scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

scholarly journals Adverse Weather Evokes Nostalgia

2018 ◽  
Vol 44 (7) ◽  
pp. 984-995 ◽  
Author(s):  
Wijnand A. P. van Tilburg ◽  
Constantine Sedikides ◽  
Tim Wildschut
Keyword(s):  
Cognitive Load ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Psychological Benefits ◽  
Adverse Weather

Four studies examined the link between adverse weather and the palliative role of nostalgia. We proposed and tested that (a) adverse weather evokes nostalgia (Hypothesis 1); (b) adverse weather causes distress, which predicts elevated nostalgia (Hypothesis 2); (c) preventing nostalgia exacerbates weather-induced distress (Hypothesis 3); and (d) weather-evoked nostalgia confers psychological benefits (Hypothesis 4). In Study 1, participants listened to recordings of wind, thunder, rain, and neutral sounds. Adverse weather evoked nostalgia. In Study 2, participants kept a 10-day diary recording weather conditions, distress, and nostalgia. We also obtained meteorological data. Adverse weather perceptions were positively correlated with distress, which predicted higher nostalgia. Also, adverse natural weather was associated with corresponding weather perceptions, which predicted elevated nostalgia. (Results were mixed for rain.) In Study 3, preventing nostalgia (via cognitive load) increased weather-evoked distress. In Study 4, weather-evoked nostalgia was positively associated with psychological benefits. The findings pioneer the relevance of nostalgia as source of comfort in adverse weather.

scholarly journals Predictability of flood events in view of current meteorology and hydrology in the conditions of the Czech Republic

2008 ◽  
Vol 2 (No. 4) ◽  
pp. 156-168 ◽  
Author(s):  
L. Březková ◽  
M. Šálek ◽  
E. Soukalová ◽  
M. Starý
Keyword(s):  
Czech Republic ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Economic Losses ◽  
Hydrological Models ◽  
Flood Events ◽  
Common Error ◽  
The Czech Republic ◽  
Hydrological Forecast

In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km<sup>2</sup>) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002&ndash;2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

scholarly journals Deformation and failure of the ice bridge on the Wilkins Ice Shelf, Antarctica

2010 ◽  
Vol 51 (55) ◽  
pp. 49-55 ◽  
Author(s):  
A. Humbert ◽  
D. Gross ◽  
R. Müller ◽  
M. Braun ◽  
R.S.W. van de Wal ◽  
...  
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Compressive Strain ◽  
Ice Shelf ◽  
Deformation And Failure ◽  
Position Data ◽  
Released Energy ◽  
Bridge Bent ◽  
Narrow Bridge

AbstractA narrow bridge of floating ice that connected the Wilkins Ice Shelf, Antarctica, to two confining islands eventually collapsed in early April 2009. In the month preceding the collapse, we observed deformation of the ice bridge by means of satellite imagery and from an in situ GPS station. TerraSAR-X images (acquired in stripmap mode) were used to compile a time series. The ice bridge bent most strongly in its narrowest part (westerly), while the northern end (near Charcot Island) shifted in a northeasterly direction. In the south, the ice bridge experienced compressive strain parallel to its long axis. GPS position data were acquired a little south of the narrowest part of the ice bridge from 19 January 2009. Analysis of these data showed both cyclic and monotonic components of motion. Meteorological data and re-analysis of the output of weather-prediction models indicated that easterly winds were responsible for the cyclic motion component. In particular, wind stress on the rough ice melange that occupied the area to the east exerted significant pressure on the ice bridge. The collapse of the ice bridge began with crack formation in the southern section parallel to the long axis of the ice bridge and led to shattering of the southern part. Ultimately, the narrowest part, only 900 m wide, ruptured. The formation of many small icebergs released energy of >125 ×106 J.

Experimental evaluation of remaining strength of crossarms in Gulfport transmission line wood structures

2010 ◽  
Vol 37 (4) ◽  
pp. 638-647
Author(s):  
M. D. Pandey ◽  
V. Ho ◽  
F. McCarthy ◽  
S. B. Woodward
Keyword(s):  
Transmission Lines ◽  
Weather Conditions ◽  
Condition Assessment ◽  
Strength Distribution ◽  
Economic Losses ◽  
Continuous Exposure ◽  
Electrical Transmission ◽  
Wood Structures ◽  
Adverse Weather ◽  
Support Cost

Electrical transmission lines in Canada and elsewhere widely utilize wood structures to support electrical conductors. The Gulfport structure is one of the largest wooden structures used in Canada’s electricity transmission networks. Continuous exposure of crossarms to wide-ranging temperature, moisture, and load conditions makes them vulnerable to internal decay and failure under adverse weather conditions, such as windstorms and snowstorms. These failures can result in forced outages and customer disruptions with significant economic losses, and therefore the electrical utility industry has taken a keen interest in developing condition-assessment methods to support cost-effective replacement of aged wood crossarms. At present, there is no dataset available regarding the remaining strength of wood pole crossarms exposed to the aboveground environment for 30–40 years of service life in Canada. This paper presents a comprehensive experimental study focussed on evaluating the remaining strength distribution in a sample of crossarms removed from service. The experimental data presented in the paper are essential to assess remaining life and develop guidelines for condition assessment of crossarms.

scholarly journals COLLAPSE WARNING SYSTEM USING LSTM NEURAL NETWORKS FOR CONSTRUCTION DISASTER PREVENTION IN EXTREME WIND WEATHER

2021 ◽  
Vol 27 (4) ◽  
pp. 230-245
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Warning System ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Strong Wind ◽  
Wind Force

Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

scholarly journals Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Michael S. Fox-Rabinovitz ◽  
Alexei A. Belochitski
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Simulated Data ◽  
Boreal Winter ◽  
Ensemble Technique ◽  
Numerical Weather ◽  
Cloud Resolving Model ◽  
Convection Parameterization

A novel approach based on the neural network (NN) ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP) models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR) Community Atmospheric Model (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

scholarly journals Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

2020 ◽  
Vol 5 (2) ◽  
pp. 15 ◽  
Author(s):  
Javier López Gómez ◽  
Francisco Troncoso Pastoriza ◽  
Enrique Granada Álvarez ◽  
Pablo Eguía Oller
Keyword(s):  
Local Level ◽  
Weather Prediction ◽  
Ground Truth ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Accurate Information ◽  
Numerical Weather ◽  
Weather Model ◽  
Geostatistical Interpolation ◽  
Solid Foundation

Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

The Sydney Australia Wildfires of January 1994 - Meteorological Conditions and High Resolution Numerical Modeling Experiments

1996 ◽  
Vol 6 (3) ◽  
pp. 145 ◽  
Author(s):  
MS Speer ◽  
LM Leslie ◽  
JR Colquhoun ◽  
E Mitchell
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Detailed Examination ◽  
Meteorological Conditions ◽  
Limited Area ◽  
Research Activity ◽  
Southeastern Australia ◽  
South Wales ◽  
Resolution Model

Southeastern Australia is particularly vulnerable to wildfires during the spring and summer months, and the threat of devastation is present most years. In January 1994, the most populous city in Australia, Sydney, was ringed by wildfires, some of which penetrated well into suburban areas and there were many other serious fires in coastal areas of New South Wales (NSW). In recent years much research activity in Australia has focussed on the development of high resolution limited area models, for eventual operational prediction of meteorological conditions associated with high levels of wildfire risk. In this study, the period January 7-8, 1994 was chosen for detailed examination, as it was the most critical period during late December 1993/early January 1994 for the greater Sydney area. Routine forecast guidance from the Australian Bureau of Meteorology's operational numerical weather prediction (NWP) models was very useful in that both the medium and short range models predicted synoptic patterns suggesting extreme fire weather conditions up to several days in advance. However, vital information of a detailed nature was lacking. A new high resolution model was run at the operational resolution of 150 km and the much higher resolutions of 25 km and 5 km. The new model showed statistically significant greater skill in predicting details of wind, relative humidity and temperature patterns both near the surface and above the boundary layer. It also produced skilful predictions of the Forest Fire Danger Index.

Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

&lt;p&gt;Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.&lt;/p&gt;

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