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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.


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Journal ArticleDOI
TL;DR: In this article, the authors compared two linear regression methods for replacing data that was out of range or missing using the nearest climate stations using three years of historical data and the current climate year to estimate linear regression coefficients.
Abstract: Automated weather collection systems are replacing climate instruments read by a person. However, data-loggers fail and data has to be estimated from adjacent climate stations. This article compares two linear regression methods for replacing data that was out of range or missing using the nearest climate stations. The first method uses three years of historical data and the second method uses the current climate year to estimate linear regression coefficients. The missing data in most cases can be estimated by using the current climate year and a good data block before the bad data. It is not necessary to wait to collect good data after the bad data block to estimate the linear regression coefficients used to replace the missing data based on good data from a nearby climate station.

8 citations

Journal ArticleDOI
TL;DR: This study investigated temperature measurement errors introduced when sampling interval was increased from 15 to 60 min, and when actual in-canopy conditions were represented by temperature measurements collected by standard-placement sensors in each of three crops and assessed the impact of these errors on outcomes of decision aids for grass stem rust as well as grape and hops powdery mildews.
Abstract: Many plant disease epidemic models, and the disease management decision aids developed from them, are created based on temperature or other weather conditions measured in or above the crop canopy at intervals of 15 or 30 min Disease management decision aids, however, commonly are implemented based on hourly weather measurements made from sensors sited at a standard placement of 15 m above the ground or are estimated from off-site weather measurements We investigated temperature measurement errors introduced when sampling interval was increased from 15 to 60 min, and when actual in-canopy conditions were represented by temperature measurements collected by standard-placement sensors (15 m above the ground, outside the canopy) in each of three crops (grass seed, grape, and hops) and assessed the impact of these errors on outcomes of decision aids for grass stem rust as well as grape and hops powdery mildews Decreasing time resolution from 15 to 60 min resulted in statistically significant underestimates of daily maximum temperatures and overestimates of daily minimum temperatures that averaged 02 to 04°C Sensor location (in-canopy versus standard-placement) also had a statistically significant effect on measured temperature, and this effect was significantly less in grape or hops than in the grass seed crop Effects of these temperature errors on performance of disease management decision aids were affected by magnitude of the errors as well as the type of decision aid The grape and hops powdery mildew decision aids used rule-based indices, and the relatively small (±08°C) differences in temperature observed between in-canopy and standard placement sensors in these crops resulted in differences in rule outcomes when actual in-canopy temperatures were near a threshold for declaring that a rule had been met However, there were only minor differences in the management decision (ie, fungicide application interval) The decision aid for grass stem rust was a simulation model, for which temperature recording errors associated with location of the weather station resulted in incremental (not threshold) effects on the model of pathogen growth and plant infection probability Simple algorithms were devised to correct the recorded temperatures or the computed infection probability to produce outcomes similar to those resulting from in-canopy temperature measurements This study illustrates an example of evaluating (and, if necessary, correcting) temperature measurement errors from weather station sensors not located within the crop canopy, and provides an estimate of uncertainty in temperature measurements associated with location and sampling interval of weather station sensors

8 citations

Journal ArticleDOI
TL;DR: A new method for creating realistic climate forcing for manipulation experiments and applying it to the UHasselt Ecotron experiment becomes able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability.
Abstract: Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change.

8 citations

Proceedings ArticleDOI
01 Sep 2012
TL;DR: In this paper, the authors investigated the effect of external factors on top-oil temperature by looking into the weather, i.e., wind velocity, on a 63MVA-ONAF 55/140 kV transformer unit operated in ONAN cooling mode.
Abstract: Standard estimation of top-oil temperature uses a thermal model related to load changes and variation of ambient temperature Attempts have been done to improve the accuracy of top-oil temperature calculations by introducing internal properties into the model ie oil viscosity and winding resistance The interest of this paper is to investigate the effect of external factors on top-oil temperature by looking into the weather, ie wind velocity The results are compared with measurements on a 63MVA-ONAF 55/140 kV transformer unit, which is operated in ONAN cooling mode The unit is located in subarctic climate, and it is equipped with a monitoring system and a weather station

8 citations

Journal ArticleDOI
TL;DR: A fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photvoltaic systems installed within the same region.
Abstract: In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202347
202293
2021124
2020123
2019131
2018131