advances in Meteorology publishes research in all areas of meteorology and climatology. Topics include forecasting techniques and applications, meteorological modelling, data analysis, atmospheric chemistry and physics, and climate change.
James Cleverly, the journal’s Chief Editor, is based at the University of Technology in Sydney, Australia. His research interests include carbon, water and energy fluxes of arid-land Acacia swales; physics of the atmospheric surface layer and interactions with terrestrial ecosystems.
Latest ArticlesMore articles
Projected Changes in Precipitation Extremes over Shaanxi Province, China, in the 21st Century
Extreme precipitation events, which have intensified with global warming, will have a pernicious influence on society. It would be desirable to understand how they will evolve in the future as global warming becomes more serious with time. Thus, the primary objective of this study is to provide a comprehensive understanding of the changing characteristics of the precipitation extremes in the 21st century over Shaanxi Province, a climate-sensitive and environmentally fragile area located in the east of northwestern China, based on a consecutive simulation of the 21st century conducted by the regional climate model RegCM4 forced by the global climate model HadGEM2-ES at high resolution under middle emission scenario of the Representative Concentration Pathway 4.5 (RCP4.5). Basic validation of the model performance was carried out, and six extreme precipitation indices (EPIs) were used to assess the intensity and frequency of the extreme precipitation events over Shaanxi Province. The results show that RegCM4 reproduces the observed characteristics of extreme precipitation events over Shaanxi Province well. Overall for the domain, the EPIs excluding consecutive dry days (CDD) have a growing tendency during 1980–2098 although they exhibit spatial variability over Shaanxi Province. Some areas in the arid northern Shaanxi may have more heavy rainfalls by the middle of the 21st century but less wet extreme events by the end of the 21st century. And the humid central and southern regions would suffer more precipitation-related natural hazards in the future.
ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network
Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.
Multidimensional Meteorological Variables for Wind Speed Forecasting in Qinghai Region of China: A Novel Approach
The accurate, efficient, and reliable forecasting of wind speed is a hot research topic in wind power generation and integration. However, available forecasting models focus on forecasting the wind speed using historical wind speed data and ignore multidimensional meteorological variables. The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. Then, the original wind speed will be decomposed into a series of intrinsic model functions with specified numbers of frequencies. A quadratic model that considers the two-way interactions between factors is used to pursue accurate forecasting. To reduce the model complexity, Gram–Schmidt-based feature selection (GSFS) is applied to extract the important meteorological factors. Finally, all the forecasting values of IMFs will be summed by assigning weights that are carefully determined by the whale optimization algorithm (WOA). The proposed forecasting approach has been applied on six datasets that were collected in Qinghai province and is compared with several state-of-the-art wind speed forecasting models. The forecasting results demonstrate that the proposed model can represent the nonlinearity of the wind speed and deliver better results than the competitors.
Spatiotemporal Variability in the Hydrometeorological Time-Series over Upper Indus River Basin of Pakistan
This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.
Sensitivity of Summer Precipitation over Korea to Convective Parameterizations in the RegCM4: An Updated Assessment
This study investigates the performance of the latest version of RegCM4 in simulating summer precipitation over South Korea, comparing nine sensitivity experiments with different combinations of convective parameterization schemes (CPSs) between land and ocean. In addition to the gross pattern of seasonal and monthly mean precipitation, the northward propagation of the intense precipitation band and statistics from extreme daily precipitation are thoroughly evaluated against gridded and in situ station observations. The comparative analysis of 10-year simulations demonstrates that no CPS shows superiority in both quantitative and qualitative aspects. Furthermore, a nontrivial discrepancy among the different observation datasets makes a robust assessment of model performance difficult. Regardless of the CPS over the ocean, the simulations with the Kain–Fritsch scheme over land show a severe dry bias, whereas the simulations with the Tiedtke scheme over land suffer from a limited accuracy in reproducing spatial distributions due to the excessive orographic precipitation. In general, the simulations with the Emanuel scheme over land are better at capturing the major characteristics of summer precipitation over South Korea, despite not all statistical metrics showing the best performance. When applying the Emanuel scheme to both land and the ocean, precipitation tends to be slightly overestimated. This deficiency can be alleviated by using either the Tiedtke or Kain–Fritsch schemes over the ocean instead. As few studies have applied and evaluated the Tiedtke and Kain–Fritsch schemes to the Korean region within the RegCM framework, and this study introduces the potential of these new CPSs compared with the more frequently selected Emanuel scheme, which is particularly beneficial to RegCM users.
Ten Years of Aerosol Effects on Single-Layer Overcast Clouds over the US Southern Great Plains and the China Loess Plateau
Using almost 10 years of observations of clouds and aerosols from the US Southern Great Plains (SGP) atmospheric observatory and the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) in China, the impact of aerosols on single-layer overcast clouds over continental land for different regimes were investigated. Atmospheric conditions at the two sites were first compared in an attempt to isolate the influence of aerosols on cloud properties from dynamic and thermodynamic influences. Cloud types and amounts are similar at the two sites. The dominant aerosol types at the SGP and SACOL sites are sulphate and dust, respectively, with greater aerosol optical depths (AODs) and absorption at the SACOL site. Aerosol first indirect effect (FIE) ranges from 0.021 to 0.152 and from −0.078 to 0.047 at the SGP and SACOL sites, respectively, when using the AOD below cloud base as CCN proxy. Although differences exist, the influence of meteorological conditions on the FIE at the two sites is consistent. FIEs are easily detected under descending motion and dry condition. The FIE at the SGP site is larger than that at the SACOL site, which suggests that the cloud albedo effect is more sensitive under relatively cleaner atmospheric conditions and the dominating aerosol at the SACOL site has less hygroscopicity. The radiative forcing of the FIE over the SGP site is −3.2 W m−2 for each 0.05 increment in FIE. Cloud durations generally prolong as aerosol loading increases, which is consistent with the hypothesis of the aerosol second indirect effect. The negative relationship between cloud duration time and aerosol loading when aerosol loading reaches a large value further might suggest a semidirect effect.