Article of the Year 2020
Evaluating the Dependence between Temperature and Precipitation to Better Estimate the Risks of Concurrent Extreme Weather EventsRead 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.
Dr Jamie Cleverly, the journal’s Chief Editor, is based at James Cook University in Cairns, Australia. Their 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
Comparison of Multi-Satellite Precipitation Data from the Global Precipitation Measurement Mission and Tropical Rainfall Measurement Mission Datasets: Seasonal and Diurnal Cycles
A comparison was made between seasonal and diurnal variations in the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) version 06B Final run and TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 products from April 2014 to March 2019. As for earlier IMERG versions, systematic differences between IMERG version 06B precipitation and TMPA precipitation data were larger over the oceans than over land. Systematic annual mean differences between the IMERG and TMPA data over the oceans were smaller for IMERG version 06B than for earlier IMERG versions, possibly because of updated calibration processes. The mean differences between the IMERG version 06B and TMPA data for tropical oceans were relatively smtropical Pacific for all four seasons were not. The diurnal amplitudes of the IMERG were smaller than those of the TMPA over most continents, and the differences increased with mean diurnal amplitudes. The diurnal amplitudes of the IMERG were larger than those of the TMPA data over the oceans. The differences between the phases of the precipitation diurnal harmonics in the IMERG and TMPA datasets varied widely in all four seasons, but the mean phases were almost the same over both the oceans and the land. The sources of the differences in diurnal precipitation amplitudes in the Bay of Bengal and along the west coast of Central America, which showed large diurnal ranges and rather different diurnal amplitudes, were assessed. Differences in seasonal means caused differences in diurnal amplitudes in the Bay of Bengal, but for Central America, differences in diurnal amplitudes were associated with seasonal mean diurnal amplitudes.
Geometric and Physical Characteristics of Precipitation Clouds in Tibetan Plateau
This paper examines the basic geometric and physical characteristics of precipitation clouds over the Tibetan Plateau, based on the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data from 1998 to 2015, using the minimum bounding rectangle (MBR) method. The results show that about 60% of the precipitation clouds occur with a scale of approximately 18 km (length) and 15 km (width), and the proportion of precipitation clouds with a length longer than 100 km and a width wider than 90 km is less than 1%. Most of the precipitation cloud exhibits a shape between square and long strips in the horizontal direction and lanky in the vertical direction. The average rainfall intensity of precipitation clouds is between 0.5 and 6 mm h−1. The average length and width of precipitation clouds show a logarithmic, linear relationship. The distribution of raindrops in precipitation clouds is relatively compact. With the expansion of the area, the precipitation clouds gradually become squatty. The relationship between physical and geometric parameters of precipitation clouds shows that with the precipitation cloud area expanding, the average rainfall rate of precipitation clouds also increases. Heavy convective rainfall is more likely to occur in larger precipitation clouds. For the precipitation clouds of the same size, the area fraction and contribution of convective precipitation are lower than that of stratiform precipitation.
Numerical Modeling the Flood and Pollutant Transport Processes in Residential Areas with Different Land Use Types
Large-scale flooding causes widespread disaster, and harmful pollutant concentration in water following flood affects public safety and the environment. In this study, a numerical model for solving the 2D shallow water equations and the solute transport equation is proposed to simulate overland flood and pollutant transport caused by floods. The present model is verified by comparing the predictions with the analytical solutions and simulation results; sufficiently high computational accuracy is achieved. The model is also used to simulate flood inundation and pollution spread in the area of Hun and Taizi Lane (HTL) in China due to river dike breaches; the results show that the coupling model has excellent performance for simulating the flooding process and the temporal and spatial distribution of pollutants in urban or rural areas. We use remote sensing techniques to acquire the land coverage in the area of HTL based on Landsat TM satellites. The impacts of changed land use on mitigation of flooding waves and pollutant spread are investigated; the results indicate that the land cover changes have an obvious influence on the evolution process of flood waves and pollutant transport in the study areas, where the transport of pollutants is very dynamic during flood inundation in HTL area. Furthermore, the motion of pollutants considering anisotropic diffusion is more reasonable than that due to isotropic dispersion in simulating pollutant transport associated with the flood in urban or farmland environments.
Relationship between the Formation of PM2.5 and Meteorological Factors in Northern China: The Periodic Characteristics of Wavelet Analysis
China’s rapid urbanisation and industrialisation have led to frequent haze in China in recent years. Although many measures to control haze have been implemented, no significant improvement has been observed, and haze still exists. In this study, we used wavelet transform to investigate the changes in PM2.5 on the time scale, the relationship amongst meteorological factors, and the causes and changes in haze formation and take measures to prevent haze. Results indicated the following: (1) The peak of PM2.5 changes in winter in the past three years primarily occurred in the range from 11:00 to 13:00 and 20:00 to 22:00. (2) Multiple cycles of daily average PM2.5 concentrations existed in 3–5 d, 6–14 d, 6–21 d, and 16–27 d, with a significant oscillation in 6–14 d and stable cycle characteristics. (3) The meteorological factors promoted the formation of haze to a certain extent. When haze occurred, the near-surface wind speed was only 1 m/s, which was not conducive to the spread of pollutants. (4) The formation of haze was affected by the interaction of various factors; the photochemical reactions of NO2 and O3 also exacerbated the formation of pollutants. This study provided a clear direction for the prevention and prediction of haze. Furthermore, the government must take relevant measures to reduce pollutant emissions and ensure the air quality of cities in winter.
A Comparative Study of CO2 Emission Forecasting in the Gulf Countries Using Autoregressive Integrated Moving Average, Artificial Neural Network, and Holt-Winters Exponential Smoothing Models
Forecasting is the process of making predictions based on past and present data, with the most common method being trend analysis. Forecasting models are becoming increasingly crucial in uncovering the intricate linkages between large amounts of imprecise data and uncontrollable variables. The main purpose of this article is to compare CO2 emission forecasts in Gulf countries. In this study, the autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and holt-Winters exponential smoothing (HWES) forecasting models are used to anticipate CO2 emissions in the Gulf countries on an annual basis. This study attempts to predict time series data on CO2 emissions in the Gulf countries using statistical tools. The current analysis relied on secondary data gathered from the United States Energy Information Administration (EIA). The study’s findings show that the ARIMA (1,1,1), Holt-Winters exponential smoothing, ARIMA (1,1,2), and ARIMA (2,1,2) models outperform the artificial neural network model in estimating CO2 emissions in the Gulf countries. This study gives information on the current state of CO2 emission forecasts. This study will aid the researcher’s understanding of CO2 emissions forecasts. In addition, government agencies can use the findings of this study to develop strategic plans.
Attribution Analysis of Runoff Variation in the Yue River Watershed of the Qinling Mountains
In recent decades, global climate change, especially human activities, has led to profound changes in the hydrological cycle and hydrological processes in watersheds. Taking the Yue River watershed in the Qinling Mountains in China as the study area, the Mann–Kendall test and Pettitt mutation test method were used to analyze the various characteristics of hydrological and climatic elements from 1960 to 2018. Then, the elastic coefficient method based on the Budyko framework was used to estimate the elastic coefficient of runoff change on each influencing factor. The results showed that the annual runoff decreased at a rate of 0.038 × 108 m3/a (), and a significant abrupt change occurred in 1990. The annual precipitation and potential evapotranspiration (ET0) increased and decreased, with change rates of 0.614 mm/a and −0.811 mm/a (), respectively. The elasticity coefficients of precipitation, ET0, and the underlying surface were 1.95, −0.95, and −0.85, respectively, indicating that annual runoff was most sensitive to the change in precipitation, followed by the change in ET0, and had the lowest sensitivity to the change in the underlying surface. Underlying surface change is the main factor of runoff decrease; the contribution is 89.07%. The total contribution of climate change to runoff change is 10.93%, in which the contributions of precipitation and ET0 are 17.59% and −6.66%, respectively. The NDVI reflecting underlying surface change has been increasing since 1990, which is an important reason for the runoff decrease.