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.

Estimation of non-catastrophic weather impacts for retail industry

Abstract: Purpose – Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. The purpose of this paper is to develop a model and to analyze the relationship between weather and retail shopping behavior (i.e. store traffic and sales). Design/methodology/approach – The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represen...

Drivers of Firn Density on the Greenland Ice Sheet Revealed by Weather Station Observations and Modeling

Abstract: Recent Arctic atmospheric warming induces more frequent surface melt in the accumulation area of the Greenland ice sheet. This increased melting modifies the near-surface firn structure and density and may reduce the firn’s capacity to retain meltwater. Yet few long-term observational records are available to determine the evolution and drivers of firn density. In this study, we compile and gap-fill Greenland Climate Network (GC-Net) automatic weather station data from Crawford Point, Dye-2, NASA-SE, and Summit between 1998 and 2015. These records then force a coupled surface energy balance and firn evolution model. We find at all sites except Summit that increasing summer turbulent heat fluxes to the surface are compensated by decreasing net radiative fluxes. After evaluating the model against firn cores, we find that, starting from 2006, the density of the top 20 m of firn at Dye-2 increased by 11%, decreasing the pore volume by 18%. Crawford Point and Summit show stable near-surface firn density over 1998–2010 and 2000–2015 respectively, while we calculate a 4% decrease of firn density at NASA-SE over 1998–2015. For each year, the model identifies the drivers of density change in the top 20-m firn and quantifies their contributions. The key driver, snowfall, explains alone 72 to 92% of the variance in day-to-day change in firn density while melt explains from 7 to 33%. Our result indicates that correct estimates of the magnitude and variability of precipitation are necessary to interpret or simulate the evolution of the firn. Plain Language Summary Arctic warming has led tomore intensemelt on the Greenland ice sheet. In recent decades this melt moved upglacier and started to alter the structure of perennial snow, or firn, in areas where melt was rarely recorded. In this study, we process 12–17 years of observations from four weather stations located in the vast high-elevation area of the ice sheet. From these climate records, we calculate how much melt occurred each summer and why (e.g., warm air or sunlight absorption). We found that heat transfer from the air to the surface has become more intense but is compensated by a brightening of the surface, causing less sunlight to be absorbed and used for melting. We use a computer model that simulates firn evolution and shows a good match with independent observations of the firn density. Our simulations identify increasing firn density at a first site, stable density at two sites, and decreasing firn density at the last one. Day-to-day and year-to-year changes in the density of the top 20m of firn were mostly due to the snowfall variability followed by surface melt. This work underlines the importance of accurate precipitation estimates in order to understand firn evolution.

Short-term local weather forecast using dense weather station by deep neural network

Abstract: This paper proposes a very-short-term, i.e., less than 1-hour, local weather forecast method. In general, a short-term weather forecast within 3 hours is difficult due to lack of surface weather data and limitations of computation resources. However, such a short-term prediction is getting more and more anticipated in several industrial situations such as transportation, retailing business, agriculture, and energy management as well as our daily life. To keep up with this huge demands, services based on very-short-term weather forecast began to be provided. Data sources for such kind of forecast are private company-owned surface sensor networks in addition to nation-owned surface sensors. Some surface weather sensors of private companies are more densely distributed than nation-owned sensors. We call these surface weather sensor network as dense weather stations. Among them, for example, POTEKA sensors are located in roughly every 2 to 3 km and provide observed data every minute through mobile network. Those dense sensors are spreading its locations over the world. However, a data mining technique for such a device has not been well developed. In this paper, we propose a deep learning architecture specifically developed for the short-term weather forecasting based on the dense weather station device. Our proposal consists of two folds: point prediction model and tensor prediction model. The point prediction model is useful for forecasting exactly on the location of the dense weather station. The tensor prediction model interpolate the prediction of the point prediction model to cover whole range of locations around the interested area. It is shown that our model outperforms the existing state-of-the-art methods such as XGBoost and support vector machines using a large real observed data.

Present weather-sensor tests for measuring drifting snow

Abstract: In Antarctica, blowing snow accounts for a major component of the surface mass balance near the coast. Measurements of precipitation and blowing snow are scarce, and therefore collected data would allow testing of numerical models of mass flux over this region. A present weather station (PWS), Biral VPF730, was set up on the coast at Cap Prud'homme station, 5 km from Dumont d'Urville (DDU), principally to quantify precipitation. Since we expected to be able to determine blowing-snow fluxes from the PWS data, we tested this device first on our experimental site, the Lac Blanc pass. An empirical calibration was made with a snow particle counter. Although the physics of the phenomenon was not well captured, the flux outputs were better than those from FlowCapts. The first data from Antarctica were reanalyzed. The new calibration seems to be accurate for estimating the high blowingsnow flux with an interrogation of the precipitation effects.

Crowdsourcing of weather observations at national meteorological and hydrological services in Europe

Abstract: . National Meteorological and Hydrological Services (NMHSs) increase their efforts to deliver impact-based weather forecasts and warnings. At the same time, a desired increase in cost-efficiency prompts these services to automatize their weather station networks and to reduce the number of human observers, which leads to a lack of “ground truth” information about weather phenomena and their impact. A possible alternative is to encourage the general public to submit weather observations, which may include crucial information especially in high-impact situations. We wish to provide an overview of the state and properties of existing collaborations between NMHSs and voluntary weather observers or storm spotters across Europe. For that purpose, we performed a survey among 30 European NMHSs, from which 22 NMHSs returned our questionnaire. This study summarizes the most important findings and evaluates the use of “crowdsourced” information. 86 % of the surveyed NMHSs utilize information provided by the general public, 50 % have established official collaborations with spotter groups, and 18 % have formalized them. The observations are most commonly used for a real-time improvement of severe weather warnings, their verification, and an establishment of a climatology of severe weather events. The importance of these volunteered weather and impact observations has strongly risen over the past decade. We expect that this trend will continue and that storm spotters will become an essential part in severe weather warning, like they have been for decades in the United States of America. A rising number of incoming reports implies that quality management will become an increasing issue, and we finally discuss an idea how to handle this challenge.