Articles | Volume 21, issue 4
https://doi.org/10.5194/nhess-21-1297-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1297-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
26 Apr 2021
Review article |
| 26 Apr 2021
| 26 Apr 2021
Review article: Observations for high-impact weather and their use in verification
Deutscher Wetterdienst, Offenbach am Main, 63067, Germany
Arpae Emilia-Romagna, Bologna, 40122, Italy
Elizabeth Ebert
Bureau of Meteorology, Docklands, Victoria, 3008, Australia
Raghavendra Ashrit
National Centre for Medium Range Weather Forecasting (NCMRWF), Noida,
201307, India
Barbara Casati
MRD/ECCC, Dorval (QC), H9P 1J3, Canada
Jing Chen
Center of Numerical Weather Prediction, CMA, Beijing, 100081, China
Caio A. S. Coelho
Centre for Weather Forecast and Climate Studies, National Institute
for Space Research, Cachoeira Paulista, 12630-000, Brazil
Manfred Dorninger
Department of Meteorology and Geophysics, University of Vienna, Vienna, 1090, Austria
Eric Gilleland
Research Applications Laboratory, National Center for Atmospheric
Research, Boulder, 80301, Colorado, USA
Thomas Haiden
ECMWF, Reading, RG2 9AX, UK
Stephanie Landman
South African Weather Service, Pretoria, 0001, South Africa
Marion Mittermaier
MetOffice, Exeter, EX1 3PB, UK
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Michele Salmi, Chiara Marsigli, and Manfred Dorninger
Adv. Sci. Res., 19, 29–38, https://doi.org/10.5194/asr-19-29-2022, https://doi.org/10.5194/asr-19-29-2022, 2022
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High resolution, probabilistic weather prediction systems are increasingly able to model lightning activity with unprecedented accuracy. Is the probabilistic approach skillful when applied to localized, deep convection? This work shows that the ensemble prediction system maintained by the German Weather Service is able to provide a useful forecast of lightning activity at a scale of around 200 km and that the probabilistic approach can anticipate possible lack of accuracy in both time and space.
This article is included in the Encyclopedia of Geosciences
Jeff Da Costa, Elizabeth Ebert, David Hoffmann, Hannah Louise Cloke, and Jessica Neumann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3892, https://doi.org/10.5194/egusphere-2025-3892, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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This paper examines why multiple early indicators of the July 2021 floods in Luxembourg did not lead to better anticipatory action. Using a value chain approach and the Waterdrop Model, it identifies how thresholds, procedures, and institutional responsibilities limited the use of available forecast information under uncertainty. The findings show how aligning information with decision processes can improve timely disaster response.
This article is included in the Encyclopedia of Geosciences
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
This article is included in the Encyclopedia of Geosciences
Llorenç Lledó, Thomas Haiden, and Matthieu Chevallier
Hydrol. Earth Syst. Sci., 28, 5149–5162, https://doi.org/10.5194/hess-28-5149-2024, https://doi.org/10.5194/hess-28-5149-2024, 2024
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High-quality observational datasets are essential to perform forecast verification and improve weather forecast services. When it comes to verifying precipitation, a high-resolution, global-coverage and good-quality dataset is not yet available. This research analyses the strengths and shortcomings of four observational products that employ complementary measurement techniques to estimate surface precipitation. Satellites provide good spatial coverage, but other products are still more accurate.
This article is included in the Encyclopedia of Geosciences
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
This article is included in the Encyclopedia of Geosciences
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
This article is included in the Encyclopedia of Geosciences
Brian Golding, Elizabeth Ebert, David Hoffmann, and Sally Potter
Adv. Sci. Res., 20, 85–90, https://doi.org/10.5194/asr-20-85-2023, https://doi.org/10.5194/asr-20-85-2023, 2023
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In 2021, several weather disasters occurred in which conditions surpassed recorded extremes. Comparative analysis of the warnings issued for these disasters shows that the conditions were generally forecast but that lack of preparedness and/or communication failures led to loss of life in particularly vulnerable groups.
This article is included in the Encyclopedia of Geosciences
David Hoffmann, Elizabeth E. Ebert, Carla Mooney, Brian Golding, and Sally Potter
Adv. Sci. Res., 20, 73–79, https://doi.org/10.5194/asr-20-73-2023, https://doi.org/10.5194/asr-20-73-2023, 2023
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The weather information value chain is a framework that describes how information is produced, communicated, and used in an end-to-end warning system for weather and hazard monitoring. A project under the WMO aims to explore value chain approaches to describe and evaluate high-impact weather events. The project developed a template for high-impact weather event case study collection, which allows scientists and practitioners to assess the effectiveness of warning value chains.
This article is included in the Encyclopedia of Geosciences
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
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Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
This article is included in the Encyclopedia of Geosciences
Michele Salmi, Chiara Marsigli, and Manfred Dorninger
Adv. Sci. Res., 19, 29–38, https://doi.org/10.5194/asr-19-29-2022, https://doi.org/10.5194/asr-19-29-2022, 2022
Short summary
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High resolution, probabilistic weather prediction systems are increasingly able to model lightning activity with unprecedented accuracy. Is the probabilistic approach skillful when applied to localized, deep convection? This work shows that the ensemble prediction system maintained by the German Weather Service is able to provide a useful forecast of lightning activity at a scale of around 200 km and that the probabilistic approach can anticipate possible lack of accuracy in both time and space.
This article is included in the Encyclopedia of Geosciences
Marion Mittermaier, Rachel North, Jan Maksymczuk, Christine Pequignet, and David Ford
Ocean Sci., 17, 1527–1543, https://doi.org/10.5194/os-17-1527-2021, https://doi.org/10.5194/os-17-1527-2021, 2021
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Regions of enhanced chlorophyll-a concentrations can be identified by applying a threshold to the concentration value to a forecast and observed field (or analysis). These regions can then be treated and analysed as features using diagnostic techniques to consider of the evolution of the chlorophyll-a blooms in space and time. This allows us to understand whether the biogeochemistry in the model has any skill in predicting these blooms, their location, intensity, onset, duration and demise.
This article is included in the Encyclopedia of Geosciences
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
This article is included in the Encyclopedia of Geosciences
Lesetja E. Lekoloane, Mary-Jane M. Bopape, Tshifhiwa Gift Rambuwani, Thando Ndarana, Stephanie Landman, Puseletso Mofokeng, Morne Gijben, and Ngwako Mohale
Weather Clim. Dynam., 2, 373–393, https://doi.org/10.5194/wcd-2-373-2021, https://doi.org/10.5194/wcd-2-373-2021, 2021
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We analysed a tornadic supercell that tracked through the northern Highveld region of South Africa for 7 h. We found that atmospheric conditions were conducive for tornado-associated severe storms over the region. A 4.4 km resolution model run by the South African Weather Service was able to predict this supercell, including its timing. However, it underestimated its severity due to underestimations of other important factors necessary for real-world development of these kinds of storms.
This article is included in the Encyclopedia of Geosciences
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
Short summary
Short summary
Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
This article is included in the Encyclopedia of Geosciences
Cited articles
Anderson, G. and Klugmann, D.: A European lightning density analysis using 5 years of ATDnet data, Nat. Hazards Earth Syst. Sci., 14, 815–829, https://doi.org/10.5194/nhess-14-815-2014, 2014.
Barras, H., Hering, A., Martynov, A., Noti, P.-A., Germann, U., and Martius,
O.: Experiences with >50 000 crowdsourced hail reports in
Switzerland, B. Am. Meteorol. Soc., 100, 1429–1440, https://doi.org/10.1175/BAMS-D-18-0090.1, 2019.
Bazlova, T., Bocharnikov, N., and Solonin, A.: Aviation operational
nowcasting systems, 3rd European Nowcasting Conference, Madrid, 24–26 April
2019, available at: https://enc2019.aemet.es/ (last access: 21 April 2021), 2019.
Ben Bouallegue, Z., Haiden, T., Weber, N. J., Hamill, T. M., and Richardson,
D. S.: Accounting for Representativeness in the Verification of Ensemble
Precipitation Forecasts, Mon. Weather Rev., 148, 2049–2062,
https://doi.org/10.1175/MWR-D-19-0323.1, 2020.
Betz, H. D., Schmidt, K., Laroche, P., Blanchet, P., Oettinger, W. P.,
Defer, E., Dziewit, Z., and Konarski, J.: LINET – An international
lightning detection network in Europe, Atmos. Res., 91, 564–573,
https://doi.org/10.1016/j.atmosres.2008.06.012, 2009.
Blakeslee, R. and Koshak, W.: LIS on ISS: Expanded Global Coverage and
Enhanced Applications, The Earth Observer, 28, 4–14, 2016.
Bosart, L. F. and Landin, M. G.: An assessment of thunderstorm probability
forecasting skill, Weather Forecast., 9, 522–531, https://doi.org/10.1175/1520-0434(1994)009<0522:AAOTPF>2.0.CO;2, 1994.
Boutle, I. A., Finnenkoetter, A., Lock, A. P., and Wells, H.: The London
Model: forecasting fog at 333 m resolution, Q. J. Roy. Meteor. Soc., 142, 360–371, https://doi.org/10.1002/qj.2656, 2016.
Brown, K. and Buchanan, P.: An objective verification system for
thunderstorm risk forecasts, Meteorol. Appl., 26, 140–152,
https://doi.org/10.1002/met.1748, 2018.
Brunet, D., Sills, D., and Driedger, N.: On the evaluation of probabilistic
thunderstorm forecasts and the automated generation of thunderstorm threat
areas during Environment Canada Pan Am Science Showcase, Weather Forecast.,
34, 1295–1319, https://doi.org/10.1175/WAF-D-19-0011.1, 2019.
Bullock, R. G., Brown, B. G., and Fowler, T. L.: Method for Object-Based
Diagnostic Evaluation, NCAR Technical Note NCAR/TN-532+STR, 84 pp., https://doi.org/10.5065/D61V5CBS, 2016.
Capitanio, M.: Occurrence and forecast of low stratus clouds over
Switzerland, Master's thesis, Eidgenössische Technische Hochschule,
Zürich, 2013.
Caumont, O.: Towards quantitative lightning forecasts with convective-scale
Numerical Weather Prediction systems, Aeronautical Meteorology scientific
conference, 6–10 November 2017, Meteo-France, Toulouse, France, 2017.
Cermak, J. and Bendix, J.: A novel approach to fog/low stratus detection
using Meteosat 8 data, Atmos. Res., 87, 279–292, https://doi.org/10.1016/j.atmosres.2007.11.009, 2008.
Corbosiero, K. L. and Galarneau, T. J.: Verification of a daily thunderstorm
probability forecast contest using the National Lightning Detection Network,
23rd Conference on Weather Analysis and Forecasting, 1–5 June, Omaha,
Nebraska, 2009.
Corbosiero, K. L. and Lazear, R. A.: Verification of thunderstorm occurrence
using the National Lightning Detection Network, Sixth Conference on the
Meteorological Applications of Lightning Data, 6–10 January, Austin, Texas,
available at: http://www.atmos.albany.edu/facstaff/kristen/tsclimo/tsclimo.html (last access: 21 April 2021), 2013.
Cummins, K. L. and Murphy, M. J.: An overview of lightning locating systems:
history, techniques, and data uses, with an in-depth look at the U.S. NLDN,
IEEE Transaction on Electromagnetic Compatibility, 51, 499–518, https://doi.org/10.1109/TEMC.2009.2023450, 2009.
Davis, C. A., Brown, B. G., and Bullock, R. G.: Object-based verification of
precipitation forecasts. Part I: Methodology and application to mesoscale
rain areas, Mon. Weather Rev., 134, 1772–1784, https://doi.org/10.1175/MWR3145.1, 2006.
De Coning, E., Gijben, M., Maseko, B., and Van Hemert, L.: Using satellite
data to identify and track intense thunderstorms in south and southern
Africa, S. Afr. J. Sci., 111, 1–9, 2015.
de Rosa, M., Picchiani, M., Sist, M., and Del Frate, F.: A novel
multispectral algorithm for the detection, tracking and nowcasting of the
thunderstorms using the Meteosat Second Generation images, 2nd European
Nowcasting Conference, Offenbach, 3–5 May 2017,
available at: http://eumetnet.eu/enc-2017/ (last access: 21 April 2021), 2017.
Derrien, M. and Le Gléau, H.: MSG/SEVIRI cloud mask and type from
SAFNWC, Int. J. Remote S., 26, 4707–4732, https://doi.org/10.1080/01431160500166128, 2005.
Dorninger, M., Ghelli, A., and Lerch, S.: Editorial: Recent developments and
application examples on forecast verification, Met. Apps., 27, 1–3, https://doi.org/10.1002/met.1934, 2020.
Dotzek, N., Groenemeijer, P., Feuerstein, B., and Holzer, A. M.: Overview of
ESSL's severe convective storm research using the European
Severe Weather Database ESWD, Atmos. Res., 93, 575–586, https://doi.org/10.1016/j.atmosres.2008.10.020, 2009.
Ebert, E. E.: Fuzzy verification of high-resolution gridded forecasts: a
review and proposed framework, Met. Apps., 15, 51–64, https://doi.org/10.1002/met.25, 2008.
Ehrler, A.: A methodology for evaluating fog and low stratus with satellite
data, Master Thesis, ETH Zürich, Department of Earth
Sciences, Zürich, 2018.
Flora, M. L., Skinner, P. S., Potvin, C. K., Reinhart, A. E., Jones, T. A.,
Yussouf, N., and Knopfmeier, K. H.: Object-Based Verification of Short-Term,
Storm-Scale Probabilistic Mesocyclone Guidance from an Experimental
Warn-on-Forecast System, Weather Forecast., 34, 1721–1739, https://doi.org/10.1175/WAF-D-19-0094.1, 2019.
Gaia, M., Della Bruna, G., Hering, A., Nerini, D., and Ortelli, V.: Hybrid
human expert and machine-based thunderstorm warnings: a novel operation
nowcasting system for severe weather at MeteoSwiss, 2nd European Nowcasting
Conference, Offenbach, 3–5 May 2017, available at: http://eumetnet.eu/enc-2017/ (last access: 21 April 2021), 2017.
Ghude, S. D., Bhat, G.
S., Prabhakaran, T., Jenamani, R. K., Chate, D. M., Safai, P. D., Karipot, A. K., Konwar, M.,
Pithani, P., Sinha, V., Rao, P. S. P., Dixit, S. A., Tiwari, S., Todekar, K., Varpe, S., Srivastava, A. K., Bisht, D. S., Murugavel, P., Ali, K., Mina, U., Dharua, M., Rao, J. Y., Padmakumari, B., Hazra, A., Nigam, N., Shende, U., Lal, D. M., Chandra, B. P., Mishra, A. K., Kumar, A., Hakkim, H., Pawar, H., Acharja, P., Kulkarni, R., Subharthi, C., Balaji, B., Varghese, M., Bera, S., and Rajeevan, M.: Winter fog experiment over the Indo-Gangetic plains of India,
Curr. Sci. India, 112, 767–784, https://doi.org/10.18520/cs/v112/i04/767-784, 2017.
Gijben, M.: The Lightning Climatology of South Africa, S. Afr. J. Sci., 108, 740, 2012.
Gijben, M. and De Coning, E.: Using Satellite and Lightning Data to Track
Rapidly Developing Thunderstorms in Data Sparse Regions, Atmosphere, 8,
67, https://doi.org/10.3390/atmos8040067, 2017.
Gilleland, E., Ahijevych, D., Brown, B. G., Casati, B., and Ebert, E. E.:
Intercomparison of Spatial Forecast Verification Methods, Weather Forecast.,
24, 1416–1430, https://doi.org/10.1175/2009WAF2222269.1, 2009.
Goessling, H. F. and Jung, T.: A probabilistic verification score for
contours: Methodology and application to Artic ice-edge forecasts, Q. J. Roy.
Meteor. Soc., 144, 735–743, https://doi.org/10.1002/qj.3242, 2018.
Gultepe, I.: Fog and Boundary Layer Clouds: Introduction, in: Fog and Boundary Layer Clouds: Fog Visibility and Forecasting, edited by: Gultepe, I., Pageoph Topical Volumes, Birkhäuser, Basel, https://doi.org/10.1007/978-3-7643-8419-7_1, 2007.
Hacker, M. and Bott, A.: Modeling the spatial and temporal variability of
fog in the Namib Desert with COSMO-PAFOG, ICCARUS 2018, 26–28 February, DWD,
Offenbach, Germany, 2018.
Harrison, S., Potter, S., Prasanna, R., Doyle, E. E. H., and Johnston, D.:
Volunteered Geographic Information for people-centred severe weather early
warning: A literature review, The Australasian Journal of Disaster and
Trauma Studies, 24, 3–21, 2020.
Hitchens, N. M., Brooks, H. E., and Kay, M. P.: Objective Limits on Forecasting Skill of Rare Events, Weather Forecast., 28, 525–534, https://doi.org/10.1175/WAF-D-12-00113.1, 2013.
Holzer, A. M. and Groenemeijer, P.: Report on the testing of EUMETSAT
NWC-SAF products at the ESSL Testbed 2017, available at:
https://www.essl.org/media/Testbed2017/20170929ESSLTestbedReportforNWCSAFfinal.pdf (last access: 21 April 2021),
2017.
Jolliffe, I. T. and Stephenson, D. B.: Forecast Verification: A
Practitioner's Guide in Atmospheric Science, Second Edition, John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK, https://doi.org/10.1002/9781119960003, 2012.
Keller, M., Fuhrer, O., Schmidli, J., Stengel, M., Stöckli, R., and
Schär, C.: Evaluation of convection-resolving models using satellite
data: The diurnal cycle of summer convection over the Alps, Meteorol. Z., 25, 165–179, https://doi.org/10.1127/metz/2015/0715, 2015.
Magnusson, L.: Collection of Severe Weather Event articles from ECMWF
Newsletters 2014–2019, ECMWF, Reading, UK,
available at: https://www.ecmwf.int/sites/default/files/medialibrary/2019-04/ecmwf-nl-severe-events.pdf (last access: 21 April 2021),
2019.
Mahringer, G.: Terminal aerodrome forecast verification in Austro Control
using time windows and ranges of forecast conditions, Meteorol. Appl., 15,
113–123, https://doi.org/10.1002/met.62, 2008.
Mäkelä, A., Tuomi, T. J., and Haapalainen, J.: A decade of high‐latitude lightning location: Effects of the evolving location network in Finland, J. Geophys. Res., 115, D21124, https://doi.org/10.1029/2009JD012183, 2010.
Marsigli, C., Alferov, D., Astakhova, E., Duniec, G., Gayfulin, D., Gebhardt, C., Interewicz, W., Loglisci, N., Marcucci, F., Mazur, A., Montani, A., Tsyrulnikov, M., and Walser, A.: Studying perturbations for the representation of
modeling uncertainties in Ensemble development (SPRED Priority Project):
Final Report, COSMO Technical Report No. 39, https://doi.org/10.5676/DWDpub/nwv/cosmo-tr39, 2019.
Morales, G., Calvo, J., Román-Cascón, C., and Yagüe, C.:
Verification of fog and low cloud simulations using an object oriented
method, Assembly of the European Geophysical Union (EGU), Vienna, Austria, 7–12
April, EGU2013-8474, 2013.
Müller, M. D., Masbou, M., and Bott, A.: Three-dimensional fog
forecasting in complex terrain, Q. J. Roy.
Meteor. Soc., 136, 2189–2202, https://doi.org/10.1002/qj.705, 2010.
Müller, R., Haussler, S., Jerg, M., Asmus, J., Hungershöfer, K., and
Leppelt, T.: SATICUS - Satellite based detection of thunderstorms
(Cumulonimbus Cb), 2nd European Nowcasting Conference, Offenbach, 3–5 May 2017, available at: http://eumetnet.eu/enc-2017/ (last access: 21 April 2021), 2017.
Pardowitz, T.: A statistical model to estimate the local vulnerability to severe weather, Nat. Hazards Earth Syst. Sci., 18, 1617–1631, https://doi.org/10.5194/nhess-18-1617-2018, 2018.
Pardowitz, T. and Göber, M.: Forecasting weather related fire brigade
operations on the basis of nowcasting data, in: LNIS Vol. 8, RIMMA Risk
Information Management, Risk Models and Applications, CODATA Germany,
Berlin, edited by: Kremers, H. and Susini, A., ISBN 978-3-00-056177-1,
2017.
Rempel, M., Senf, F., and Deneke, H.: Object-based metrics for forecast
verification of convective development with geostationary satellite data,
Mon. Weather Rev., 145, 3161–3178, https://doi.org/10.1175/MWR-D-16-0480.1, 2017.
Riede, H., Acevedo-Valencia, J. W., Bouras, A., Paschalidi, Z., Hellweg, M.,
Helmert, K., Hagedorn, R., Potthast, R., Kratzsch, T., and Nachtigal, J.:
Passenger car data as a new source of real-time weather information for
nowcasting, forecasting and road safe, ENC2019, Third European Nowcasting
Conference, Book of Abstracts, 24–26 April 2019, AEMET, Madrid, Spain, 2019.
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall
accumulations from high-resolution forecasts of convective events, Mon.
Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2007.
Roberts, R. D., Anderson, A. S., Nelson, E., Brown, B. G., Wilson, J. W.,
Pocernich, M., and Saxen, T.: Impacts of forecaster involvement on
convective storm initiation and evolution Nowcasting, Weather
Forecast., 27, 1061–1089, https://doi.org/10.1175/WAF-D-11-00087.1, 2012.
Rossi, P. J., Hasu, V., Halmevaara, K., Mäkelä, A., Koistinen, J.,
and Pohjola, H.: Real-time hazard approximation of long-lasting convective
storms using emergency data, J. Atmos. Ocean. Tech., 30,
538–555, https://doi.org/10.1175/JTECH-D-11-00106.1, 2013.
Roux, B.: ACCESS sensitivity experiments for fog cases at Perth Airport,
Bureau Research Report 033, ISBN 978-1-925738-04-9, 2017.
Schmid, F., Bañon, L., Agersten, S., Atencia, A., de Coning, E., Kann,
A., Wang, Y., and Wapler, K.: Conference Report: Third European Nowcasting
Conference, Meteorol. Z., 28, 447–450, https://doi.org/10.1127/metz/2019/0983, 2019.
Schuster, S. S., Blong, R. J., Leigh, R. J., and McAneney, K. J.: Characteristics of the 14 April 1999 Sydney hailstorm based on ground observations, weather radar, insurance data and emergency calls, Nat. Hazards Earth Syst. Sci., 5, 613–620, https://doi.org/10.5194/nhess-5-613-2005, 2005.
Steeneveld, G. J., Ronda, R. J., and Holtslag, A. A. M.: The challenge of
forecasting the onset and development of radiation fog using mesoscale
atmospheric models, Bound.-Lay. Meteorol., 154, 265–289, https://doi.org/10.1007/s10546-014-9973-8, 2015.
Tsonevsky, I., Doswell, C. A., and Brooks, H. E.: Early warnings of severe
convection using the ECMWF extreme forecast index, Weather Forecast., 33,
857–871, https://doi.org/10.1175/WAF-D-18-0030.1, 2018.
Valachova, M. and Sykorova, P.: Convective Storm Nowcasting by remote
sensing data combination, Book of Abstract, 2nd European Nowcasting
Conference, 3–5 May 2017, Headquarters of DWD, Offenbach, Germany, 2017.
Wapler, K., Goeber, M., and Trepte, S.: Comparative verification of different nowcasting systems to support optimisation of thunderstorm warnings, Adv. Sci. Res., 8, 121–127, https://doi.org/10.5194/asr-8-121-2012, 2012.
Wapler, K., Harnisch, F., Pardowitz, T., and Senf, F.: Characterisation and
predictability of a strong and a weak severe convective event – a
multi-data approach, Meteorol. Z., 24, 393–410, https://doi.org/10.1127/metz/2015/0625,
2015.
Wapler, K., Banon Peregrin, L. M., Buzzi, M., Heizenreder, D., Kann, A.,
Meirold-Mautner, I., Simon, A., and Wang, Y.: Conference Report 2nd European
Nowcasting Conference, Meteorol. Z., 27, 81–84, https://doi.org/10.1127/metz/2017/0870,
2018.
Wapler, K., de Coning, E., and Buzzi, M.: Nowcasting, Reference Module in
Earth Systems and Environmental Sciences, Elsevier, Amsterdam, https://doi.org/10.1016/B978-0-12-409548-9.11777-4, 2019.
Wernli, H., Paulat, M., Hagen, M., and Frei, C.: SAL – A novel quality
measure for the verification of quantitative precipitation forecasts, Mon.
Weather Rev., 136, 4470–4487, https://doi.org/10.1175/2008MWR2415.1, 2008.
Westerhuis, S., Eugster, W., Fuhrer, O., and Bott A.: Towards an improved
representation of radiation fog in the Swiss numerical weather prediction
models, ICCARUS 2018, 26–28 February, DWD, Offenbach, Germany, 2018.
Williams, H.: Social sensing of natural hazards, 41st EWGLAM and
26th SRNWP Meeting, Sofia, Bulgaria, available at: http://srnwp.met.hu/ (last access: 21 April 2021), 2019.
Wilson, C. and Mittermaier, M.: Observation needed for verification of
additional forecast products, Twelfth Workshop on Meteorological Operational
Systems, 2–6 November 2009, Conference Paper, ECMWF, Reading, UK, 2009.
Wiwatwattana, N., Sangkhatad, K., and Srivatanakul, T.: An analysis and case
study of using mobile crowdsourcing for weather forecast verification, 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS),
Beijing, 251–254, https://doi.org/10.1109/ICSESS.2015.7339048, 2015.
WMO-No.1198: Guidelines for Nowcasting Techniques, 2017 Edition,
World Meteorological Organization, Geneva, Switzerland, ISBN 978-92-63-11198-2,
2017.
WMO-No.8: Guide to Instruments and Methods of Observation, 2018 Edition,
World Meteorological Organization, Geneva, Switzerland, ISBN
978-92-63-10008-5, 2018.
Zhang, Q., Li, L., Ebert, B., Golding, B., Johnston, D., Mills, B., Panchuk,
S., Potter, S., Riemer, M., Sun, J., and Taylor, A.: Increasing the value of
weather-related warnings, Sci. Bull., 64, 647–649, https://doi.org/10.1016/j.scib.2019.04.003, 2019.
Zhou, B. and Du, J.: Fog prediction from a multimodel mesoscale ensemble
prediction system, Weather Forecast., 25, 303–322, https://doi.org/10.1175/2009WAF2222289.1, 2010.
Short summary
This paper reviews new observations for the verification of high-impact weather and provides advice for their usage in objective verification. New observations include remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and reports from insurance companies. This work has been performed in the framework of the Joint Working Group on Forecast Verification Research (JWGFVR) of the WMO.
This paper reviews new observations for the verification of high-impact weather and provides...
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