scholarly journals Assessing local daily temperatures by means of novel analog approaches: a case study based on the city of Augsburg, Germany

2021 ◽  
Author(s):  
Christian Merkenschlager ◽  
Stephanie Koller ◽  
Christoph Beck ◽  
Elke Hertig
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Modeling ◽  
Selection Process ◽  
Urban Climate ◽  
Domain Size ◽  
Normal Vector ◽  
Circulation Patterns ◽  
The City

AbstractWithin the scope of urban climate modeling, weather analogs are used to downscale large-scale reanalysis-based information to station time series. Two novel approaches of weather analogs are introduced which allow a day-by-day comparison with observations within the validation period and which are easily adaptable to future periods for projections. Both methods affect the first level of analogy which is usually based on selection of circulation patterns. First, the time series were bias corrected and detrended before subsamples were determined for each specific day of interest. Subsequently, the normal vector of the standardized regression planes (NVEC) or the center of gravity (COG) of the normalized absolute circulation patterns was used to determine a point within an artificial coordinate system for each day. The day(s) which exhibit(s) the least absolute distance(s) between the artificial points of the day of interest and the days of the subsample is/are used as analog or subsample for the second level of analogy, respectively. Here, the second level of analogy is a second selection process based on the comparison of gridded temperature data between the analog subsample and the day of interest. After the analog selection process, the trends of the observation were added to the analog time series. With respect to air temperature and the exceedance of the 90th temperature quantile, the present study compares the performance of both analog methods with an already existing analog method and a multiple linear regression. Results show that both novel analog approaches can keep up with existing methods. One shortcoming of the methods presented here is that they are limited to local or small regional applications. In contrast, less pre-processing and the small domain size of the circulation patterns lead to low computational costs.

Assessment of future heat events for the city of Augsburg by means of a normal vector based analog approach

2020 ◽  
Author(s):  
Christian Merkenschlager ◽  
Christoph Beck ◽  
Elke Hertig
Keyword(s):  
Time Series ◽  
Urban Areas ◽  
General Circulation ◽  
Heat Waves ◽  
Selection Process ◽  
General Circulation Models ◽  
Normal Vector ◽  
Urban Structure ◽  
Data Set ◽  
The City

<p>Under enhanced anthropogenic greenhouse gas forcing heat waves are only one example of climatic risks mankind has to deal with. Especially in urban areas where most of the people will live until the end of the 21<sup>st</sup> century heat waves are a serious risk factor since the urban heat island will reinforce such events. For the city of Augsburg, new analog methods are utilized for assessing the development and impacts of heat waves taking into account the varying urban structure.</p><p>For model calibration the temperature data from the Augsburg-Mühlhausen weather station operated by the German Weather Service (DWD) and atmospheric circulation variables of the ERA5 reanalysis data set were used to analyze the recent temperature development. For this purpose, the least deviation of the normal vector was used to determine a subsample of analogs corresponding to the day of interest. The normal vector was derived from the regression plane of the prevailing circulation on the respective day. Subsequently, the temperature patterns were used to define the analog day from the subsample. For future periods, the same method was applied to model data for two representative concentration pathways (RCP4.5, RCP8.5) of different general circulation models (GCM: ACCESS1-0, CNRM-CM5, MPI-ESM-LR). Thus, we derive future time series of analogs corresponding to events prevailing in the observational period. To account for projected trends of the GCMs, the trends of all time-series were first removed and, after the analog selection process, added again according to the trends of the GCMs.</p><p>Temperature extremes are defined as days with temperatures exceeding the 90<sup>th</sup> quantile (Q90) and heat days are defined as days where at least two temperature indices (TMIN, TMEAN, TMAX) exceed Q90. When at least three consecutive days are defined as heat day a heat wave is proclaimed. Analysis have shown that under consideration of RCP8.5 (RCP4.5) and all model runs the number of heat days in the end of the 21<sup>st</sup> century will be nine (five) times higher than within the reference period 1970-2000. Furthermore, the mean duration of heatwaves will extend by factor four (two), whereby heat waves of more than 30 (15) consecutive days are possible.</p>

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry

2020 ◽  
Author(s):  
Dinh Ho Tong Minh ◽  
Yen-Nhi NGO ◽  
Thu Trang Lê ◽  
Trung Chon Le ◽  
Hong Son Bui ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
European Space Agency ◽  
Ground Subsidence ◽  
Ho Chi Minh City ◽  
Vertical Movements ◽  
Space Agency ◽  
Permanent Scatterers ◽  
The City ◽  
East West

Ho Chi Minh City (HCMC), the most populous city and the economic center of Viet Nam, has faced ground subsidence in recent decades. This work aims at providing an unprecedented spatial extent coverage of the subsidence in HCMC in both horizontal and vertical components using Interferometric Synthetic Aperture Radar (InSAR) time series. For this purpose, an advanced InSAR technique PSDS (Permanent Scatterers and Distributed Scatterers) was applied to two big European Space Agency (ESA) Sentinel-1 datasets composed of 96 ascending and 202 descending images, acquired from 2014 to 2020 over HCMC area. A time series of 33 Cosmos SkyMED images was also used for comparison purpose. The combination of ascending and descending satellite passes allows the decomposition of the light of sight velocities into horizontal East-west and vertical components. By taking into account the presence of the horizontal East-west movement, our finding indicates that the precision of the decomposed vertical velocity can be improved up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most severe in areas along the Sai Gon river in the northwest-southeast axis and the southwest of the city with the maximum value up to 80 mm/year, consistent with findings in the literature. The magnitude of horizontal East-West velocities is relatively small and a large-scale westward motion can be observed in the northwest of the city at a rate of 2-5 mm/year. Together, these results reinforced the remarkable suitability of ESA's Sentinel-1 SAR for subsidence applications even for non-Europe countries such as Vietnam and Southeast Asia.

scholarly journals Extraction of Urban Information for Seismic Hazard and Risk Assessment in Lima, Peru Using Satellite Imagery

2013 ◽  
Vol 8 (2) ◽  
pp. 328-345 ◽  
Author(s):  
Masashi Matsuoka ◽  
◽  
Hiroyuki Miura ◽  
Saburoh Midorikawa ◽  
Miguel Estrada ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Surface Model ◽  
Building Inventory ◽  
Hazard And Risk ◽  
Hazard And Risk Assessment ◽  
Landsat Satellite ◽  
Time Series Images ◽  
The City ◽  
Area Data

Lima City, Peru, is, like Japan, on the verge of a strike by a massive earthquake. Building inventory data for the city need to be created for earthquake damage estimation, so the city was subjected to the extraction of spatial distribution of building age from Landsat satellite time-series images and an assessing building height from ALOS/PRISM images. Interband calculation of Landsat time-series images gives various indices relevant to land covering. The transition of indices was evaluated to clarify urban sprawl taking place in the northern, southern, and eastern parts of Lima City. Built-up area data were created for buildings by age. The height of large-scale mid-to-highrise buildings was extracted by applying spatial filtering for a DSM (Digital Surface Model) generated from stereovision PRISM images. As a result, buildings with a small square measure, color similar to that of their surroundings, or complicated shapes turned out to be difficult to detect.

scholarly journals Contrasts in Innovation: Pittsburgh Then and Now

2017 ◽  
Author(s):  
Michael J Madison
Keyword(s):  
Steel Industry ◽  
Large Scale ◽  
Production Model ◽  
Local Conditions ◽  
Industrial Economy ◽  
Industrial Regions ◽  
Post Industrial ◽  
The Relationship ◽  
The City

Assessments of the relationship among law, innovation, and economic growth often begin with one or more propositions of law or law practice and predict how changes might affect innovation or business practice. This approach is problematic when applied to questions of regional economic development, because historic and contemporary local conditions vary considerably. This paper takes a different tack. It takes a snapshot of one recovering post-industrial economy, in Pittsburgh, Pennsylvania, USA. For most of the 20th century, Pittsburgh's steelmakers were leading examples worldwide of American economic prowess. Pittsburgh was so vibrant with industry that a late 19th century travel writer called Pittsburgh "hell with the lid taken off," and he meant that as a compliment. In the early 1980s, however, Pittsburgh's steel economy collapsed, a victim of changing worldwide demand for steel and the industry's inflexible commitment to a large-scale integrated production model. As the steel industry collapsed, the Pittsburgh region collapsed, too. Unemployment in some parts of the Pittsburgh region peaked at 20%. More than 100,000 manufacturing jobs disappeared. Tens of thousands of residents moved away annually. Over the last 30 years, Pittsburgh has slowly recovered, building a new economy that balances limited manufacturing with a broad range of high quality services. In 2009, President Barack Obama took note of the region's rebirth by selecting the city to host a summit of the Group of 20 (G-20) finance ministers. The paper describes the characteristics of Pittsburgh today and measures the state of its renewal. It considers the extent, if any, to which law and the legal system have contributed to Pittsburgh's modern success, and it identifies lessons that this Pittsburgh case study might offer for other recovering and transitioning post-industrial regions.

Emotional and Subjective Volunteered Geographical Information

2017 ◽  
pp. 97-112 ◽  
Author(s):  
Jiri Panek
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Spatial Data ◽  
Large Scale ◽  
Geographical Information ◽  
Emotional Information ◽  
Spatial Features ◽  
Spatial Locations ◽  
The City

Crowdsroucing of emotional information can take many forms, from social networks data mining to large-scale surveys. The author presents the case-study of emotional mapping in Ostrava´s district Ostrava-Poruba, Czech Republic. Together with the local administration, the author crowdsourced the emotional perceptions of the location from almost 400 citizens, who created 4,051 spatial features. Additional to the spatial data there were 1,244 comments and suggestions for improvements in the district. Furthermore, the author is looking for patterns and hot-spots within the city and if there are any relevant linkages between certain emotions and spatial locations within the city.

scholarly journals Urban anomalies in response to rainstorms based on smartphone location data: a case study of eight cities in China

2019 ◽  
Author(s):  
Jiawei Yi ◽  
Yunyan Du ◽  
Fuyuan Liang ◽  
Tao Pei ◽  
Ting Ma ◽  
...  
Keyword(s):  
Time Series ◽  
Rainfall Intensity ◽  
Mainland China ◽  
Food Delivery ◽  
Location Based Services ◽  
Location Data ◽  
City Population ◽  
The City ◽  
Method Analysis

Abstract. This study explored city residents’ collective geo-tagged behaviors in response to rainstorms using the number of location request (NLR) data generated by smartphone users. We examined the rainstorms, flooding, NLR anomalies, as well as the associations among them in eight selected cities across the mainland China. The time series NLR clearly reflects cities’ general diurnal rhythm and the total NLR is moderately correlated with the total city population. Anomalies of NLR were identified at both the city and grid scale using the S-H-ESD method. Analysis results manifested that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating city residents request more location-based services (e.g. map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, sensitivity of the city residents’ collective geo-tagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which lead to increased location-based requests as shown by positive anomalies on the time series NLR.

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

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