Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.

Daily Weather and Tree Growth at the Tropical Treeline of North America

Abstract: We present here the 2001–2004 results of observational field studies aimed at quantifying tropical timberline climate and radial increment of Pinus hartwegii Lindl. trees on Nevado de Colima, in the middle of the North American Monsoon region. An automated weather station was installed at 3760 m a.s.l., 19°34.778′N latitude, 103°37.180′W longitude, within a forest where multi-century tree-ring records had been previously developed. At the same time, automated electronic sensors for recording tree growth at 30-min intervals were set up at two sites within a 1-km radius from the weather station. Meteorological observations recorded every 30 min were summarized on a daily basis. Time-series patterns are reported for atmospheric pressure, precipitation, incoming solar radiation, air and soil temperature, relative humidity, soil moisture, and wind speed and direction. Of particular interest is the sudden decrease in air temperature after the onset of the monsoon season, which determines very high rela...

Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation

Abstract: Climate is one of the single most important factors affecting watershed ecosystems and water resources. The effect of climate variability and change has been studied extensively in some places; in many places, however, assessments are hampered by limited availability of long-term continuous climate data. Weather generators provide a means of synthesizing long-term climate data that can then be used in natural resource assessments. Given their potential, there is the need to evaluate the performance of the generators; in this study, three commonly used weather generators—CLImate GENerator (CLIGEN), Long Ashton Research Station Weather Generator (LARS-WG), and Weather Generators (WeaGETS) were compared with regard to their ability to capture the essential statistical characteristics of observed data (distribution, occurrence of wet and dry spells, number of snow days, growing season temperatures, and growing degree days). The study was based on observed 1966–2015 weather station data from the Western Lake Erie Basin (WLEB), from which 50 different realizations were generated, each spanning 50 years. Both CLIGEN and LARS-WG performed fairly well with respect to representing the statistical characteristics of observed precipitation and minimum and maximum temperatures, although CLIGEN tended to overestimate values at the extremes. This generator also overestimated dry sequences by 18%–30% and snow-day counts by 12%–19% when considered over the entire WLEB. It (CLIGEN) was, however, well able to simulate parameters specific to crop growth such as growing degree days and had an added advantage over the other generators in that it simulates a larger number of weather variables. LARS-WG overestimated wet sequence counts across the basin by 15%–38%. In addition, the optimal growth period simulated by LARS-WG also exceeded that obtained from observed data by 16%–29% basin-wide. Preliminary results with WeaGETS indicated that additional evaluation is needed to better define its parameters. Results provided insights into the suitability of both CLIGEN and LARS-WG for use with water resource applications.

Forecasting wildlife die-offs from extreme heat events

Abstract: Extreme heat events pose increasing challenges to biodiversity conservation worldwide, yet our ability to predict the time, place and magnitude of their impacts on wildlife is limited. Extreme heat events in Australia are known to kill thousands of flying-foxes (Pteropus spp.), and such die-offs are expected to become more frequent and widespread in the future under anthropogenic climate change. There is a growing need for predicting when and where such heat-related die-offs would occur, to facilitate short-term wildlife management and conservation actions. In this study, we used gridded hourly air temperature forecasts [Australian Community Climate and Earth-System Simulator (ACCESS-R) Numerical Weather Prediction (NWP) model] from the Australian Bureau of Meteorology to predict flying-fox heat-related mortality based on an empirically determined threshold of 42.0°C. We tested the accuracy and precision of this model using a twofold evaluation of the ACCESS-R NWP forecast air temperature during a recorded extreme heat event with in situ air temperature measurements and interpolated weather station data. While our results showed a slight discrepancy between the modelled and measured air temperatures, there was no significant difference in the forecast's accuracy to predict die-offs during an extreme heat event and the overall summer period. We evaluated the accuracy of mortality predictions based on different air temperature thresholds (38.0, 40.0, 42.0 and 44.0°C). Our results revealed a significant probability of flying-fox mortality occurrence when forecast air temperature was ≥42.0°C, while the 24- and 48-h forecasts accurately predicted 77 and 73% of the die-offs, respectively. Thus, the use of 42.0°C forecast air temperature from the ACCESS-R NWP model can predict flying-fox mortality reliably at the landscape scale. In principle, the forecaster can be used for any species with known thermal tolerance data and is therefore a promising new tool for prioritizing adaptation actions that aim to conserve biodiversity in the face of climate change.

IoT Based Low-cost Weather Station and Monitoring System for Precision Agriculture in India

Abstract: In recent times it is seen that the climatic and weather conditions not only in India but also in other countries have become uncertain and unpredictable, which may have devastating effects on the agriculture production. India being an agricultural country, most of the farmers largely rely on monsoons and agricultural production is weather dependent. The environmental factors like temperature, humidity, moisture, precipitation and many other parameters keep on changing rapidly and unpredictably. This unpredictable nature, variability of climatic or weather conditions makes the life of farmers quite miserable as they are unable to take proper decisions at the right time. Thus, it is the need of the hour to have a real-time, local weather station which would keep the farmers informed well in advance about the prevailing weather conditions so that they can take appropriate decisions at the right time and save their crops from loss. Precision Agriculture (PA) is an art of using the latest available technologies in the agriculture domain so as to make traditional agriculture more profitable and sustainable while reducing the wastage of resources. The penetration of internet into India is very deep and very fast, especially due to the Jio mania by Reliance Jio Infocomm Limited last year, high speed internet is now possible even in rural areas. This paper proposes a IoT based real-time local weather station for PA, that would provide farmers a means of automizing their agricultural practices (irrigation, fertilization, harvesting) at the right time. The proposed system would also aid the farmers to carry out the agricultural tasks on real-time bases, which in turn helps them to use the agricultural resources in efficient way and at the time when needed by the crops. The proposed weather system is a small step towards the development of PA system considering the Indian scenarios.