scholarly journals Surface and Aerodynamic Parameters Estimation for Urban and Rural Areas

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 147
Author(s):  
Roberto Sozzi ◽  
Giampietro Casasanta ◽  
Virginia Ciardini ◽  
Sandro Finardi ◽  
Igor Petenko ◽  
...  
Keyword(s):  
Rural Areas ◽  
Roughness Length ◽  
Prediction Models ◽  
Weather Prediction ◽  
Mediterranean Area ◽  
Parameters Estimation ◽  
Urban Site ◽  
Displacement Height ◽  
Spatial And Temporal Scales ◽  
Urban And Rural Areas

Numerical weather prediction models require an accurate parametrization of the energy budget at the air-ground interface, that can be obtained only through long-term atmospheric boundary layer measurements at different spatial and temporal scales. Despite their importance, such measurements are still scarce even in well-characterized areas. In this paper, a three-year dataset from four micrometeorological stations run by the Regional Agency for Environmental Protection of Lazio was analyzed to estimate albedo, zero-displacement height, roughness length and surface properties over Rome and its suburbs, characterizing differences and interconnections between urban, suburban and rural areas of the same municipality. The integral albedo coefficient at the zenith for the urban station was found to be almost twice that for suburban and rural stations. The zero-displacement height of the urban site was strongly dependent on wind direction, with values varying between 12.0 and 17.8 m, while the roughness length (≈1.5 m) was almost independent of upwind direction, but it was significantly higher than the typical values calculated for rural stations (≈0.4 m). The apparent thermal capacities and thermal conductivity at all the non-urban sites were in fair agreement with each other and typical of soils with relatively low water content, as expected for a relatively dry Mediterranean area like Rome, while the apparent thermal diffusivity reflected the presence of different soil types.

scholarly journals Measurement report: Exploring NH<sub>3</sub> behavior in urban and suburban Beijing: comparison and implications

2021 ◽  
Vol 21 (6) ◽  
pp. 4561-4573
Author(s):  
Ziru Lan ◽  
Weili Lin ◽  
Weiwei Pu ◽  
Zhiqiang Ma
Keyword(s):  
Relative Humidity ◽  
Rural Areas ◽  
Urban Site ◽  
Mixing Ratio ◽  
Mixing Ratios ◽  
Different Seasons ◽  
Rural Sites ◽  
Highly Correlated ◽  
Urban And Rural Areas ◽  
Suburban Site

Abstract. Ammonia (NH3) plays an important role in particulate matter formation; hence, its atmospheric level is relevant to human health and climate change. Due to different relative distributions of NH3 sources, concentrations of atmospheric NH3 may behave differently in urban and rural areas. However, few parallel long-term observations of NH3 exist to reveal the different behaviors of NH3 concentrations at urban and rural sites in a same region. In this study, online ammonia analyzers were used to continuously observe atmospheric NH3 concentrations at an urban site and a suburban site in Beijing from 13 January 2018 to 13 January 2019. The observed mixing ratio of NH3 averaged 21±14 ppb (range of 1.6–133 ppb) at the urban site and 22±15 ppb (range of 0.8–199 ppb) at the suburban site. The NH3 mixing ratios at the urban and suburban sites exhibited similar seasonal variations, with high values in summer and spring and low values in autumn and winter. The hourly mean NH3 mixing ratios at the urban site were highly correlated (R=0.849, P<0.01) with those at the suburban site; however, the average diurnal variations in the NH3 mixing ratios at the urban and suburban sites differed significantly, which implies different contributions from NH3 sources and sinks at the urban and suburban sites. In addition to the emission sources, meteorological factors were closely related to the changes in the NH3 concentrations. For the same temperature (relative humidity) at the urban and suburban sites, the NH3 mixing ratios increased with relative humidity (temperature). Relative humidity was the factor with the strongest influence on the NH3 mixing ratio in different seasons at the two sites. The relationships between the NH3 concentrations and temperature (relative humidity) varied from season to season and showed differences between the urban and suburban sites. The reasons for the different relationships need to be investigated in future studies. Higher wind speed mainly from the northwest sector lowered the NH3 mixing ratios at both sites. Similarly to other primary pollutants in Beijing, the NH3 mixing ratios were high when impacted by air masses from the southern sector.

scholarly journals Degradation Prediction of Rail Tracks: A Review of the Existing Literature

2018 ◽  
Vol 12 (1) ◽  
pp. 88-104 ◽  
Author(s):  
Najwa Elkhoury ◽  
Lalith Hitihamillage ◽  
Sara Moridpour ◽  
Dilan Robert
Keyword(s):  
Rural Areas ◽  
Prediction Models ◽  
Main Concern ◽  
Complex Matrix ◽  
Common Goal ◽  
Track Geometry ◽  
The Past ◽  
Operational Costs ◽  
Railway Infrastructure ◽  
Urban And Rural Areas

In the past few decades, the railway infrastructure has been widely expanded in urban and rural areas, making it the most complex matrix of rail transport networks. Safe and comfortable travel on railways has always been a common goal for transportation engineers and researchers, and requires railways in excellent condition and well-organized maintenance practices. Degradation of rail tracks is a main concern for railway organizations as it affects the railway’s behaviour and its parameters, such as track geometry, speed, traffic and loads. Therefore, the prediction of the degradation of rail tracks is very important in order to optimise maintenance needs, reduce maintenance and operational costs of railways, and improve rail track conditions.This paper provides a comprehensive review of rail degradation prediction models, their parameters, and the strengths and weaknesses of each model. A comprehensive discussion of existing research and a comparison of different models of degradation of rail tracks is also provided. Finally, this review presents concluding remarks on the limitations of existing studies and provides recommendations for further research and appraisal practices.

scholarly journals On the potential of sub-mm passive MW observations from geostationary satellites to retrieve heavy precipitation over the Mediterranean Area

2006 ◽  
Vol 7 ◽  
pp. 387-394 ◽  
Author(s):  
S. Pinori ◽  
F. Baordo ◽  
C. M. Medaglia ◽  
A. Mugnai ◽  
B. Bizzarri
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
Radiative Transfer Model ◽  
General Interest ◽  
Transfer Model ◽  
Microphysical Properties ◽  
Crete Island ◽  
Potential Use

Abstract. The general interest in the potential use of the mm and sub-mm frequencies up to 425 GHz resolution from geostationary orbit is increasing due to the fact that the frequent time sampling and the comparable spatial resolution relative to the "classical" (≤89 GHz) microwave frequencies would allow the monitoring of precipitating intense events for the assimilation of rain in now-casting weather prediction models. In this paper, we use the simulation of a heavy precipitating event in front of the coast of Crete island (Greece) performed by the University of Wisconsin - Non-hydrostatic Modeling System (UW-NMS) cloud resolving model in conjunction with a 3D-adjusted plane parallel radiative transfer model to simulate the upwelling brightness temperatures (TB's) at mm and sub-mm frequencies. To study the potential use of high frequencies, we first analyze the relationships of the simulated TB's with the microphysical properties of the UW-NMS simulated precipitating clouds, and then explore the capability of a Bayesian algorithm for the retrieval of surface rain rate, rain and ice water paths at such frequencies.

Identification of Polycyclic Aromatic Hydrocarbon and Heavy Metal in PM10 from Urban and Rural School Ambient

2017 ◽  
Vol 5 (2) ◽  
pp. 98-103
Author(s):  
Norrimi Rosaida Awang ◽  
Hemamalini Manogaran ◽  
Nur Dalila Che Omar ◽  
Siti Hajar Ya’akob ◽  
Nor Hizami Hassin
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Particulate Matter ◽  
Polycyclic Aromatic Hydrocarbon ◽  
Aromatic Hydrocarbons ◽  
Rural Areas ◽  
Rural Site ◽  
Urban Site ◽  
Polycyclic Aromatic ◽  
Urban And Rural Areas

Particulate matter (PM10) is a major pollutant of concern as it can be adsorbed with toxicsubstances like heavy metals and organic compounds like polycylic aromatic hydrocarbons(PAH). Automobile exhaust was among the most significant contributor towards the particulatematter problem in roadside areas of Malaysia. In addition, most of the school in Malaysia locatedclose to the roadside to ease accessibility that consequently further increase health risk amongchildren. In this study, aerosol samples from school ambient in urban area (Kota Bharu) and ruralarea (Jeli) were analysed for PAH and concentration of heavy metal (Pb, Zn and C) identification.Result found the concentration of PM10 was higher in urban site than rural site with meanconcentration Kota Bharu and Jeli is 52.57?g/m3 and 39.21 ?g/m3 respectively. The 1-methylnaphthalene was the only PAH detected in both urban and rural areas. Result suggestedthat the concentration of lead (Pb) is the most prominent heavy metal measured in Jeli, whileconcentration of zinc (Zn) is the most significant of heavy metal in Kota Bharu.

scholarly journals Empirical values and assumptions in the convection of numerical models

2021 ◽  
Author(s):  
Anahí Villalba-Pradas ◽  
Francisco J. Tapiador
Keyword(s):  
Climate Models ◽  
Prediction Models ◽  
Numerical Models ◽  
Global Climate ◽  
Weather Prediction ◽  
Global Climate Models ◽  
Spatial And Temporal Scales ◽  
Weather Events ◽  
Wide Range ◽  
Convection Schemes

Abstract. Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in Numerical Weather Prediction models, Global Climate Models, and Earth System Models (NWPs, GCMs, and ESMs) as the key physical processes occur at scales much lower than the model grid size. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection.

Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts

2014 ◽  
Vol 29 (4) ◽  
pp. 1024-1043 ◽  
Author(s):  
David John Gagne ◽  
Amy McGovern ◽  
Ming Xue
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Spatial And Temporal Scales ◽  
Small Areas ◽  
Probabilistic Forecasts ◽  
Quantitative Precipitation Forecasts ◽  
Wide Variability ◽  
Added Uncertainty

Abstract Probabilistic quantitative precipitation forecasts challenge meteorologists due to the wide variability of precipitation amounts over small areas and their dependence on conditions at multiple spatial and temporal scales. Ensembles of convection-allowing numerical weather prediction models offer a way to produce improved precipitation forecasts and estimates of the forecast uncertainty. These models allow for the prediction of individual convective storms on the model grid, but they often displace the storms in space, time, and intensity, which results in added uncertainty. Machine learning methods can produce calibrated probabilistic forecasts from the raw ensemble data that correct for systemic biases in the ensemble precipitation forecast and incorporate additional uncertainty information from aggregations of the ensemble members and additional model variables. This study utilizes the 2010 Center for Analysis and Prediction of Storms Storm-Scale Ensemble Forecast system and the National Severe Storms Laboratory National Mosaic & Multi-Sensor Quantitative Precipitation Estimate as input data for training logistic regressions and random forests to produce a calibrated probabilistic quantitative precipitation forecast. The reliability and discrimination of the forecasts are compared through verification statistics and a case study.

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