scholarly journals Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Julie M. Mallon ◽  
Keith L. Bildstein ◽  
William F. Fagan
Keyword(s):  
Weather Conditions ◽  
Weather Data ◽  
Movement Activity ◽  
Cathartes Aura ◽  
Thermal Development ◽  
Daily Scale ◽  
Migrating Birds ◽  
Increasing Precipitation ◽  
Turkey Vultures ◽  
Local Weather

Abstract Background Migrating birds experience weather conditions that change with time, which affect their decision to stop or resume migration. Soaring migrants are especially sensitive to changing weather conditions because they rely on the availability of environmental updrafts to subsidize flight. The timescale that local weather conditions change over is on the order of hours, while stopovers are studied at the daily scale, creating a temporal mismatch. Methods We used GPS satellite tracking data from four migratory Turkey Vulture (Cathartes aura) populations, paired with local weather data, to determine if the decision to stopover by migrating Turkey Vultures was in response to changing local weather conditions. We analyzed 174 migrations of 34 individuals from 2006 to 2019 and identified 589 stopovers based on variance of first passage times. We also investigated if the extent of movement activity correlated with average weather conditions experienced during a stopover, and report general patterns of stopover use by Turkey Vultures between seasons and across populations. Results Stopover duration ranged from 2 h to more than 11 days, with 51 % of stopovers lasting < 24 h. Turkey Vultures began stopovers immediately in response to changes in weather variables that did not favor thermal soaring (e.g., increasing precipitation fraction and decreasing thermal updraft velocity) and their departure from stopovers was associated with improvements in weather that favored thermal development. During stopovers, proportion of activity was negatively associated with precipitation but was positively associated with temperature and thermal updraft velocity. Conclusions The rapid response of migrating Turkey Vultures to changing weather conditions indicates weather-avoidance is one of the major functions of their stopover use. During stopovers, however, the positive relationship between proportion of movement activity and conditions that promote thermal development suggests not all stopovers are used for weather-avoidance. Our results show that birds are capable of responding rapidly to their environment; therefore, for studies interested in external drivers of weather-related stopovers, it is essential that stopovers be identified at fine temporal scales.

scholarly journals Experiencing climate change: revisiting the role of local weather in affecting climate change awareness and related policy preferences

2021 ◽  
Vol 167 (3-4) ◽  
Author(s):  
Lea Gärtner ◽  
Harald Schoen
Keyword(s):  
Climate Change ◽  
Fixed Effects ◽  
Reference Frames ◽  
Weather Conditions ◽  
Policy Preferences ◽  
Weather Data ◽  
Standard Errors ◽  
Theoretical Argument ◽  
Local Weather

AbstractOver the last few years, climate change has risen to the top of the agenda in many Western democracies, backed by a growing share of voters supporting climate protection policies. To understand how and why these changes came about, we revisit the question whether personal experiences with increasingly unusual local weather conditions affect people’s beliefs about climate change and their related attitudes. We first take a closer look at the theoretical underpinnings and extend the theoretical argument to account for the differential impact of different weather phenomena, as well as the role of prior beliefs and individual reference frames. Applying mixed-effects regressions to a novel dataset combining individual-level multi-wave panel survey data from up to 18,010 German voters collected from 2016 to 2019 with weather data from 514 weather stations, we show that personally experiencing unusual or extreme local weather did not shape people’s awareness of climate change as a political problem or their climate policy preferences in a sustained manner. Even among people who may be considered most likely to exhibit such effects, we did not detect them. Moreover, we demonstrate that the common modeling strategy of combining fixed-effects regression with clustered standard errors leads to severely reduced standard errors and substantively different results. We conclude that it cannot be taken for granted that personally experiencing extreme weather phenomena makes a difference in perceptions of climate change and related policy preferences.

scholarly journals Effect of the presence of moisture at the wheel–rail interface during dew and damp conditions

2017 ◽  
Vol 232 (4) ◽  
pp. 979-989 ◽  
Author(s):  
BT White ◽  
R Nilsson ◽  
U Olofsson ◽  
AD Arnall ◽  
MD Evans ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Weather Conditions ◽  
Local Scale ◽  
Weather Data ◽  
Friction Modifiers ◽  
Precipitation Temperature ◽  
Rail Industry ◽  
Low Levels ◽  
Local Weather ◽  
Insight Into

Incidents involving low levels of adhesion between the wheel and rail are a recurrent issue in the rail industry. The problem has been mitigated using friction modifiers and traction enhancers, but a significant number of incidents still occur throughout the year. This study looks at the environmental conditions that surround periods of low adhesion in order to provide an insight into why low adhesion events occur. Network Rail Autumn data, which provided details on the time and location of low adhesion incidents, were compared against weather data on a national and then local scale. Low adhesion incidents have often been attributed to contamination on the rails, such as organic leaf matter, but these incidents also occur when no contamination is visible. The time, date and location of incidents were linked to local weather data to establish any specific weather conditions that could lead to these events. The effects of precipitation, temperature and humidity on rails were analysed in order to further the understanding of low adhesion in the wheel–rail contact, which will lead to adopting better methods of mitigating this problem.

scholarly journals Seasonal Variation in Prevalence of Mycoplasma hyopneumoniae and Other Respiratory Pathogens in Peri-Weaned, Post-Weaned, and Fattening Pigs with Clinical Signs of Respiratory Diseases in Belgian and Dutch Pig Herds, Using a Tracheobronchial Swab Sampling Technique, and Their Associations with Local Weather Conditions

Pathogens ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1202
Author(s):  
Frédéric A. C. J. Vangroenweghe ◽  
Olivier Thas
Keyword(s):  
Respiratory Diseases ◽  
Clinical Signs ◽  
Sampling Technique ◽  
Weather Conditions ◽  
Mycoplasma Hyopneumoniae ◽  
Weather Data ◽  
Respiratory Syndrome Virus ◽  
Respiratory Pathogens ◽  
Local Weather ◽  
Fattening Pigs

Besides Mycoplasma hyopneumoniae (M. hyopneumoniae), many other viruses and bacteria can concurrently be present in pigs. These pathogens can provoke clinical signs, known as porcine respiratory disease complex (PRDC). A sampling technique on live animals, namely tracheobronchial swab (TBS) sampling, was applied to detect different PRDC pathogens in pigs using PCR. The objective was to determine prevalence of different PRDC pathogens and their variations during different seasons, including correlations with local weather conditions. A total of 974 pig farms and 22,266 pigs were sampled using TBS over a 5-year period. TBS samples were analyzed using mPCR and results were categorized and analyzed according to the season of sampling and local weather data. In samples of peri-weaned and post-weaned piglets, influenza A virus in swine (IAV-S), porcine reproductive and respiratory syndrome virus—European strain (PRRSV1), and M. hyopneumoniae were found as predominant pathogens. In fattening pigs, M. hyopneumoniae, porcine circovirus type 2 (PCV-2) and PRRSV1 were predominant pathogens. Pathogen prevalence in post-weaned and finishing pigs was highest during winter, except for IAV-S and A. pleuropneumoniae, which were more prevalent during autumn. Associations between prevalence of several PRDC pathogens, i.e., M. hyopneumoniae, PCV-2 and PRRSV, and specific weather conditions could be demonstrated. In conclusion, the present study showed that many respiratory pathogens are present during the peri-weaning, post-weaning, and fattening periods, which may complicate the clinical picture of respiratory diseases. Interactions between PRDC pathogens and local weather conditions over the 5-year study period were demonstrated.

scholarly journals Arduino Sensor Integrated Drone for Weather Indices: A Prototype for Pre-flight Preparation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Theodore Karachalios ◽  
Dimitris Kanellopoulos ◽  
Fotis Lazarinis
Keyword(s):  
Meteorological Data ◽  
Objective Assessment ◽  
Weather Conditions ◽  
Barometric Pressure ◽  
Local Area ◽  
Weather Data ◽  
Map Reading ◽  
Micro Scale ◽  
Arduino Microcontroller ◽  
Local Weather

Commercial weather stations can effectively collect weather data for a specified area. However, their ground sensors limit the amount of data that can be logged, thus failing to collect precise meteorological data in a local area such as a micro-scale region. This happens because weather conditions at a micro-scale region can vary greatly even with small altitude changes. For now, drone operators must check the local weather conditions to ensure a safe and successful flight. This task is often a part of pre-flight preparations. Since flight conditions (and most important flight safety) are greatly affected by weather, drone operators need a more accurate localized weather map reading for the flight area. In this paper, we present the Arduino Sensor Integrated Drone (ASID) with a built-in meteorological station that logs the weather conditions in the vertical area where the drone will be deployed. ASID is an autonomous drone-based system that monitors weather conditions for pre-flight preparation. The operation of the ASID system is based on the Arduino microcontroller running automatic flight profiles to record meteorological data such as temperature, barometric pressure, humidity, etc. The Arduino microcontroller also takes photos of the horizon for an objective assessment of the visibility, the base, and the number of clouds.

Association of Wind Speed and the Occurrence of Sickle Cell Acute Painful Episodes: Results of a Case-Crossover Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3402-3402
Author(s):  
Vikki G. Nolan ◽  
Yuqing Zhang ◽  
Timothy Lash ◽  
Paola Sebastiani ◽  
Martin H. Steinberg
Keyword(s):  
Wind Speed ◽  
Crossover Study ◽  
Sickle Cell ◽  
Induction Time ◽  
Weather Conditions ◽  
Weather Data ◽  
Case Crossover ◽  
Measure Of Association ◽  
Local Weather

Abstract The role of weather as a possible trigger of sickle cell acute painful episodes has been debated for over 30 years. Early studies based on anecdotal evidence, such as patients reporting pain during the colder parts of the day or when swimming in the cold ocean on a particularly hot day, argued for an association between weather and the occurrence of pain. Recently published studies have shown an association with cold and rainy seasons and with windy weather and low humidity. Other studies however, have found no associations. A limitation of these studies is that they are based on seasonal trend data, mean monthly temperatures, hospital-wide visit rates, but not data at the individual level. To more accurately describe the role of weather as a trigger of painful events, we conducted a case-crossover study of the association of local weather conditions with the occurrence of individual pain crises. From the Cooperative Study of Sickle Cell Disease, 813 patients with 3,580 acute painful episodes were identified. For each pain episode, the hazard period was defined as the 48 hours preceding the onset of pain, and control periods were two periods of 48 hours, two weeks before, and two weeks after the pain crisis. Local weather data including temperature, wind speed and relative humidity, were downloaded from weather-source.com for each of the 23 participating centers for the years 1979 through 1982. Weather data were merged with clinical data and the association between the occurrence of pain crises and local weather conditions were studied using conditional logistic regression. We found an association between wind speed and the onset of pain, specifically wind speed during the 24 hour period preceding the onset of pain. Continuous measures of wind speed, mean and median wind speed during the 24 first hours of the hazard/control windows, showed significant associations with the occurrence of pain (p = 0.03 and p = 0.009, respectively). Analyzing wind speed as a categorical trait, dichotomized at the median (10 mph) for the same 24 hour period, showed a 14% increase (95% CI: 4% – 12%) in odds of pain, when comparing the high wind speed to lower wind speed (p = 0.005). To determine the most likely induction time, average wind speeds were determined for 4 hour intervals and their association with the onset of pain analyzed. Assuming a non-specific induction time will bias the measure of association toward the null, the interval with the highest OR should contain the most relevant induction time. We found that the interval from 13 hours to 16 hours prior to onset of pain has the largest measure of association [OR =1.01 (1.00 – 1.02), p = 0.026]. These results are in agreement with another study that found an association between wind speed and hospital visits for pain in the United Kingdom (Jones et. al, BJH 2005). These findings lend support to recent physiological and clinical studies that have suggested that skin cooling is associated with sickle vasoocclusion (Mohan et al. Clin Sci, 1998), and perhaps pain (Resar et al., J Pediatr 1991). Though pain is a common complication, and likely to have many potential triggers, physicians may wish to advise patients to take precautions on windy days by limiting skin exposure.

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.

Sea breeze and local weather conditions in a coastal Mediterranean mountain setting

Weather ◽  
2014 ◽  
Vol 69 (7) ◽  
pp. 184-190 ◽  
Author(s):  
Carla Mora
Keyword(s):  
Sea Breeze ◽  
Weather Conditions ◽  
Local Weather

Repeated Use of an Abandoned Vehicle by Nesting Turkey Vultures (Cathartes aura)

2010 ◽  
Vol 44 (1) ◽  
pp. 73-75 ◽  
Author(s):  
Lawrence D. Igl ◽  
Stephen L. Peterson
Keyword(s):  
Cathartes Aura ◽  
Repeated Use ◽  
Turkey Vultures

Analog Models for Empirical-Statistical Downscaling

2021 ◽  
Author(s):  
María Laura Bettolli
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Weather Conditions ◽  
Climate Information ◽  
Analog Method ◽  
Weather Typing ◽  
Regional Climates ◽  
Local Weather

Global climate models (GCM) are fundamental tools for weather forecasting and climate predictions at different time scales, from intraseasonal prediction to climate change projections. Their design allows GCMs to simulate the global climate adequately, but they are not able to skillfully simulate local/regional climates. Consequently, downscaling and bias correction methods are increasingly needed and applied for generating useful local and regional climate information from the coarse GCM resolution. Empirical-statistical downscaling (ESD) methods generate climate information at the local scale or with a greater resolution than that achieved by GCM by means of empirical or statistical relationships between large-scale atmospheric variables and the local observed climate. As a counterpart approach, dynamical downscaling is based on regional climate models that simulate regional climate processes with a greater spatial resolution, using GCM fields as initial or boundary conditions. Various ESD methods can be classified according to different criteria, depending on their approach, implementation, and application. In general terms, ESD methods can be categorized into subgroups that include transfer functions or regression models (either linear or nonlinear), weather generators, and weather typing methods and analogs. Although these methods can be grouped into different categories, they can also be combined to generate more sophisticated downscaling methods. In the last group, weather typing and analogs, the methods relate the occurrence of particular weather classes to local and regional weather conditions. In particular, the analog method is based on finding atmospheric states in the historical record that are similar to the atmospheric state on a given target day. Then, the corresponding historical local weather conditions are used to estimate local weather conditions on the target day. The analog method is a relatively simple technique that has been extensively used as a benchmark method in statistical downscaling applications. Of easy construction and applicability to any predictand variable, it has shown to perform as well as other more sophisticated methods. These attributes have inspired its application in diverse studies around the world that explore its ability to simulate different characteristics of regional climates.

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