Next Article in Journal
Aerosol Optical Properties and Determination of Aerosol Size Distribution in Wuhan, China
Previous Article in Journal
Benefits of European Climate Policies for Mercury Air Pollution
Previous Article in Special Issue
Water Vapor, Temperature and Wind Profiles within Maize Canopy under in-Field Rainwater Harvesting with Wide and Narrow Runoff Strips
Article Menu

Export Article

Open AccessArticle
Atmosphere 2014, 5(1), 60-80; doi:10.3390/atmos5010060

Data Mining Methods to Generate Severe Wind Gust Models

Geoinformatics Research Centre (GRC), Auckland University of Technology (AUT), 2-14 Wakefield St, Auckland 1142, New Zealand
*
Author to whom correspondence should be addressed.
Received: 10 November 2013 / Revised: 12 December 2013 / Accepted: 18 December 2013 / Published: 13 January 2014
(This article belongs to the Special Issue Agrometeorology: From Scientific Analysis to Operational Application)
View Full-Text   |   Download PDF [2236 KB, uploaded 13 January 2014]   |  

Abstract

Gaining knowledge on weather patterns, trends and the influence of their extremes on various crop production yields and quality continues to be a quest by scientists, agriculturists, and managers. Precise and timely information aids decision-making, which is widely accepted as intrinsically necessary for increased production and improved quality. Studies in this research domain, especially those related to data mining and interpretation are being carried out by the authors and their colleagues. Some of this work that relates to data definition, description, analysis, and modelling is described in this paper. This includes studies that have evaluated extreme dry/wet weather events against reported yield at different scales in general. They indicate the effects of weather extremes such as prolonged high temperatures, heavy rainfall, and severe wind gusts. Occurrences of these events are among the main weather extremes that impact on many crops worldwide. Wind gusts are difficult to anticipate due to their rapid manifestation and yet can have catastrophic effects on crops and buildings. This paper examines the use of data mining methods to reveal patterns in the weather conditions, such as time of the day, month of the year, wind direction, speed, and severity using a data set from a single location. Case study data is used to provide examples of how the methods used can elicit meaningful information and depict it in a fashion usable for management decision making. Historical weather data acquired between 2008 and 2012 has been used for this study from telemetry devices installed in a vineyard in the north of New Zealand. The results show that using data mining techniques and the local weather conditions, such as relative pressure, temperature, wind direction and speed recorded at irregular intervals, can produce new knowledge relating to wind gust patterns for vineyard management decision making.
Keywords: weather extremes; wind speed and direction; artificial neural networks; self-organising map (SOM) method weather extremes; wind speed and direction; artificial neural networks; self-organising map (SOM) method
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Shanmuganathan, S.; Sallis, P. Data Mining Methods to Generate Severe Wind Gust Models. Atmosphere 2014, 5, 60-80.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top