Computational fluid dynamics (CFD) is an effective technique to investigate atmospheric processes at a local scale. For example, in near-source atmospheric dispersion applications, the effects of meteorology, air pollutant sources, and buildings can be included. A prerequisite is to establish horizontally homogeneous atmospheric conditions, prior to the inclusion of pollutant sources and buildings. This work investigates modelling of the atmospheric surface layer under neutral and stable boundary layer conditions, respectively. Steady-state numerical solutions of the Reynolds Averaged Navier-Stokes (RANS) equations were used, including the the k-ε turbulence model. Atmospheric profiles derived from the Cooperative Atmosphere-Surface Exchange Study-99 (CASES-99) were used as reference data. The results indicate that the observed profiles of velocity and potential temperature can be adequately reproduced using CFD, while turbulent kinetic energy showed less agreement with the observations under the stable conditions. The results are discussed in relation to the boundary conditions and sources, and the observational data.

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the Man catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the Man catchment is 13.01 ton ha-1 year-1, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the Man catchment has negligible soil erosion rate (less than 2 ton ha-1 year-1). Its spatial distribution found on flat land of upper Man catchment. It has been detected that 26% Area of Man catchment has moderate to extremely severe soil erosion rate (greater than 10 ton ha-1 year-1). Its spatial distribution has been found on undulated topography of the middle Man catchment. It is proposed to treat this area by catchment area treatment activity.

Due to the urban heat island effect getting more evident in the cities in Taiwan, the urban climate has become an essential factor in urban development. Taiwan is located on the border of tropical and subtropical climate zones, the climate condition is hot and humid, and the city shows high-density development. The dense urban development has increased the heat storage capacity of the ground and buildings. However, if only apply the climate stations set by the Central Meteorological Bureau to observe the climate data, the predicted results would differ from the actual urban climate conditions due to the small number of these stations and the too far distance between them. Therefore, this study employs the Local Climate Zone (LCZ), which can classify the land features by considering both land use and land cover and can be freely generated from satellite images. The LCZ classification method can view the type of the city through the height and density of obstacles. This study also combines the urban canopy model (UCM) of the mesoscale climate prediction model Weather Research and Forecasts (WRF). This approach can calculate vertical and horizontal planes of the city, such as building volume, road width, the influence of streets and roofs, roof heat capacity, building wall heat capacity, etc., to predict the climatic conditions in different lands in the study area. Simultaneously, to understand the actual distribution of urban climate more accurately, this study used the microclimate measurement network built in the research area to produce pedestrian-level temperature distribution and compared the estimated results with the actual measured values for urban climate assessment. This study can understand the cause of urban heat islands and assist urban planners in more appropriately formulate heat island mitigation strategies in different regions.

Street canyons restrict natural ventilation flow due to their geometric complexity, and hence cause the increase in air pollution related issues which are detrimental to the health of residents in and around such areas. To mitigate these issues, many research articles explore new designs and how arrange barriers/obstacles to improve roadside air quality and ventilation within the urban street canyon (Gallagher et al., 2015; Huang et al., 2021).

These obstacles are generally categorized into two types – porous and non-porous. Porous barriers include vegetated shrubs and trees, while non-porous barriers include parked cars, low boundary walls, etc (Gallagher et al., 2015). Further categorization includes design strategies that can be applied to new developments or in-street modifications that can be applied to existing street canyons (Huang et al., 2021). These barriers can reduce the air pollution in multiple ways – such as dispersion, deposition and even chemical transformation (in the case of gaseous pollutants) (Abhijith et al., 2017; Druckman et al., 2019). A recent review by Huang et al. (2021) strongly suggests that combining solid and porous barriers offers more potential than individual use. Moreover, new developments can benefit from added design flexibility by the use of lift-up building design and building porosity as a promising way of improving ventilation (Tse et al., 2017).

This paper reviews the different research studies conducted on obstacles/barriers in an urban canyon which helps improve air quality. It shall highlight potential future research avenues, while also pointing out gaps in the research (such as the lack of real-world data experimental validation) that should be addressed going forward.

Abhijith, K. V. et al. (2017) doi: 10.1016/j.atmosenv.2017.05.014

Druckman, A. et al. (2019) doi: 10.1016/j.envint.2019.105181

Gallagher, J. et al. (2015) doi: 10.1016/j.atmosenv.2015.08.075

Huang, Y. et al. (2021) doi: 10.1016/j.envpol.2021.116971

Tse, K. T. et al. (2017) doi: 10.1016/j.buildenv.2017.03.011

Atmospheric profiles are key inputs in correcting the atmospheric effects of thermal infrared (TIR) remote sensing data for estimating Land Surface Temperature (LST). This study is a first insight into the feasibility of using the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for LST retrieval. WRF numerical simulations were performed to downscaling NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the use of these profiles in the atmospheric correction of TIR data applying the Radiative Transfer Equation (RTE) inversion single-channel approach. The MODerate resolution atmospheric TRANsmission (MODTRAN) model and Landsat 8 TIRS10 (10.6–11.2 µm) band were taken for the method application. The accuracy evaluation was performed using in situ radiosondes in Southern Brazil. We included in the comparative analysis the NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles directly from the NCEP CFSv2 reanalysis. The atmospheric correction parameters from ACPC, followed by CFSv2, had better agreement with the ones calculated using in situ radiosondes. When applied into the RTE to retrieve LST, the best results (RMSE) were, in descending order: CSFv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). The finds suggest that increasing the horizontal resolution of reanalysis profiles does not particularly improve the accuracy of RTE-based LST retrieval. However, the WRF results are yet satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and also recommend the NCEP CFSv2 reanalysis profiles for TIR remote sensing atmospheric correction and LST single-channel retrieval.

NEXRAD radars detect biological scatterers in the atmosphere, i.e., birds and insects, without distinguishing between them. A method is proposed to discriminate bird and insect echoes. Multiple scans are collected for mass migration of birds (insects) and coherently averaged along their different aspects to improve the data quality. Additional features are also computed to capture the dependence of bird (insect) echoes on their aspect, range, and spatial locality. Next, ridge classifier and decision tree machine learning algorithms are trained on the collected data. For each method, classifiers are trained, first with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed on a held-out test data set. Further case studies on roosting birds, bird migration and insect migration cases, are conducted to investigate the performance of the classifiers when applied to new scenarios. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well on both the test data and in the case studies. This classifier is recommended for operational use on the US Next-Generation Radars (NEXRAD) in conjunction with the existing Hydrometeor Classification Algorithm (HCA). The HCA would be used first to separate biological from non-biological echoes, then the ridge classifier could be applied to categorize biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that can detect diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

The evaluation of the Nowcasting and very short-range prediction system of the National Meteorological Service of Cuba is presented. The WRF numerical weather model is the primary tool employed in the system. The assessment is done for the relative humidity, precipitation, temperature, wind and pressure during 2019 and for the simulation domain of highest spatial resolution (3km). The measurements of the meteorological surface stations were used in the analysis. As result the system has good ability to forecast the aforementioned variables, its behavior is better in the pressure and temperature fields, while the worst results were obtained for precipitation. Although there was not much difference between the four initialization (00:00, 06:00, 12:00 and 18:00 UTC), the initialization at 12:00 UTC stood out among the others because, in general, it had better performance in the forecast of the variables studied.

The generalized extreme value distribution (GEVD) has been widely used to model the extreme events in many areas. It is however limited to using only block maxima, which motivated to model the GEVD dealing with r-largest order statistics (rGEVD). The rGEVD which uses more than one extreme per block can significantly improves the performance of the GEVD. The four parameter kappa distribution (K4D) is a generalization of some three-parameter distributions including the GEVD. It can be useful in fitting data when three parameters in the GEVD are not sufficient to capture the variability of the extreme observations. The K4D still uses only block maxima. In this study, we thus extend the K4D to deal with r-largest order statistics as analogy as the GEVD is extended to the rGEVD. The new distribution is called the r-largest four parameter kappa distribution (rK4D). We derive a joint probability density function (PDF) of the rK4D, and the marginal and conditional cumulative distribution functions and PDFs. The maximum likelihood method is considered to estimate parameters. The usefulness and some practical concerns of the rK4D are illustrated by applying it to Venice sea-level data. This example study shows that the rK4D gives better fit but larger variances of the parameter estimates than the rGEVD. Some new $r$-largest distributions are derived as special cases of the rK4D, such as the $r$-largest logistic (rLD), generalized logistic (rGLD), and generalized Gumbel distributions (rGGD).

Particulate matter (PM) is released from mobile sources depending on the type of fossil fuel used in combustion. Long-term exposure to PM can affect both lungs and heart. According to the USEPA, PM exposure can cause a variety of problems like premature deaths, nonfatal heart attacks, irregular heartbeat, asthma, reduced lung function, and respiratory issues. Therefore, it is necessary to predict the downwind concentrations near highways for regulatory work. The current study concentrates on developing an analytical line source dispersion model for particulate matter in different particle size ranges. Available line source models do not consider explicitly different ranges of particle size present in the exhaust. The present study discusses the development of a dispersion model to predict downwind concentrations of PM by incorporating a range of particle sizes. The deposition of particles is also considered during development. Emission rate, wind speed, wind direction, atmospheric turbulence, and dry deposition velocity of the particles are the model inputs. The sensitivity of the model is determined by varying the independent input variables.

The nowcasting and very short-term prediction system (SisPI, for its acronym in Spanish) is among the tools used by the National Meteorological Service of Cuba, for the quantitative precipitation forecast (QPF). SisPI uses the WRF model as the core of its forecasts and one of the challenges to overcome is to improve the precision of the QPF. With this purpose, in this work we present the results of the application of a bias correction method based on artificial neural networks. The method is applied to the highest resolution domain of SisPI (3km), and the correction is made from the precipitation estimation GPM satellite product. Results shows higher correlation with the artificial neural network model in relation to the values ​​predicted by SisPI (0.76 and 0.34 respectively). The mean square error applying the artificial neural network model is 3.69, improving the performance of SisPI with 6.78. In general, the bias correction has good ability to correct the precipitation forecast provided by SisPI, being less evident in cases where precipitation is reported and SisPI is not capable of forecasting it. In cases of overestimation by SisPI (which happens quite frequently), the correction achieves the best results.