scholarly journals Reduced-Order Modeling of Stratospheric Winds and Its Application in High-Altitude Balloon Trajectory Simulations

2017 ◽  
Vol 56 (6) ◽  
pp. 1753-1766 ◽  
Author(s):  
Sai Sudha Ramesh ◽  
Kian Meng Lim ◽  
Heow Pueh Lee ◽  
Boo Cheong Khoo
Keyword(s):  
High Altitude ◽  
Weather Conditions ◽  
Temporal Variations ◽  
Weather Data ◽  
Compact Representation ◽  
Station Keeping ◽  
Quasi Biennial Oscillation ◽  
Wind Profiles ◽  
Fourier Model ◽  
Trajectory Simulations

AbstractThe knowledge of weather conditions at the stratosphere is important for the planning and execution of high-altitude balloon flights, which require an accurate modeling of weather data over a period of time. Various methods based on statistical analysis, artificial neural networks, and cluster analysis have been employed to model the temporal variation of weather parameters. In the present study, a proper orthogonal decomposition (POD) method has been used to study the spatial as well as temporal variations of wind data in Singapore. The use of POD facilitates a compact representation of the weather dataset and aids in faster computation of wind profiles for use in balloon trajectory simulation. Further, the results reveal the existence of the quasi-biennial oscillation phenomenon, which is characteristic of equatorial easterly–westerly winds. This phenomenon enables the development of a Fourier prediction model, which can be used in real-time balloon trajectory simulations. The Fourier model is observed to be sensitive to wind velocity fluctuations, especially in the vicinity of alternating wind directions. However, it provides a reasonable projection of balloon trajectory, which can be used in preliminary planning and testing of high-altitude flights. Thus, a prior knowledge of wind profiles based on POD or a Fourier model aids in balloon station keeping. A simple case of altitude-controlled balloon flight is presented, and the results highlight the advantages of the present method in balloon station keeping.

scholarly journals Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1630
Author(s):  
Jane Jean Kiam ◽  
Eva Besada-Portas ◽  
Axel Schulte
Keyword(s):  
High Altitude ◽  
Weather Conditions ◽  
Mission Planning ◽  
Weather Data ◽  
Mission Success ◽  
Resolution Imaging ◽  
Solar Powered ◽  
High Level ◽  
Imaging Sensors ◽  
The Cost

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

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.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

Meteorological study for Gangotri Glacier and its comparison with other high altitude meteorological stations in central Himalayan region

2007 ◽  
Vol 38 (1) ◽  
pp. 59-77 ◽  
Author(s):  
Pratap Singh ◽  
Umesh K. Haritashya ◽  
Naresh Kumar
Keyword(s):  
High Altitude ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Meteorological Station ◽  
Himalayan Region ◽  
Valley Wind ◽  
Maximum Rainfall ◽  
Garhwal Himalayas ◽  
Wind Speeds ◽  
Gangotri Glacier

In spite of the vital role of high altitude climatology in melting of snow and glaciers, retreat or advancement of glaciers, flash floods, erosion and sediment transport, etc., weather conditions are not much studied for the high altitude regions of Himalayas. In this study, a comprehensive meteorological analysis has been made for the Gangotri Meteorological Station (Bhagirathi Valley, Garhwal Himalayas) using data observed for four consecutive melt seasons (2000–2003) covering a period from May to October for each year. The collected meteorological data includes rainfall, temperature, wind speed and direction, relative humidity, sunshine hours and evaporation. The results and their distribution over the different melt seasons were compared with available meteorological records for Dokriani Meteorological Station (Dingad Valley, Garhwal Himalayas) and Pyramid Meteorological Station (Khumbu Valley, Nepal Himalayas). The magnitude and distribution of temperature were found to be similar for different Himalayan regions, while rainfall varied from region to region. The influence of the monsoon was meagre on the rainfall in these areas. July was recorded to be the warmest month for all the regions and, in general, August had the maximum rainfall. For all the stations, daytime up-valley wind speeds were 3 to 4 times stronger than the nighttime down-valley wind speeds. It was found that the Gangotri Glacier area experienced relatively low humidity and high evaporation rates as compared to other parts of the Himalayas. Such analysis reveals the broad meteorological characteristics of the high altitude areas of the Central Himalayan region.

scholarly journals On the Relationship between Teleconnections and Taiwan's Streamflow: Evidence of Climate Regime Shift and Implications for Seasonal Forecasting

2016 ◽  
Author(s):  
Chia-Jeng Chen ◽  
Tsung-Yu Lee
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Phase Relationship ◽  
Temporal Variations ◽  
Pacific Basin ◽  
Climate Regime ◽  
Quasi Biennial Oscillation ◽  
Teleconnection Patterns ◽  
Integrated Response ◽  
The Relationship

Abstract. Interannual variations of catchment streamflow represent an integrated response to anomalies in regional moisture transport and atmospheric circulations, ultimately linked to large-scale climate oscillations. This study investigates the relationship between Taiwan's long-term summertime (July to September, JAS) streamflow and manifold teleconnection patterns. Lagged correlation analysis is conducted to calculate how JAS streamflow data derived at 28 upstream and 13 downstream gauges in Taiwan correlate with 14 teleconnection indices in the concurrent or preceding seasons. Out of the many indices, the West-Pacific and Pacific-Japan (PJ) patterns, both of which play a critical role in determining cyclonic activity in the western North Pacific basin, exhibit the highest concurrent correlations (most significant r = 0.48) with the JAS flows in Taiwan. At a one-month lead time, on the other hand, the Quasi-Biennial Oscillation significantly correlate with the JAS flows (most significant r = −0.66), indicating some forecasting utility. By further examining the correlation results using a 20-year moving window, peculiar temporal variations and possible climate regime shifts (CRS) can be revealed. To identify suspicious, abrupt changes in the correlation, a CRS test is employed. The late 1970s and 1990s are identified as two significant change points, and during the intermediate period, a marked in-phase relationship (r ~ 0.9) between Taiwan's streamflow and the PJ index is observed. It is verified that the two shifts are in concordance with the alteration of large-scale circulations in the Pacific basin. Discussion about the changes in pattern correlation and composite maps before and after the change point is carried out, and our results suggest that empirical forecasting techniques should take into account the effect of CRS on predictor screening.

scholarly journals Review of Weather Forecast Services for Ship Routing Purposes

2019 ◽  
Vol 26 (4) ◽  
pp. 80-89
Author(s):  
Marcin Życzkowski ◽  
Joanna Szłapczyńska ◽  
Rafał Szłapczyński
Keyword(s):  
Collision Avoidance ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Weather Data ◽  
Ocean Engineering ◽  
Ship Routing ◽  
Engineering Research ◽  
Fishing Vessels ◽  
Analysis Of Stability

Abstract Weather data is nowadays used in a variety of navigational and ocean engineering research problems: from the obvious ones like voyage planning and routing of sea-going vessels, through the analysis of stability-related phenomena, to detailed modelling of ships’ manoeuvrability for collision avoidance purposes. Apart from that, weather forecasts are essential for passenger cruises and fishing vessels that want to avoid the risk associated with severe hydro-meteorological conditions. Currently, there is a wide array of services that offer weather predictions. These services include the original sources – services that make use of their own infrastructure and research models – as well as those that further postprocess the data obtained from the original sources. The existing services also differ in their update frequency, area coverage, geographical resolution, natural phenomena taken into account and finally – output file formats. In the course of the ROUTING project, primarily addressing ship weather routing accounting for changeable weather conditions, the necessity arose to prepare a report on the state-of-the-art in numerical weather prediction (NWP) modelling. Based on the report, this paper offers a thorough review of the existing weather services and detailed information on how to access the data offered by these services. While this review has been done with transoceanic ship routing in mind, hopefully it will also be useful for a number of other applications, including the already mentioned collision avoidance solutions.

scholarly journals An IoT based Low-cost Weather Monitoring System for Smart Farming

2021 ◽  
Author(s):  
L.P.S.S.K. Dayananda ◽  
A. Narmilan ◽  
P. Pirapuraj
Keyword(s):  
Liquid Crystal ◽  
Mobile Phone ◽  
Real Time ◽  
Monitoring System ◽  
Liquid Crystal Display ◽  
Low Cost ◽  
Weather Conditions ◽  
Weather Data ◽  
The Internet ◽  
Arduino Uno

Background: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity. Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature, moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet, weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions. Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2 level, wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the Arduino Uno software. Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile phone. This smart weather station was used to monitor real-time weather conditions while sending weather information to the farmer’s mobile phone, allowing him to make better decisions to increase yield.

scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

Forecasting Forced Migration by Coupling an Agent-based Simulation Approach with Weather Data

2021 ◽  
Author(s):  
Diana Suleimenova ◽  
Alireza Jahani ◽  
Hamid Arabnejad ◽  
Derek Groen
Keyword(s):  
River Discharge ◽  
South Sudan ◽  
Forced Migration ◽  
Weather Conditions ◽  
Weather Data ◽  
Movement Speed ◽  
Precipitation Data ◽  
Agent Based ◽  
Displaced People ◽  
Modelling Approach

<p>There are nearly 80 million people forcibly displaced worldwide, of which 26 million are refugees and 45 million are internally displaced people (IDPs) (UNHCR, 2020). It is difficult to foresee and accurately forecast forced migration trends due to the severity and instability of conflicts or crises. However, it is possible to capture relevant aspects of this complex phenomenon and propose an approach forecasting future migration trends. Hence, we present an agent-based modelling approach, namely FLEE, that predicts the distribution of incoming refugees from a conflict origin to neighbouring countries (Suleimenova et al., 2017). Our aim is to assist governments, organisations and NGOs to efficiently allocate humanitarian resources, manage crises and save lives.</p><p>To construct a forced migration model, we obtain relevant data from three sources: the United Nations High Commissioner for Refugees (UNHCR, https://data2.unhcr.org) providing the number of forcibly displaced people in the conflict, the camp locations in neighbouring countries and their population capacities; the Armed Conflict Location and Event Data Project (ACLED, https://acled-data.com) for conflict locations and dates of battles; and the OpenStreetMaps platform (https://openstreetmap.org) to geospatially interconnect camp and conflict locations with other major settlements that reside en-route between these locations. Consequently, we simulate the constructed model using the FLEE code (https://github.com/djgroen/flee-release) and obtain the distribution of incoming forced displacement across destination camps. We were able to reproduce key trends in refugee counts found in the UNHCR data across Burundi, Central African Republic and Mali (Suleimenova et al., 2017), as well as investigated the impact of policy decisions, such as camp and border closures, in the South Sudan conflict (Suleimenova and Groen, 2020).</p><p>In our recent collaboration with Save the Children, we focus on an ongoing conflict in Ethiopia’s Tigray region and forecast IDP numbers within the region and refugee arrival counts in Sudan. We found that the number of arrivals in Sudan seem to depend strongly on whether the conflict will erupt in the east or in the west of Tigray. This seems to be a larger factor than the actual intensity of the conflict.</p><p>Moreover, our modelling approach allows us to investigate possible effects of weather conditions on forcibly displaced people by coupling FLEE with precipitation data, seasonal flood and river discharge levels. The purpose of coupling with the European Centre for Medium-Range Weather Forecasts (ECMWF) data is to identify the effect of weather conditions on the behaviour and movement speed of forced migrants.</p><p>The overall strategy is the static coupling of weather data where we have analysed 40 years of precipitation data for South Sudan to identify the precipitation range (minimum and maximum levels) as triggers which by the agents’ movement speed changes accordingly. Besides, we have used daily river discharge data from Global flood forecasting system (GloFAS) to explore the threshold for closing the link considering values of river discharge for return periods of 2, 5 and 20 years. Currently, we only use a simple rule with one threshold to define the river distance for a given link, which we aim to investigate further.</p><p><strong>References</strong><br>1. UNHCR (2020). Figures at a Glance, Available at: https://www.unhcr.org/figures-at-a-glance.html.<br>2. Suleimenova D., Bell D. and Groen D. (2017) “A generalized simulation development approach for predicting refugee destinations”. Scientific Reports 7:13377. (https://doi.org/10.1038/s41598-017-13828-9).<br>3. Suleimenova D. and Groen D. (2020) “How policy decisions affect refugee journeys in SouthSudan: A study using automated ensemble simulations”. Journal of Artificial Societies and Social Simulation 23(1)2, pp. 1-17. (https://doi.org/10.18564/jasss.4193).</p>

Early recognizing of a hurricanes from public satellite images

2021 ◽  
Author(s):  
A.A. Kuzmitsky ◽  
M.I. Truphanov ◽  
O.B. Tarasova ◽  
D.V. Fedosenko
Keyword(s):  
Satellite Images ◽  
A Priori ◽  
Vortex Formation ◽  
Weather Conditions ◽  
Local Area ◽  
Weather Data ◽  
Polar Coordinate System ◽  
Practical Implementation ◽  
Characteristic Points ◽  
Moving Points

One of the key tasks associated with the fast identification of powerful tropical hurricanes, the assessment of the growth of their power, is the formation of such an input dataset, which is based on data that are technically easy and accurately recorded and calculated using existing sources located in the open accessibility. The presented work is based on the analysis of satellite images as the main data sources, and on weather data as peripheral. An obvious advantage of satellite images in comparison with other sources of data on weather conditions is their high spatial resolution, as well as the ability to obtain data from various satellites, which increases the timeliness and accuracy of retrieving primary information. The developed approach consists in performing the following main interconnected iteratively performed groups of subtasks: calculation of feature points describing the location of individual cloud areas at different points in time by using different descriptors; comparison of the same cloud areas at specified times to analyze the local directions of cloud movements; tracking of cloudiness for specified time intervals; calculation of local features for selected points of cloudiness to recognize the origin and analyze turbulence; the formation of the dynamics of changes in the local area near the trajectory of the point; recognition of primary characteristic features characterizing the transformation of local turbulences into a stable vortex formation; identification of signs of the growing of a hurricane and assessment of the primary dynamics of the increase in its power; generalization and refinement of a priori given features by analyzing similar features of known cyclones. To detect points, a modified algorithm for finding them has been introduced. To describe the points, additional descriptors are introduced based on the normalized gradient measured for the neighborhood of neighboring points and cyclically changing in the polar coordinate system. A comparative analysis of the results of applying the created method and algorithm when compared with known similar solutions revealed the following distinctive features: introduction of additional invariant orientations of features when describing characteristic points and greater stability of detecting characteristic points when analyzing cloudiness, identification of cloudiness turbulence and analysis of changes in their local characteristics and movement parameters, formation of a set of generalizing distributions when analyzing a set of moving points for the subsequent recognition of the signs of a hurricane at its initial stages of formation. The developed approach was tested experimentally in the analysis of hurricanes video recordings and their movement in the Atlantic region for the period from 2010 to 2020. The developed general approach and a specific algorithm for estimating hurricane parameters based on cloud analysis are presented. The approach is applicable for practical implementation and allows accumulating data for detecting hurricanes in real time based on publicly available data for the development of a physical and mathematical model.

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