The significance of local meteorological stations in research planning

Strong, downslope winds commonly disrupt traffic on the Dempster Highway near the Yukon-NWT border; road closures occur on 25% of winter days. Wind characteristics from regional and local weather stations were analysed and associated with synoptic conditions. Downslope winds form due to a mountain ridge along the territorial border. The ridge is a boundary for wind characteristics: easterlies prevail in the Yukon, westerlies in the NWT, and both in Wright Pass where the highway crosses the border. The ECCC Rock River weather station has recorded 672 easterly windstorms since 2006 and a maximum wind speed of 137 km/h. Just 12% of the windstorms occurred in summer, with the remainder equally distributed during the other seasons. Easterly windstorms were commonly associated with extratropical cyclones in the Gulf of Alaska and north-south pressure gradients, and westerly windstorms with south-north gradients and cyclones over the Beaufort Sea.

Snow meltwater (snowmelt) that seeps into the subsurface is a major factor contributing to the development of landslides during the spring in mountainous areas of the Rocky Mountain region. An examination of historical temperature data in relation to spring season landslide occurrences reveals an association between the landslide events and intervals of rising temperatures that accelerate the production of snow meltwater. Historical climatic data recorded at local weather stations located near the landslide sites are used to show the association and to identify a temperature threshold that may be useful for forecasting the onset of spring season landslides. Historical daily temperature maximums and minimums for unmonitored landslide sites are estimated by applying an elevation correction factor to historical temperature data from nearby weather stations. The proposed temperature threshold (a 6-day moving average of daily maximum temperature of 58 F) is defined by the number and temporal distribution of snowmelt related landslide events. The results of the study suggest that real-time temperature data recorded at weather stations throughout the Rocky Mountain region is potentially a valuable source of information that may be useful for forecasting the onset of spring season landslides.

Agriculture sector utilizes almost 80% of the available fresh water for irrigation. Conventional irrigation practices are very inefficient and add to fresh water scarcity drastically. Agriculture 4.0 is the technology that can be adopted for precision irrigation. Evapotranspiration, soil and crop transpiration are the major factors that decides the demand of irrigation. To estimate the evapotranspiration, soil and crop transpiration local weather condition needs to be gathered, for which local weather station are required. The development of Internet of things (IoT)-based weather station wireless senor network node (WSN) is discussed in the present work. The weather station node developed gathers the air temperature, humidity, wind conditions and provides the data to the IoT cloud server. In the end, challenges in front of WSN Agriculture 4.0 are discussed.KeywordsAir temperatureCrop transpirationEvapotranspirationInternet of thingsRelative humiditySoil transpirationWeather stationWind directionWind speedWSN

<p>Across the globe, exposure to environmental heat stress may impose increased health and safety hazards to active populations such as athletes and workers. Monitoring heat stress is a key component of a well-designed heat mitigation policy. Yet, the cost of several hand-held heat stress sensors may pose a financial barrier for use in many circumstances. Numerous areas, however, have existing networks of weather stations that could potentially be incorporated into monitoring heat stress. Currently, the Japanese Ministry of the Environment has set up a network of weather stations across the city to monitor environmental conditions in preparation of the 2021 Tokyo Olympic and Paralympic games. Our research question asks how representative are the values recorded at local weather stations (often located over a natural surface) to playing field conditions with various surfaces and microclimate conditions. In the U.S. the WeatherSTEM network has over 600 stations scattered across the country and measures a suite of variables relevant to heat stress including air temperature, humidity, wind speed, solar radiation and models the wet bulb globe temperature (WBGT) values. This study will compare measurements from a local WeatherSTEM station with on-site measurements taken over three different playing surfaces (grass, synthetic turf, and hardcourt tennis) in a humid subtropical climate in Athens, Georgia. U.S. It will also compare WBGT values computed using different models. Our results may provide insight not only for sports but also for the workplace which take place over various surface types and environments.</p>

Urban microclimate prediction based on weather station data and artificial neural network

Sheep are vulnerable to hypothermia shortly after birth and shearing. Since the 1970’s sheep weather alerts have been reported at a regional scale by the media up to 24 hours prior to a chill event. The SMS and email weather warning system was designed as an enhanced service to provide sheep producers with advanced warnings of forth-coming chill events, based on local weather forecasts, with personalized chill warnings delivered by SMS and email. A trial was conducted with 30 sheep producers who selected one or more local weather stations and a low, medium or high sensitivity threshold to control the frequency at which messages were sent. Sensitivity thresholds were calculated for each weather station from historical data. Numerical forecast data were sourced from the Bureau of Meteorology, and an email and SMS sent each morning whenever forecast chill exceeded the warning threshold within the 7-day forecast period. Participants were interviewed by telephone after a 2-month trial. The alerts were found to be clear and reasonably accurate, but produced an unexpected high number of false warnings at some sites. The SMS format was well received, and farmers were generally happy to continue the trial. False warnings were attributed to over-prediction of wind speeds at some sites relative to on-ground weather stations, most of which were in northern Victoria.

Weather status measurement and prediction have become an important part of human life because of their many applications in most walks of life. The conventional methods of operating such standard weather stations need expert workers, which adds extra cost to running such stations. In this paper, a local wireless weather station suitable for installation in a university campus or other similar establishments has been designed and implemented based on an Arduino Uno microcontroller to measure needed environmental parameters such as temperature, rainfall, humidity, wind speed, and wind direction. These measured parameters are sent to a web server that broadcasts the weather status over a wireless local area network (Wi-Fi) to the clients as a webpage. The main aim of this work is to design and implement a low-cost wireless weather station that does not exceed 100 USD and compare the measurement parameters with a standard weather station of the type (GRWS100). The practically recorded results of such parameters over one week from this designed prototype station were in good agreement with the standard weather measurements recorded via the typically installed station over the same period.

The article considers the problem of water loss due to the evaporation from the surface of ponds and reservoirs under conditions of intense anthropogenic load and climate changes, focusing on the Ingulets river basin as an example of the one of the most regulated river systems in the steppe zone of Ukraine. Taking into account the growing shortage of water resources, especially after the explosion of the Kakhovka HPP, the relevance of studying evaporation from water surfaces of artificial reservoirs in the southern regions is increasing. Estimating water losses due to the evaporation from the surface of ponds and reservoirs in the Ingulets River basin is an important aspect of water resources management in the region. Available data on the areas of artificial water bodies and average evaporation rates were used to calculate total water losses. Based on a spatial analysis of over 1,200 water bodies in the Inhulets river basin, including an assessment of the areas and volumes of reservoirs and ponds, a quantitative assessment of water evaporation from their surface was carried out for 1961-1990 and 1991-2023. The research methodology is based on the processing of long term meteorological data from local weather stations, as well as the use of satellite and archival information from available sites. Climatic conditions in the studied region demonstrate a steady warming trend: over the past 30 years, the average annual temperature in the Ingulets river basin has increased by an average of 1,3°C with slight decreases in precipitation over the studied period. It has been found that under modern conditions, evaporation can exceed the inflow from atmospheric precipitation, which turns artificial reservoirs into active factors in reducing the water balance of the Ingulets river basin. In 1991-2023, evaporation rates from the surface of water bodies increased by 13,7% (107 mm) for the Znamyanka weather station, by 7,6% (68 mm) for the Komisarivka weather station, and by 9,1% (88 mm) for the Kryvyi Rih weather station. Particular attention is paid to assessing the water management efficiency of the functioning of such facilities in the context of environmental safety, hydrological stability, and adaptation to the climate changes. The obtained results are important for the development of regional water resources management strategies, improvement of methods for calculating water losses, and ecological and economic optimization of the water fund structure in small and medium-sized river basins.

Disease forecasts from regional or remotely sensed meteorological data free growers from infield weather data monitoring and may improve disease forecast implementation. This study was initiated to validate potato early blight forecast models in Colorado and to determine the influence of sources of meteorological data on forecast accuracy. Hourly temperatures were recorded by Campbell Scientific CR-10, Pessl Instruments μMetos Model MCR300, and Spectrum Technologies Model 450 WatchDog weather stations and data loggers within potato fields, field-specific temperature estimations generated by mPOWER3/EMERGE from off-site weather stations, and regional COAGMET CR-10 weather stations. Mean hourly temperature deviations between mPOWER3/EMERGE or in-field stations and COAGMET varied from 0.93°C greater to 1.11°C less than COAGMET observations. Initial appearance of early blight lesions was predicted using a 300 physiological day threshold in commercial fields in each year from 1998 to 2001 and in experimental plots in each year from 1997 to 2001 as determined by COAGMET meteorological observations. All sources of meteorological data generated early blight forecasts within 6 days of each other across all locations and years. COAGMET weather stations should free potato growers and integrated pest management personnel from collecting in-field microclimatic data and speed the implementation of disease forecasting.

In China, meteorological forecasting relies on meteorological data obtained from regional and national stations. However, there were discrepancies between the data collected from the meteorological station at the passion fruit growing base and the data from regional and national stations. Consequently, the high and low temperature disaster indicators determined by the meteorological station at the passion fruit growing base cannot be applied to meteorological forecasting. To address this issue and facilitate the monitoring and early warning of high and low temperature disasters in passion fruit cultivation in Fujian, China, we used multi-source hourly temperature data (including the data from meteorological observation stations in passion fruit growing bases, the nearest regional stations, and national surface conventional meteorological observation stations) in three cities in southwestern Fujian (Longyan, Sanming, and Zhangzhou) spanning the years 2020 to 2022. By employing comprehensive statistical analysis methods (0.5 interval division and Cumulative frequency), we identified that passion fruit in southwestern Fujian was susceptible to high temperature disasters during the blooming-fruiting period, as well as low temperature disasters during the sprouting period. Consequently, we developed high and low temperature disaster indicators based on data from regional and national stations for different phenological periods of passion fruit in this region.

Core Ideas Increased bias in ET due to various data sources where generally utilized standard weather data poses similar biases. Increased understanding of the complex relationship among the various weather input and vegetation indices. Results showed local weather station data closely represented site condition indicating the difficulties and challenges in ET modeling using other commonly available and widely adopted data sources. ABSTRACTComplex interactions among meteorological data and vegetation indices are incompletely understood in relation to evapotranspiration (ET) calculations for larger spatial domains with higher spatial and temporal resolution. Objectives of this study were to evaluate contributions of inputs to uncertainty in ET calculations and to enhance understanding of interactions among weather data, vegetative indices, and resistances utilized in biophysical ET model. We evaluated individual and combined effects of weather variables and vegetation indices using BAITSSS (Backward‐Averaged Iterative Two‐Source Surface temperature and energy balance Solution). Local weather station (LWS) data at a lysimeter site were obtained for irrigated corn (Zea mays L.) during the growing season (May to September, 2016) at Bushland, Texas. Gridded meteorological data were obtained from North American Land Data Assimilation System (NLDAS) (∼ 12.5 km) and remotely‐sensed vegetation indices (Landsat 30 m). Standard weather station (SWS) data were obtained from a grass reference near lysimeter site. The r2 and RMSE of ET simulated using LWS data and measured vegetation indices were 0.90 and 0.85 mm for daily ET, and 0.90 and 0.10 mm, for hourly ET, compared to lysimeter ET (less than 4% cumulative error). However, r2 and RMSE were 0.74 and 1.64 mm for daily ET, and 0.81 and 0.14 mm for hourly ET using gridded data, with positive bias (∼ 25% from NLDAS data). Simulated ET from SWS data exhibited similar behavior to gridded data with increased ET up to 21%. Results quantify difficulties in ET modeling using commonly available and widely adopted data sources.

Water is a very limited resource in the Sonoran Desert region of Caborca, Sonora, Mexico. For an efficient use of irrigation water, a method of calculating water requirements of the crops is needed. Potential evapotranspiration (Eto) value obtained with the Penman-Monteith model from a regional weather station was not dependable, since some parameters, such as sensible heat flux in the soil, are estimated from a fixed rate with net radiation (Rn), also an estimated value. The weather station did not have a sensor for heat flux in its network. Studies in northwestern Mexico have indicated that it is feasible to adapt the use of the Makkink model, because a single measurement of solar radiation and temperature would be required. We compared the daily Makkink Eto against the Class A pan method (control) Eto during 75 days and found a value of 0.81 mm/day less with the Makkink model. To fit the Makkink model to regional conditions, we ran the Makkink model varying the value of C constant (from 0.5 to 0.95), and found that a value of C = 0.87 substituted for C = 0.65 (original value) has an daily average difference of 0.09 mm/day less with respect to the control. This could be because there are few clouds in the region, and a greater proportion of global radiation arrives at the surface from the earth or the crops in form of net radiation.

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

<strong class="journal-contentHeaderColor">Abstract.</strong> To address ambitious goals of carbon neutrality set at national and city scales, a number of atmospheric networks have been deployed to monitor greenhouse gas (GHG) concentrations in and around cities. To convert these measurements into estimates of emissions from cities, atmospheric models are used to simulate the transport of various tracer gases and help interpret these measurements. We set up a modelling framework using the Weather Research and Forecasting (WRF) model applied at a high spatial resolution (up to 400 m) to simulate the atmospheric transport of GHGs and interpret the observations provided by the Munich Urban Carbon Column Network (MUCCnet). Building on previous analyses using similar measurements performed within a campaign for the city of Berlin and its surroundings (Zhao et al., 2019), our modelling framework has been improved regarding the initialization of tagged tracers, model settings, and input data. To assess the model performance, we validate the modelled output against two local weather stations and observed column GHG concentrations provided by MUCCnet from 1 to 30 August 2018. The modelled wind matches well with the measurements from the weather stations, with wind speeds slightly overestimated. The measured slant column concentrations of GHGs and their variability are generally reproduced by the model, with a bias in CO₂ of around 3.7 ppm that can be attributed to the initial and boundary conditions used. The differential column method (DCM) has been applied to cancel out the influence from the background concentrations of CO₂. We optimize its application by selecting suitable days on which the assumption of the DCM holds true: a relatively uniform air mass travels over the city, passing from an upwind site to a downwind site. In particular, the Stochastic Time-Inverted Lagrangian Transport model (STILT) is used here and driven by our WRF modelled meteorological fields to obtain footprints (i.e., the potential areas of influence for signals observed at specific points), further used for interpreting measurement results. Combining these footprints with knowledge of local emission sources, we find evidence of CH₄ sources near Munich that are missing or underestimated in the emission inventory used. This demonstrates the potential of this data-model framework to constrain local sources and improve emission inventories.

To test fusion effects of LAPS( Local Analysis and Prediction System) and STMAS( Space Time Multiscale Analysis System) system at different spatial scales,different resolution conditions on the surface temperature,LAPS and STMAS fusion test were performed in Beijing,North China and the Country 3 different areas,using the regional automatic weather station observations and GFS( Global Forecast System) background information on 1,5,10 and 20 km four kinds of grid resolution,the differences of LAPS and STMAS fusion effect was analyzed from algorithm angle,and a year of hourly fusion analysis results by LAPS and STMAS was statistical analyzed using the national automatic station data. The results showed that: LAPS and STMAS both have good performance in the observation-intensive areas,STMAS can resolve the small features in the observations,and the fusion results by STMAS are more closer to the actual observation and that by LAPS relatively smooth obvious,and possibly losing observational information; the confusion of the longwave and shortwave information in data sparse areas can be avoided because of the multi-scale analysis of STMAS that the background field can be corrected more efficiently using observations; in addition,the irrational nature of the terrain height adjustment has affect the fusion effect of LAPS and STMAS in the mountains.