Drag-Based Ensemble Model (DBEM) to predict the heliospheric propagation of CMEs

2021 ◽  
Author(s):  
Jasa Calogovic ◽  
Mateja Dumbović ◽  
Davor Sudar ◽  
Bojan Vršnak ◽  
Karmen Martinić ◽  
...  
Keyword(s):  
Space Weather ◽  
Web Application ◽  
Situational Awareness ◽  
Solar Wind Speed ◽  
Absolute Error ◽  
Computational Time ◽  
Ensemble Model ◽  
Arrival Times ◽  
Weather Forecasters ◽  
Input Parameters

<p><span>The Drag-based Model (DBM) is an analytical model for heliospheric propagation of Coronal Mass Ejections (CMEs) that predicts the CME arrival time and speed at Earth or any other given target in the solar system. It is based on the equation of motion and depends on initial CME parameters, background solar wind speed, w and the drag parameter γ. A very short computational time of DBM (< 0.01s) allowed us to develop the Drag-Based Ensemble Model (DBEM) that considers the variability of model input parameters by making an ensemble of n different input parameters to calculate the distribution and significance of the DBM results. Using such an approach, we apply DBEM to determine the most likely CME arrival times and speeds, quantify the prediction uncertainties and calculate the confidence intervals. Recently, a new DBEMv3 version was developed including the various improvements and Graduated Cylindrical Shell (GCS) option for the CME geometry input as well as the CME propagation visualizations. Thus, we compare the DBEMv3 with previous DBEM versions (e.g. DBEMv2), evaluate it and determine the DBEMv3 performance and errors by using various CME-ICME lists. Compared to the previous versions, the DBEMv3 provides very similar results for all calculated output parameters with slight improvement in the performance. Based on the evaluation performed for 146 CME-ICME pairs, the DBEMv3 performance with mean error (ME) of -11.3 h, mean absolute error (MAE) of 17.3 h was obtained, similar to previous DBM and DBEM evaluations. Fully operational DBEMv3 web application was integrated as one of the ESA Space Situational Awareness portal services (https://swe.ssa.esa.int/current-space-weather) providing an important tool for space weather forecasters.</span></p>

Predicting heliospheric propagation of CMEs with probabilistic Drag-Based Ensemble Model (DBEM)

2020 ◽  
Author(s):  
Jaša Čalogović ◽  
Mateja Dumbović ◽  
Bojan Vršnak ◽  
Davor Sudar ◽  
Manuela Temmer ◽  
...  
Keyword(s):  
Solar Wind ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Input Parameter ◽  
Source Region ◽  
Space Environment ◽  
Computational Time ◽  
Ensemble Model ◽  
The Earth ◽  
Input Parameters

<p><span>Understanding space weather driven by the solar activity is crucial as it can affect various human technologies, health as well as it can have important implications for the space environment near the Earth and the Earth’s atmosphere. In order to better asses space weather forecasts various empirical, drag-based and MHD models have been developed to predict the arrival time of CMEs. One of them is the analytical Drag-based Model (DBM) applying the equation of CME motion which is determined by the drag force from the background solar wind acting on the CME. DBM predictions depend on various initial parameters such as CME launch speed, background solar wind speed and empirically derived drag parameter as well CME’s angular half-width and longitude of CME source region for a DBM CME cone geometry. Since many of input parameters may be inaccurate or unreliable due to limited observations, the Drag-Based Ensemble Model (DBEM) was developed that considers the variability of model input parameters by making an ensemble of a number of different input parameters to calculate a distribution and significance of DBM results. DBM has the advantage of having very short computational time (< 0.01s) and DBEM ensemble runs with many thousand members can be performed within few seconds on a normal computer. Using such approach, DBEM can determine the most likely CME arrival times and speeds, quantify the prediction uncertainties and calculate the forecast confidence intervals. Recently, DBEM web interface was also integrated as one of the ESA Space Situational Awareness web portal space weather services (http://swe.ssa.esa.int/heliospheric-weather). We’ll present the recent DBEM developments together with the validation of its predictions using observations and other models as well as the input parameter sensitivity tests.</span></p>

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.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3380 ◽  
Author(s):  
Sepehr Makhsous ◽  
Mukund Bharadwaj ◽  
Benjamin E. Atkinson ◽  
Igor V. Novosselov ◽  
Alexander V. Mamishev
Keyword(s):  
Web Application ◽  
Dietary Assessment ◽  
Three Dimensional ◽  
Absolute Error ◽  
Nutritional Intake ◽  
Diabetic Patients ◽  
Dietary Recall ◽  
Global Epidemic ◽  
User Input ◽  
3D Measurement System

Diabetes is a global epidemic that impacts millions of people every year. Enhanced dietary assessment techniques are critical for maintaining a healthy life for a diabetic patient. Moreover, hospitals must monitor their diabetic patients’ food intake to prescribe a certain amount of insulin. Malnutrition significantly increases patient mortality, the duration of the hospital stay, and, ultimately, medical costs. Currently, hospitals are not fully equipped to measure and track a patient’s nutritional intake, and the existing solutions require an extensive user input, which introduces a lot of human errors causing endocrinologists to overlook the measurement. This paper presents DietSensor, a wearable three-dimensional (3D) measurement system, which uses an over the counter 3D camera to assist the hospital personnel with measuring a patient’s nutritional intake. The structured environment of the hospital provides the opportunity to have access to the total nutritional data of any meal prepared in the kitchen as a cloud database. DietSensor uses the 3D scans and correlates them with the hospital kitchen database to calculate the exact consumed nutrition by the patient. The system was tested on twelve volunteers with no prior background or familiarity with the system. The overall calculated nutrition from the DietSensor phone application was compared with the outputs from the 24-h dietary recall (24HR) web application and MyFitnessPal phone application. The average absolute error on the collected data was 73%, 51%, and 33% for the 24HR, MyFitnessPal, and DietSensor systems, respectively.

scholarly journals A rapidly deployed, interactive, online visualization system to support fatality management during the coronavirus disease 2019 (COVID-19) pandemic

2020 ◽  
Vol 27 (12) ◽  
pp. 1943-1948
Author(s):  
Smiti Kaul ◽  
Cameron Coleman ◽  
David Gotz
Keyword(s):  
North Carolina ◽  
Web Application ◽  
Situational Awareness ◽  
Tabular Data ◽  
Local Public Health ◽  
Visualization System ◽  
Data Collection Process ◽  
Public Health Officials ◽  
State And Local ◽  
Future Work

Abstract Objective To create an online visualization to support fatality management in North Carolina. Materials and Methods A web application aggregates online datasets for coronavirus disease 2019 (COVID-19) infection rates and morgue utilization. The data are visualized through an interactive, online dashboard. Results The web application was shared with state and local public health officials across North Carolina. Users could adjust interactive maps and other statistical charts to view live reports of metrics at multiple aggregation levels (eg, county or region). The application also provides access to detailed tabular data for individual facilities. Discussion Stakeholders found this tool helpful for providing situational awareness of capacity, hotspots, and utilization fluctuations. Timely reporting of facility and county data were key, and future work can help streamline the data collection process. There is potential to generalize the technology to other use cases. Conclusions This dashboard facilitates fatality management by visualizing county and regional aggregate statistics in North Carolina.

scholarly journals The Space Weather Threat to Situational Awareness, Communications, and Positioning Systems

2015 ◽  
Vol 43 (9) ◽  
pp. 3086-3098 ◽  
Author(s):  
Dale C. Ferguson ◽  
Simon Peter Worden ◽  
Daniel E. Hastings
Keyword(s):  
Space Weather ◽  
Situational Awareness ◽  
Positioning Systems

scholarly journals Implementation of Bessel's method for solar eclipses prediction in the WRF-ARW model

2016 ◽  
Vol 16 (9) ◽  
pp. 5949-5967 ◽  
Author(s):  
Alex Montornès ◽  
Bernat Codina ◽  
John W. Zack ◽  
Yolanda Sola
Keyword(s):  
Solar Radiation ◽  
Solar Eclipse ◽  
Weather Prediction ◽  
Grid Point ◽  
Absolute Error ◽  
Resource Assessment ◽  
Surface Radiation ◽  
Computational Time ◽  
Arw Model ◽  
Solar Eclipses

Abstract. Solar eclipses are predictable astronomical events that abruptly reduce the incoming solar radiation into the Earth's atmosphere, which frequently results in non-negligible changes in meteorological fields. The meteorological impacts of these events have been analyzed in many studies since the late 1960s. The recent growth in the solar energy industry has greatly increased the interest in providing more detail in the modeling of solar radiation variations in numerical weather prediction (NWP) models for the use in solar resource assessment and forecasting applications. The significant impact of the recent partial and total solar eclipses that occurred in the USA (23 October 2014) and Europe (20 March 2015) on solar power generation have provided additional motivation and interest for including these astronomical events in the current solar parameterizations.Although some studies added solar eclipse episodes within NWP codes in the 1990s and 2000s, they used eclipse parameterizations designed for a particular case study. In contrast to these earlier implementations, this paper documents a new package for the Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model that can simulate any partial, total or hybrid solar eclipse for the period 1950 to 2050 and is also extensible to a longer period. The algorithm analytically computes the trajectory of the Moon's shadow and the degree of obscuration of the solar disk at each grid point of the domain based on Bessel's method and the Five Millennium Catalog of Solar Eclipses provided by NASA, with a negligible computational time. Then, the incoming radiation is modified accordingly at each grid point of the domain.This contribution is divided in three parts. First, the implementation of Bessel's method is validated for solar eclipses in the period 1950–2050, by comparing the shadow trajectory with values provided by NASA. Latitude and longitude are determined with a bias lower than 5  ×  10−3 degrees (i.e.,  ∼  550 m at the Equator) and are slightly overestimated and underestimated, respectively. The second part includes a validation of the simulated global horizontal irradiance (GHI) for four total solar eclipses with measurements from the Baseline Surface Radiation Network (BSRN). The results show an improvement in mean absolute error (MAE) from 77 to 90 % under cloudless skies. Lower agreement between modeled and measured GHI is observed under cloudy conditions because the effect of clouds is not included in the simulations for a better analysis of the eclipse outcomes. Finally, an introductory discussion of eclipse-induced perturbations in the surface meteorological fields (e.g., temperature, wind speed) is provided by comparing the WRF–eclipse outcomes with control simulations.

scholarly journals Even moderate geomagnetic pulsations can cause fluctuations of foF2 frequency of the auroral ionosphere

2020 ◽  
Author(s):  
Nadezda Yagova ◽  
Alexander Kozlovsky ◽  
Evgeny Fedorov ◽  
Olga Kozyreva
Keyword(s):  
Space Weather ◽  
Critical Frequency ◽  
Dynamic Pressure ◽  
Solar Wind Speed ◽  
Pressure Fluctuations ◽  
Weather Conditions ◽  
Geomagnetic Pulsations ◽  
Station Pair ◽  
Using Data ◽  
Magnetic Meridian

Abstract. Using data of the ionosonde in Sodankyla, (SOD, 67° N, 27° E, Finland), variations of the critical frequency of o-mode radiowave reflected from ionospheric F2 layer (foF2) in 1–5 mHz frequency range and their possible association with long period (Pc5/Pi3) geomagnetic pulsations are studied. For that, a technique of automatic detection of the foF2 critical frequency from an ionogram is developed and applied to daytime Pc5/Pi3 geomagnetic pulsations and foF2 fluctuations during several months of years 2014–2015 near the maximum of 24-th Solar cycle. The variations of foF2 are compared with the Pc5/Pi3 geomagnetic pulsations at SOD station, and the influence of pulsations' spatial scale is analyzed with the data of a station pair located at the same magnetic meridian but separated in latitude. The variations of foF2 are in the majority of cases decoupled from the geomagnetic pulsations on the ground. Meanwhile, the analysis of geomagnetic and foF2 variations show intervals with noticeable coherence for both horizontal components. These coherent pulsations are predominantly registered in the afternoon sector of the magnetic local time (MLT). Statistically, their spectral content, polarization and spatial distribution differ from averaged parameters of post-noon Pc5 pulsations. The pulsations, coherent to foF2 fluctuations, demonstrate features typical for Alfven field-line resonance. The analysis of space weather conditions favorable for the occurrence of coherent geomagnetic/foF2 pulsations show that these pulsations are registered mostly under moderately disturbed conditions. Comparison of space weather parameters for all the intervals analyzed and the intervals of high geomagnetic/foF2 coherence show that the latter correspond mostly to intermediate values of indexes of geomagnetic (Dst) and auroral (AE) activity, solar wind speed and dynamic pressure fluctuations.

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