Advances in Meteorology

Terrestrial and marine photosynthetic organisms emit trace gases, including isoprene and monoterpenes. The resulting emissions can impact the atmosphere through oxidative chemistry and formation of secondary organic aerosol. Large uncertainty exists as to the magnitude of the marine sources of these compounds, their controlling factors, and contribution to marine aerosol. In recent years, the number of relevant studies has increased substantially, necessitating the review of this topic. Isoprene emissions vary with plankton species, chlorophyll concentration, light, and other factors. Remote marine boundary layer isoprene mixing ratios can reach >300 pptv, and extrapolated global ocean fluxes range from <1 to >10 Tg C year ⁻¹. Modeling studies using surface chlorophyll concentration as an isoprene emissions proxy suggest variable atmospheric impacts. More information is needed, including emission fluxes of isoprene and monoterpenes from various biogeographical areas, the effects of species and nutrient limitation on emissions, and the aerosol yields via condensation and nucleation, in order to better quantify the atmospheric impacts of marine isoprene and monoterpenes.

Increasing heat will be a significant problem for Central European cities in the future. Shading devices are discussed as a method to mitigate heat stress on citizens. To analyze the physical processes, which are characteristic of shading in terms of urban human-biometeorology, experimental investigations on the thermal effects of shading by a building and shading by tree canopies were conducted in Freiburg (Southwest Germany) during typical Central European summer weather. Urban human-biometeorology stands for the variables air temperatureTa, mean radiant temperatureTmrt, and physiologically equivalent temperature PET, that is the human-biometeorological concept to assess the thermal environment which was applied. The measuring setup consists of specific human-biometeorological stations, which enable the direct or indirect determination ofTa,Tmrt, and PET. With respect to both shading devices, theTareduction did not exceed 2°C, while PET as a measure for human heat stress was lowered by two thermal sensation steps according to the ASHRAE scale. AsTmrthas the role of a key variable for outdoor thermal comfort during Central European summer weather, all radiant flux densities relevant to the determination ofTmrtwere directly measured and analyzed in detail. The results show the crucial significance of the horizontal radiant flux densities forTmrtand consequently PET.

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

In this paper, hourly observations of precipitation, wind, and PM2.5 and PM10 concentrations in Qinhuangdao from 2016 to 2018 were used to study the effects of precipitation and wind on PM2.5 and PM10 concentrations. The results show that precipitation has a certain wet scavenging effect on PM2.5 and PM10, and the scavenging effect on PM10 is greater than that on PM2.5. Precipitation above moderate rainfall is concentrated from May to September, and light rain in winter increases the concentration of pollutants. The changes of PM2.5 before and after precipitation are related to the initial concentration of PM2.5 before precipitation, precipitation intensity, and precipitation duration. The scavenging effect of precipitation on PM10 is closely related to the initial concentration of PM10 before precipitation. The higher the initial concentration of PM10 is, the greater the removal amount of precipitation will be. Moderate or above pollution weather mainly occurs in the northeast, southwest, and west wind meteorological conditions; the more westerly the wind, the more the pollution; north wind and northwest wind have the most obvious scavenging effect on PM2.5 and Pm10; when the wind speed increases to 2 m/s, the concentration of PM2.5 and PM10 can be reduced; when the wind speed is more than 4 m/s, the concentration of PM10 increases under the south wind, southeast wind, east wind, and northeast wind.

The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

Precipitation is an essential input parameter for land surface models because it controls a large variety of environmental processes. However, the commonly sparse meteorological networks in complex terrains are unable to provide the information needed for many applications. Therefore, downscaling local precipitation is necessary. To this end, a new machine learning method, LASSO algorithm (least absolute shrinkage and selection operator), is used to address the disparity between ERA-Interim forecast precipitation data (0.25° grid) and point-scale meteorological observations. LASSO was tested and validated against other three downscaling methods, local intensity scaling (LOCI), quantile-mapping (QM), and stepwise regression (Stepwise) at 50 meteorological stations, located in the high mountainous region of the central Alps. The downscaling procedure is implemented in two steps. Firstly, the dry or wet days are classified and the precipitation amounts conditional on the occurrence of wet days are modeled subsequently. Compared to other three downscaling methods, LASSO shows the best performances in precipitation occurrence and precipitation amount prediction on average. Furthermore, LASSO could reduce the error for certain sites, where no improvement could be seen when LOCI and QM were used. This study proves that LASSO is a reasonable alternative to other statistical methods with respect to the downscaling of precipitation data.

Recently, microwave communication networks have been shown to be valuable tools for rainfall monitoring, based on the well-known Power-Law which relates rain-rate to attenuation in microwave frequencies. However, once precipitation other than pure rain exists (e.g., snow), the Power-Law relation is no longer accurate. In this paper we propose a model which relates the induced attenuation to rain, snow, and sleet. Based on this model we propose estimating the total accumulated precipitation, regardless of the precipitation type, using measurements from multiple microwave links. Our technique takes advantage of the commercial communication networks, need for redundancy, which dictates the use of multiple microwave links at the same area. We show that by using measurements from at least three microwave links better estimation of the total accumulated precipitation fall can be provided, when rain, snow, sleet, or a mixture of them coexists. To demonstrate the proposed approach, it has been applied on actual microwave links attenuation measurements, which were provided by a cellular carrier. The estimation results were compared with Rain-Gauges and disdrometer measurements and show very good agreement and improved accuracy.

The exposure of students to traffic-emitted carbon monoxide (CO) in their daily walk to school is evaluated, with a particular emphasis on the effect of trees and route choice. The study is focused on the city centre of Aveiro, in central Portugal. Time evolution of the georeferenced location of an individual is tracked with a GPS for different alternative walking routes to a school. Spatial distribution of CO concentration is simulated with a computational fluid dynamics (CFD) model. An exposure model is developed that associates the georeferenced location of the student with the computed air quality levels (at an average breathing height) for that specific grid cell. For each individual, the model calculates the instantaneous exposure at each time frame and the mean value for a given period. Results show a general benefit induced by the trees over the mean exposure of the student in each route. However, in the case of instantaneous exposure values, this is not consistent along the entire period. Also, the variability of the estimated exposure values indicates the potential error that can be committed when using a single value of air quality as a surrogate of air pollution exposure.

Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb–Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762–1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981–2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. Then, the impacts on WYPA for the period 2021–2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021–2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. The comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5.

The Nile Delta major cities, particularly Cairo, experienced stagnant air pollution episodes, known as Black Cloud, every year over the past decade during autumn. Low-elevated thermal inversion layers play a crucial role in intensifying pollution impacts. Carbon monoxide, ozone, atmospheric temperature, water vapor, and methane measurements from the tropospheric emission spectrometer (TES) on board the Aura have been used to assess the dominant component below the inversion layer. In this study, time series analysis, autocorrelations, and cross correlations are performed to gain a better understanding of the connections between those parameters and their local effect. Satellite-based data were obtained for the years 2005–2010. The parameters mentioned were investigated throughout the whole year in order to study the possible episodes that take place in addition to their change from year to year. Ozone and carbon monoxide were the two major indicators to the most basic episodes that occur over Cairo and the Delta region.