Regional Weather Data Research Articles

Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data

Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m−3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.

Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research.

New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysis based on Guillain-Barré Syndrome (GBS) surveillance data.

BackgroundBetween January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern (NE) region of Brazil. As a result, two waves of Guillain-Barré Syndrome (GBS) were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies to other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the disease symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate) and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil.MethodologyA mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using a plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number , and to infer the true ZIKV epidemic dynamics.Findings and conclusionsThe GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% confidence interval: 17.1%—29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as ρ = 0.0061% (95% CI: 0.0050%—0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, . Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.

Transient simulation and modeling of photovoltaic-PEM water electrolysis

ABSTRACTThis work aims at analyzing the transient behavior of a photovoltaic-Polymer Electrolyte Membrane (PV-PEM) water electrolyser system based on regional weather data. The goal of a dynamic modeling is to check how the system works against the load variations. In this study, the PV array is used for power supply then, a controller and power conditioning to provide clean energy for electrolyser. The electrolyser model is developed in MATLAB and it is used in a TRNSYS environment. The performance of a PEM electrolyser can be illustrated by the polarization curve which is voltage–load current (V–I) characteristics. Moreover, a mathematical modeling for computing dynamic voltage based on current and all operation parameters in electrolyser stacks have been developed. Then the transient behavior of the PV-PEM system is analyzed over the seasonal condition base on Vienna average weather information.

Examining Agricultural Workplace Micro and Macroclimate Data Using Decision Tree Analysis to Determine Heat Illness Risk.

This study was designed to examine the associations between regional weather data and agricultural worksite temperatures in Florida. Florida farmworkers (n = 105) were each monitored using iButton technology paired with simultaneous data from regional weather stations. Conditional inference tree models were developed for (1) regional environmental temperatures and iButton (worksite) temperatures, and (2) regional heat index (HI) and iButton HI. Worksite temperatures were partitioned by regional temperature at the primary node of 29.1°C. Worksite HI was partitioned at nodes of 33.0°C, 36.0°C, 37.0°C, and 40.0°C. The nodes at 33.0°C and 40.0°C mirror the National Weather Service's category entry points for "extreme caution" and "danger" regarding the risk of developing heat-related illness. Regional weather data have the potential to provide estimations of worksite environmental conditions allowing employers to quickly implement strategies to protect workers.

Effects of weather conditions on drinking water distribution pipe failures in the Netherlands

Abstract The influence of the weather parameters of temperature, wind and drought on pipe failure of drinking water distribution pipes was studied for the Netherlands. Several data sources were used relating weather effects to pipe failure: pipe failure data, regional weather data from different weather stations in the Netherlands, soil settlement data obtained from satellites and (modelled) pressure data. For asbestos-cement (AC) and cast iron (CI) pipes, temperature was an important factor. CI pipes showed increased pipe failures at low temperatures, which confirms results from previous studies, whereas AC pipes showed increased pipe failures at high temperatures. Pipe failure rates were higher for pipes that on average received higher internal pressures. This study also showed that wind resulted in additional pipe failures caused by uprooting of trees during a severe storm. With respect to drought, in some regions in the Netherlands, increased pipe failures during periods of drought were found. A small influence of soil settlement on pipe failure was found using remote-sensing techniques for a small area (5 × 10 km) in the Netherlands.

Energy saving potential of a counter-flow regenerative evaporative cooler for various climates of China: Experiment-based evaluation

Recently there has been growing interest in regenerative evaporative coolers (REC), which can reduce the temperature of the supply air to below the wet-bulb of intake air and approach its dew-point. In this paper, we designed, fabricated and experimentally tested a counter-flow REC in laboratory. The REC’s core heat and mass exchanger was fabricated using stacked sheets composed of high wicking evaporation (wickability of available materials was measured) and waterproof aluminium materials. The developed REC system has a much higher cooling performance compared to conventional indirect evaporative cooler. However, the decision to use the REC for China buildings depends on a dedicated evaluation of the net energy saved against the capital expended. Such an evaluation requires the hourly-based data on the availability of cooling capacity provided by the REC for various climates. The paper used an experiment-based method to estimate the cooling capacity and energy savings provided by the proposed REC for China’s various climates. By using the experimental results and regional hourly-based weather data, the energy saving potential of the REC against an equivalent-sized mechanical air conditioner alone was analysed. The results indicate that, for all selected regions, the REC could reduce 53–100% of cooling load and 13–58% of electrical energy consumption annually.

Emergence of white pine needle damage in the northeastern United States is associated with changes in pathogen pressure in response to climate change.

The defoliation of the eastern white pine (Pinus strobus) across the northeastern United States is an escalating concern threatening the ecological health of northern forests and economic vitality of the region's lumber industry. First documented in the spring of 2010 affecting 24328 hectares in the state of Maine, white pine needle damage (WPND) has continued to spread and is now well established in all New England states. While causal agents of WPND are known, current research is lacking in both sampling distribution and the specific environmental factor(s) that affect the development and spread of this disease complex. This study aims to construct a more detailed distribution map of the four primary causal agents within the region, as well as utilize long-term WPND monitoring plots and data collected from land-based weather stations to develop a climatic model to predict the severity of defoliation events in the proceeding year. Sampling results showed a greater distribution of WPND than previously reported. WPND was generally found in forest stands that compromised >50% eastern white pine by basal area. No single species, nor a specific combination of species had a dominating presence in particular states or regions, thus supporting the disease complex theory that WPND is neither caused by an individual species nor by a specific combination of species. In addition, regional weather data confirmed the trend of increasing temperature and precipitation observed in this region with the previous year's May, June, and July rainfall being the best predictor of defoliation events in the following year. Climatic models were developed to aid land managers in predicting disease severity and accordingly adjust their management decisions. Our results clearly demonstrate the role changing climate patterns have on the health of eastern white pine in the northeastern United States.

ESTUDO DO COMPORTAMENTO DE OVIPOSIÇÃO DO AEDES AEGYPTI EM DOIS BAIRROS SOB A INFLUÊNCIA DO CLIMA SEMIÁRIDO NO MUNICÍPIO DE SALINAS

The mosquito Aedes aegypti (Diptera: Culicidae) is the main vector of human arborvirus in Brazil.Oviposition behavior is important for the survival and dispersal of A. aegypti. The objective of this studywas to evaluate the oviposition behavior and its relationship to meteorological factors. Twenty ovitrapswere installed in two neighborhoods in the urban area of the city of Salinas, MG, southeastern Brazil.The traps were inspected weekly for the presence of eggs in the water and on the paddle. Simultaneously,regional weather data were analyzed. The results showed the Ovitrap Positivity Index (OPI) variedgreatly, from 0 to 90%, between the weeks and boroughs sampled, demonstrating the vector’s greatpopulation fluctuation. Among the analyzed climatic factors, rainfall strongly influenced oviposition,followed by relative air humidity. The number of eggs found on the paddle was significantly higher thanin the water (p<0.05). However, a significant increase of eggs in the water was noticed when the relativehumidity dropped below 40%. This seems to be an important behavior to maintain the population inperiods of drought. Through the low number of eggs (<30) found in 56% of positive traps, we concludedthat skip-oviposition behavior occurred, contributing to the prevalence of mosquitoes during thesampled period. It is concluded that the occurrence of water oviposition may be higher than described inthe literature, and should be studied due to its evolutionary and epidemiological importance.

한국형 위성항법시스템의 UDRE 모니터링 분석

This paper is about analysis of UDRE monitoring method for Korean Satellite navigation system, which is the correction parameter of satellite measurements. New receiver clock bias and tropospheric delay error estimation method to make pseudo-range residual for UDRE monitoring is proposed. Saastamoinen model and Neill mapping function are used for estimate the tropospheric delay and EKF is used for estimgate the receiver clock bias. Through the satellite measurements and regional weather data received directly from the domestic is using for UDRE monitoring analysis, more suitable UDRE monitoring threshold can be deducted and it is expected to be utilized for fault detection technique of Korean Satellite Navigation System.

Study on the future weather data considering the global and local climate change for building energy simulation

Energy simulations are often used to evaluate the indoor thermal environment and energy consumption of buildings. In such simulations, it is common to use regional weather data that are typically based on current or past weather conditions. However, most buildings have a lifespan of several decades, during which climate can change gradually. Therefore, the design of energy conservation systems and energy simulations should incorporate climate change predictions to ensure that buildings are adaptable to future climatic conditions.The present study aims to construct future (i.e., 2030s) standard weather data for use in architectural design using numerical meteorological models. For this purpose, we adopted a dynamical downscaling method, used global climate model (GCM) data as the initial and boundary conditions for input into a regional climate model (RCM), and physically downscaled the data using the RCM.We constructed a prototype of the future (i.e., 2031–2035) standard weather data based on version 4 of the Model for Interdisciplinary Research on Climate (MIROC) and the Weather Research and Forecasting (WRF) model. The results confirm that the weather data generated via the dynamical downscaling method can predict local climate. Subsequently, we simulated building thermal load consumption using regional climate data. By comparing the results for the present (2007) and future (2034), we estimated the impact of climate change on the energy performance of a detached house. In particular, the sensible heat load for the house was predicted to increase by 15% under the conditions considered.

A COMPARATIVE STUDY OF THE THERMAL COMFORT OF DIFFERENT BUILDING MATERIALS IN SANA’A

This study describes the impact of alternative building material envelope systems on the overall thermal performance of four selected materials used in Sana’a, Yemen. These systems included adobe, brick, stone and concrete block. After thorough on-site investigation and data collection, the information, along with regional weather data were input into the Ecotect energy simulation software for thermal performance evaluation. The objective was to search for affordable and energy-efficient construction techniques suitable for settlements and incorporating traditional cultural values in a arid upland region characterized by cold winters and warm, dry summers. This was pursued by analyzing temperature and measurements within buildings constructed from a variety of traditional and modern materials. The thermal behavior and comfort, the patterns of energy use and the appropriateness of the different building techniques and materials are analyzed, compared and discussed. We demonstrate how a building envelope reacts to outdoor conditions through graphic illustration and show ways in which the research can be extended by the creation of simulations using Ecotect software. This research contributes to the promotion of passive and low energy architecture towards a sustainable future.

The Impact of Urban Wind Environments on Natural Ventilation

The aim of this paper is to illustrate the impact of urban wind environments when assessing the availability of natural ventilation. A numerical study of urban airflow for a complex of five building blocks located at the University of Reading, UK is presented. The computational fluid dynamics software package ANSYS was used to simulate six typical cases of urban wind environments and to assess the potential for natural ventilation. The study highlights the impact of three typical architectural forms (street canyons, semi-enclosures and courtyards) on the local wind environment. Simulation results were also compared with experimental data collected from six locations on the building complex. The study demonstrates that ventilation strategies formed using regional weather data may have a propensity to over-estimate the potential for natural ventilation and cooling, due to the impact of urban form which creates a unique microclimate. Characteristics of urban wind flow patterns are presented as a guideline and can be used to assess the design and performance of natural or hybrid ventilation and the opportunity for passive cooling.

A computer model to simulate control of parasitic gastroenteritis in sheep on UK farms

A computer model that simulates the population dynamics and epidemiology of three major species of parasitic nematodes of sheep found in the UK ( Telodorsagia [ Ostertagia] spp., Haemonchus spp. and Trichostrongylus spp.) is described. The model has been developed as a tool for veterinarians and advisors to aid in the implementation of integrated parasite control strategies designed to optimise anthelmintic usage and delay the development of resistance on UK farms. The model represents the parasite lifecycle, flock dynamics and the response of individuals with different susceptible and resistant genotypes to the major broad-spectrum classes of anthelmintic available in the UK. Where possible, UK data have been used for the model parameters. The model allows worm control simulations on individual UK farms. Inputs include environmental and farm management variables which impact on the epidemiology of the disease, e.g. regional weather data; flock stocking rates; initial pasture larval contamination levels and species proportions; lambing dates; timing of flock movements to clean pastures; and removal of lambs during the year. Farm management data, as well as nematode egg outputs and grass larval counts, were collected from eight UK farms over a 1-year period for initial validation of the model outputs. The management data for each farm were used as inputs for each model run and model outputs for nematode egg counts from ewes and lambs were compared to the observed data for each farm. Statistical analysis of results shows a positive correlation for observed and simulated counts and regression analysis suggests an acceptable fit between the data. Comparison of observed and simulated outputs for resistance were possible for only one farm due to low numbers of worms developing in the laboratory tests. Additional studies will be necessary before resistance data can be reliably compared. Further validation studies are proposed to ensure that the model is robust and applicable across a diverse range of farm types. The model will be used to demonstrate the advantage, in terms of delaying resistance development, of current guidelines for anthelmintic use and management practices for worm control in sheep.

Predicting light leaf spot (Pyrenopeziza brassicae) risk on winter oilseed rape (Brassica napus) in England and Wales, using survey, weather and crop information

Data from surveys of winter oilseed rape crops in England and Wales in growing seasons with harvests in 1987–99 were used to construct statistical models to predict, in autumn (October), the incidence of light leaf spot caused by Pyrenopeziza brassicae on winter oilseed rape crops the following spring (March/April), at both regional and individual crop scales. Regions (groups of counties) with similar seasonal patterns of incidence (percentage of plants affected) of light leaf spot were defined by using principal coordinates analysis on the survey data. At the regional scale, explanatory variables for the statistical models were regional weather (mean summer temperature and mean monthly winter rainfall) and survey data for regional light leaf spot incidence (percentage of plants with affected pods) in July of the previous season. At the crop scale, further explanatory variables were crop cultivar (light leaf spot resistance rating), sowing date (number of weeks before/after 1 September), autumn fungicide use and light leaf spot incidence in autumn. Risk of severe light leaf spot (&gt; 25% plants affected) in a crop in spring was also predicted, and uncertainty in predictions was assessed. The models were validated using data from spring surveys of winter oilseed rape crops in England and Wales from 2000 to 2003, and reasons for uncertainty in predictions for individual crops are discussed.

Using long-term experiments to evaluate models for assessing climatic impacts on future crop production

Crop models are indispensable to assess crop production under climate change, and they need evaluation preferably on long-term experiments. The certainty of estimates depends on the detail of input data and selection of an appropriate model. Aggregation may allow the use of simpler models, but it is unknown how this increases the prediction uncertainty. The purpose of our study was to assess the efficiency of a meteorological dataset from regional and local sources using two different models simulating sugar beet growth. Experimental data (1961-1992) originated from the Static Fertilization Experiment in Bad Lauchstädt, Germany. Regional weather (temperature, radiation and rainfall) was retrieved from the European database. Biomass of sugar beet was simulated with a relatively simple single-season model (BB) and a more complex mechanistic multi-season model (CA). Simulations compared best to de-trended observations using CA with local daily weather data (R2=0.55). Using model CA with regional weather data decreased the prediction certainty to that for BB (R2=0.45 and 0.46). The complex model (CA) overestimated biomass (+1.4 t/ha) whereas the simple model (BB) under-estimated observed biomass above 21 t /ha (−3.8 t/ha). An estimate of initial soil water content proved essential for the application of the BB model under continental climate. The results suggest that the predictive capacity of simple and complex models for crop growth is similar when only regional input data are available.

Modelling a forest lepidopteran: phenological plasticity determines voltinism which influences population dynamics

Mnesampela privata is an Australian geometrid moth that is considered to have resource-driven outbreaks. An autumnal oviposition/larval development cycle is considered the “norm” in this species, but spring/summer activity has also been observed. This apparent plasticity of phenology and probable concomitant changes in voltinism have not been considered as possible causes of moth outbreaks. We developed GumMoth, a phenological model for M. privata that uses temperature to predict development times of immatures. Photoperiod determines whether individuals undergo direct development or pupal diapause. We used known catch dates of moths (in the same moth-active season or 365-day period) to simulate population phenologies on the Australian mainland (27°28′S to 38°14′S) and in Tasmania (40°54′S to 42°57′S). GumMoth successfully simulated phenological patterns that agreed with published records and demonstrated for the first time that multivoltinism is possible in M. privata. In seven paired simulations using dates for first and last moth catches, the earliest moth activity resulted in the pupae of four out of 11 generations undergoing diapause, whereas the latest moth activity resulted in pupae of five out of eight generations undergoing diapause (diapause individuals emerged the following moth-active season). Almost two-thirds of offspring of spring/summer active moths reached adulthood within that same moth-active season (i.e. no delay in development), whereas those of autumn active moths always reached adulthood the following moth-active season (i.e. development was delayed). Records from foresters and in the scientific literature confirmed that the more common autumn phenology is supplemented by spring/summer activity in many localities. At half of these localities, populations that required insecticide control or caused substantial defoliation also developed. Because plantation eucalypts are suitable M. privata hosts for only four to five tree-growth seasons after planting, local moth populations must increase quickly if an outbreak is to occur. We suggest that large populations are more likely when plantations are colonised in the first moth-active season after planting and population growth commences in the spring/summer of each subsequent year. Foresters can use catch dates and regional weather data in GumMoth to estimate the number and phenology of generations of M. privata. This information can be used to assess risk posed by regional populations and thereby adjust future surveillance intensity.

Regional Environmental Prediction Over the Pacific Northwest

This paper examines the potential of regional environmental prediction by focusing on the local forecasting effort in the Pacific Northwest. A consortium of federal, state, and local agencies have funded the development and operation of a multifaceted numerical prediction system centered at the University of Washington that includes atmospheric, hydrologic, and air quality models, the collection of real-time regional weather data sources, and a number of real-time applications using both observations and model output. The manuscript reviews northwest modeling and data collection systems, describes the funding and management system established to support and guide the effort, provides some examples of regional real-time applications, and examines the national implications of regional environmental prediction.

Predicting Ross River virus epidemics from regional weather data.

Diseases caused by arboviruses cause extensive mortality and morbidity throughout the world. Weather directly affects the breeding, abundance, and survival of mosquitoes, the principal vector of many arboviruses. The goal of this study was to test whether climate variables could predict with high levels of accuracy (more than 70%) epidemics of one arbovirus, Ross River virus disease. Weather data from two regions in southeastern Australia were matched with Ross River virus disease data for the period 1991 to 1999. Our aim was to develop simple models for the probability of the occurrence of an epidemic in an area in a given year. Two predictable epidemic patterns emerged, after either high summer rainfalls or high winter rainfalls. A prerequisite relating to host-virus dynamics was lower than average spring rainfall in the preepidemic year. The sensitivity of the model was 96% for Region 1 and 73% for Region 2. Early warning of weather conditions conducive to outbreaks of Ross River virus disease is possible at the regional level with a high degree of accuracy. Our models may have application as a decision tool for health authorities to use in risk-management planning.

ROMPEX—The Rocky Mountain Peaks Experiment of 1985: Preliminary Assessment

Abstract During the late summer of 1985 a field experiment was conducted to investigate mountaintop winds over a broad area of the Rocky Mountains extending from south central Wyoming through northern New Mexico. The principal motivation for this experiment was to further investigate an unexpectedly strong and potentially important wind cycle observed at mountaintop in north central Colorado during August 1984. These winds frequently exhibited nocturnal maxima of 20 to 30 m·s−1 from southeasterly directions and often persisted for eight to ten hours. It appears that these winds originate as outflow from intense mesoscale convective systems that form daily over highland areas along the Continental Divide. However, details of the spatial extent and variability of these winds could not be determined from “routine” regional weather data that are mostly collected in valleys. Although synoptic conditions during much of the 1985 experiment period did not favor diurnally recurring convection over the study area, ...