Meteorological Changes Research Articles

A Comprehensive Methodology for the Statistical Characterization of Solar Irradiation: Application to the Case of Morocco

The prediction and characterization of solar irradiation relies mostly on either the use of complex models or on complicated mathematical techniques, such as artificial neural network (ANN)-based algorithms. This mathematical complexity might hamper their use by businesses and project developers when assessing the solar resource. In this study, a simple but comprehensive methodology for characterizing the solar resource for a project is presented. It is based on the determination of the best probability distribution function (PDF) of the solar irradiation for a specific location, assuming that the knowledge of statistical techniques may be more widely extended than other more complex mathematical methods. The presented methodology was tested on 23 cities across Morocco, given the high interest in solar investments in the country. As a result, a new database for solar irradiation values depending on historical data is provided for Morocco. The results show the great existing variety of PDFs for the solar irradiation data at the different months and cities, which demonstrates the need for undertaking a proper characterization of the irradiation when the assessment of solar energy projects is involved. When it is simply needed to embed the radiation uncertainty in the analysis, as is the case of the techno-economic valuation of solar energy assets, the presented methodology can reach this objective with much less complexity and less demanding input data. Moreover, its application is not limited to solar resource assessment, but can also be easily used in other fields, such as meteorology and climate change studies.

Biomonitoring of polycyclic aromatic hydrocarbons (PAHs) by Murraya paniculata (L.) Jack in South Kolkata, West Bengal, India: spatial and temporal variations.

Attempts have been made in the present study for ascertaining the concentrations of atmospheric polycyclic aromatic hydrocarbons (PAHs) using passive biosamplers in preference to conventional air sampling methods. Mechanical stirring, sonication, Soxhlet technique and microwave-assisted Soxhlet extraction (MASE) were employed to extract PAHs from an evergreen plant (Murraya paniculata) leaves (having long life-span) sampled from polluted places of South Kolkata, India, with dense population and heavy traffic. Effects of extraction methods and operational parameters (solvent and time) on the recovery levels of PAHs were also investigated. Purified extracts, acquired through adsorption chromatography, were subjected to GC-MS and HPLC-UV analyses for qualitative and quantitative assessment of PAHs. Spatio-temporal distribution of accumulated PAHs across the sampling sites was monitored over premonsoon, postmonsoon and winter supported by pollutant source characterization. The results displayed that the extraction yields of Soxhlet (272.07 ± 26.15μgg-1) and MASE (280.17 ± 15.46μgg-1) were the highest among the four techniques. Conditions of extraction with toluene for 6h were found to be most favorable for PAHs. In spatio-temporal analysis, total concentrations of PAHs in the foliar samples varied from 200.98 ± 2.72 to 550.79 ± 10.11μgg-1 dry weight, and the highest values being recorded in the samples of Exide More because of daylong inexorable traffic flow/crowding increasing the burden of ambient PAHs. Widespread changes in meteorology exerted influence on seasonal concentrations of PAHs in plant leaves, and extent of leaf contamination by PAHs was observed extreme in winter followed by postmonsoon and then, premonsoon. Foliar accretion of PAHs differed in the study sites with diverse sources of emission from motor vehicles, fossil fuel and biomass burning along with other human interferences.

Spatial Correlation between Urban Planning Patterns and Vulnerability to Flooding Risk: A Case Study in Murcia (Spain)

Cities in the Spanish Mediterranean regions have undergone an extensive process of urban growth in recent decades. This urban transformation has often failed to consider the variable of flooding in its planning. Such a situation, combined with the current meteorological changes derived from climate change phenomena that increasingly cause less frequent but more extreme rainfall events in this part of the planet, has caused a sharp increase in the vulnerability of many urban areas against flooding. This research aims to analyze, from a spatiotemporal approach, in the case study of Murcia, a Mediterranean city in southeastern Spain, the existing spatial statistical correlation between urban planning patterns of growth of the city and the increase in risk due to its current vulnerability to flooding. Using GIS-based multivariate indicators and geostatistical analysis, the behavior patterns of said correlation will be numerically evaluated, and possible future trends and scenarios for this problem will be raised.

Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods

In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, CODMn, and NH3-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction.

Assessing climate change impact on river flow extreme events in different climates of Iran using hybrid application of LARS-WG6 and rainfall-runoff modeling of deep learning

Climate change and its side effects on weather variables and water resources is one of the recent international concerns. This research aims to project future changes in weather variables due to climate change for different emission scenarios of RCP2.6, RCP4.5 and RCP8.5 to assess and compare climate change impact on river flow variation based on hybrid application of deep learning and LARS-WG6 in different climates of Iran (CSA: cold semi-arid; HT: humid temperate; CA: cold arid). For this purpose, best combination of daily rainfall and discharges data with different lags were selected as inputs in rainfall-runoff modeling using Convolutional Neural Networks (CNN) model. Predicted meteorological data by the LARS-WG6 in the future (2021-2040) were then used as inputs for the selected CNN model and consequently runoff was predicted. Obtained findings showed that projected rainfall changes in 2021-2040 compared to the base period in scenarios of RCP2.6, 4.5 and 8.5 will be +14, +11, +6% in CA, +8, +1, +2% in CSA and +3.7, -1, -2.6% in HT regions, respectively. Whereas, maximum discharge in CA climate in scenario RCP2.6 is projected to raise about 18%, in the HT climate shows a reduce of about 5% in RCP8.5 and in CSA will decrease by about 5% in RCP2.6. Considering the effect of climate as well as seasonal changes in each region on the amount and direction of the climate change impacts on meteorological and hydrologic variables changes, assessing climate change impact in each region is necessary to extract adaptation strategies in water resources management.

Aerosol radiative feedback enhances particulate pollution over India: A process understanding

In this study, we investigate the role of aerosol direct radiative feedback on regional meteorological changes and subsequent distribution of air pollutant with the focus on particulate matter PM2.5 and ozone (O3) over India. WRF-Chem simulations have been applied for Baseline case with both aerosol direct and indirect effects and another simulation including only aerosol indirect effects. The aerosol direct radiative feedback on the meteorology and air quality were investigated by taking the differences between these two simulations. A comprehensive model evaluation showed the model had the capacity to reproduce the observations and well captured the temporal and spatial variation in meteorological and aerosol fields. We found that the reduction in incoming shortwave solar radiation, temperature at 2 m and planetary boundary layer height annually and across all the season due to aerosol radiation interaction (ARI). The concentration of PM2.5 and gaseous precursors like SO2 and NO2 showed enhancements, while O3 concentration mostly showed a reduction. Spatial and annual average PM2.5 concentration was increased by +6%, SO2 concentration was increased by +1.4%, NO2 concentration was increased by +3.4%, and O3 concentration was reduced by −1.4%. Largest regional increases of 40% in particulate pollution induced by ARI occurred over north-western Indian region. The increase in PM2.5 concentration was highly related to stabilisation induced through meteorological variables and increases in primary aerosol concentration. The decrease in O3 concentration was highly related increases in NO2 and SO2 concentration. In the most polluted regions of India, ARI significantly enhance surface-level particulate pollution and related premature mortality, of particular concern considering likely future increasing trends. This study highlights the importance of inclusion and better representations of ARI and for implementing effective mitigation plans.

The AirGAM 2022r1 air quality trend and prediction model

Abstract. This paper presents the AirGAM 2022r1 model – an air quality trend and prediction model developed at the Norwegian Institute for Air Research (NILU) in cooperation with the European Environment Agency (EEA) over 2017–2021. AirGAM is based on nonlinear regression GAMs – generalised additive models – capable of estimating trends in daily measured pollutant concentrations at air quality monitoring stations, discounting for the effects of trends and time variations in corresponding meteorological data. The model has been developed primarily for the compounds NO2, O3, PM10, and PM2.5. Meteorological input data consist of temperature, wind speed and direction, planetary boundary layer height, relative and absolute humidity, cloud cover, and precipitation over the period considered. The exact set of meteorological variables used in the model depends on the compound selected for analysis. In addition to meteorological variables introduced in the model as covariates, i.e. explanatory variables for the concentration levels, the model also incorporates time variables such as the day of the week, day of the year, and overall time, which is related to the model's trend term. The trend analysis is performed at each station separately. Thus, the model only considers the temporal features of concentrations and meteorology at a station, rather than any spatial correlations or dependencies between stations. AirGAM is implemented using the R language for statistical computing and, in particular, the GAM package mgcv. In the model, meteorological and time covariates are represented and estimated as smooth nonlinear functions of the corresponding variables. Thus, the trend term is defined and estimated as a smooth nonlinear function of time over the period selected for analysis. Once fitted to training data, the model may be used as a prediction tool capable of predicting air pollutant concentrations for new sets of meteorological and time data which are not in the training set – e.g. for cross-validation or forecasting purposes. The model does not explicitly use emissions or background concentrations – these are sought to be implicitly represented through the estimated nonlinear relations between meteorology, time, and concentrations. In addition to meteorology-adjusted trends, the program also produces unadjusted trends – i.e. trends based on the same regression set-up but only including the time covariates. Both types of trends can be output in the same run, making it possible to compare them. Ideally, the meteorology-adjusted trend will show the trend in concentration mainly due to changes in emissions or physicochemical processes not induced by changes in meteorology. AirGAM has been developed and tested primarily in trend studies based on measurement data hosted by the EEA, including the AirBase data (before 2013) and the Air Quality e-Reporting (AQER) data from 2013 and onwards. Still, the model is general and could be applied in other regions with other input data. The EEA data provide daily or hourly surface measurements at individual monitoring stations in Europe. For input meteorological data, we extract time series from the gridded meteorological re-analysis (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for each monitoring station. The paper presents results with the model for all AirBase/AQER stations in Europe from the latest EEA trend study for 2005–2019.

Drivers of 2015–2021 trends in cold winter surface PM2.5 in the Harbin-Changchun megalopolis in China: Meteorology vs. anthropogenic emission

Harbin-Changchun megalopolis (HCM) is a typical representative of the cold urban agglomeration region in China. Although the average annual PM2.5 concentration has experienced an evolutionary process continuous deterioration to gradual improvement in the past decade, but severe haze episodes still occur during cold winter periods. WRF-CMAQ model and observed dataset of surface PM2.5 concentration were conducted in this study to identify the drivers of 2015–2021 trends in cold winter surface PM2.5 in the HCM by separating contributions from anthropogenic emissions and meteorology changes. Meteorological condition change was the dominant driver of 2015–2021 trends in cold winter surface PM2.5 in the HCM. The years with most unfavorable, typical, and most favorable meteorological conditions for PM2.5 concentration reduction during January from 2015 to 2021 for the whole HCM region were 2020, 2017, and 2018, respectively. The major controlling meteorological factors that affect the surface PM2.5 concentration change were relative humidity, maximum wind direction and speed in cold winter in the HCM. In addition, we found that the emission cut policy began to play a practical role in the air quality improvement in the HCM from year of 2018. The emission reduction during the 2017–2021 resulted in an approximately 19% (15.7 μg/m3) decrease of PM2.5 concentrations in January. Furthermore, sensitivity experiments demonstrated that residential emission cut was the key measures for future air quality improvement in cold winter in the HCM.

Spatio-Temporal Changes and Influencing Factors of Meteorological Dry-Wet in Northern China during 1960–2019

In northern China, precipitation fluctuates greatly and drought occurs frequently, which mark some of the important threats to agricultural and animal husbandry production. Understanding the meteorological dry-wet change and the evolution law of drought events in northern China has guiding significance for regional disaster prevention and mitigation. Based on the standardized precipitation index (SPI), this paper explored the spatio-temporal evolution of meteorological dry-wet in northern China. Our results showed that arid area (AA) and semi-arid area (SAA) in the west showed a trend of wetting at inter-annual and seasonal scales, while humid area (HA) and semi-humid area (SHA) in the east showed a different dry-wet changing trend at different seasons under the background of inter-annual drying. AA and HA showed obvious “reverse fluctuation” characteristics in summer. The drought frequency (DF) and drought intensity (DI) were high in the east and low in the west, and there was no significant difference in drought duration (DD) and drought severity (DS) between east and west. The DD, DS and DI of AA and SAA showed a decreasing trend, while the DD and DS of HA and SHA showed a slight increasing trend, and the DS decreased. In summer and autumn, the main influencing factors of drying in the east and wetting in the west were PNA, WP, PDO and TP1, and the fluctuations of NAO-SOI, NAO-AMO and PNA-NINO3.4 jointly determined the characteristics of SPI3 reverse fluctuations of HA and AA in summer.

Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China?

To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (−15%) and transportation (−90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.

Decay stages and meteorological factors affect microbial community during leaf litter in situ decomposition

• Decay stages and meteorological factors affect leaf litter’s microbial community.• Bacteria and fungi were mainly affected by OC, TN, pH, and water content of leaf litter.• Bacterial (6.6) and fungal (3.6) Shannon indexes were the largest after 125 days.• Microbial diversity and decay stage directly regulated the litter mass-loss rate.Litter microorganisms play a crucial role in the biological decomposition in forest ecosystems; however, the coupling effect of meteorological and substrate changes on it during the different stages of leaf decomposition in situ remains unclear. Hence, according to meteorological factors dynamics, a one-year field litter of Quercus wutaishanica in situ decomposition experiment was designed for four decay stages in a warm temperate forest. Microbial community composition was characterized using Illumina sequencing of fungal ITS and bacterial 16S genes. Bacterial (6.6) and fungal (3.6) Shannon indexes were the largest after 125 days’ litter decomposition (October). The relative abundance of Acidobacteria after 342 days and Bacteroidetes after 125 days were 3 and 24 times higher than after 31 days, respectively. Some non-dominant species (bacteria: Firmicutes, Planctomycotes, and Verrucomicrobia; fungi: Chytridiomycota and Glomeromomycota) may be absent or present at different decomposition stages due to litter properties or meteorological factors. Chemoheterotrophy and aerobic-chemoheterotrophy were the dominant bacterial functional groups, and the dominant fungal functional groups were saprotrophs, pathotrophs, and symbiotrophs. Precipitation and relative humidity significantly affected bacteria. Temperature, sunlight intensity, and net radiation significantly affected fungi. Besides, among the relative contributions of changes in bacterial and fungal community structure, leaf litter properties alone explained the variation of 5.51% and 10.63%. Microbial diversity and decay stage directly affected the litter mass-loss rate, with meteorological factors (precipitation, relative humidity, air temperature, and sunlight intensity) being indirect. Our findings highlight the importance of microbial diversity for leaf litter decomposition and the influence of meteorological factors.

The Impact of Meteorological Changes on Human Comfort in Residential Buildings

This research article investigates the effect of meteorological changes on the comfort of residents in Imo State, Nigeria, which is situated in a tropical monsoon climate. The study aims to assess the state of the indoor environment in residential buildings in Imo State, considering thermal changes caused by meteorological variations. A case study approach was adopted, focusing on a selected residential building in Umuasonye, Orji, Owerri North, Imo State. The study spanned a period of 10 years, from 2011 to 2021, and included physical observation, measurement, and qualitative analysis of the collected data. A case study approach was employed to overcome limitations in accessing a large sample size of individual residential buildings. The selected residential building in Umuasonye, Orji, Owerri North, Imo State served as a representative example for the study. Data collection involved physical observation, measurement, and qualitative analysis over a 10-year period, from 2011 to 2021. This approach allowed for a comprehensive understanding of the indoor environment and its relationship with meteorological changes. Findings reveal significant climate and weather changes in Imo State, leading to thermal variations in building spaces. The study assesses the impact on resident comfort and identifies adaptive strategies employed. During dry seasons, residents utilized cooling systems, ventilation, and reduced hot water usage. In wet seasons, measures such as closing windows, wearing thick clothing, and increased hotwater usage were observed. This research provides valuable insights into the impact of meteorological changes on resident comfort in Imo State, Nigeria. It highlights the importance of climate-responsive building design and systems, showcasing adaptive strategies employed by residents. The case study approach contributes to knowledge for architects, building industry stak eholders, and users interested in promoting climate-responsive buildings and systems

Understanding Meteorological Changes Following Severe Defoliation during a Strong Hurricane Landfall: Insights from Hurricane Michael (2018)

Abstract Despite prompting persistent meteorological changes, severe defoliation following a tropical cyclone (TC) landfall has received relatively little attention and is largely overlooked within hurricane preparedness and recovery planning. Changes to near-track vegetation can modify evapotranspiration for months after tropical cyclone passage, thereby altering boundary layer moisture and energy fluxes that drive the local water cycle. This study seeks to understand potential spatial and temporal changes in defoliation-driven meteorological conditions using Hurricane Michael (2018) as a testbed. In this sensitivity study, two Weather Research and Forecasting (WRF) Model simulations, a normal-landscape and a post-TC scenario, are compared to determine how a defoliation scar placed along Michael’s path alters surface heat fluxes, temperature, relative humidity, and precipitation near the storm’s track. In the month following the foliage reduction, WRF resolves a 0.7°C 2-m temperature increase, with the greatest changes occurring at night. Meanwhile, the simulations produce changes to the sensible and latent heat fluxes of +8.3 and −13.9 W m−2, respectively, while average relative humidity decreases from 73% to 70.1%. Although the accumulated precipitation in the defoliated simulation was larger along a narrow corridor paralleling and downwind of the hurricane track, neither simulation satisfactorily replicated post-Michael precipitation patterns as recorded by NCEP Stage IV QPE, casting doubt as to whether the downwind enhancement was exclusively due to the defoliation scar. This sensitivity analysis provides insight into the types of changes that may be possible following rapid and widespread defoliation during a TC landfall.

Impact of Meteorological Changes on Air Pollution in Turpan and Its Countermeasures

Impact of Meteorological Changes on Air Pollution in Turpan and Its Countermeasures

Dynamic Response of Shrubbery Fire to Meteorological Changes in Yunnan Province

This paper takes shrubland fires as the research object and selects typical shrubland forest fire cases that occurred throughout Yunnan Province from 1954 to 2018 as the research samples, and the shrubland fire loss elements and meteorological data are studied by SPSS correlation analysis and MATLAB gray prediction model to analyze shrubland fires and meteorological elements. The results showed that there were significant correlations between shrubland fires and meteorological data; the shrubland fire over fire area, forest damage area, and economic loss composite data index showed positive correlations with annual maximum temperature, annual average minimum temperature, annual average temperature, and annual sunshine hours; the annual average relative humidity, precipitation days, and the composite index of the same day meteorological data showed significant negative correlations, etc. In exploring the main meteorological factors affecting the distribution of forest fires, we establish a meteorological fire model to provide a real and credible scientific basis and theoretical foundation for future forest fire prevention and prediction.

The Environmental Consequence of Automobile and Motor-bike Traffic Noise Intensity on the Activity of African Grey Parrots (Psithacus erittacus) in Limbe Botanic Garden, Southwest Region, Cameroon

Highway traffic noise pollution is unavoidably generated by human activity, and has caused serious health problems to the human society. However, it’s also a challenge to many wildlife species, though, continous exposure to the noise stimuli has seemingly given them adaptation. Understanding the ability of wildlife species to cope with noise pollution is complex, thus, requires research. Therefore, the objective of this study was to explore automobile and motor bike traffic noise intensity on African grey parrots’ activity in Limbe Botanic Garden. The research data was collected for a period of three months from 8:00 am - 5:00 pm each day. Traffic noise pollution is difficult to avoid in urban settings because of huge human population and its activity, however, its interference with other sounds is common, and wildlife population living in cities, such as birds are affected. The study revealed a positive significance X2 = 25.796 df=2 P=0.000 and X2 =39.489 df=6 P=0.000 on photoperiod and meteorological conditions respectively. Moreover, meteorological changes in the study area recorded significance on sun 75%, cloud 15%, wind 6%, and rain 4% respectively. Furthermore, noise intensity of automobile and motor bike traffic around the botanic garden area showed a significance X2 = 5.657 df=8 P<0.05, and r = 0.209 P=0.000 on behavior and parrots' food availability respectively. The highest parrots’ activity recorded during this study was on contact calls 54%, and the least was roosting 3%. Though a comprehensive inventory is not carried out on the parrot population in Cameroon, there are signs, these birds are already facing population reduction; principally because of the pet-trade driven by the craving human love for the colourful plumage morphology and the talented sound-mimicking behavior of the parrots. There is a necessity for further study to be carried out on the birds’ population census in the country. The study found that traffic noise intensity at all levels have a negative influence on the aggregation activity of grey parrots, reason why they avoid the vegetation along the streets of the garden. Finally, oil palm fruit-feeding was a dominant activity recorded in this study, hence, we recommend more oil palm trees be planted in the garden far from major roads to enrich the feeding environment of the grey parrots.

USING OBSERVED HAIL TO QUALITY EXTREME HAIL CLIMATE IN VIET NAM. PART I: DATA QUALITY CONTROL AND CLIMATOLOGICAL CHARACTERISTICS OF HAIL

In this study, we focus on studying data and calculating climatic features of hail. In this study, a hail dataset was created by combining historic records from 186 meteorological stations for the period 1961 - 2021. The data are obtained from several sources including observations from the Viet Nam Meteorological and Hydrological Administration (VNMHA), annual reports of the Viet Nam Institute of Meteorology, Hydrology and Climate change (IMHEN) and the Viet Nam Disaster Management Authority (VDMA). The data used in this study is defined from the official data sources. Therefore, the data quality control shows that this data may be considered good available data of hail. Research results for 1961 - 2021 show that the highest number of hail events was observed in the Northwest (743 events), and in the Northeast (557 events) subregions; the lowest number of hail events was observed in the South Central (16 events) and the South (27 events) subregions. The higher frequency duration of hail occurs from March to May, with the peak in April. The largest hail diameter, recorded in Vietnam is 12 cm at Bac Ha station on March 27, 2013; the common largest hail diameter size in history at stations is around 3 - 5 cm at the Northern stations and 1 - 3 cm at the southern stations.

Demonstrating Regional Climate and Meteorological Sensitivity in Landfill Methane Inventories from Historical Californian Databases

The urgency to manage global methane emissions has been acknowledged with international pledges to reduce 2020 levels by 30% by 2030. Carbon management requires effective tools to monitor changes, including those from significant sources including waste disposed on land. The first order degradation model used to determine landfill methane emissions, has been described by researchers as highly variable, insensitive and inadequate, despite recent attempts to explain microclimate impacts on methane oxidation. The development of detailed regional inventories is hampered by these variables. The availability of historical waste management and meteorological data in California, enables a theoretical review and modelling of meteorological moisture changes with methane generation data in a region of decadal drought. This study identifies a novel approach in the modelling of regional optimisation of variable seasonal parameters of moisture and methane oxidation based on the adjustment of the methane correction factor (MCF) generally assumed to be MCF = 1 for managed sites, that is optimised as MCF<sub>site</sub> ≠ 1 as the average regional MCF<sub>all sites</sub>, → 1 (Range: <1, ≥1). Regional annual unmitigated methane emitted in December 2010 after methane recovery, oxidation and flaring is estimated at 0.40 million tonnes, falling to 0.31 million tonnes in 2011 and back to 0.40 million tonnes in 2012 (Pr < 0.01, n = 370). as meteorological conditions returned to the changing decadal norm. Meteorological and climate sensitivity is demonstrated in relation to the regional water balance, spatial distribution of landfill site moisture levels, satellite imagery of 2012 wildfire intensity ranges, the 2011 El Nino impacts and independent data sources. The method provides accurate regional methane assessments inclusive of soil and cover material oxidation. This will primarily benefit developing countries where landfill remains a dominant option, enabling the development of database linked satellite monitoring and detailed regional landfill climate emission inventories.

On the Buildings and Location of Armagh Observatory

We consider the buildings of Armagh Observatory and present their location to a few tens of centimetres or approximately 0.01 arcsecond in latitude and longitude, referred to the position of the Ordnance Survey (OS) benchmark (OSBM) inscribed on the historic eighteenth-century building. This has Irish Grid (IG) coordinates (287829.309BM, 345757.815BM) and a height above mean sea level of 61.08 m. It lies approximately 12.1 ± 0.15 m west and 7.9 ± 0.1 m south of the Observatory’s earliest transit telescopes and 1.5 ± 0.05 m east and 1.7 ± 0.1 m south of the 1795 Troughton telescope. Ground level at the Observatory has an orthometric height of 61.9 ± 0.1 m, approximately 2 metres below that given in the Astronomical Almanac. Accurate measurements of height have implications for values of atmospheric pressure reduced to mean sea level, for precise comparisons between weather stations, and for weather forecasting. Our position for the Observatory’s earliest transit telescopes lies within ~0.4 m of the coordinate origin of the 1835 OS 6-inch County Armagh maps, highlighting the link between astronomy and the first high-accuracy maps of Ireland. An 1834 plan and measurements of the surviving north meridian marks suggest that the transit room’s east windows were moved ∼0.8 m east when the sector tower was added around 1840. This resolves an unexplained discrepancy between the present distance (3.0 m) between the centres of the west and east windows of the transit room and the original distance (~2.2 m) inferred by other means. We suggest that we may havefound parts of early transit instruments believed lost and give improved coordinates for the meridian marks. This work has applications to the history of astronomy, geodesy, meteorology, and climate change.

Radiative effects and feedbacks of anthropogenic aerosols on boundary layer meteorology and fine particulate matter during the COVID-19 lockdown over China

The COVID-19 epidemic has exerted significant impacts on human health, social and economic activities, air quality and atmospheric chemistry, and potentially on climate change. In this study, an online coupled regional climate–chemistry–aerosol model (RIEMS-Chem) was applied to explore the direct, indirect, and feedback effects of anthropogenic aerosols on radiation, boundary layer meteorology, and fine particulate matter during the COVID-19 lockdown period from 23 January to 8 April 2020 over China. Model performance was validated against a variety of observations for meteorological variables, PM2.5 and its chemical components, aerosol optical properties, as well as shortwave radiation flux, which demonstrated that RIEMS-Chem was able to reproduce the spatial distribution and temporal variation of the above variables reasonably well. During the study period, direct radiative effect (DRE) of anthropogenic aerosols was stronger than indirect radiative effect (IRE) in most regions north of the Yangtze River, whereas IRE dominated over DRE in the Yangtze River regions and South China. In North China, DRE induced larger changes in meteorology and PM2.5 than those induced by IRE, whereas in South China, the changes by IRE were remarkably larger than those by DRE. Emission reduction alone during the COVID-19 lockdown reduced PM2.5 concentration by approximately 32 % on average over East China. As a result, DRE at the surface was weakened by 15 %, whereas IRE changed little over East China, leading to a decrease in total radiative effect (TRE) by approximately 7 % in terms of domain average. The DRE-induced changes in meteorology and PM2.5 were weakened due to emission reduction, whereas the IRE-induced changes were almost the same between the cases with and without emission reductions. By aerosol radiative and feedback effects, the COVID-19 emission reductions resulted in 0.06 °C and 0.04 °C surface warming, 1.6 and 4.0 μg m−3 PM2.5 decrease, 0.4 and 1.3 mm precipitation increase during the lockdown period in 2020 in terms of domain average over North China and South China, respectively, whereas the lockdown caused negligible changes on average over East Asia.