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.

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Abstract: This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Abstract: Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

A containerized service for clustering and categorization of weather records in the cloud

Abstract: This paper presents a containerized service for clustering and categorization of weather records in the cloud. This service considers a scheme of microservices and containers for organizations and end-users to manage/process weather records from the acquisition, passing through the prepossessing and processing stages, to the exhibition of results. In this service, a specialized crawler acquires records that are delivered to a microservice of distributed categorization of weather records, which performs clustering of acquired data (the temperature and precipitation) by spatiotemporal parameters. The clusters found are exhibited in a map by a geoportal where statistic microservice also produce results regression graphs on-the-fly. To evaluate the feasibility of this service, a case study based on 33 years of daily records captured by the Mexican weather station network (EMAS-CONAGUA) has been conducted. Lessons learned in this study about the performance of record acquisition, clustering processing, and mapping exhibition are described in this paper. Examples of utilization of this service revealed that end-users can analyze weather parameters in an efficient, flexible and automatic manner.

Development of a system for collecting and processing sky images and meteorological data used for weather prediction

Abstract: Weather prediction is a crucial element for power management in photovoltaic power plants (PVPP). In this paper, we propose a novel system for collecting essential data used for local short-term weather prediction. Image data consists of all-sky ground-based images obtained by an all-sky camera system with a fish-eye lens. Our proposed weather station collects meteorological data into database. The data include air temperature, humidity, wind speed, relative pressure, and spectrum of solar radiation. First, the whole setup for obtaining all-sky images is described, and setup for weather station is proposed. Then, our all-sky image database is characterized. Finally, to test sky images an experiment was performed to determine sky condition (clear sky, partly cloudy, mostly cloudy, overcast) with the use of a deep convolutional neural network (CNN). The accuracy of this method reached 97,80%.

Weather derivatives: managing risk with market-based instruments

Abstract: Accurate pricing of weather derivatives is critically dependent upon correct specification of the underlying weather process. We test among six likely alternative processes using maximum likelihood methods and data from the Fresno, CA weather station. Using these data, we find that the best process is a mean-reverting geometric Brownian process with discrete jumps and ARCH errors. We describe a pricing model for weather derivatives based on such a process.