Can we predict Dry Air Intrusions using an Artificial Neural Network?

2020 ◽  
Author(s):  
Stav Nahum ◽  
Shira Raveh-Rubin ◽  
Jonathan Shlomi ◽  
Vered Silverman
Keyword(s):  
Neural Network ◽  
Weather Conditions ◽  
Climate Projections ◽  
Data Sets ◽  
High Resolution Data ◽  
Weather Forecasts ◽  
Dry Air ◽  
Identification Method ◽  
Weather Events ◽  
Trained Neural Network

<p>Dry-air intrusions (DIs) descending from the upper troposphere toward the surface are often associated with abrupt modification of the atmospheric boundary layer,air-sea interface, and high impact weather events. Understanding the triggering mechanism of DIs is important to predict the likelihood of their occurrence in both weather forecasts and future climate projections.</p><p>The current identification method of DIs is based on a systematic costly Lagrangian method that requires high vertical resolution of the wind field at sub-daily intervals. Therefore, the accurate prediction of surface weather conditions is potentially limited. Moreover, large case to case variability of these events makes it challenging to compose an objective algorithm for predicting the timing and location of their initiation.    </p><p>Here we test the ability of deep neural networks, originally designed for computer vision purposes, to identify the DI phenomenon based on instantaneous 2-dimensional maps of commonly available atmospheric parameters. Our trained neural network is able to successfully predict DI origins using three instantaneous 2-D maps of geopotential heights.</p><p>Our results demonstrate how machine learning can be used to overcome the limitations of the traditional identification method, introducing the possibility to evaluate and quantify the occurrence of DIs instantaneously, avoiding costly computations and the need for high resolution data sets which are not available for most atmospheric data sets. In particular, for the first time, it is possible to predict the occurrence of DI events up to two days before the actual descent is complete.</p>

scholarly journals Neural Networks for Postprocessing Ensemble Weather Forecasts

2018 ◽  
Vol 146 (11) ◽  
pp. 3885-3900 ◽  
Author(s):  
Stephan Rasp ◽  
Sebastian Lerch
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Distribution ◽  
Predictor Variables ◽  
Specific Information ◽  
Observation Data ◽  
Neural Network Approach ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Trained Neural Network

Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.

scholarly journals ASCAT Wind Superobbing Based on Feature Box

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Boheng Duan ◽  
Weimin Zhang ◽  
Haijin Dai
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Weather Conditions ◽  
Error Characteristic ◽  
Data Sets ◽  
Observation Data ◽  
High Resolution Data ◽  
Correlated Error ◽  
Meteorological Features ◽  
Resolution Data

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing.

Alpha particle microdosimetry calculations using a shallow neural network

2022 ◽  
Author(s):  
Peter Wagstaff ◽  
Pablo Minguez Gabina ◽  
Ricardo Mínguez ◽  
John C Roeske
Keyword(s):  
Neural Network ◽  
Alpha Particle ◽  
Transfer Functions ◽  
Single Cells ◽  
Data Sets ◽  
Cell Nuclei ◽  
Data Set ◽  
Testing Data ◽  
Data Points ◽  
Trained Neural Network

Abstract A shallow neural network was trained to accurately calculate the microdosimetric parameters, <z1> and <z1 2> (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 µm), cell radii (2.5–20 µm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71×10-7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80×10-7. The 95th percentile testing data errors were within ±1.4% for <z1> outputs and ±2.8% for <z1 2> outputs, respectively. Cell survival was also predicted using actual vs. neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.

Investigation of Ship and Platform Accidents Due to Severe Weather Events: Results of the MAXWAVE Project

2006 ◽  
Author(s):  
S. Lehner ◽  
W. Rosenthal
Keyword(s):  
Weather Conditions ◽  
Rogue Waves ◽  
Radar Data ◽  
Severe Weather ◽  
Data Sets ◽  
Extreme Waves ◽  
Weather Events ◽  
Marine Radar ◽  
Satellite Radar ◽  
Remote Sensing Techniques

Heavy sea states and severe weather conditions have caused the loss of more than 200 super carriers within the last 20 years. In many cases single ‘rogue waves’ of abnormal height as well as groups of extreme waves have been reported by crew members of such vessels. The European Project MAXWAVE dealt with both theoretical aspects of extreme waves as well as new techniques to observe these waves using different remote sensing techniques. The final goal was to improve the understanding of the physical processes responsible for the generation of extreme waves and to identify geophysical conditions in which such waves are most likely to occur. This paper gives a summary of the results of the MAXWAVE projects with emphasis on the analysis of the marine and satellite radar data sets. Two dimensional sea surface elevation fields are derived from marine radar data and complex Spaceborne Synthetic Aperture Radar (SAR) images. Several ship and offshore platform accidents are analysed and new warning criteria are discussed.

scholarly journals Feature Analysis on Atmosphere Laser Communication Channel Under Rainy Condition in the Tibetan Plateau

2015 ◽  
Vol 9 (1) ◽  
pp. 147-152
Author(s):  
Wei Xiao
Keyword(s):  
Neural Network ◽  
Tibetan Plateau ◽  
Communication Channel ◽  
Laser Communication ◽  
Data Sets ◽  
The Tibetan Plateau ◽  
Analysis Process ◽  
Trained Neural Network ◽  
Summer Rain

In the Tibetan Plateau, due to lack of raw experimental data sets and proper data analysis method, investigations on atmosphere laser communication channel (ALCC), especially under rainy condition, are rarely concerned by researchers. Neural network group and optimal weight initialization technology (OWIT) are adopted in the analysis process. Firstly, construct neural network group according to different season’s conditions. Secondly, utilize existed raw data sets of ALCC under rainy condition to choose matching initial weight sets with OWIT. Thirdly, train neural network group until expected requirement is met. Finally, load rain data sets from the Tibetan Plateau (Lhasa for example) on trained neural network group to achieve the ultimate channel quality of ALCC. Actual results show that spring rain has the best quality of ALCC, followed by winter rain, summer rain and autumn rain.

PERSONALIZED APPROACH TO THE ORGANIZATION OF MEDICAL CARE FOR PATIENTS WITH WEATHER DEPENDENCE

2020 ◽  
pp. 35-39
Author(s):  
Klepikov O.V. ◽  
Kolyagina N.M. ◽  
Berezhnova T.A. ◽  
Kulintsova Ya.V.
Keyword(s):  
Medical Care ◽  
Cardiovascular System ◽  
Performance Indicators ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Outpatient Clinics ◽  
Vulnerable Groups ◽  
Pathological Effect ◽  
Weather Forecasts ◽  
Personalized Approach

Relevance. Today, in preventive medicine, climatic conditions that have a pathological effect on the functional state of a person are increasingly being updated. the occurrence of exacerbations of many diseases can be causally associated with various weather conditions. Aim: to develop the main tasks for improving the organization of medical care for weather-dependent patients with diseases of the cardiovascular system. Material and methods. The assessment of personnel, material and technical support and the main performance indicators of an outpatient clinic was carried out on the example of the Voronezh city polyclinic No. 18 to develop the main tasks for improving the organization of medical care for weather-dependent patients with diseases of the cardiovascular system. Results. The main personnel problem is the low staffing of district therapists and specialists of a narrow service. One of the priorities for reducing the burden on medical hospitals is the organization of inpatient replacement medical care on the basis of outpatient clinics. The indicators for the implementation of state guarantees for the outpatient network for 2018, which were fully implemented, are given. The analysis of the planned load performance by polyclinic specialists is presented. Cardiological and neurological services carry out measures to reduce the risk of exacerbations of diseases with cerebral atherosclerosis, hypertension, and major neurological nosologies. Conclusion. Improving the organization of medical care for weather-dependent patients with cardiovascular diseases are: informing patients about the sources of specialized medical weather forecasts in the region, organizing the work of the medical prevention office, implementing an interdepartmental approach to providing health care to the most vulnerable groups of the population.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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