Extraction and Visual Analysis of Potential Vorticity Banners around the Alps

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.

[1]  R. Houze,et al.  The MAP special observing period , 2001 .

[2]  D. Lüthi,et al.  Evaluation of the convection‐resolving climate modeling approach on continental scales , 2017 .

[3]  Michael Sprenger,et al.  The Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP , 2002 .

[4]  Robert S. Laramee,et al.  The State of the Art in Flow Visualisation: Feature Extraction and Tracking , 2003, Comput. Graph. Forum.

[5]  David H. Eberly,et al.  Ridges in Image and Data Analysis , 1996, Computational Imaging and Vision.

[6]  Natascha Kljun,et al.  Frontal modification and lee cyclogenesis in the Alps: A case study using the ALPEX reanalysis data set , 2001 .

[7]  Gordon L. Kindlmann,et al.  Rendering and Extracting Extremal Features in 3D Fields , 2018, Comput. Graph. Forum.

[8]  C. Runge.  Ueber die numerische Auflösung von Differentialgleichungen , 1895 .

[9]  Hans-Christian Hege,et al.  Vortex and Strain Skeletons in Eulerian and Lagrangian Frames , 2007, IEEE Transactions on Visualization and Computer Graphics.

[10]  C. Schär,et al.  Low-Level Potential Vorticity and Cyclogenesis to the Lee of the Alps , 1998 .

[11]  David C. Banks,et al.  Vortex tubes in turbulent flows: identification, representation, reconstruction , 1994, Proceedings Visualization '94.

[12]  Olga Sorkine-Hornung,et al.  Topology‐based Smoothing of 2D Scalar Fields with C1‐Continuity , 2010, Comput. Graph. Forum.

[13]  Tino Weinkauf,et al.  Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields , 2017, Comput. Graph. Forum.

[14]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[15]  Rüdiger Westermann,et al.  Robust Detection and Visualization of Jet-Stream Core Lines in Atmospheric Flow , 2018, IEEE Transactions on Visualization and Computer Graphics.

[16]  C. Schär,et al.  Heavy precipitation in a changing climate: Does short‐term summer precipitation increase faster? , 2015 .

[17]  J. Steppeler,et al.  Meso-gamma scale forecasts using the nonhydrostatic model LM , 2003 .

[18]  H. Ertel.  Ein neuer hydrodynamischer Erhaltungssatz , 1942, Naturwissenschaften.

[19]  P. Debye,et al.  Näherungsformeln für die Zylinderfunktionen für große Werte des Arguments und unbeschränkt veränderliche Werte des Index , 1909 .

[20]  Hans-Peter Seidel,et al.  Saddle connectors - an approach to visualizing the topological skeleton of complex 3D vector fields , 2003, IEEE Visualization, 2003. VIS 2003..

[21]  Holger Theisel,et al.  Rotation Invariant Vortices for Flow Visualization , 2016, IEEE Transactions on Visualization and Computer Graphics.

[22]  Guido Gerig,et al.  Towards a shape model of white matter fiber bundles using diffusion tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[23]  Holger Theisel,et al.  The State of the Art in Vortex Extraction , 2018, Comput. Graph. Forum.

[24]  Hans-Christian Hege,et al.  Cores of Swirling Particle Motion in Unsteady Flows , 2007, IEEE Transactions on Visualization and Computer Graphics.

[25]  Roger C. Strawn,et al.  Computer Visualization Of Vortex Wake Systems , 1998 .

[26]  B. Hoskins.  The role of potential vorticity in symmetric stability and instability , 1974 .

[27]  B. Hoskins,et al.  On the use and significance of isentropic potential vorticity maps , 2007 .

[28]  T. Hoefler,et al.  Kilometer-Scale Climate Models: Prospects and Challenges , 2020 .

[29]  Simon K. Siedersleben,et al.  The Missing Link between Terrain-Induced Potential Vorticity Banners and Banded Convection , 2016 .

[30]  Michael Sprenger,et al.  Global Climatologies of Eulerian and Lagrangian Flow Features based on ERA-Interim , 2017 .

[31]  Hans Hagen,et al.  A Survey of Topology‐based Methods in Visualization , 2016, Comput. Graph. Forum.

[32]  Tobias Gysi,et al.  Towards a performance portable, architecture agnostic implementation strategy for weather and climate models , 2014, Supercomput. Front. Innov..

[33]  D. Lüthi,et al.  Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19 , 2016 .

[34]  Hans-Peter Seidel,et al.  Path Line Attributes - an Information Visualization Approach to Analyzing the Dynamic Behavior of 3D Time-Dependent Flow Fields , 2009, Topology-Based Methods in Visualization II.

[35]  H. Miura,et al.  Identification of Tubular Vortices in Turbulence , 1997 .

[36]  Bernhard Preim,et al.  Ieee Transactions on Visualization and Computer Graphics 1 Blood Flow Clustering and Applications in Virtual Stenting of Intracranial Aneurysms , 2022 .

[37]  H. Wernli,et al.  Life Cycle Study of a Diabatic Rossby Wave as a Precursor to Rapid Cyclogenesis in the North Atlantic—Dynamics and Forecast Performance , 2011 .

[38]  Rüdiger Westermann,et al.  Visualization in Meteorology—A Survey of Techniques and Tools for Data Analysis Tasks , 2018, IEEE Transactions on Visualization and Computer Graphics.

[39]  B. Hoskins,et al.  Conditional symmetric instability - a possible explanation for frontal rainbands , 1979 .

[40]  D. Lüthi,et al.  Structure and dynamics of an Alpine potential‐vorticity banner , 2003 .

[41]  D. Schultz,et al.  Convective Snowbands Downstream of the Rocky Mountains in an Environment with Conditional, Dry Symmetric, and Inertial Instabilities , 2010 .

[42]  Brian Cabral,et al.  Imaging vector fields using line integral convolution , 1993, SIGGRAPH.

[43]  Ronald Peikert,et al.  The "Parallel Vectors" operator-a vector field visualization primitive , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[44]  Torsten Hoefler,et al.  Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0 , 2017 .