Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management
Abstract
:1. Introduction
2. Data
- CAPPI: reflectivity fields at a certain height (1 km, 3 km and 5 km CAPPIs were available) interpolated from PPI elevations. It is a good indicator of rainfall intensity.
- VIL: Product equivalent to the total amount of water available on a vertical column of the atmosphere. Large values indicate either large hail occurrence, large amounts of small hail, or very high rainfall intensity.
- ETM (echo top maximum): Corresponds to the maximum height at which there is a minimum reflectivity intensity of 20 dBZ. It is a good indicator of the top of storm clouds.
- DVIL: This incorporates ETM information to the VIL product, making it a better severe weather indicator with less dependence on seasonality, as it normalizes the height of the precipitable column (see, e.g., [26] for more information). This is a derived product, and is not directly calculated by the radar software. It can be obtained using Equation (1). Thus, thunderstorms may be compared and analyzed in terms of their severity with less seasonal dependence. The utility of this product makes even more sense if works such as [23] are considered. They concluded that VIL and ETM can aid ATC in route operations into the US.
3. Methodology
3.1. Identification and Characterization of Storm Structures
3.2. Tracking
3.3. Short-Term Nowcasting
3.4. Analysis of Results
4. Case Studies
5. Results
5.1. Individual Tracking and Nowcasting of Storm Cells for the Three Events
5.1.1. Milano Case, 11 May 2019
5.1.2. Venice Case, 7 July 2019
5.1.3. Bergamo Case, 6 August 2019
5.2. MODE Software for the Nowcasting Approach of SINOPTICA
5.3. Application of Nowcasting to Air Traffic Controller Support
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
4D-CARMA | Four-Dimensional Cooperative Arrival Manager |
ADS-B | Automatic Dependent Surveillance—Broadcast |
AMAN | Arrival Manager system |
ATC | Air Traffic Control |
ATM | Air Traffic Management |
CAPPI | Constant Altitude Plan Position Indicator |
CORTEC | Continuity Tracking Radar Echoes by Correlation |
DLR | German Aerospace Center |
DVIL | Density of the Vertical Integrated Liquid |
ESWD | European Severe Weather Database |
ETM | Echo Top Maximum |
MODE | Method for Object-Based Evaluation |
MXP | Malpensa airport |
PPI | Plan Position Indicator |
RaNDeVIL | Radar Nowcasting with Density of VIL |
ROI | Region Of Interest |
SCIT | Storm Cell Identification and Tracking |
References
- Reitmann, S.; Alam, S.; Schultz, M. Advanced Quantification of Weather Impact on Air Traffic Management. In Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), Vienna, Austria, 17–21 June 2019. [Google Scholar]
- Ohneiser, O.; Kleinert, M.; Muth, K.; Gluchshenko, O.; Ehr, H.; Gross, N.; Temme, M.-M. Bad Weather Highlighting: Advanced Visualization of Severe Weather and Support in Air Traffic Control Displays. In Proceedings of the 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), San Diego, CA, USA, 8–12 September 2019. [Google Scholar] [CrossRef]
- Department of the Air Force Washington DC. Weather for Aircrews. In Air Force Handbook 11-203; Department of the Air Force Washington DC: Washington, DC, USA, 1997; Volume 1, p. 234. [Google Scholar]
- Esbrí, L.; Rigo, T.; Llasat, M.C.; Aznar, B. Identifying Storm Hotspots and the Most Unsettled Areas in Barcelona by Analysing Significant Rainfall Episodes from 2013 to 2018. Water 2021, 13, 1730. [Google Scholar] [CrossRef]
- Rigo, T.; Llasat, M.C.; Esbrí, L. The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar. Geomatics 2021, 1, 347–368. [Google Scholar] [CrossRef]
- Kalchikhin, V.V.; Kobzev, A.A. Use of an optical rain gauge in a system of monitoring severe weather phenomena. IOP Conf. Ser. Earth Environ. Sci. 2018, 211, 012063. [Google Scholar] [CrossRef]
- Andrieu, H.; Creutin, J.D.; Delrieu, G.; Faure, D. Use of a weather radar for the hydrology of a mountainous area. Part I: Radar measurement interpretation. J. Hydrol. 1997, 193, 1–25. [Google Scholar] [CrossRef]
- Harrison, D.L.; Driscoll, S.J.; Kitchen, M. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorol. Appl. 2000, 7, 135–144. [Google Scholar] [CrossRef] [Green Version]
- Ye, B.Y.; Lee, G.W.; Park, H.M. Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm. Adv. Atmos. Sci. 2015, 32, 1217–1230. [Google Scholar] [CrossRef]
- Heymsfield, G.M.; Ghosh, K.K.; Chen, L.C. An interactive system for compositing digital radar and satellite data. J. Clim. Appl. Meteorol. 1983, 22, 705–713. [Google Scholar] [CrossRef]
- Rigo, T.; Farnell, C. Using maximum Vertical Integrated Liquid (VIL) maps for identifying hail-affected areas: An operative application for agricultural purposes. Tethys 2019, 16, 15–24. [Google Scholar] [CrossRef]
- Wilson, J.W.; Crook, N.A.; Mueller, C.K.; Sun, J.; Dixon, M. Nowcasting Thunderstorms: A Status Report. Bull. Am. Meteorol. Soc. 1998, 79, 2079–2099. [Google Scholar] [CrossRef]
- Dixon, M.; Wiener, G. TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology. J. Atmos. Ocean. Technol. 1993, 10, 785–797. [Google Scholar] [CrossRef]
- Johnson, J.T.; MacKeen, P.L.; Witt, A.; Mitchell, E.D.; Stumpf, G.J.; Eilts, M.D.; Thomas, K.W. The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather. Forecast. 1998, 13, 263–276. [Google Scholar] [CrossRef]
- Kyznarová, H.; Novák, P. CELLTRACK―Convective cell tracking algorithm and its use for deriving life cycle characteristics. Atmos. Res. 2009, 93, 317–327. [Google Scholar] [CrossRef]
- Bellon, A.; Zawadzki, I.; Kilambi, A.; Lee, H.C.; Lee, Y.H.; Lee, G. McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique. Asia-Pacific J. Atmos. Sci. 2010, 46, 369–381. [Google Scholar] [CrossRef]
- Atencia, A.; Kann, A.; Wang, Y.; Meier, F. Localized variational blending for nowcasting purposes. Meteorol. Zeit 2020, 29, 247–261. [Google Scholar] [CrossRef]
- del Moral, A.; Rigo, T.; Llasat, M.C. A radar-based centroid tracking algorithm for severe weather surveillance: Identifying split/merge processes in convective systems. Atmos. Res. 2018, 213, 110–120. [Google Scholar] [CrossRef]
- Chandrasekar, V. AI in Weather Radars. In Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 21–25 September 2020; pp. 1–3. [Google Scholar] [CrossRef]
- Zhou, K.; Zheng, Y.; Dong, W.; Wang, T. A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data. J. Atmos. Ocean. Technol. 2020, 37, 927–942. [Google Scholar] [CrossRef] [Green Version]
- Woo, W.C.; Wong, W.K. Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting. Atmosphere 2017, 8, 48. [Google Scholar] [CrossRef] [Green Version]
- Lin, H.H.; Tsai, C.C.; Liou, J.C.; Chen, Y.C.; Lin, C.Y.; Lin, L.Y.; Chung, K.S. Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme. Atmosphere 2020, 11, 1166. [Google Scholar] [CrossRef]
- Rubnich, M.; Delaura, R. An Algorithm to Identify Robust Convective Weather Avoidance Polygons in En Route Airspace. In Proceedings of the 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Fort Worth, TX, USA, 13–15 September 2010; p. 9164. [Google Scholar] [CrossRef] [Green Version]
- Temme, M.-M.; Gluchshenko, O.; Nöhren, L.; Kleinert, M.; Ohneiser, O.; Muth, K.; Ehr, H.; Groß, N.; Temme, A.; Lagasio, M.; et al. Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager. Aerospace 2023, 10, 210. [Google Scholar] [CrossRef]
- Vulpiani, G.; Pagliara, P.; Negri, M.; Rossi, L.; Gioia, A.; Giordano, P.; Alberoni, P.; Cremonini, R.; Ferraris, L.; Marzano, F.S. The Italian Radar Network within the National Early-Warning System for Multi-Risks Management. In Proceedings of the Fifth European Conference on Radar in Meteorology and Hydrology, Helsinki, Finland, 30 June–4 July 2008. [Google Scholar]
- Edwards, R.; Thompson, R.L. Nationwide comparisons of hail size with WSR-88D vertically integrated liquid water and derived thermodynamic sounding data. Weather Forecast. 1998, 13, 277–285. [Google Scholar] [CrossRef]
- Groenemeijer, P.; Púcik, T.; Holzer, A.M.; Antonescu, B.; Riemann-Campe, K.; Schultz, D.M.; Kühne, T.; Feuerstein, B.; Brooks, H.E.; Doswell, C.A.; et al. Severe Convective Storms in Europe: Ten Years of Research and Education at the European Severe Storms Laboratory. Bull. Am. Meteorol. Soc. 2017, 98, 2641–2651. [Google Scholar] [CrossRef]
- Rigo, T.; Carmen Llasat, M. Forecasting hailfall using parameters for convective cells identified by radar. Atmos. Res. 2016, 169, 366–376. [Google Scholar] [CrossRef]
- Qi, Y.; Zhang, J.; Zhang, P. A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates. Q. J. R. Meteorol. Soc. 2013, 139, 2233–2240. [Google Scholar] [CrossRef]
- Seo, B.-C.; Krajewski, W.F.; Qi, Y. Utility of Vertically Integrated Liquid Water Content for Radar-Rainfall Estimation: Quality Control and Precipitation Type Classification. Atmos. Res. 2020, 236, 104800. [Google Scholar] [CrossRef]
- Rigo, T.; Llasat, M.C. A methodology for the classification of convective structures using meteorological radar: Application to heavy rainfall events on the Mediterranean coast of the Iberian Peninsula. Nat. Hazards Earth Syst. Sci. 2004, 4, 59–68. [Google Scholar] [CrossRef] [Green Version]
- del Moral, A.; del Carmen Llasat, M.; Rigo, T. Connecting flash flood events with radar-derived convective storm characteristics on the northwestern Mediterranean coast: Knowing the present for better future scenarios adaptation. Atmos. Res. 2020, 238, 104863. [Google Scholar] [CrossRef]
- López, L. Convección Atmosférica Severa: Pronóstico e Identificación de Tormentas de Granizo. Ph.D. Thesis, Universidad de León, León, Spain, 2003; p. 207. [Google Scholar]
- Ceperuelo, M.; Llasat, M.C.; López, L.; García-Ortega, E.; Sánchez, J.L. Study of 11 September 2004 hailstorm event using radar identification of 2-D systems and 3-D cells. Adv. Geosci. 2006, 7, 215–222. [Google Scholar] [CrossRef] [Green Version]
- Davis, C.; Brown, B.; Bullock, R. Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Weather Rev. 2006, 134, 1772–1784. [Google Scholar] [CrossRef] [Green Version]
- Brown, B.G.; Gotway, J.H.; Bullock, R.; Gilleland, E.; Fowler, T.; Ahijevych, D.; Jensen, T. The Model Evaluation Tools (MET): Community Tools for Forecast Evaluation. In Proceedings of the 25th Conference on International Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, USA, 10 January 2009; Volume 9, p. 6. [Google Scholar]
- Newman, K.; Opatz, J.; Jensen, T.; Prestopnik, J.; Soh, H.; Goodrich, L.; Brown, B.; Bullock, R.; Halley Gotway, J. The MET Version 10.1.2 User’s Guide. Developmental Testbed Center. 2022. Available online: https://github.com/dtcenter/MET/releases (accessed on 19 June 2023).
- Davis, C.; Brown, B.; Bullock, R. Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Weather Rev. 2006, 134, 1785–1795. [Google Scholar] [CrossRef] [Green Version]
- Mazzarella, V.; Milelli, M.; Lagasio, M.; Federico, S.; Torcasio, R.C.; Biondi, R.; Realini, E.; Llasat, M.C.; Rigo, T.; Esbrí, L.; et al. Is an NWP-Based Nowcasting System Suitable for Aviation Operations? Remote Sens. 2022, 14, 4440. [Google Scholar] [CrossRef]
- Harwood, K.; Sanford, B.D.; Lee, K.K. Developing ATC Automation in the Field: It Pays to Get Your Hands Dirty. Air Traffic Control Q. 1998, 6, 45–70. [Google Scholar] [CrossRef]
RaNDeVIL Experiment | Centroid Distance [Grid Units] | Area Ratio | Total Observed Area [Grid Units] | Total Forecasted Area [Grid Units] | Intersection Area [Grid Units] | Total Interest |
---|---|---|---|---|---|---|
Milano | 1.485 | 0.750 | 2532 | 1701 | 1241 | 0.993 |
Bergamo | 1.278 | 0.611 | 1922 | 1064 | 884 | 0.974 |
Venice | 1.060 | 0.618 | 3758 | 2223 | 1881 | 0.972 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Esbrí, L.; Rigo, T.; Llasat, M.C.; Biondi, R.; Federico, S.; Gluchshenko, O.; Kerschbaum, M.; Lagasio, M.; Mazzarella, V.; Milelli, M.; et al. Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management. Atmosphere 2023, 14, 1238. https://doi.org/10.3390/atmos14081238
Esbrí L, Rigo T, Llasat MC, Biondi R, Federico S, Gluchshenko O, Kerschbaum M, Lagasio M, Mazzarella V, Milelli M, et al. Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management. Atmosphere. 2023; 14(8):1238. https://doi.org/10.3390/atmos14081238
Chicago/Turabian StyleEsbrí, Laura, Tomeu Rigo, María Carmen Llasat, Riccardo Biondi, Stefano Federico, Olga Gluchshenko, Markus Kerschbaum, Martina Lagasio, Vincenzo Mazzarella, Massimo Milelli, and et al. 2023. "Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management" Atmosphere 14, no. 8: 1238. https://doi.org/10.3390/atmos14081238
APA StyleEsbrí, L., Rigo, T., Llasat, M. C., Biondi, R., Federico, S., Gluchshenko, O., Kerschbaum, M., Lagasio, M., Mazzarella, V., Milelli, M., Parodi, A., Realini, E., & Temme, M. -M. (2023). Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management. Atmosphere, 14(8), 1238. https://doi.org/10.3390/atmos14081238