Improving Meteorological Models Using Ships’ Weather Data Communicated via AIS

2021 ◽  
Author(s):  
Gregory Johnson ◽  
Kenneth Dykstra ◽  
Sophie Ordell ◽  
Brian Tetreault ◽  
Kevin Kohlmann
Keyword(s):  
Weather Data ◽  
Meteorological Models

scholarly journals Meteorology as Citizen Science

2020 ◽  
Author(s):  
Eirik Samuelsen ◽  
Per Pippin Aspaas
Keyword(s):  
Citizen Science ◽  
The Arctic ◽  
Weather Data ◽  
Large Network ◽  
Weather Forecasts ◽  
Project Smart ◽  
Weather Stations ◽  
Meteorological Models ◽  
Meteorological Institute ◽  
Weather Service

Eirik Samuelsen, senior meteorologist at the Norwegian Meteorological Institute (Met) and UiT The Arctic University of Norway, discusses the importance of citizen science to current meteorology in Norway. Amateurs contribute to the improvement of weather forecasts in various ways, from anecdotic but valuable feedback on errors in the forecast to a large network of private weather stations providing precious data for the free-to-use weather service www.yr.no. Besides yr.no as such, which is maintained by the Norwegian Broadcasting Corporation (NRK), there is mention of Eiriks værblogg (Eirik's weather blog) on Facebook, the network of Netatmo Weather Stations, the open database of meteorological models and weather data thredds.met.no, and the project Smart Senja. First published online September 23, 2020.

WEB SYSTEM FOR WEATHER DATA PROCESSING TO HVAC DESIGN

2018 ◽  
Author(s):  
Raama Alves ◽  
Thamires Bernardes ◽  
MANOEL ANTONIO FONSECA COSTA
Keyword(s):  
Data Processing ◽  
Weather Data ◽  
Web System

Essai d'application de modèles météorologiques au calcul du moment d'apparition de deux phases phénologiques | Application of Meteorological Models for the Calculation of the Beginning of Two Phenological Phases

2000 ◽  
Vol 151 (10) ◽  
pp. 385-397
Author(s):  
Bernard Primault
Keyword(s):  
Sunshine Duration ◽  
Fruit Trees ◽  
Swiss Alps ◽  
Winter Solstice ◽  
Phenological Data ◽  
Thermal Thresholds ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Models ◽  
Phenological Phases

Many years ago, a model was elaborated to calculate the«beginning of the vegetation's period», based on temperatures only (7 days with +5 °C temperature or more). The results were correlated with phenological data: the beginning of shoots with regard to spruce and larch. The results were not satisfying, therefore, the value of the two parameters of the first model were modified without changing the second one. The result, however, was again not satisfying. Research then focused on the influence of cumulated temperatures over thermal thresholds. Nevertheless, the results were still not satisfying. The blossoming of fruit trees is influenced by the mean temperature of a given period before the winter solstice. Based on this knowledge, the study evaluated whether forest trees could also be influenced by temperature or sunshine duration of a given period in the rear autumn. The investigation was carried through from the first of January on as well as from the date of snow melt of the following year. In agricultural meteorology, the temperature sums are often interrelated with the sunshine duration, precipitation or both. However,the results were disappointing. All these calculations were made for three stations situated between 570 and 1560 m above sea-level. This allowed to draw curves of variation of the two first parameters (number of days and temperature) separately for each species observed. It was finally possible to specify the thus determined curves with data of three other stations situated between the first ones. This allows to calculate the flushing of the two tree species, if direct phenological observation is lacking. This method, however, is only applicable for the northern part of the Swiss Alps.

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

scholarly journals Distribution and incidence of viruses in Irish seed potato crops

2015 ◽  
Vol 54 (2) ◽  
pp. 98-106 ◽  
Author(s):  
F. Hutton ◽  
J.H. Spink ◽  
D. Griffin ◽  
S. Kildea ◽  
D. Bonner ◽  
...  
Keyword(s):  
Potato Virus ◽  
Significant Proportion ◽  
Potato Production ◽  
Weather Data ◽  
Management Approach ◽  
Potato Seed ◽  
Virus Diseases ◽  
Aphid Control ◽  
Daily Weather Data ◽  
Potato Crops

Abstract Virus diseases are of key importance in potato production and in particular for the production of disease-free potato seed. However, there is little known about the frequency and distribution of potato virus diseases in Ireland. Despite a large number of samples being tested each year, the data has never been collated either within or across years. Information from all known potato virus testing carried out in the years 2006–2012 by the Department of Agriculture Food and Marine was collated to give an indication of the distribution and incidence of potato virus in Ireland. It was found that there was significant variation between regions, varieties, years and seed classes. A definition of daily weather data suitable for aphid flight was developed, which accounted for a significant proportion of the variation in virus incidence between years. This use of weather data to predict virus risk could be developed to form the basis of an integrated pest management approach for aphid control in Irish potato crops.

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Cloud Radar Observations of Diurnal and Seasonal Cloudiness over Reunion Island

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 868
Author(s):  
Jonathan Durand ◽  
Edouard Lees ◽  
Olivier Bousquet ◽  
Julien Delanoë ◽  
François Bonnardot
Keyword(s):  
Sea Breeze ◽  
Satellite System ◽  
Research Infrastructure ◽  
Weather Data ◽  
Reunion Island ◽  
Wet Season ◽  
Trade Winds ◽  
Cloud Radar ◽  
Réunion Island ◽  
Global Navigation Satellite

In November 2016, a 95 GHz cloud radar was permanently deployed in Reunion Island to investigate the vertical distribution of tropical clouds and monitor the temporal variability of cloudiness in the frame of the pan-European research infrastructure Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS). In the present study, reflectivity observations collected during the two first years of operation (2016–2018) of this vertically pointing cloud radar are relied upon to investigate the diurnal and seasonal cycle of cloudiness in the northern part of this island. During the wet season (December–March), cloudiness is particularly pronounced between 1–3 km above sea level (with a frequency of cloud occurrence of 45% between 12:00–19:00 LST) and 8–12 km (with a frequency of cloud occurrence of 15% between 14:00–19:00 LST). During the dry season (June–September), this bimodal vertical mode is no longer observed and the vertical cloud extension is essentially limited to a height of 3 km due to both the drop-in humidity resulting from the northward migration of the ITCZ and the capping effect of the trade winds inversion. The frequency of cloud occurrence is at its maximum between 13:00–18:00 LST, with a probability of 35% at 15 LST near an altitude of 2 km. The analysis of global navigation satellite system (GNSS)-derived weather data also shows that the diurnal cycle of low- (1–3 km) and mid-to-high level (5–10 km) clouds is strongly correlated with the diurnal evolution of tropospheric humidity, suggesting that additional moisture is advected towards the island by the sea breeze regime. The detailed analysis of cloudiness observations collected during the four seasons sampled in 2017 and 2018 also shows substantial differences between the two years, possibly associated with a strong positive Indian Ocean Southern Dipole (IOSD) event extending throughout the year 2017.

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.

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