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.

Land Surface Phenology and Seasonality Using Cool Earthlight in Croplands of Eastern Africa and the Linkages to Crop Production

Abstract: Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003–2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land surface variables that blends data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (AMSR-E) from 2002 to 2011 with data from AMSR2 from 2012 to 2015 with a Chinese microwave radiometer to fill the gap. These time series were analyzed in terms of either cumulative precipitable water vapor-days (CVDs) or cumulative actual evapotranspiration-days (CETaDs), rather than as days of the year. Time series of the land surface variables displayed unimodal seasonality at study sites in Ethiopia and South Sudan, in contrast to bimodality at sites in Tanzania. Interannual moisture variability was at its highest at the beginning of the growing season affecting planting times of crops, while it was lowest at the time of peak moisture. Actual evapotranspiration (ETa) from the simple surface energy balance (SSEB) model was sensitive to track both unimodal and bimodal rainfall patterns. ETa as a function of CETaD was better fitted by a quadratic model (r2 > 0.8) than precipitable water vapor was by CVDs (r2 > 0.6). Moisture time to peak (MTP) for the land surface variables showed strong, logical correspondence among variables (r2 > 0.73). Land surface parameters responded to El Nino-Southern Oscillation and the Indian Ocean Dipole forcings. Area under the curve of the diel difference in vegetation optical depth showed correspondence to crop production and yield data collected by local offices, but not to the data reported at the national scale. A long-term seasonal Mann–Kendall rainfall trend showed a significant decrease for Ethiopia, while the decrement was not significant for Tanzania. While there is significant potential for passive microwave data to augment cropland status and food security monitoring efforts in the region, more research is needed before these data can be used in an operational environment.

Integrating neural network models with computational fluid dynamics (CFD) for site-specific wind condition

Abstract: Most building energy simulations tend to neglect microclimates in building and system design, concentrating instead on building and system efficiency. Energy simulations utilize various outdoor variables from weather data, typically from the average weather record of the nearest weather station that is located in an open field, near airports and parks. The weather data may not accurately represent the physical microclimate of the site, and may therefore reduce the accuracy of simulation results. For this reason, this paper investigates utilizing computational fluid dynamics (CFD) with neural network (NN) model to predict site-specific wind parameters for energy simulation. The CFD simulation is used to find selected samples of site-specific wind conditions. Findings from CFD simulation are used as training data for NN. A trained NN predicts site-specific hourly wind conditions for a typical year. The outcome of the site-specific wind condition from the neural network is used as wind condition input for the energy simulation. The results of energy simulation using typical weather station data and site-specific weather data are compared in this paper, in order to find the possibility of using site-specific weather condition by NN with CFD to yield more realistic and robust ES results.

An IoT-based Smart Garden with Weather Station System

Abstract: Internet of Things (IoT) consists of devices that connect to the internet and communicate with each other. It enables these devices to collect and exchange data with a consumer. This paper presents an IoT-based Smart Garden with Weather Station system, which can be used to monitor the growth of plant every day and predict the probability for raining. Why this IoT-based device is been created? Many people are interested in growing the plants are always forget on watering the plants. Hence, in this study, the device is equipped with a water pump, where it can be monitored and controlled by using a smartphone. In addition, the devices also consist of four main sensors, which are Barometric Pressure, DHT11 Temperature, and Humidity Sensor, Soil Moisture Sensor and Light intensity module sensor. The Soil and Light Intensity sensor used to measure the value by percentages. Besides, two actuators, which are the water pump and LED light can be used remotely or by using a button on the devices. The LED is purposely to replicate the sunlight and make the plant grow faster. This IoT-based Smart Garden with Weather Station System can record the data and send the result to user through the smartphone application named as Blynk apps. This research is beneficial, and the system can be easily managed by all users such as researcher or farmer, and children.