scholarly journals Assimilating Weather Data into a Digital Event Gazetteer of Airborne Parachute Operations during the French Indochina War

2017 ◽  
Vol 10 (1) ◽  
pp. 19-34
Author(s):  
Johnathan P. Kirk ◽  
Gordon A. Cromley

Abstract Modern datasets cataloging historical events, known as digital event gazetteers, feature spatiotemporal data regarding events that enable analysis through parameters including location and other descriptive information of those events. Weather and climate data represent two dimensions of spatiotemporal information, which can enhance understanding of historical events. A recently published digital event gazetteer of airborne parachute operations [opérations aéroportées (OAPs)] during and prior to the French Indochina War, spanning from 1945 to 1954, represents an opportunity to associate discrete historical events with weather information. This study outlines a methodology for assimilating weather data into the construct of a digital event gazetteer and then demonstrates example analyses of how the weather and climate conditions in Indochina may relate to OAPs during the war. A synoptic classification, utilizing the self-organizing maps procedure, is performed using daily mean sea level pressure data from 1945 to 2010, from a twentieth-century reanalysis dataset, to characterize weather patterns over the Indochina Peninsula. Since observations are sparse during the years of the conflict, the resulting weather patterns are associated with modern precipitation observations in the area, as a representation of wet and dry patterns during the war. The appropriate daily weather pattern is then assigned to each OAP in order to investigate its relationship with the weather and climate patterns of Indochina, including the influence of monsoon seasons, and how the resulting precipitation patterns affected combat operations across the theater. Additionally, specific OAPs of various missions are analyzed to investigate how weather patterns may have affected operation planning during the French Indochina War.

2019 ◽  
Vol 279 ◽  
pp. 03007
Author(s):  
Ján Hollý ◽  
Adela Palková

The issue of climate change is undeniably demonstrating its presence. Consequently, there is a rising need to be prepared for upcoming threats by any means possible. One of the precautions includes obtaining the information characterizing the expected impact of global warming. This will allow authorities and other stakeholders to act accordingly in time. The article presents the assessment of the extent of impact of energy-related construction solutions in dwelling type unit situated in Central Europe region under the 21st century climate conditions. The findings represent eventual demands of energy for cooling and heating and its prospective savings. This is conducted by consecutively and automatically changing the parameters in individual simulation runs. As a basis for simulations, regionally scaled weather data of three different climate areas are used. These data are based on the emission scenarios by IPCC and are reaching to the year 2100. The selection of assessed parameters and climate data application are briefly explained in the article. The results of simulations are evaluated and recommended solutions are stated in regard to the specific energy-related construction changes. The aim is to successfully mitigate and adapt to the climate change phenomenon.


2011 ◽  
Vol 7 (6) ◽  
pp. 4173-4221
Author(s):  
H. Castebrunet ◽  
N. Eckert ◽  
G. Giraud

Abstract. Snow avalanche activity is controlled to a large extent by snow and weather patterns. However, its response to climate fluctuations remains poorly documented. Previous studies have focused on direct extraction of trends in avalanche and winter climate data, and this study employs a time-implicit method to model annual avalanche activity in the French Alps during the 1958–2009 period from its most representative climatic drivers. Modeled snow and weather data for different elevations and aspects are considered as covariates that explain actual observed avalanche counts, modeled instability indexes, and a combination of both avalanche activity indicators. These three series present relatively similar fluctuations over the period and good consistency with historically harsh winters. A stepwise procedure is used to obtain regression models that accurately represent trends as well as high and low peaks with a small number of physically meaningful covariates, showing their climatic relevance. The activity indicators and their regression models seen as time series show, within a high interannual variability, a predominant bell-shaped pattern presumably related to a short period of colder and snowier winters around 1980, as well as a very slight but continuous increase between 1975 and 2000 concomitant with warming. Furthermore, the regression models quantify the respective weight of the different covariates, mostly temperature anomalies and south-facing snowpack characteristics to explain the trends and most of the exceptional winters. Regional differences are discussed as well as seasonal variations between winter and spring activity and confirm rather different snow and weather regimes influencing avalanche activity over the Northern and Southern Alps, depending on the season.


2021 ◽  
Author(s):  
Raed Hamed ◽  
Anne Van Loon ◽  
Jeroen Aerts ◽  
Dim Coumou

<p>The US agriculture system supplies more than one third of globally traded soybean, of which 90% is produced under rainfed agriculture. This makes the commodity particularly sensitive to weather and climate variability. Previous research has shown that annually averaged climate conditions explain about a third of global crop yield variability. Additionally, although less studied so far, crops are sensitive to specific short-term weather conditions, isolated or co-occurring at key moments throughout the growing season. Here we aim to identify key within-season weather and climate variables that can explain soybean yield variability in the US while exploring synergies between drivers that can have compounding impacts. The study combines weather data from reanalysis and satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yield estimates using statistical methods that account for interaction effects. We also analyze the historic changes in identified key driving conditions in order to explore the effects of current climatic trends on yields. Our preliminary results indicate positive yield response to higher minimum temperature early and late in the season whereas the largest effect on soybeans is driven by the harmful co-occurrence of high temperature and low moisture levels during the summer flowering period significantly reducing yields on average in the US  by one standard deviation. The magnitude of the response to climate drivers varies across the spatial domain highlighting the need to focus on local and season specific management strategies. On the bright side, recent trends in temperature have not increased the likelihood of low yields. This is because the overall warming conditions reduce the risk of frost early and late in the season. Conversely, a peculiar cooling trend during the summer period attributed to agricultural land use is beneficial for yields when crops are most sensitive to high temperatures. Our study provides a detailed understanding of the current relationship between climate and soybean yields in the US. This is particularly relevant for adaptation and mitigation strategies aimed at avoiding low yields in a context of increasing food demand and climate change.</p>


Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5332
Author(s):  
Krzysztof Grygierek ◽  
Izabela Sarna

Today, there is a great deal of emphasis on reducing energy use in buildings for both economic and environmental reasons. Investors strongly encourage the insulating of buildings. Buildings without cooling systems can lead to a deterioration in thermal comfort, even in transitional climate areas. In this article, the effectiveness of natural ventilation in a passive cooling building is analyzed. Two options are considered: cooling with external air supplied to the building by fans, or by opening windows (automatically or by residents). In both cases, fuzzy controllers for the cooling time and supply airflow control are proposed and optimized. The analysis refers to a typical Polish single-family building. Simulations are made with the use of the EnergyPlus program, and the model is validated based on indoor temperature measurement. The calculations were carried out for different climate data: standard and future (warmed) weather data. Research has shown that cooling with external air can effectively improve thermal comfort with a slight increase in heating demand. However, to be able to reach the potential of such a solution, fans should be used.


2021 ◽  
Vol 2021 (002) ◽  
pp. 1-14
Author(s):  
Nancy Westcott ◽  
◽  
Jason Cooper ◽  
Karen Andsager ◽  
Leslie Stoecker ◽  
...  

The Climate Data Modernization Program Forts and Volunteer Observer Database (CDMP-Forts) currently consists of 450 keyed and 355 quality-controlled stations for the period 1788–1892, reaching across the United States. In conjunction with the Global Historical Climate Network (GHCN) daily data, this resource is invaluable for examining 19th century weather and climate in the United States. CDMP-Forts is incomplete, however, with a considerable amount of data remaining to be digitally transcribed and quality controlled. It is the intent of this paper to provide an overview of the processes involved in rescuing these data and to show important ways these data can be used and the considerations that may have to be taken to create meaningful analyses. Finally, the dataset is placed in the context of other global datasets and efforts to rescue historical weather data.


2014 ◽  
Author(s):  
Jamey Kain ◽  
Sarah Zhang ◽  
Mason Klein ◽  
Aravinthan Samuel ◽  
Benjamin de Bivort

Organisms use various strategies to cope with fluctuating environmental conditions. In diversified bet-hedging, a single genotype exhibits phenotypic heterogeneity with the expectation that some individuals will survive transient selective pressures. To date, empirical evidence for bet-hedging is scarce. Here, we observe that individual Drosophila melanogaster flies exhibit striking variation in light- and temperature-preference behaviors. With a modeling approach that combines real world weather and climate data to simulate temperature preference-dependent survival and reproduction, we find that a bet-hedging strategy may underlie the observed inter-individual behavioral diversity. Specifically, bet-hedging outcompetes strategies in which individual thermal preferences are heritable. Animals employing bet-hedging refrain from adapting to the coolness of spring with increased warm-seeking that inevitably becomes counterproductive in the hot summer. This strategy is particularly valuable when mean seasonal temperatures are typical, or when there is considerable fluctuation in temperature within the season. The model predicts, and we experimentally verify, that the behaviors of individual flies are not heritable. Finally, we model the effects of historical weather data, climate change, and geographic seasonal variation on the optimal strategies underlying behavioral variation between individuals, characterizing the regimes in which bet-hedging is advantageous.


2012 ◽  
Vol 8 (2) ◽  
pp. 855-875 ◽  
Author(s):  
H. Castebrunet ◽  
N. Eckert ◽  
G. Giraud

Abstract. Snow avalanche activity is controlled to a large extent by snow and weather patterns. However, its response to climate fluctuations remains poorly documented. Previous studies have focused on direct extraction of trends in avalanche and winter climate data, and this study employs a time-implicit method to model annual avalanche activity in the French Alps during the 1958–2009 period from its most representative climatic drivers. Modelled snow and weather data for different elevations and aspects are considered as covariates that explain actual observed avalanche counts, modelled instability indexes, and a combination of both avalanche activity indicators. These three series present relatively similar fluctuations over the period and good consistency with historically harsh winters. A stepwise procedure is used to obtain regression models that accurately represent trends as well as high and low peaks with a small number of physically meaningful covariates, showing their climatic relevance. The activity indicators and their regression models seen as time series show, within a high interannual variability, a predominant bell-shaped pattern presumably related to a short period of colder and snowier winters around 1980, as well as a very slight but continuous increase between 1975 and 2000 concomitant with warming. Furthermore, the regression models quantify the respective weight of the different covariates, mostly temperature anomalies and south-facing snowpack characteristics to explain the trends and most of the exceptional winters. Regional differences are discussed as well as seasonal variations between winter and spring activity and confirm rather different snow and weather regimes influencing avalanche activity over the Northern and Southern Alps, depending on the season.


Author(s):  
James Hawkes ◽  
Nicolau Manubens ◽  
Emanuele Danovaro ◽  
John Hanley ◽  
Stephan Siemen ◽  
...  

<div>Every day, ECMWF produces ~120TiB of raw weather data, represented as a six-dimensional dataset. This data is used to produce approximately 30TiB of user-defined products, which are disseminated worldwide. The raw data is also stored in the world's largest meteorological archive (MARS), currently holding over 300 PiB of primary data -- which is also served around the world on demand. As the resolution of ECMWFs global weather models increase over the next few years, the amount of raw data produced per day will increase into the petabytes, and the distribution of products and archived data becomes impossible. In-situ, on-the-fly data extraction and processing are required to sustain and increase the accessibility of ECMWFs big weather data.</div><div> </div><div>To meet these requirements, ECMWF is developing Polytope -- an open-source service which allows users to request arbitrary n-dimensional stencils ("polytopes") of data from highly-structured n-dimensional datasets. The data extraction is performed server-side (collocated with the data), allowing for large data reduction prior to transmission and less complexity for the user. For example, a user could request a polytope describing a flight path -- simultaneously crossing temporal and spatial axes. Polytope will return just a few bytes of data rather than large structured arrays of geo-spatial data which must be further post-processed by the user.</div><div> </div><div>Polytope is being partly developed under LEXIS, an EU-funded Horizon 2020 project which focuses on large-scale HPC & cloud workflows. The emphasis of LEXIS is on how HPC and cloud systems interact; how they can share data; and methods to compose workflows of tasks running on both cloud and HPC systems. Polytope will be used to provide a cross-centre weather and climate data API which connects to multiple high-performance data sources across Europe, and serves multiple cloud environments with this data.</div><div> </div><div>This poster will present the early developments and future vision of Polytope. It will also illustrate how it is used within the LEXIS project to enable complex weather and climate workflows, involving global forecasts, regional forecasts and cloud-based simulations.</div>


2021 ◽  
Vol 13 (11) ◽  
pp. 5918
Author(s):  
Giacomo Chiesa ◽  
Yingyue Li

Urban heat island and urban-driven climate variations are recognized issues and may considerably affect the local climatic potential of free-running technologies. Nevertheless, green design and bioclimatic early-design analyses are generally based on typical rural climate data, without including urban effects. This paper aims to define a simple approach to considering urban shapes and expected effects on local bioclimatic potential indicators to support early-design choices. Furthermore, the proposed approach is based on simplifying urban shapes to simplify analyses in early-design phases. The proposed approach was applied to a sample location (Turin, temperate climate) and five other climate conditions representative of Eurasian climates. The results show that the inclusion of the urban climate dimension considerably reduced rural HDD (heating degree-days) from 10% to 30% and increased CDD (cooling degree-days) from 70% to 95%. The results reveal the importance of including the urban climate dimension in early-design phases, such as building programming in which specific design actions are not yet defined, to support the correct definition of early-design bioclimatic analyses.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter

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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