Articles | Volume 4, issue 3
https://doi.org/10.5194/gc-4-361-2021
© 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/gc-4-361-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Marine meteorological forecasts for coastal ocean users – perceptions, usability and uptake
Christo Rautenbach
CORRESPONDING AUTHOR
Coastal and Estuarine Processes, National Institute for Water and Atmospheric Research (NIWA), Hamilton, New Zealand
Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South Africa
Department of Oceanography and Marine Research Institute, University of Cape Town, Cape Town, South Africa
Berill Blair
Environmental Policy Group, Wageningen University and Research, Wageningen, the Netherlands
Related authors
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Preprint under review for GMD
Short summary
Short summary
This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
Christo Rautenbach, Julia C. Mullarney, and Karin R. Bryan
Geosci. Model Dev., 14, 4241–4247, https://doi.org/10.5194/gmd-14-4241-2021, https://doi.org/10.5194/gmd-14-4241-2021, 2021
Short summary
Short summary
The simulation of ocean waves is important for various reasons, e.g. ship route safety and coastal vulnerability assessments. SWAN is a popular tool with which ocean waves may be predicted. Simulations using this tool can be computationally expensive. The present study thus aimed to understand which typical parallel-computing SWAN model set-up will be most effective. There thus do exist configurations where these simulations are most time-saving and effective.
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Preprint under review for GMD
Short summary
Short summary
This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
Christo Rautenbach, Julia C. Mullarney, and Karin R. Bryan
Geosci. Model Dev., 14, 4241–4247, https://doi.org/10.5194/gmd-14-4241-2021, https://doi.org/10.5194/gmd-14-4241-2021, 2021
Short summary
Short summary
The simulation of ocean waves is important for various reasons, e.g. ship route safety and coastal vulnerability assessments. SWAN is a popular tool with which ocean waves may be predicted. Simulations using this tool can be computationally expensive. The present study thus aimed to understand which typical parallel-computing SWAN model set-up will be most effective. There thus do exist configurations where these simulations are most time-saving and effective.
Cited articles
Alexander, S., Atsbeha, E., Negatu, S., Kirksey, K., Brossard, D., Holzer,
E., and Block, P.: Development of an interdisciplinary, multi-method approach
to seasonal climate forecast communication at the local scale, Clim. Change,
162, 2021–2042, https://doi.org/10.1007/s10584-020-02845-9, 2020.
Barnes, M. A. and Rautenbach, C.: Toward Operational Wave-Current
Interactions Over the Agulhas Current System, J. Geophys. Res.-Ocean.,
125, e2020JC016321, https://doi.org/10.1029/2020JC016321, 2020.
Blair, B., Lee, O. A., and Lamers, M.: Four paradoxes of the user-provider
interface: A responsible innovation framework for sea ice services,
Sustainability, 12, 448, https://doi.org/10.3390/su12020448, 2020.
Borgatti, S. P., Everett, M. G., and Freeman, L. C.: Ucinet for Windows:
Software for social network analysis, UCINET [code], available at: https://sites.google.com/site/ucinetsoftware/home (last access: 20 December 2020), 2002.
Bremer, S., Wardekker, A., Dessai, S., Sobolowski, S., Slaattelid, R., and
van der Sluijs, J.: Toward a multi-faceted conception of co-production of
climate services, Clim. Serv., 13, 42–50,
https://doi.org/10.1016/j.cliser.2019.01.003, 2019.
Caulkins, D. and Hyatt, S. B.: Using Consensus Analysis to Measure Cultural
Diversity in Organizations and Social Movements, Field Method., 11,
5–26, https://doi.org/10.1177/1525822X9901100102, 1999.
Chiswell, S. M., Bostock, H. C., Sutton, P. J., and Williams, M. J.: Physical
oceanography of the deep seas around New Zealand: a review, New Zeal. J.
Mar. Freshw. Res., 49, 286–317, https://doi.org/10.1080/00288330.2014.992918, 2015.
Compton, J. S.: Pleistocene sea-level fluctuations and human evolution on
the southern coastal plain of South Africa, Quat. Sci. Rev., 30,
506–527, https://doi.org/10.1016/j.quascirev.2010.12.012, 2011.
Demuth, J. L., Lazo, J. K., and Morss, R. E.: Exploring Variations in
People's Sources, Uses, and Perceptions of Weather Forecasts, Weather. Clim.
Soc., 3, 177–192, https://doi.org/10.1175/2011WCAS1061.1, 2011.
de Vos, M. and Rautenbach, C.: Investigating the connection between metocean
conditions and coastal user safety: An analysis of search and rescue data,
Saf. Sci., 117, 217–228, https://doi.org/10.1016/j.ssci.2019.03.029, 2019.
Doksæter Sivle, A. and Kolstø, S. D.: Use of online weather
information in everyday decision-making by laypeople and implications for
communication of weather information, Meteorol. Appl., 23, 650–662,
https://doi.org/10.1002/met.1588, 2016.
Doswell, C. A.: Societal impacts of severe thunderstorms and tornadoes:
lessons learned and implications for Europe, Atmos. Res., 67–68,
135–152, https://doi.org/10.1016/S0169-8095(03)00048-6, 2003.
Douglas, M. and Wildavsky, A.: Risk and culture: an essay on the selection
of technical and environmental dangers, University of California Press,
Berkeley, California, USA, ISBN-13 978-0-5200-4491-3,
ISBN-10 0520044916 1982.
Dunn, M.: New Zealand Painting: A Concise History, Revised, Auckland
University Press, Auckland, New Zealand, ISBN 978-1-8694-0297-6, 2003.
Ebert, E., Brown, B., Göber, M., Haiden, T., Mittermaier, M., Nurmi, P.,
Wilson, L., Jackson, S., Johnston, P., and Schuster, D.: The WMO challenge to
develop and demonstrate the best new user-oriented forecast verification
metric, Meteorol. Z., 27, 435–440, https://doi.org/10.1127/metz/2018/0892,
2018.
Findlay, K.: Operation Phakisa and unlocking South Africa 's ocean economy,
J. Indian Ocean Reg., 14, 248–254, https://doi.org/10.1080/19480881.2018.1475857,
2018.
Finnis, J., Shewmake, J. W., Neis, B., and Telford, D.: Marine Forecasting
and Fishing Safety: Improving the Fit between Forecasts and Harvester Needs,
J. Agromedicine, 24, 324–332, https://doi.org/10.1080/1059924X.2019.1639576, 2019.
Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S., and Combs, B.: How
safe is safe enough? A psychometric study of attitudes towards technological
risks and benefits, Policy Sci., 9, 127–152, https://doi.org/10.1007/BF00143739,
1978.
Garro, L. C.: Intracultural variation in causal accounts of diabetes: A
comparison of three Canadian Anishinaabe (ojibway) communities, Cult. Med.
Psychiatry, 20, 381–420, https://doi.org/10.1007/BF00117086, 1996.
Godoi, V. A., Bryan, K. R., Stephens, S. A., and Gorman, R. M.: Extreme waves
in New Zealand waters, Ocean Model., 117, 97–110,
https://doi.org/10.1016/j.ocemod.2017.08.004, 2017.
Hewitt, C. D.: Climate services in the UK Met Office – challenges and
solutions, J. South. Hemisph. Earth Syst. Sci., 70, 139–142, https://doi.org/10.1071/es19030, 2020.
Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L., Braman,
D., and Mandel, G.: The polarizing impact of science literacy and numeracy on
perceived climate change risks, Nat. Clim. Chang., 2, 732–735,
https://doi.org/10.1038/nclimate1547, 2012.
Katz, R. W. and Lazo, J. K.: Economic Value of Weather and Climate
Forecasts, Oxford University Press, UK, 2011.
Keith, H.: The Big Picture: The History of New Zealand Art from 1642, Random
House New Zealand Ltd, New Zealand, 2007.
Kim, H. L., Ratan, A., Perry, G. H., Montenegro, A., Miller, W., and
Schuster, S. C.: Khoisan hunter-gatherers have been the largest population
throughout most of modern-human demographic history, Nat. Commun., 5,
5692, https://doi.org/10.1038/ncomms6692, 2014.
Kirchhoff, C. J., Carmen Lemos, M., and Dessai, S.: Actionable Knowledge for
Environmental Decision Making: Broadening the Usability of Climate Science,
Annu. Rev. Environ. Resour., 38, 393–414,
https://doi.org/10.1146/annurev-environ-022112-112828, 2013.
Kuonen, J., Conway, F., and Strub, T.: Relating Ocean Condition Forecasts to
the Process of End-User Decision Making: A Case Study of the Oregon
Commercial Fishing Community, Mar. Technol. Soc. J., 53, 53–66,
https://doi.org/10.4031/MTSJ.53.1.1, 2019.
Lamers, M., Duske, P., and van Bets, L.: Understanding user needs: a
practice-based approach to exploring the role of weather and sea ice
services in European Arctic expedition cruising, Polar Geogr., 41,
262–278, https://doi.org/10.1080/1088937X.2018.1513959, 2018.
Lazo, J. K., Morss, R. E., and Demuth, J. L.: 300 Billion Served, B. Am.
Meteorol. Soc., 90, 785–798, https://doi.org/10.1175/2008BAMS2604.1, 2009.
Lee, I., Choi, B., Kim, J., and Hong, S.-J.: Culture-Technology Fit: Effects
of Cultural Characteristics on the Post-Adoption Beliefs of Mobile Internet
Users, Int. J. Electron. Commer., 11, 11–51,
https://doi.org/10.2753/JEC1086-4415110401, 2007.
Lemos, M. C., Kirchhoff, C. J., and Ramprasad, V.: Narrowing the climate
information usability gap, Nat. Clim. Chang., 2, 789–794,
https://doi.org/10.1038/nclimate1614, 2012.
Li, X., Hess, T. J., and Valacich, J. S.: Why do we trust new technology? A
study of initial trust formation with organizational information systems, J.
Strateg. Inf. Syst., 17, 39–71, https://doi.org/10.1016/j.jsis.2008.01.001, 2008.
Lichtenstein, S. and Slovic, P. (Eds.): The Construction of Preference,
Cambridge University Press, Cambridge, USA, 2006.
Lim, H. and Park, J.-S.: The Effects of National Culture and Cosmopolitanism
on Consumers' Adoption of Innovation: A Cross-Cultural Comparison, J. Int.
Consum. Mark., 25, 16–28, https://doi.org/10.1080/08961530.2013.751793, 2013.
Lövbrand, E.: Co-producing European climate science and policy: a
cautionary note on the making of useful knowledge, Sci. Public Policy,
38, 225–236, https://doi.org/10.3152/030234211X12924093660516, 2011.
Martinsons, M. G. and Westwood, R. I.: Management information systems in the
Chinese business culture: An explanatory theory, Inf. Manag., 32,
215–228, https://doi.org/10.1016/S0378-7206(96)00009-2, 1997.
Meadow, A. M., Ferguson, D. B., Guido, Z., Horangic, A., Owen, G., and Wall,
T.: Moving toward the deliberate coproduction of climate science knowledge,
Weather. Clim. Soc., 7, 179–191, https://doi.org/10.1175/WCAS-D-14-00050.1, 2015.
Medin, D. L., Ross, N., Atran, S., Burnett, R. C., and Blok, S. V.:
Categorization and reasoning in relation to culture and expertise, in
Psychology of Learning and Motivation, Academic
Press, Cambridge, MA, USA, pp. 1–41, 2002.
Miller, M. L., Kaneko, J., Bartram, P., Marks, J., and Brewer, D. D.:
Cultural Consensus Analysis and Environmental Anthropology: Yellowfin Tuna
Fishery Management in Hawaii, Cross-Cultural Res., 38, 289–314,
https://doi.org/10.1177/1069397104264278, 2004.
Ministry for Culture and Heritage: New Zealand history, available
at:
https://nzhistory.govt.nz/culture/nz-painting-history/further-information
(last access: 26 November 2020), 2014.
Naves, L. C., Simeone, W. E., Lowe, M. E., Valentine, E. M., Stickwan, G.,
and Brady, J.: Cultural Consensus on Salmon Fisheries and Ecology in the
Copper River, Alaska, Arctic, 68, 210, https://doi.org/10.14430/arctic4482, 2015.
O'Connor, R. E., Yarnal, B., Dow, K., Jocoy, C. L., and Carbone, G. J.:
Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts,
Risk Anal., 25, 1265–1275, https://doi.org/10.1111/j.1539-6924.2005.00675.x, 2005.
Oliver, E. and Oliver, W. H.: The Colonisation of South Africa: A unique
case, HTS Teol. Stud./Theol. Stud., 73, a4498, https://doi.org/10.4102/hts.v73i3.4498,
2017.
Paris, C. M., Musa, G., and Thirumoorthi, T.: A comparison between Asian and
Australasia backpackers using cultural consensus analysis, Curr. Issues
Tour., 18, 175–195, https://doi.org/10.1080/13683500.2014.920771, 2015.
Ramos, M.-H., Mathevet, T., Thielen, J., and Pappenberger, F.: Communicating
uncertainty in hydro-meteorological forecasts: mission impossible?,
Meteorol. Appl., 17, 223–235, https://doi.org/10.1002/met.202, 2010.
Rautenbach, C., Daniels, T., de Vos, M., and Barnes, M. A.: A coupled wave,
tide and storm surge operational forecasting system for South Africa:
validation and physical description, Nat. Hazards, 103, 1407–1439,
https://doi.org/10.1007/s11069-020-04042-4, 2020.
Reyes-García, V., Paneque-Gálvez, J., Luz, A., Gueze, M.,
Macía, M., Orta-Martínez, M., and Pino, J.: Cultural Change and
Traditional Ecological Knowledge: An Empirical Analysis from the Tsimane' in
the Bolivian Amazon, Hum. Organ., 73, 162–173,
https://doi.org/10.17730/humo.73.2.31nl363qgr30n017, 2014.
Ribeiro, N.: Do tourists do what they say they do? An application of the
cultural consensus and cultural consonance models to tourism research, in
42nd travel and tourism research association (TTRA) annual conference,
London, 19–21 June 2011, 2011.
Rito, T., Richards, M. B., Fernandes, V., Alshamali, F., Cerny, V., Pereira,
L., and Soares, P.: The First Modern Human Dispersals across Africa, edited
by: Gilbert, T., PLoS One, 8, e80031, https://doi.org/10.1371/journal.pone.0080031,
2013.
Romney, A. K., Weller, S. C., and Batchelder, W. H.: Culture as Consensus: A
Theory of Culture and Informant Accuracy, Am. Anthropol., 88, 313–338,
https://doi.org/10.1525/aa.1986.88.2.02a00020, 1986.
Sherman-Morris, K.: Tornado warning dissemination and response at a
university campus, Nat. Hazards, 52, 623–638,
https://doi.org/10.1007/s11069-009-9405-0, 2010.
Silver, A.: Watch or warning? Perceptions, preferences, and usage of
forecast information by members of the Canadian public, Meteorol. Appl.,
22, 248–255, https://doi.org/10.1002/met.1452, 2015.
Stewart, A. E.: Minding the weather: The measurement of weather salience,
B. Am. Meteorol. Soc., 90, 1833–1841, https://doi.org/10.1175/2009BAMS2794.1,
2009.
Stewart, A. E., Lazo, J. K., Morss, R. E., and Demuth, J. L.: The
Relationship of Weather Salience with the Perceptions and Uses of Weather
Information in a Nationwide Sample of the United States, Weather. Clim.
Soc., 4, 172–189, https://doi.org/10.1175/WCAS-D-11-00033.1, 2012.
Strong, A. E. and White, T. L.: Using paired cultural modelling and cultural
consensus analysis to maximize programme suitability in local contexts,
Health Policy Plan., 35, 115–121, https://doi.org/10.1093/heapol/czz096, 2020.
Sturrock, K. and Rocha, J.: A Multidimensional Scaling Stress Evaluation
Table, Field Method., 12, 49–60, https://doi.org/10.1177/1525822X0001200104, 2000.
Van Holt, T., Bernard, H. R., Weller, S., Townsend, W., and Cronkleton, P.:
Influence of the Expert Effect on Cultural Models, Hum. Dimens. Wildl.,
21, 169–179, https://doi.org/10.1080/10871209.2015.1110736, 2016.
Vaughan, C. and Dessai, S.: Climate services for society: Origins,
institutional arrangements, and design elements for an evaluation framework,
Wiley Interdiscip. Rev. Clim. Chang., 5, 587–603, https://doi.org/10.1002/wcc.290,
2014.
Vaughan, C., Dessai, S., and Hewitt, C.: Surveying climate services: What can
we learn from a bird's-eye view?, Weather. Clim. Soc., 10, 373–395,
https://doi.org/10.1175/WCAS-D-17-0030.1, 2018.
Vogel, C. and O'Brien, K.: Who can eat information? Examining the
effectiveness of seasonal climate forecasts and regional climate-risk
management strategies, Clim. Res., 33, 111–122, https://doi.org/10.3354/cr033111,
2006.
Wagner, P. M., Hughes, N., Bourbonnais, P., Stroeve, J., Rabenstein, L.,
Bhatt, U., Little, J., Wiggins, H., and Fleming, A.: Sea-ice information and
forecast needs for industry maritime stakeholders, Polar Geogr., 43,
160–187, https://doi.org/10.1080/1088937X.2020.1766592, 2020.
Weller, S. C.: Cultural Consensus Theory: Applications and Frequently Asked
Questions, Field Method., 19, 339–368, https://doi.org/10.1177/1525822X07303502,
2007.
Weller, S. C., Baer, R. D., Garcia de Alba Garcia, J., and Salcedo Rocha, A.
L.: Explanatory models of diabetes in the U.S. and Mexico: The
patient–provider gap and cultural competence, Soc. Sci. Med., 75,
1088–1096, https://doi.org/10.1016/j.socscimed.2012.05.003, 2012.
Williams, C. A., Miller, P. W., Black, A. W., and Knox, J. A.: Throwing
Caution to the Wind: National Weather Service Wind Products as Perceived by
a Weather-Salient Sample, J. Oper. Meteorol., 5, 103–120,
https://doi.org/10.15191/nwajom.2017.0509, 2017.
Wolcott, A. and Macaskill, J.: New Zealand: Integration of Traditional Maori
Art and Art Education Curricula, J. Multi-Cultural Cross-Cultural Res. Art
Educ., 15, 24–32, 1997.
Worden, N.: New Approaches to VOC History in South Africa, South African
Hist. J., 59, 3–18, https://doi.org/10.1080/02582470709464770, 2007.
Zulkafli, Z., Perez, K., Vitolo, C., Buytaert, W., Karpouzoglou, T., Dewulf,
A., De Bièvre, B., Clark, J., Hannah, D. M., and Shaheed, S.: User-driven
design of decision support systems for polycentric environmental resources
management, Environ. Model. Softw., 88, 58–73,
https://doi.org/10.1016/j.envsoft.2016.10.012, 2017.
Short summary
We aim to address a proposed disconnect between science and the public. In this case, it is imarine meteorological information and the users of these data. Here, the focus is not only on the perceptions, usability and uptake of extreme event forecasts but rather on general, everyday situations. A survey was conducted in two Southern Hemisphere countries, South Africa and New Zealand, and subgroups within the communities (recreational and commercial users) were identified and elucidated.
We aim to address a proposed disconnect between science and the public. In this case, it is...
Altmetrics
Final-revised paper
Preprint