Real-Time Weather Analytics

2020 ◽  
Vol 17 (4) ◽  
pp. 15-31
Author(s):  
Lavanya K. ◽  
Sathyan Venkatanarayanan ◽  
Anay Anand Bhoraskar
Keyword(s):  
Data Storage ◽  
Weather Forecasting ◽  
Modern Science ◽  
Weather Forecast ◽  
Forecast Model ◽  
Data Availability ◽  
Third Party ◽  
Weather Data ◽  
Storage Devices ◽  
Weather Forecast Model

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.

scholarly journals A novel weather parameters prediction scheme and their effects on crops

2022 ◽  
Vol 12 (1) ◽  
pp. 639
Author(s):  
Naveen Lingaraju ◽  
Hosaagrahara Savalegowda Mohan
Keyword(s):  
Weather Forecasting ◽  
Fitness Function ◽  
Research Work ◽  
Geographical Location ◽  
Weather Forecast ◽  
Forecast Model ◽  
Weather Data ◽  
Prediction Scheme ◽  
Weather Parameters ◽  
Technological World

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

scholarly journals Statistical downscaling of MM5 model output to better assess avalanche threats

2010 ◽  
Vol 51 (54) ◽  
pp. 14-18 ◽  
Author(s):  
K. Srinivasan ◽  
Ajay Kumar ◽  
Jyoti Verma ◽  
Ashwagosha Ganju
Keyword(s):  
Statistical Downscaling ◽  
Winter Season ◽  
Threat Assessment ◽  
Weather Forecast ◽  
Western Himalaya ◽  
Forecast Model ◽  
Weather Data ◽  
Model Output ◽  
Mm5 Model ◽  
Weather Forecast Model

AbstractIn this study, we use MM5 weather-forecast model output and observed surface weather data from 11 stations in the western Himalaya to develop a statistical downscaling model (SDM) to better predict precipitation, 10 m wind speed and 2 m temperature. The analysis covers three consecutive winters: 2004/05, 2005/06 and 2006/07. The performance of the SDM was assessed using an independent dataset from the 2007/08 winter season. This assessment shows that the SDM technique substantially improves the forecast over specific station locations, which is important for avalanche-threat assessment.

scholarly journals A Distributed File-Based Storage System for Improving High Availability of Space Weather Data

2019 ◽  
Vol 9 (23) ◽  
pp. 5024
Author(s):  
Andrian ◽  
Kim ◽  
Ju
Keyword(s):  
Data Storage ◽  
Space Weather ◽  
File System ◽  
Single Point ◽  
Storage System ◽  
Science Research ◽  
Data Availability ◽  
Weather Data ◽  
Time Data ◽  
Replication Time

In space science research, the Indonesia National Institute of Aeronautics and Space (LAPAN) is concerned with the development of a system that provides actual information and predictions called the Space Weather Information and Forecast Services (SWIFtS). SWIFtS is supported by a data storage system that serves data, implementing a centralized storage model. This has some problems that impact to researchers as the primary users. The single point of failure and also the delay in data updating on the server is a significant issue when researchers need the latest data, but the server is unable to provide it. To overcome these problems, we proposed a new system that utilized a decentralized model for storing data, leveraging the InterPlanetary File System (IPFS) file system. Our proposed method focused on the automated background process, and its scheme would increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Moreover, we also included system monitoring for real-time data flow from each node and information of node status that combines active and passive approaches. For system evaluation, the experiment was performed to determine the performance of the proposed system compared to the existing system by calculating mean replication time and the mean throughput of a node. As expected, performance evaluations showed that our proposed scheme had faster file replication time and supported high throughput.

scholarly journals Some Experimental Lessons on Digital Filtering in the ALADIN-France 3DVAR Based on Near-Ground Examination

2011 ◽  
Vol 139 (3) ◽  
pp. 774-785 ◽  
Author(s):  
Claude Fischer ◽  
Ludovic Auger
Keyword(s):  
Data Assimilation ◽  
Digital Filter ◽  
Three Dimensional ◽  
Weather Forecast ◽  
Forecast Model ◽  
Horizontal Wind ◽  
Screen Level ◽  
Incremental Formulation ◽  
Weather Forecast Model ◽  
Observation Impact

Abstract This paper deals with the characteristics and effects of digital filter initialization, as implemented in the operational three-dimensional variational data assimilation (3DVAR) system of the Aire Limitée Adaptation Dynamique Développement International (ALADIN)-France regional weather forecast model. First, a series of findings on the properties of the initialization of the model are discussed. Examples of initial spinup linked with inertia–gravity wave occurrence are shown, and the major sources for their generation are listed. These experimental results are compared with past and present experiences concerning the use and need for digital filter initialization. Furthermore, the impacts of switching to an incremental formulation of the filter in data assimilation mode are demonstrated. Second, the effects of the filter formulation on the results of an observation impact study are illustrated. The latter consists of implementing screen-level, 10-m horizontal wind information into the ALADIN 3DVAR analysis. There can, indeed, be some delicate interference between observation impact evaluation and the effects of filtering, at least on short-term forecasts. The paper is concluded with some general considerations on the experimental evaluation of spinup and the link between the assimilation system design and model state filtering.

scholarly journals A Multi-purpose Weather Forecast Model for the Mondsee Catchment

GI_Forum ◽  
2015 ◽  
Vol 1 ◽  
pp. 600-609 ◽  
Author(s):  
Hermann Klug ◽  
Liviu Oana
Keyword(s):  
Weather Forecast ◽  
Forecast Model ◽  
Weather Forecast Model
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