scholarly journals Machine Learning in Python for Weather Forecast based on Freely Available Weather Data

2018 ◽  
Author(s):  
Erik Abrahamsen ◽  
Ole Magnus Brastein ◽  
Bernt Lie
Keyword(s):  
Machine Learning ◽  
Weather Forecast ◽  
Weather Data

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 303
Author(s):  
Eloise S. Fogarty ◽  
David L. Swain ◽  
Greg M. Cronin ◽  
Luis E. Moraes ◽  
Derek W. Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Trials ◽  
Satellite System ◽  
False Positives ◽  
Sensor Data ◽  
Weather Data ◽  
Integrated Sensor ◽  
Detection Model ◽  
Independent Field ◽  
Grazing Sheep

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

Brazilian aviation nowcasting system: current stage and some results

2021 ◽  
Author(s):  
Vinícius Almeida ◽  
Gutemberg França ◽  
Francisco Albuquerque Neto ◽  
Haroldo Campos Velho ◽  
Manoel Almeida ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Weather Data ◽  
Vertical Acceleration ◽  
In Situ Data ◽  
Forecasting System ◽  
Under Construction ◽  
Convective Weather

<p>Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.</p>

scholarly journals Review of Weather Forecast Services for Ship Routing Purposes

2019 ◽  
Vol 26 (4) ◽  
pp. 80-89
Author(s):  
Marcin Życzkowski ◽  
Joanna Szłapczyńska ◽  
Rafał Szłapczyński
Keyword(s):  
Collision Avoidance ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Weather Data ◽  
Ocean Engineering ◽  
Ship Routing ◽  
Engineering Research ◽  
Fishing Vessels ◽  
Analysis Of Stability

Abstract Weather data is nowadays used in a variety of navigational and ocean engineering research problems: from the obvious ones like voyage planning and routing of sea-going vessels, through the analysis of stability-related phenomena, to detailed modelling of ships’ manoeuvrability for collision avoidance purposes. Apart from that, weather forecasts are essential for passenger cruises and fishing vessels that want to avoid the risk associated with severe hydro-meteorological conditions. Currently, there is a wide array of services that offer weather predictions. These services include the original sources – services that make use of their own infrastructure and research models – as well as those that further postprocess the data obtained from the original sources. The existing services also differ in their update frequency, area coverage, geographical resolution, natural phenomena taken into account and finally – output file formats. In the course of the ROUTING project, primarily addressing ship weather routing accounting for changeable weather conditions, the necessity arose to prepare a report on the state-of-the-art in numerical weather prediction (NWP) modelling. Based on the report, this paper offers a thorough review of the existing weather services and detailed information on how to access the data offered by these services. While this review has been done with transoceanic ship routing in mind, hopefully it will also be useful for a number of other applications, including the already mentioned collision avoidance solutions.

Development of a Probabilistic Convective Weather Forecast Threshold Parameter for Flight-Routing Decisions

2013 ◽  
Vol 28 (5) ◽  
pp. 1175-1187 ◽  
Author(s):  
Kapil Sheth ◽  
Thomas Amis ◽  
Sebastian Gutierrez-Nolasco ◽  
Banavar Sridhar ◽  
Daniel Mulfinger
Keyword(s):  
Traffic Control ◽  
Weather Forecast ◽  
Threshold Value ◽  
Air Traffic ◽  
Weather Data ◽  
Traffic Data ◽  
Threshold Parameter ◽  
Probability Threshold ◽  
Convective Cells ◽  
Convective Weather

Abstract This paper presents a method for determining a threshold value of probabilistic convective weather forecast data. By synchronizing air traffic data and an experimental probabilistic convective weather forecast product, it was observed that aircraft avoid areas of specific forecasted probability. Both intensity and echo top of the forecasted weather were synchronized with air traffic data to derive the probability threshold parameter. This value can be used by dispatchers for flight planning and by air traffic managers to reroute streams of aircraft around convective cells. The main contribution of this paper is to provide a method to compute the probability threshold parameters using a specific experimental probabilistic convective forecast product providing hourly guidance up to 6 h. Air traffic and weather data for a 4-month period during the summer of 2007 were used to compute the parameters for the continental United States. The results are shown for different altitudes, times of day, aircraft types, and airspace users. Threshold values for each of the 20 Air Route Traffic Control Centers were also computed. Additional details are presented for seven high-altitude sectors in the Fort Worth, Texas, center. For the analysis reported here, flight intent was not considered and no assessment of flight deviation was conducted since only aircraft tracks were used.

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>.  When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

Weather Maps, Forecasts, and Data

2000 ◽  
Author(s):  
C. David Whiteman
Keyword(s):  
Weather Forecast ◽  
Ground Level ◽  
Weather Conditions ◽  
Low Pressure ◽  
Weather Data ◽  
Dimensional Structure ◽  
Synoptic Scale ◽  
Pressure System ◽  
Surface Weather ◽  
Comprehensive Picture

Weather maps prepared by the National Weather Service summarize and synthesize weather data to provide a comprehensive picture of weather conditions at a given time. They are the basis of weather maps used on television to show precipitation, high and low pressure centers, and fronts. Weather maps are produced using both surface data and data from specified pressure levels. Data are plotted and contoured by computer, and analysts use satellite photos, satellite video loops, weather forecast models, and extrapolations from previous frontal and pressure system analyses to locate fronts and pressure centers. An example of a surface weather chart is presented in figure 9.1. A 500-mb chart for the same date and time was presented in figure 5.1. Symbols are used on weather maps to indicate synoptic-scale features. High and low pressure weather systems (highs and lows) are indicated by the letters H and L, with isobars labeled in millibars. Lines indicating frontal positions (section 6.2) represent the position on the ground of boundaries between air masses. Additional meteorological variables, such as temperature, are often analyzed on the same map using dashed or colored lines. Pressure, temperature, and other data from the reporting stations are plotted in coded form at the station locations. A station model specifies the positions in which different types of data are plotted relative to the station location. Figure 9.1 used an abbreviated station model. A complete station model is shown in figure 9.2. Figures 9.3 — 9.7 show additional symbols used in station models to indicate total sky cover, winds, pressure tendency, cloud types, and present weather types, respectively. A surface weather chart, with the symbols indicating fronts and high and low pressure centers and the information included in the station model, provides a snapshot of synoptic-scale conditions at ground level. By overlaying charts for several pressure levels (section 5.1.3), changes with altitude can be identified and the three-dimensional structure of the atmosphere at a given point in time can be visualized. By comparing consecutive charts, the rate of movement of fronts and the rates of development of high and low pressure centers can be determined.

Rain on your radar : Engaging with weather data visualizations as part of everyday routines

2020 ◽  
Author(s):  
Eef Masson ◽  
Karin van Es
Keyword(s):  
Weather Forecast ◽  
Daily Basis ◽  
Weather Data ◽  
Structured Interviews ◽  
Short Term ◽  
Demographic Profiles ◽  
Usage Patterns ◽  
Data Visualizations

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.

Sign in / Sign up

Export Citation Format

Share Document