Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.883 IF 2.883
  • IF 5-year value: 3.321 IF 5-year
    3.321
  • CiteScore value: 3.07 CiteScore
    3.07
  • SNIP value: 1.336 SNIP 1.336
  • IPP value: 2.80 IPP 2.80
  • SJR value: 1.024 SJR 1.024
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 81 Scimago H
    index 81
  • h5-index value: 43 h5-index 43
Volume 16, issue 8
Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016
https://doi.org/10.5194/nhess-16-1807-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Situational sea awareness technologies for maritime safety...

Nat. Hazards Earth Syst. Sci., 16, 1807–1819, 2016
https://doi.org/10.5194/nhess-16-1807-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Review article 09 Aug 2016

Review article | 09 Aug 2016

Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case

Jenny Pistoia et al.
Related authors  
Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-113,https://doi.org/10.5194/gmd-2019-113, 2019
Manuscript under review for GMD
Short summary
Induced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errors
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019,https://doi.org/10.5194/tc-13-2001-2019, 2019
Short summary
Circulation of the Turkish Straits System under interannual atmospheric forcing
Ali Aydoğdu, Nadia Pinardi, Emin Özsoy, Gokhan Danabasoglu, Özgür Gürses, and Alicia Karspeck
Ocean Sci., 14, 999–1019, https://doi.org/10.5194/os-14-999-2018,https://doi.org/10.5194/os-14-999-2018, 2018
Short summary
OSSE for a sustainable marine observing network in the Sea of Marmara
Ali Aydoğdu, Timothy J. Hoar, Tomislava Vukicevic, Jeffrey L. Anderson, Nadia Pinardi, Alicia Karspeck, Jonathan Hendricks, Nancy Collins, Francesca Macchia, and Emin Özsoy
Nonlin. Processes Geophys., 25, 537–551, https://doi.org/10.5194/npg-25-537-2018,https://doi.org/10.5194/npg-25-537-2018, 2018
Short summary
High-resolution observations in the western Mediterranean Sea: the REP14-MED experiment
Reiner Onken, Heinz-Volker Fiekas, Laurent Beguery, Ines Borrione, Andreas Funk, Michael Hemming, Jaime Hernandez-Lasheras, Karen J. Heywood, Jan Kaiser, Michaela Knoll, Baptiste Mourre, Paolo Oddo, Pierre-Marie Poulain, Bastien Y. Queste, Aniello Russo, Kiminori Shitashima, Martin Siderius, and Elizabeth Thorp Küsel
Ocean Sci., 14, 321–335, https://doi.org/10.5194/os-14-321-2018,https://doi.org/10.5194/os-14-321-2018, 2018
Short summary
Related subject area  
Sea, Ocean and Coastal Hazards
Comparing the efficiency of hypoxia mitigation strategies in an urban, turbid tidal river via a coupled hydro-sedimentary–biogeochemical model
Katixa Lajaunie-Salla, Aldo Sottolichio, Sabine Schmidt, Xavier Litrico, Guillaume Binet, and Gwenaël Abril
Nat. Hazards Earth Syst. Sci., 19, 2551–2564, https://doi.org/10.5194/nhess-19-2551-2019,https://doi.org/10.5194/nhess-19-2551-2019, 2019
Machine learning analysis of lifeguard flag decisions and recorded rescues
Chris Houser, Jacob Lehner, Nathan Cherry, and Phil Wernette
Nat. Hazards Earth Syst. Sci., 19, 2541–2549, https://doi.org/10.5194/nhess-19-2541-2019,https://doi.org/10.5194/nhess-19-2541-2019, 2019
Short summary
Reconstructing patterns of coastal risk in space and time along the US Atlantic coast, 1970–2016
Scott B. Armstrong and Eli D. Lazarus
Nat. Hazards Earth Syst. Sci., 19, 2497–2511, https://doi.org/10.5194/nhess-19-2497-2019,https://doi.org/10.5194/nhess-19-2497-2019, 2019
Short summary
Ensemble models from machine learning: an example of wave runup and coastal dune erosion
Tomas Beuzen, Evan B. Goldstein, and Kristen D. Splinter
Nat. Hazards Earth Syst. Sci., 19, 2295–2309, https://doi.org/10.5194/nhess-19-2295-2019,https://doi.org/10.5194/nhess-19-2295-2019, 2019
Short summary
Environmental controls on surf zone injuries on high-energy beaches
Bruno Castelle, Tim Scott, Rob Brander, Jak McCarroll, Arthur Robinet, Eric Tellier, Elias de Korte, Bruno Simonnet, and Louis-Rachid Salmi
Nat. Hazards Earth Syst. Sci., 19, 2183–2205, https://doi.org/10.5194/nhess-19-2183-2019,https://doi.org/10.5194/nhess-19-2183-2019, 2019
Short summary
Cited articles  
Boria, R. A., Olson, L. E., Goodman, S. M., and Anderson, R. P.: Spatial filtering to reduce sampling bias can improve the performance of ecological niche models, Ecol. Model., 275, 73–77, https://doi.org/10.1016/j.ecolmodel.2013.12.012, 2014.
Brasseur, P., Bahurel, P., Bertino, L., Birol, F., Brankart, J.-M., Ferry, N., Losa, S., Remy, E., Schröter, J., Skachko, S., Testut, C.-E., Tranchant, B., Van Leeuwen, P. J., and Verron, J.: Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments, Q. J. Roy. Meteor. Soc., 131, 3561–3582, https://doi.org/10.1256/qj.05.142, 2005.
Casey, K., Brandon, T., Cornillon, P., and Evans, R.: The Past, Present and Future of the AVHRR Pathfinder SST Program, Oceanography from Space: Revisited, 2010.
Dobricic, S. and Pinardi, N.: An oceanographic three-dimensional variational data assimilation scheme, Ocean Model., 22, 89–105, https://doi.org/10.1016/j.ocemod.2008.01.004, 2008.
Evans, R., Harrison, M., Graham, R., and Mylne, K.: Joint Medium-Range Ensembles from The Met. Office and ECMWF Systems, Mon. Weather Rev., 128, 3104–3127, https://doi.org/10.1175/1520-0493(2000)128<3104:JMREFT>2.0.CO;2, 2000.
Publications Copernicus
Download
Short summary
In this work we developed a new multi-model super-ensemble method to estimate sea surface temperature, an important product of ocean analysis systems. We find that ensemble size, quality, type of members and the training period length are all important elements of the MMSE methodology and require careful calibration. We show that with a rather limited but overconfident data set (with a low bias of the starting ensemble members) the RMSE analysis can be improved.
In this work we developed a new multi-model super-ensemble method to estimate sea surface...
Citation