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  • 1
    Publication Date: 2023-11-02
    Description: Extra‐tropical cyclones are an important source of weather variability in the mid‐latitudes. Multiple occurrences in a short period of time at a particular location are denominated serial cyclone clustering (SCC), and potentially lead to large societal impacts. We investigate the relationship between SCC affecting Western Europe and large‐scale weather regimes (WRs) in the North Atlantic‐European region in boreal winter. We find that SCC in low latitudes (45°N) is predominantly associated with the anticyclonic Greenland Blocking WR. In contrast, SCC in mid and high latitudes (55°N, 65°N) is mostly linked to different cyclonic WRs. Thereby, SCC occurs typically within a well‐established WR that builds up prior to SCC and decays after SCC. Thus, SCC events are closely associated with recurrent, quasi‐stationary and persistent large‐scale flow patterns (WRs). This mutual relationship reveals the potential of WRs in forecasting storm series and associated impacts on sub‐seasonal to seasonal time scales.
    Description: Plain Language Summary: Serial cyclone clustering describes the occurrence of multiple extra‐tropical cyclones within a certain time frame and a spatially restricted region. Since extra‐tropical cyclones can be associated with strong winds and heavy precipitation, multiple occurrences can lead to large cumulative impacts in the affected areas. We analyze the relationship between serial cyclone clustering (SCC) in Western Europe and so‐called weather regimes (WRs) in the North Atlantic‐European region in boreal winter. These regimes describe slow evolving and enduring large‐scale atmospheric circulation patterns. Relationships with certain regime types are identified but depend on the latitude at which the clustered frequency of extra‐tropical cyclones is found. When SCC occurs in low latitudes (45°N), it mostly appears coincident with anticyclonic large‐scale flow patterns. In contrast, SCC in mid and high latitudes (55°N, 65°N) often occurs simultaneously with different cyclonic regimes. We find that periods of SCC occur typically within WR life cycles pointing to the fact that both, the WRs and SCC periods, are interlinked. This relationship may facilitate forecasting storm series and associated impacts on time scales beyond 2 weeks.
    Description: Key Points: A close relationship is found between serial cyclone clustering (SCC) at 5°W and weather regimes (WRs) in the North Atlantic‐European region. SCC in mid and high latitudes (55°N, 65°N) is mainly associated with cyclonic and in low latitudes (45°N) with anticyclonic WR life cycles. Regardless of the selected latitude, SCC occurs mostly during an active regime life cycle and is manifested in a well‐established WR.
    Description: German Research Foundation
    Description: AXA Research Fund http://dx.doi.org/10.13039/501100001961
    Description: Helmholtz Association http://dx.doi.org/10.13039/501100009318
    Description: BMBF ClimXtreme
    Description: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim
    Keywords: ddc:551.5 ; serial cyclone clustering ; weather regimes ; atmospheric dynamics ; sub‐seasonal prediction
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2019
    Description: Automatic building extraction using a single data type, either 2D remotely-sensed images or light detection and ranging 3D point clouds, remains insufficient to accurately delineate building outlines for automatic mapping, despite active research in this area and the significant progress which has been achieved in the past decade. This paper presents an effective approach to extracting buildings from Unmanned Aerial Vehicle (UAV) images through the incorporation of superpixel segmentation and semantic recognition. A framework for building extraction is constructed by jointly using an improved Simple Linear Iterative Clustering (SLIC) algorithm and Multiscale Siamese Convolutional Networks (MSCNs). The SLIC algorithm, improved by additionally imposing a digital surface model for superpixel segmentation, namely 6D-SLIC, is suited for building boundary detection under building and image backgrounds with similar radiometric signatures. The proposed MSCNs, including a feature learning network and a binary decision network, are used to automatically learn a multiscale hierarchical feature representation and detect building objects under various complex backgrounds. In addition, a gamma-transform green leaf index is proposed to truncate vegetation superpixels for further processing to improve the robustness and efficiency of building detection, the Douglas–Peucker algorithm and iterative optimization are used to eliminate jagged details generated from small structures as a result of superpixel segmentation. In the experiments, the UAV datasets, including many buildings in urban and rural areas with irregular shapes and different heights and that are obscured by trees, are collected to evaluate the proposed method. The experimental results based on the qualitative and quantitative measures confirm the effectiveness and high accuracy of the proposed framework relative to the digitized results. The proposed framework performs better than state-of-the-art building extraction methods, given its higher values of recall, precision, and intersection over Union (IoU).
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by MDPI
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  • 3
    Publication Date: 2018-02-25
    Description: Remote Sensing, Vol. 10, Pages 355: Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network Remote Sensing doi: 10.3390/rs10020355 Authors: Haiqing He Min Chen Ting Chen Dajun Li Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images, with complex background variations are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method, which can significantly improve the matching performance of multi-temporal remote sensing images with complex background variations, is better than the state-of-the-art matching methods. In our experiments, the proposed method obtained a large number of evenly distributed matches (at least 10 times more than other methods) and achieved a high accuracy (less than 1 pixel in terms of root mean square error).
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by MDPI Publishing
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  • 4
    Publication Date: 2018-07-01
    Description: Slantwise convection, the process by which moist symmetric instability is released, has often been linked to banded clouds and precipitation, especially in frontal zones within extratropical cyclones. Studies also suggest that the latent heat release associated with slantwise convection can lead to a spinup of surface frontogenesis, which can enhance the rapid intensification of extratropical cyclones. However, most of these studies considered only local areas or short time durations. In this study, we provide a novel statistical investigation of the global climatology of the potential occurrence of slantwise convection, in terms of conditional symmetric instability, and its relationship with precipitating systems. Using the 6-hourly ERA-Interim, two different indices are calculated, namely, slantwise convective available potential energy (SCAPE) and vertically integrated extent of realizable symmetric instability (VRS), to assess the likelihood of occurrence of slantwise convection around the globe. The degree of association is quantified between these indices and the observed surface precipitation as well as the cyclone activity. The susceptibility of midlatitude cyclones to slantwise convection at different stages of their life cycle is also investigated. As compared to the nonexplosive cyclone cases, the time evolution of SCAPE and VRS within rapidly deepening cyclones exhibit higher values before, and a more significant drop after, the onset of rapid intensification, supporting the idea that the release of symmetric instability might contribute to the intensification of storms.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 5
    Publication Date: 2016-09-01
    Description: The goal of this work is to improve understanding of the mechanisms leading to a heavy rainfall event under the combined influences of the outer circulation of Typhoon Megi (2010), the Asian monsoon, and the topography of Taiwan. Megi is a case featuring high forecast uncertainty associated with its sudden recurvature, along with remote heavy rainfall over northeastern Taiwan (especially at Yilan) and its adjacent seas during 19–23 October 2010. An ensemble simulation is conducted, and characteristic ensemble members are separated into subgroups based on either track accuracy or rainfall forecast skill. Comparisons between different subgroups are made to investigate favorable processes for precipitation and how the differences between these subgroups affect the rainfall simulation.Several mechanisms leading to this remote rainfall event are shown. The northward transport of water vapor by Megi’s outer circulation provides a moisture-laden environment over the coastal area of eastern Taiwan. Meanwhile, the outer circulation of Megi (with high ) encounters the northeasterly monsoon (with low ), and strong vertical motion is triggered through isentropic lifting in association with low-level frontogenesis over the ocean northeast of Yilan. Most importantly, the northeasterly flow advects the moisture inland to the steep mountains in south-southwestern Yilan, where strong orographic lifting further induces torrential rainfall. In addition, the analyses further attribute the uncertainty in simulating Megi’s remote rainfall to several factors, including variations of storm track, strength and extension of the northeasterly monsoon, and, above all, the impinging angle of the upstream flow on the topography.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 6
    Publication Date: 2020-04-02
    Description: Budget analysis of a tendency equation is widely utilized in numerical studies to quantify different physical processes in a simulated system. While such analysis is often post-processed when the output is made available, it is well acknowledged that the closure of a budget is difficult to achieve without temporal and/or spatial averaging. Nevertheless, the development of errors in such calculations has not been systematically investigated. In this study, an inline budget retrieval method is first developed in the WRF v3.8.1 model and tested on a 2D idealized slantwise convection case with a focus on the momentum equations. This method extracts all the budget terms following the model solver, which gives a high accuracy, with a residual term always less than 0.1 % of the tendency term. Then, taking the inline values as truth, several offline budget analyses with different commonly used simplifications are performed to investigate how they may affect the accuracy of the estimation of individual terms and the resultant residual. These assumptions include using a lower-order advection operator than the one used in the model, neglecting grid staggering, or following a mathematically equivalent but transformed format of the governing equations. Errors in these post-processed analyses are found mostly over the area where the dynamics are the most active, thus impairing the subsequent physical interpretation. A maximum 99th percentile residual can reach 〉50 % of the concurrent tendency term, indicating the danger of neglecting the residual term as done in many budget studies. This work provides general guidance not only for budget diagnoses with the WRF model but also for minimizing the errors in post-processed budget calculations.
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
    Publication Date: 2013-09-25
    Description: Using special data from the field campaign of 2008 and an ensemble Kalman filter–based vortex initialization method, this study explores the impact of different track clusters categorized under the ensemble simulations of Typhoon Sinlaku (2008) on the associated precipitation. In particular, the distinct pattern of cumulative frequencies in the 28 members is identified to correspond to three types of track clusters. The simulation integrated from the initial ensemble mean slightly underestimates the maximum amount of the observed rainfall in central Taiwan by about 30%. The quantitative evaluation based on the equitable threat score indicates that members with tracks close to the best track produce more consistent rainfall distribution in northern Taiwan although their cumulative frequencies are underestimated. For members with southwestward-biased tracks, although the cumulative frequencies are closer to the observation, the simulated rainfall pattern is less consistent with the observation in northern Taiwan and the maximum rainfall amount is overestimated. The comparison of rainfall simulation during landfall between two representative members shows that the distinct differences in the rainfall amount and distribution are primarily associated with the track differences on the windward side of the mountain. With a finer horizontal grid resolution, the rainfall accumulation becomes greater as a result of the enhancement of updraft from the better-resolved topography, yet the cumulative frequency stays nearly unchanged. Based on ensemble simulations, this study highlights that the uncertainties in rainfall patterns and amounts can be assessed from ensemble track variations, thus providing better insights into the rainfall predictability associated with typhoons near Taiwan.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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