A middleware platform for the validation and utilization of short-term weather forecast data for office buildings

2017 ◽  
Vol 149 ◽  
pp. 192-203 ◽  
Author(s):  
Junghun Lee ◽  
Sungjin Lee ◽  
Jonghun Kim ◽  
Doosam Song ◽  
Hakgeun Jeong
2019 ◽  
Vol 213 ◽  
pp. 714-723 ◽  
Author(s):  
Jingjing Cao ◽  
Junwei Tan ◽  
Yuanlai Cui ◽  
Yufeng Luo

1969 ◽  
Vol 50 (12) ◽  
pp. 947-956
Author(s):  
Carl W. Kreitzberg

Effective reasoning, analysis and communication regarding natural phenomena require the use of models to render tractable the complexities of nature. This paper attempts to put into perspective the proper roles of different types of models to maximize the effectiveness of their utilization. The advances in short term forecasting envisioned for the 1970's from full implementation of new knowledge, models and technology will materialize only if the managers and researchers join in an interagency effort to provide the operational meteorologists with the education, techniques, tools and, particularly, the challenging working environment needed to fully develop man's role in forecasting. A program to meet these requirements is outlined. The types of models discussed include: descriptive or synoptic, dynamic or analytic, numerical or physical, statistical or optimized. The uses of models discussed include: education (basic concepts), research (experimental), operations (customized). Since the operational meteorologist is responsible for the intelligent use of these types of models, he must continually update his training and properly understand the potential contributions of the models. It is anticipated that during the 1970's routine computer models will become more refined and specialized data such as trajectories and probabilities will become more common. Highly specialized products will be available from special purpose models on a special request basis as field forecasters gain access to remote terminals. Also, forecasters will have access to specialized consultants when unusual events or unusual forecast requirements arise. Background materials will be provided to the applied meteorologists so that he may gain physical understanding from educational and research models including systematic numerical experiments. Communication advances will provide for dynamic (motion picture) displays of radar, synchronous satellite, weather map and weather forecast data. Only if the operational forecasters do receive the necessary management and scientific support, will their jobs be challenging and attractive to highly motivated and qualified students; only then will the customers of specialized short term forecasts receive the benefits made feasible by science and technology.


2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.


2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.


2021 ◽  
pp. 42-46
Author(s):  
ANASTASIA SERGEEVNA NADTOCHY ◽  
◽  
DMITRIY VLADIMIROVICH FOMIN ◽  

The paper presents information on the results of short-term space weather forecasting for the Vostochny cosmodrome based on data on the electron flux density with energies above 2 MeV received from satellites from the operator's site of the Space Weather Forecast Center of the Moscow State University Institute of Nuclear Physics. The analysis of the calculated data on the level of near-Earth radiation, as a result of the use of various extrapolation methods, showed that the method of exponential smoothing is most effective for short-term space weather forecasting. Such forecasts can be used when planning launches of launch vehicles from spaceports.


Author(s):  
Eef Masson ◽  
Karin van Es

This chapter discusses visualizations of weather data, used to communicate short-term precipitation predictions to lay audiences. Focusing on the example of Buienradar, a popular Dutch weather forecast website and app, it investigates how people engage with such representations on a daily basis, how they interpret them, and how their readings of them affect their actions and decisions, shaping their day-to-day routines. The research is based on semi-structured interviews with users with different demographic profiles. Aside from establishing usage patterns or preferences and readerly strategies, the chapter also considers people’s own evaluations of their conduct in relation to the Buienradar service, and more broadly, their reflections on the significance of weather data visualizations to their lives.


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