scholarly journals The Use of Thermal Infra-Red Imagery to Elucidate the Dynamics and Processes Occurring in Fog

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Jeremy Price ◽  
Kristian Stokkereit
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Model Performance ◽  
Video Footage ◽  
Great Insight ◽  
Infra Red ◽  
Meteorological Observations ◽  
Set Up ◽  
Insight Into ◽  
Local Nature

Improving our ability to predict fog accurately is currently a high priority for Numerical Weather Prediction models. Such an endeavour requires numerous types of observations of real fog as a means to both better understand it and also provide an assessment of model performance. We consider the use of thermal infra-red imagery, used in conjunction with other meteorological observations, for the purposes of studying fog. Two cameras were used—a FLIR Systems Inc. A655sc and a FLIR Systems Inc. A65sc—which were set up to capture one image per minute. Images were then combined to provide video footage of nocturnal fog events. Results show that the imagery from such cameras can provide great insight into fog processes and dynamics, identifying interesting features not previously seen. Furthermore, comparison of imagery with conventional meteorological observations showed that the observations were often not capable of being used to delineate all of the processes affecting fog, due to their incomplete and local nature.

scholarly journals The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

2012 ◽  
Vol 140 (8) ◽  
pp. 2689-2705 ◽  
Author(s):  
Marc Berenguer ◽  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Diurnal Cycle ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Model Performance ◽  
Radar Data ◽  
Skill Scores ◽  
Diurnal Cycle Of Precipitation ◽  
Timing Of Initiation ◽  
Short Term Forecasting

Abstract This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting. The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar. Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill. The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 103°–85°W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

scholarly journals WAVE MODELLING IN A COMPLEX ESTUARY: STUDY IN PREPARATION OF FIELD MEASUREMENT CAMPAIGN EEMS-DOLLARD ESTUARY

2018 ◽  
pp. 66
Author(s):  
Patrick Oosterlo ◽  
Jentsje W. Van der Meer ◽  
Bas Hofland ◽  
Gerbrant Van Vledder
Keyword(s):  
Prediction Models ◽  
Wave Model ◽  
Wave Modelling ◽  
Measurement Campaign ◽  
The North ◽  
Wave Measurements ◽  
Shelf Sea ◽  
Wave Models ◽  
Set Up ◽  
Insight Into

This paper considers the Eems-Dollard estuary in the north of the Netherlands, which is part of the shallow shelf sea the Wadden Sea. This estuary is a highly complex area with tidal flats and deep channels and is characterised by an offshore directed wind, posing a challenge to wave prediction models. As little measurements are available, a measurement campaign is set up to provide field data for verifying and improving these wave models. The paper presents the locations that are most suited for wave measurements in the estuary, insight in the performance of the phase-averaged numerical wave model SWAN, and insight in the processes that play a role in the area around the corner of the Eemshaven. Furthermore, it presents insight into the reliability and applicability of SWAN in this highly complex area. An analysis of propagation effects is performed, as well as a comparison between the SWAN version as used for the Dutch dike safety assessment and the newest version, used for development, which includes the state of the art parameterisations of the physics. Furthermore, modelling is done for a schematised version of the area around the corner of the Eemshaven, considering several different model settings. Large differences occur in the results between the two SWAN versions. These differences are studied in more detail, and the causes of these differences are identified.

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

Convection is key to better polar low forecasts

2020 ◽  
Author(s):  
Matilda Hallerstig ◽  
Linus Magnusson ◽  
Erik Kolstad
Keyword(s):  
Prediction Models ◽  
Deterministic Model ◽  
Rapid Development ◽  
Weather Prediction ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Added Value ◽  
Grid Spacing ◽  
Polar Lows ◽  
Polar Low

<p>ECMWF HRES and Arome Arctic are the operational Numerical Weather Prediction models that forecasters in northern Norway use to predict Polar lows in the Nordic and Barents Seas. These type of lows are small, but intense mesoscale cyclones with strong, gusty winds and heavy snow showers. They cause hazards like icing, turbulence, high waves and avalanches that threaten offshore activity and coastal societies in the area. Due to their small size and rapid development, medium range global models with coarser resolutions such as ECMWF have not been able to represent them properly. This was only possible with short range high resolution regional models like Arome. When ECMWF introduced their new HRES deterministic model with 9 km grid spacing, the potential for more precise polar low forecasts increased. Here we use case studies and sensitivity tests to examine the ability of ECMWF HRES to represent polar lows. We also evaluate what added value the Arome Arctic model with 2.5 km grid spacing gives. For verification, we use coastal meteorological stations and scatterometer winds. We found that convection has a greater impact on model performance than horizontal resolution. We also see that Arome Arctic produces higher wind speeds than ECMWF HRES. To improve performance during polar lows for models with a horizontal grid spacing less than 10 km, it is therefore more important to improve the understanding and formulation of convective processes rather than simply increasing horizontal resolution.</p>

scholarly journals Adaptive Kalman Filtering for Postprocessing Ensemble Numerical Weather Predictions

2017 ◽  
Vol 145 (12) ◽  
pp. 4837-4854 ◽  
Author(s):  
Anna Pelosi ◽  
Hanoi Medina ◽  
Joris Van den Bergh ◽  
Stéphane Vannitsem ◽  
Giovanni Battista Chirico
Keyword(s):  
Kalman Filtering ◽  
Prediction Models ◽  
Southern Italy ◽  
Weather Prediction ◽  
Adaptive Method ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Weather Stations ◽  
Meteorological Observations ◽  
Numerical Weather Prediction Models

Forecasts from numerical weather prediction models suffer from systematic and nonsystematic errors, which originate from various sources such as subgrid-scale variability affecting large scales. Statistical postprocessing techniques can partly remove such errors. Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially postprocess the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training datasets do not exist. Here, a new adaptive postprocessing technique for ensemble predictions (called AEMOS) is introduced. The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the postprocessing equation. A verification study for the region of Campania in southern Italy is performed. Two years (2014–15) of daily meteorological observations of 10-m wind speed and 2-m temperature from 18 ground-based automatic weather stations are used, comparing them with the corresponding COSMO-LEPS ensemble forecasts. It is shown that the proposed adaptive method outperforms the AMOS method, while it shows comparable results to the member-by-member batch postprocessing approach.

scholarly journals Seamless postprocessing of multi-model NWP surface wind forecasts with deep learning

2021 ◽  
Author(s):  
Daniele Nerini ◽  
Jonas Bhend ◽  
Christoph Spirig ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ensemble Forecasting ◽  
Added Value ◽  
Random Errors ◽  
Ensemble Forecasts ◽  
Late Model ◽  
Set Up

<p>Hourly wind forecasts from numerical weather prediction models suffer from a range of systematic and random errors that are to a great extent related to limitations in the model grid resolution. To correct for such biases, statistical postprocessing and downscaling procedures are commonly applied so to leverage the information provided by automatic wind measurements at the surface. More recently, such techniques have been reformulated in a machine learning framework so to profit from the increased availability of data and computational resources. The results reported in the literature are promising and call for a serious evaluation of their potential for operational forecasting.</p><p>However, there remain several scientific and more applied challenges that need to be addressed before such methods can transition to real-world applications. One such challenge relates to the availability of multiple ensemble forecasts for the same point in time and space, which raises the question of how the information can be efficiently and optimally handled during postprocessing, so to provide added value to the end-user without adding technical debt to the operational system.</p><p>We propose an approach where a single deep learning model is trained to postprocess a combination of three ensemble forecasting systems, namely the high-resolution regional COSMO model with two configurations, and the ECMWF IFS ENS global ensemble forecasting system. We will show how the training is set up to provide a robust postprocessing model that can account for real time scenarios that include missing data and late model runs, while the quality of the forecasts remains comparable to a single-model approach. We found that the flexibility of the deep learning architecture translates into a robust automatic postprocessing solution that limits the maintenance burden and improves the system’s reliability.</p>

Teacher digital competence: bringing education to the next level

2020 ◽  
Vol 11 (1) ◽  
pp. 22-26
Author(s):  
S.V. Tsymbal ◽  
Keyword(s):  
Deep Learning ◽  
Learning Experiences ◽  
Digital Competence ◽  
Digital Revolution ◽  
Access Information ◽  
Set Up ◽  
Insight Into ◽  
The Way

The digital revolution has transformed the way people access information, communicate and learn. It is teachers' responsibility to set up environments and opportunities for deep learning experiences that can uncover and boost learners’ capacities. Twentyfirst century competences can be seen as necessary to navigate contemporary and future life, shaped by technology that changes workplaces and lifestyles. This study explores the concept of digital competence and provide insight into the European Framework for the Digital Competence of Educators.

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