Advances in Meteorology
 Journal metrics
Acceptance rate37%
Submission to final decision118 days
Acceptance to publication49 days
CiteScore2.900
Journal Citation Indicator0.390
Impact Factor1.962

Article of the Year 2020

Evaluating the Dependence between Temperature and Precipitation to Better Estimate the Risks of Concurrent Extreme Weather Events

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 Journal profile

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.

 Editor spotlight

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.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

The Influence of Work Zone Management on User Carbon Dioxide Emissions in Life Cycle Assessment on Highway Pavement Maintenance

The higher contribution of traffic delay to environmental impacts is urging the highway agencies to take work zone management into the maintenance schemes decision-making. Aiming to understand the role of work zone management in user CO2 emissions reduction, this paper firstly developed a practical methodological framework of traffic delay-related CO2 emissions caused by highway maintenance based on a popular life cycle user cost analysis approach in regard of the microscopic vehicle operation analysis. The method was applied in an actual freeway flexible pavement with 15-year design life in Shaanxi Province, China, covering three types of preventive maintenance, correction maintenance, and rehabilitation. In addition, the impacts of key inputs of proposed method on work zone user CO2 emissions results were checked. The results show that traffic delay attributes to 29.4% of total CO2 emissions of the life cycle of highway pavement maintenance, and 51.8% of work zone user CO2 emissions result from preventive maintenance, especially from micro vehicle operations including speed change and queue near work zone (62% of total work zone user CO2 emissions). The work zone management alternative strategies related to less traffic volume or higher highway capacity including vehicle type limitation and the limited work zone speed have an advantage in reducing the work zone CO2 emissions over changing work zone length or work zone timing. The findings in this paper may present a useful tool and reference for robustly supporting the decision-making on highway maintenance carbon mitigation in work zone traffic.

Research Article

High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations

Cloud identification methods of passive sensors are usually on the basis of different thresholds at different wavelengths. However, the high pollution levels may contribute to the misidentification of cloud mask of Advanced Himawari Imager (AHI) carried on Himawari-8. This study comprehensively analyses and demonstrates this possibility by comparing the AHI cloud-masks and space-based lidar observations based on surface observations of air-polluted loadings from January 1, 2016, to December 31, 2019. Therefore, this study comprehensively explores this impact by comparing the AHI cloud-masks and space-based lidar observations by using surface observations of air-polluted loadings from January 1, 2016, to December 31, 2019. Case studies that compare the two sensors indicate that the performance of AHI cloud detection is degenerative during aerosol events. Long-term statistical analysis demonstrates that the average hit ratio of clear (cloud) between the two sensors during the period is 79% (63%) and the consistency (hit rate) of cloud-mask between AHI and CALIOP decreases with increasing pollution levels. On the contrary, the low uncertainty ratios with 15% of cloud and 3% of clear exist in low PM2.5 levels (lower than 40 μg/m3), while the high uncertainty ratios with 47% of cloud and 15% of clear exist in high PM2.5 levels (higher than 130 μg/m3). Therefore, results demonstrate that the reliability of AHI cloud-mask is weakened by high air-polluted levels. Further improvement of AHI cloud-mask algorithm is desired because AHI products with high temporal resolution are vital in several related fields, such as climate change, aerosol-cloud interaction, and air-polluted mapping.

Research Article

Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model

As a result of considerable changes in rural areas in Northern Thailand, the frequency and intensity of haze outbreaks from particulate pollution, particularly fine particulate matter (PM2.5), has increased in this region. To supplement ground-based monitoring where PM2.5 observation is limited, this study applied a multivariate linear regression model to predict PM2.5 concentrations in 2020 using aerosol optical depth (AOD); meteorological parameters of wind velocity, temperature, and relative humidity; and gaseous pollutants such as SO2, NO2, CO, and O3 from ground-based measurements at three locations: Chiang Mai, Lampang, and Nan provinces in Northern Thailand. Two multivariate linear regression models were conducted in this study. The first model (model 1) is a generic model with meteorological parameters of aerosol optical depth (AOD), temperature, relative humidity, and wind speed. The second model (model 2) includes meteorological parameters and several gaseous pollutants, such as SO2, NO2, CO, and O3. In general, the regression model, which used hourly data from 2020 of the three provinces, adequately characterized the PM2.5 concentrations. The performance of model 2 was good for the prediction of PM2.5 concentrations at Chiang Mai (R2 = 0.52) and Lampang (R2 = 0.60). Model 2 improved the prediction of PM2.5 concentration compared to model 1 for both wet and dry seasons. However, model uncertainties were also present, which lays a foundation for further study.

Research Article

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.

Research Article

Evaluation of Lightning Prediction by an Electrification and Discharge Model in Long-Term Forecasting Experiments

Over nearly three rainy seasons of lightning activity in North China, numerical prediction experiments were carried out using the Weather Research and Forecasting model coupled with electrification and discharge schemes (WRF-Electric). The numerical forecast results were evaluated using the neighborhood-based equitable threat score (ETS) and fraction skill score (FSS) verification methods based on nationwide observational lightning data. An algorithm was used to generate the coverage of the total flash (intracloud and cloud-to-ground flashes) by fitting the cloud-to-ground flash data. The numerical results showed that the region of lightning activity could be well predicted by the mesoscale WRF-Electric model, particularly during a 6–12-hour forecasting period. The average ETS score of the 6–12-hour forecasting period was 0.34 for a 20 km neighborhood radius. The predictive skill of the model varied not only monthly but also diurnally. The model showed better forecasting skills during the main rainy season (June–July–August) and at 14 : 00–20 : 00 local time. The predictability of the model was enhanced with increasing thunderstorm scale. On the other hand, the coverage of predicted lightning activity was relatively concentrated, and the lightning flash density was higher than the observations. The main discrepancies in the model prediction were related to the design of the discharge parameterization. Thus, in discharge parameterization, the initial threshold for lightning should be modified according to the model resolution, while the magnitude of the neutralization charge in a single discharge should be referenced to the observational results.

Research Article

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena’s river catchment forecasting result is more accurate than Gummera’s catchment forecasting result, with RMSE, MAE, MAPE, and R2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

Advances in Meteorology
 Journal metrics
Acceptance rate37%
Submission to final decision118 days
Acceptance to publication49 days
CiteScore2.900
Journal Citation Indicator0.390
Impact Factor1.962
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.