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.

Instability indices and forecasting thunderstorms: the case of 30 April 2009

Abstract: . In this paper, one meteorological case study for two Iranian airports are presented. Attempts have been made to study the predefined threshold amounts of some instability indices such as vertical velocity and relative humidity. Two important output variables from a numerical weather prediction model have been used to survey thunderstorms. The climatological state of thunder days in Iran has been determined to aid in choosing the airports for the case studies. The synoptic pattern, atmospheric thermodynamics and output from a numerical weather prediction model have been studied to evaluate the occurrence of storms and to verify the threshold instability indices that are based on Gordon and Albert (2000) and Miller (1972). Using data from the Statistics and Data Center of the Iran Meteorological Organization, 195 synoptic stations were used to study the climatological pattern of thunderstorm days in Iran during a 15-yr period (1991–2005). Synoptic weather maps and thermodynamic diagrams have been drawn using data from synoptic stations and radiosonde data. A 15-km resolution version of the WRF numerical model has been implemented for the Middle East region with the assistance of global data from University Corporation for Atmospheric Research (UCAR). The Tabriz airport weather station has been selected for further study due to its high frequency of thunderstorms (more than 35 thunderstorm days per year) and the existence of an upper air station. Despite the fact that storms occur less often at the Tehran weather station, the station has been chosen as the second case study site due to its large amount of air traffic. Using these two case studies (Tehran at 00:00 UTC, 31 April 2009 and Tabriz at 12:00 UTC, 31 April 2009), the results of this research show that the threshold amounts of 30 °C for KI, −2 °C for LI and −3 °C for SI suggests the occurrence and non-occurrence of thunderstorms at the Tehran and Tabriz stations, respectively. The WRF model output of vertical velocity and relative humidity are the two most important indices for examining storm occurrence, and they have a numerical threshold of 1 m s−1 and 80%, respectively. These results are comparable to other studies that have examined thunderstorm occurrence.

Gridded meteorological data as a resource for mechanistic macroecology in coastal environments.

Abstract: Gridded weather data were evaluated as sources of forcing variables for biophysical models of intertidal animal body temperature with model results obtained using local weather station data serving as the baseline of comparison. The objective of the study was to determine which gridded data are sufficient to capture observed patterns of thermal stress. Three coastal sites in western North America were included in this analysis: Boiler Bay, Oregon; Bodega Bay, California; and Pacific Grove, California. The gridded data with the highest spatial resolution, the 32-km North American Regional Reanalysis (NARR) and the 38-km Climate Forecasting System Reanalysis (CFSR), predicted daily maximum intertidal animal temperature most similarly to the local weather station data. Time step size was important for variables that change rapidly throughout the day, such as solar radiation. There were site-based differences in the ability of the model to predict daily maximum intertidal animal temperature, with the gridded ...

Cloud Based Weather Station using IoT Devices

Abstract: This paper proposes a smart system cloud based weather station. The system uses Raspberry Pi, for collecting and observing weather data. The storing and processing of the obtained weather data is done in cloud to predicting the effect of this weather change. The system is designed to effectively monitor the ambient weather conditions such as temperature, humidity, wind speed, pressure, and rainfall etc. The objective is to design a system which is low cost, requires less maintenance, and involved minimal manual intervention. The system is built using commodity hardware Raspberry Pi, various sensors and uses WiFi as a communication medium which makes the system consume very low power and low cost of building. Smaller Raspberry Pi Zero W boards are used to collect the sensor's data and send it to the base station Raspberry Pi 3 board. The Raspberry Pi 3 then further transmits the data over WiFi to the cloud database and this data is further used to train new Machine Learning model deployed in the cloud for prediction of the effect and to observe and study various weather patterns and trends. The users can access the weather data and insights remotely, and in real time through a web application that is built using the Django Framework, and is deployed in the cloud.

Evaluation of pan evaporation modeling with two different neural networks and weather station data

Abstract: This study evaluates neural networks models for estimating daily pan evaporation for inland and coastal stations in Republic of Korea. A multilayer perceptron neural networks model (MLP-NNM) and a cascade correlation neural networks model (CCNNM) are developed for local implementation. Five-input models (MLP 5 and CCNNM 5) are generally found to be the best for local implementation. The optimal neural networks models, including MLP 4, MLP 5, CCNNM 4, and CCNNM 5, perform well for homogeneous (cross-stations 1 and 2) and nonhomogeneous (cross-stations 3 and 4) weather stations. Statistical results of CCNNM are better than those of MLP-NNM during the test period for homogeneous and nonhomogeneous weather stations except for MLP 4 being better in BUS-DAE and POH-DAE, and MLP 5 being better in POH-DAE. Applying the conventional models for the test period, it is found that neural networks models perform better than the conventional models for local, homogeneous, and nonhomogeneous weather stations.

Wireless Sensor Network for Monitoring Soil Moisture and Weather Conditions

Abstract: . A wireless sensor network (WSN) was built and deployed in three fields to monitor soil moisture status and collect weather data for irrigation scheduling. The WSN consists of soil moisture sensors, weather sensors, wireless data loggers, and a wireless modem. Soil moisture sensors were installed at three depths below the ground surface in various locations across the fields. Weather sensors were mounted on a 3-m instrument tower. An antenna mount was designed and fabricated for use in the WSN. When field equipment such as a fertilizer or chemical applicator impacted the mount, the mount was capable of protecting the antenna from damage by the equipment. In the WSN, received radio signal strength of Em50R data logger decreased as the distance from the data logger to the receiver increased. It also decreased as the distance between the top of the plant canopy and the logger’s antenna above the plant canopy decreased. The antenna of the Em50R logger required replacement above the plant canopy for effective data communication. The Em50G data logger was capable of transferring data as its antenna was inside the plant canopy. Using the WSN system, soil moisture and weather conditions including precipitation, solar radiation, wind speed, and humidity were measured every minute and the hourly averages were reported and stored at 1-h interval. The soil moisture data and weather data were automatically and wirelessly transmitted to the internet making the data available online. Data collected by the WSN have been used in irrigation scheduling research in cotton, corn and soybean crops.