Annual Weather Research Articles

A Methodology for Assembling Future Weather Files Including Heatwaves for Building Thermal Simulations from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) Climate Data

With increasing mean and extreme temperatures due to climate change, it becomes necessary to use—not only future typical conditions—but future heatwaves in building thermal simulations as well. Future typical weather files are widespread, but few researchers have put together methodologies to reproduce future extreme conditions. Furthermore, climate uncertainties need to be considered and it is often difficult due to the lack of data accessibility. In this article, we propose a methodology to re-assemble future weather files—ready-to-use for building simulations—using data from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) dynamically downscaled regional climate multi-year projections. It is the first time that this database is used to assemble weather files for building simulations because of its recent availability. Two types of future weather files are produced: typical weather years (TWY) and heatwave events (HWE). Combined together, they can be used to fully assess building resilience to overheating in future climate conditions. A case study building in Paris is modelled to compare the impact of the different weather files on the indoor operative temperature of the building. The results confirm that it is better to use multiple types of future weather files, climate models, and or scenarios to fully grasp climate projection uncertainties.

Mean Annual Weather Cycles of some Weather Variables over Warri, Delta State, Nigeria during 2009 to 2018

The importance of analysing the weather cycles of any region cannot be over emphasized considering the dynamic nature of weather and its undesirable impacts on the environment. This study examines the mean annual cyclic behaviour of some weather variables over Warri, Delta State, Nigeria using recent weather data from NiMet for a period of ten years (2009 to 2018). It was observed from the cyclic behaviours from the various weather variables for the ten years period under consideration, that the annual maximum and minimum temperature, vapour pressure and cloud amount cycles follow similar trends, likewise the undulation of the annual soil heat flux cycles. The annual rainfall, wind speed, maximum and minimum relative humidity cycles were found to fluctuate in different patterns. In periods of limited rainfall, relative humidity is at the threshold of ~ 50.0%. A roughly curvilinear fit was noticed between the annual rainfall and relative humidity. While, rainfall appears insensitive to relative humidity changes during the months with lower relative humidity, which tends to be linear during the months with higher relative humidity. Hence, as the rainfall amount increases, there is an increasing tendency towards a curvilinear relationship. The occurrence of the maximum in temperature preceding that of wind speed indicates that the prevalent winds over Warri could be thermally obsessed. The results obtained would assist in providing appropriate panacea to mitigating weather induced environmental hazards, thereby improving agriculture, efficient economic productivity and advance scientific research.

Elevated atmospheric carbon dioxide effects on maize and alfalfa in the Northeast US: A comparison of model predictions and observed data

The main feed crops on dairy farms in the northeastern U.S. are alfalfa (Medicago sativa L.) and maize (Zea mays L.). Projected changes in climate along with increased atmospheric carbon dioxide (CO2) concentration will affect future production of these crops. We evaluated maize and alfalfa yield and evapotranspiration response to CO2 enrichment predicted by three process-based cropping system models, CropSyst, DSSAT and IFSM, in six counties of Pennsylvania and New York, using 25 years of daily weather and two concentrations of atmospheric CO2 (350 and 550 ppm). Positive effects of CO2 enrichment on crop growth have been documented using plants grown under controlled CO2 conditions; of particular relevance are results obtained in fields with free-air CO2 enrichment (FACE) technology. For evaluation, simulation results were compared among models and to FACE experimental data. The three models simulated similar maize response to increasing CO2 for grain yield, total biomass yield and harvest index, with predicted responses within the ranges reported in FACE experiments. The models may have slightly overestimated the yield response of maize during wet years compared with FACE experiments. The models also simulated increased productivity of alfalfa within the bounds observed in FACE experiments. While DSSAT and IFSM predicted yield increases of about 20%, Cropsyst predicted an increase of 32%, which is in the upper bound of FACE experimental data. For both crops, models simulated a lesser reduction in evapotranspiration under increasing CO2 than that measured in FACE experiments. The larger response of alfalfa may either shift rotations towards more alfalfa, or allow for a shorter alfalfa phase in a rotation depending on market and other production constraints. While there were overestimations in simulating maize productivity under no water stress and uncertainties in simulating evapotranspiration that are relevant for local and regional hydrology, these models adequately represented crop yield response to changes in atmospheric CO2 concentration.

Common Vetch: A Drought Tolerant, High Protein Neglected Leguminous Crop With Potential as a Sustainable Food Source.

Global demand for protein is predicted to increase by 50% by 2050. To meet the increasing demand whilst ensuring sustainability, protein sources that generate low-greenhouse gas emissions are required, and protein-rich legume seeds have the potential to make a significant contribution. Legumes like common vetch (Vicia sativa) that grow in marginal cropping zones and are drought tolerant and resilient to changeable annual weather patterns, will be in high demand as the climate changes. In common vetch, the inability to eliminate the γ-glutamyl-β-cyano-alanine (GBCA) toxin present in the seed has hindered its utility as a human and animal food for many decades, leaving this highly resilient species an “orphan” legume. However, the availability of the vetch genome and transcriptome data together with the application of CRISPR-Cas genome editing technologies lay the foundations to eliminate the GBCA toxin constraint. In the near future, we anticipate that a zero-toxin vetch variety will become a significant contributor to global protein demand.

Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt

Abstract Planting date and cultivar selection are major factors in determining the yield potential of any crop and in any region. However, there is a knowledge gap in how climate scenarios affect these choices. To explore this gap, we performed a regional scale analysis (11 planting dates × 8 cultivars × 281 fields × 36 weather years × 6 climate scenarios) using the APSIM model and pSIMS software for Iowa, the leading US maize (Zea mays L.) producing state. Our objectives were to determine how the optimum planting date (optPD) changes with weather scenarios and cultivars and the potential economic implications of planting outside the optimum windows. Results indicated that the mean optPD corresponds to the US Department of Agriculture, National Agriculture Statistics Service (USDA-NASS) 18.4% planting progress (April 28th) in Iowa. The optPD was found to be advancing by –0.13 d yr−1 from 1980 to 2015. A 1 °C increase in mean temperature increased the length of the growing season by 10 days while the optPD changed by –2 to + 6 days, depending on cultivar. Under a more realistic scenario of increasing the minimum temperature by 0.5 °C, decreasing the maximum temperature by 0.5 °C, increasing spring rainfall by 10% and decreasing summer rainfall by 10%, the optPD only changed by –2 days compared to current trends, however, yield increased by 6.6%. Analysis of historical USDA-NASS planting durations indicated that on average, the planting duration (1–99% statewide reported planting progress) is 44 days, while it can be as low as 21 days in years with favorable weather. A simple economic analysis illustrated a potential revenue loss up to $340 million per year by planting maize outside the optimum window. We conclude that future investments in planting technologies to accelerate planting, especially in challenging weather years, as well as improved optPD × cultivar recommendations to farmers, will provide economic benefits and buffer climate variability.

”Juhannuksena sää saattaa olla lämmin”

Ilmasto konkretisoituu ihmisten arkeen sään kautta; ilmaston merkitys välittyy sään kokemisessa ja säälle annetuissa paikallisissa merkityksissä. Näin sään ja sille annettujen kulttuuristen merkitysten tutkiminen tuo arvokkaan lisän ilmastonmuutoksen arkisten seurausten hahmottamisessa ja voi auttaa ymmärtämään erilaisia ilmastonmuutosnäkemyksiä ja näistä juontuvia ilmastopolitiikan konflikteja. Tämän vuoksi kiinnostus arkisten sääkokemusten tutkimukseen on kasvanut viime vuosina. Artikkeli tarjoaa katsauksen tähän kansainvälisesti kehittyvään kulttuurisen ilmastotutkimuksen kenttään, sekä tarkastelee empiirisesti sään kulttuurisia merkityksiä Suomessa sanomalehtiaineiston avulla. Empiirinen analyysi pohjaa sääuutisointiin erikoisena säävuonna 2018 kahdessa sanomalehdessä: Helsingin Sanomissa ja Maaseudun Tulevaisuudessa. Sääuutisoinnista nostetaan esiin kolme keskeistä piirrettä: kesän sään merkityksellisyys, sään epätavallisuuden ja ongelmallisuuden korostaminen sekä sääpuheen kontekstisidonnaisuus. Ilmastonmuutoksen näkökulmasta tutkimus tarjoaa eväitä sopeutumistarpeiden paremmalle ymmärtämiselle.

Doing Good When Doing Well: Evidence on Real Earnings Management

We provide evidence on earnings management by exploiting temporary exogenous shocks to utility firms’ sales in the form of annual weather variation. We find that sample firms’ sales are highly sensitive to annual changes in average temperatures in the region where the firm operates, but this sensitivity disappears quickly moving down the income statement. We interpret this as indirect evidence of earnings management. In search of direct evidence, we study charitable giving decisions by sample firms and uncover a significant positive sensitivity of charitable spending to weather-driven demand shocks. Given the magnitude of this detected relation and the economic returns from charitable giving, this behavior appears to be driven by a desire to smooth earnings.

The vulnerability of homes to overheating in Myanmar today and in the future: A heat index analysis of measured and simulated data

The integration of vernacular strategies in Myanmar housing seems to have remained remarkably resilient and is still the norm even in modern dwellings. However, no regard is given to the possible impact of climate change.Using one-year monitored indoor and outdoor thermal environment data, typical and a morphed weather data representing future climate change scenarios, the authors investigated whether modern dwellings in Myanmar can provide thermal comfort in the present and future, and measured how vulnerable to overheating risks they are. Building envelope materials based on typical construction found locally and window shading were varied in the simulations, in order to understand their role in the resultant building performance.The analysis showed that the number of hours above 30 °C in 2019 doubled when compared to a typical weather year. This study contributes valuable insights into how the combined effect of air temperature and humidity will affect building thermal performance in future climate scenarios. Temperature readings reaching the 'danger' heat index threshold were 14.06% of the time in 2019 and only 5.49% in the typical weather year. This indicated that modern dwellings in Myanmar are facing two challenges: high vulnerability to extreme heatwave events, and inadequate response to increased mean air temperatures.

An assessment of the contribution of air pollution to the weathering of limestone heritage in Malta

Malta is known for its limestone megalithic temples of which many are inscribed on the UNESCO World Heritage List. A variation of this limestone was historically, and until very few years ago, a primary building material in Malta. The temples are subject to various environmental influences which until recently have led to several collapses due in part to serious stone surface and infill loss. As a protection measure, open-sided shelters have been built over three of these temples. This work assesses the degrading influence of air pollution (nitrogen dioxide, ozone, particle matter, sulfur dioxide, and acidity in rain) on the temples, in combination and comparison with the influence of other environmental factors (relative humidity, temperature, precipitation, moisture, sea salt, wind) and in this respect evaluates the potential protective effect of the shelters. The variation in air pollution weathering of limestone exposed outdoor in Malta was calculated by exposure–response functions from the ICP-materials programme and compared with measured values, and its contribution to the deterioration of the temples was evaluated. The difference between urban and rural locations in Malta, in the first year of atmospheric chemical weathering of limestone due to air pollution, was found to be about one micrometer loss of stone surface. This is probably less than the annual variations due to the influence of natural climatic factors, and small compared to the present annual variations in continental Europe. The deposition of sea salt and presence of salts on and in the limestone megaliths and changes in salt-crystallization events due to relative humidity fluctuations, inside and outside the shelters, will account for more of the variations in the first year of weathering of Globigerina limestone than variations in air pollution. The deterioration will also be related to temperature (including condensation events), wind parameters and rainfall, as well as ground water replenished from areas beyond the shelter.

Factors affecting severe weather threat index in urban areas of Turkey and Iran

BackgroundDistinguishing dynamic variations of the climate from the physical urban indicators is a challenge to assess the factors affecting weather severity. Hence, the time-series of the severe weather threat index (SWEAT) were considered in the four urban areas of Turkey and Iran to identify its affecting factors among the climatic variables and urban indicators in 2018. The SWEAT data were obtained from the upper-air sounding database of the University of Wyoming. The climatic variables were extracted from the Asia Pacific data research center (APDRC). The spatial statistics for urban expansion were collected from global human built-up and settlement extent (HBASE) data sets. A quantitative measuring of the Pearson correlation test was used to expose the relationships between dependent index (SWEAT) and independent variables (climatic and anthropogenic).ResultsResults revealed that the high and extreme severity classes of the weather condition in the Ankara, Istanbul, Mashhad, and Tehran are estimated as 7.7% (28 days), 15.3% (56 days), 1.1% (4 days), and 4.4% (16 days), respectively. The strongest values of the annual SWEAT index, exposing the unstable and severe weather conditions, were observed for Istanbul and Ankara urban regions. This result may be corresponding to the highest values of mean annual precipitation and relative humidity in addition to the largest values of urban expansion and sprawl index. The statistical correlation tests in annual scale confirmed the effective role of climatic elements of precipitation, relative humidity, and cloudiness (R from 0.94 to 0.99) and the urban expansion indicators (R from 0.86 to 0.91) in increasing annual severe weather index of SWEAT at above 85–95% of confidence level.ConclusionsThe correlations between the urban expansion indicators and outcome SWEAT index can be strengthened by some climatic elements (e.g., precipitation, humidity, and cloudiness), revealing the mediator and magnifier task. However, the mentioned correlations can be weakened by another climatic variable (i.e., air temperature), revealing a moderator and modifier task. Ultimately, investigation of the weather severity indices (e.g., SWEAT index) could be applied to identify the local and regional evidence of climate change in the urban areas.

Deviation of design air-conditioning load based on weather database of reference weather year and actual weather year

Abstract In Japan, in order to determine the capacity of air-conditioning equipment, designers usually use the weather database of Reference Weather Year (RWY) to obtain the design air-conditioning load by using software such as New HASP/ACLD and Building Energy Simulation Tool (BEST) that are often used in Japan. In recent years, with the global warming due to climate change, the weather database used to calculate air conditioning load also changes. Thus, in order to determine an appropriate capacity of air-conditioning equipment for energy conservation of buildings, the deviation of design air-conditioning load calculated using the weather database of RWY and Actual Weather Year (AWY) should be discussed. In this paper, New HASP/ACLD was used to calculate the building heat loads of eight major Japanese cities over 30 years (1981–2010) between RWY and AWY. The heat load at an exceedance probability of 2.5% is defined as the design air-conditioning load in this paper. Comparing the design air-conditioning load obtained from RWY and AWY, it is shown that it is not necessarily the most appropriate when using the RWY to calculate the design air-conditioning load, especially for heating load in winter. Additionally, it is also shown that the annual heating load time ratio has decreased and the annual cooling load time ratio has increased over the 30 years.

Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (2014–2017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M × E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M × E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.

Validation of SAM Modeling of Concentrated Solar Power Plants

The paper proposes the validation of the latest System Advisor Model (SAM) vs. the experimental data for concentrated solar power energy facilities. Both parabolic trough, and solar tower, are considered, with and without thermal energy storage. The 250 MW parabolic trough facilities of Genesis, Mojave, and Solana, and the 110 MW solar tower facility of Crescent Dunes, all in the United States South-West, are modeled. The computed monthly average capacity factors for the average weather year are compared with the experimental data measured since the start of the operation of the facilities. While much higher sampling frequencies are needed for proper validation, as monthly averaging dramatically filters out differences between experiments and simulations, computational results are relatively close to measured values for the parabolic trough, and very far from for solar tower systems. The thermal energy storage is also introducing additional inaccuracies. It is concluded that the code needs further development, especially for the solar field and receiver of the solar tower modules, and the thermal energy storage. Validation of models and sub-models vs. high-frequency data collected on existing facilities, for both energy production, power plant parameters, and weather conditions, is a necessary step before using the code for designing novel facilities.

The dynamics of livestock and its influencing factors on the Mongolian Plateau

Animal husbandry on the Mongolian Plateau is a dynamic human–natural system characterized by complex relationships with the grassland ecosystem. Sustainable use of the plateau's grassland depends on the ability of herders and decision makers to adjust the grazing pattern to adapt to extreme climate events and socioeconomic conditions. Unfortunately, the system's responses and adaptation to these changes are unclear. However, simultaneously studying the natural and human factors will improve the understanding of the adaptations of grazing and the grassland ecosystem. The goal of this comparative study was to reveal the relationship between livestock management and government policy, natural conditions, and socioeconomic conditions near the border between Mongolia and China's Inner Mongolia Autonomous Region. In this study, we used spatial and statistical data in the analysis of the dynamics of livestock. (1) China implemented a livestock reduction policy, whereas Mongolia increased livestock through privatization of livestock and the development of an economic market. In addition, Mongolian herders raised more goats. (2) The livestock population was significantly positively correlated with vegetation cover (represented by the normalized difference vegetation index) during years of severe weather in both Inner Mongolia and Mongolia. (3) The goat population was influenced significantly by the export amount in Mongolia, but by the return on investment in Inner Mongolia. (4) The livestock population was only weakly related to the rural population and the number of herder households. Our findings provide a basis for improving grassland and animal management strategies in both countries. Co-management of the grassland and livestock by herders and the government will be needed to balance herder livelihoods with the grassland ecosystem.

Can we phase-out all of them? Probabilistic assessments of security of electricity supply for the German case

Against the backdrop of expansions of intermittent renewable energy capacity, planned nuclear phase-outs, and current debates on the additional mothballing of coal-fired power plants in central Europe, there is a substantial increase in uncertainty regarding security of electricity supply. In this context, we present a probabilistic and scenario-based analysis of impacts on security of supply for Germany as a case-study in the medium-term perspective (years 2020, 2022, and 2023). For our analysis, we introduce the energy security assessment module of the JERICHO energy system model from RWTH Aachen University. Our model allows for an hourly probabilistic simulation of security of supply and accounts for stochastic characteristics of (non-)availability of conventional generators, renewable feed-in, electricity demand, and import potentials from central-European countries. To increase the robustness of results, we use a comprehensive dataset of temperature, wind speeds, solar radiation and water levels from weather years 1986 to 2015 as input for our simulations. Our results indicate that Germany is unlikely to maintain its quasi-absolute levels of security of supply in the near future with a mean Loss of Load Expectation (LoLE) of up to 2.6 h in scenario year 2023. However, this does not necessarily imply that future levels of security of supply are insufficient. There is need for further assessments on identifying threshold levels for security of supply, which are economically efficient and acceptable for society. Our results also indicate that the sensitivity of security of supply for meteorological changes increases with reductions of conventional power plants. Finally, we demonstrate that the dependency of security of supply on imports from neighboring countries increases substantially calling for increased international coordination of national energy policies.

Development of reference summer weather years for analysis of overheating risk in buildings

ABSTRACTOverheating in buildings arising from climatic extreme heat events has been identified as a health concern to vulnerable occupants. However, there have been very limited studies to generate suitable weather data to evaluate by simulation the overheating risk and its effect on the comfort and health of occupants. This paper develops a methodology to identify reference summer weather years (RSWY) for overheating risk analysis. The methodology includes generation of historical climate data, and development of a heat stress metric for the definition and characterization of heat events. The Standard Effective Temperature was selected among a short list of popular metrics, modified and named t-SET to account for transient heat events, activity levels of occupants, and thermoregulatory controls of sleeping subjects. The t-SET model predictions compared well with measured body temperatures of subjects undergoing multi-stage activities under hot conditions. The t-SET index was used to generate RSWY for selected Canadian cities.

UK annual weather summary 2019

UK annual weather summary 2019

Forecasting Long-Term Electricity Demand Time Series Using Artificial Neural Networks

Accurate forecast of electricity load are increasingly important. We present a method to forecast long-term weather-dependent hourly electricity load using artificial neural networks. The fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer are trained using historic data from 2006 to 2015. Input parameters comprise calendrical information, annual peak loads and weather data. The results are benchmarked against the method to forecast electric loads used in the current mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the common approach as used by entso-e shows an average error of 4.8% using peak load scaling. Further, we conduct forecasts for Germany, Sweden, Spain, and France for scenario year 2025 and assess parameter variations. Our approach can serve to increase prediction accuracy of future electricity loads.

Is building energy simulation based on TMY representative: A comparative simulation study on doe reference buildings in Toronto with typical year and historical year type weather files

The goal of this study is to determine whether the use of Typical Year (TY) type weather file is adequate for estimating heating and cooling load in Building Energy Simulation (BES). Two Typical Year (TY) Type weather files – Canadian Weather Year for Energy Calculation(CWEC) 1990s and CWEC 2016 are compared with Historical Year (HY) weather data Canadian Weather Energy and Engineering Datasets(CWEEDs) 1998 to 2014. Three stages of analysis are completed – 1) comparison of the cumulative distribution of Dry Bulb Temperature and Global Horizontal Irradiance; 2) analysis of extreme weather event occurrences through comparing temperature-based weather indices; 3) comparison of BES results from simulating DOE prototype buildings. Results show a hotter summer and warmer winter in CWEC 2016 indicated by a shift of median temperature in stage 1, increased hot weather characterizing indices and decreased cold weather characterizing indices in stage 2 when compared with CWEC 1990s. This translates to a lower heating energy use and higher cooling energy use in stage 3 with simulation using CWEC 2016. The total 17-year heating and cooling energy sum estimation using CWEC 2016 shows lower percentage difference for all buildings, ranging from 0 to -8% difference, where most buildings are -1% or -2% from the estimation using historical weather data. However, neither simulation using CWEC 1990s nor CWEC 2016 yield consistent accuracy when compared to the yearly Historical simulation results, since the occurrence of years with hotter/colder temperature shows a periodical cycle, some years have more hot weather events, whereas other years have more cold weather event. This study concludes that Typical Year simulation alone is not sufficient for Building Energy Simulation because 1) past weather data might not be a good indication of recent or future weather events due to changes in climate; 2) the update cycle of TY type weather files are infrequent and therefore most recent climate may not be included in the analysis. Future work should focus on the development of a commonly accepted practice in using multi-year simulation, as well as a commonly accepted format for future or stochastic weather file format.

Predicting the periodic risk of anthrax in livestock in Victoria, Australia, using meteorological data.

Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95-0.97) and OR 0.98 (95% CI 0.97-0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11-0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29-1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27-0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95-0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10days immediately before the case (OR 1.46 (95% CI 1.35-1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.