Teaching a Weather Forecasting Class in the 2020s

Lars van Galen Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands

Search for other papers by Lars van Galen in
Current site
Google Scholar
PubMed
Close
,
Oscar Hartogensis Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands

Search for other papers by Oscar Hartogensis in
Current site
Google Scholar
PubMed
Close
,
Imme Benedict Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands

Search for other papers by Imme Benedict in
Current site
Google Scholar
PubMed
Close
, and
Gert-Jan Steeneveld Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands

Search for other papers by Gert-Jan Steeneveld in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gert-Jan Steeneveld, gert-jan.steeneveld@wur.nl

Abstract

We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gert-Jan Steeneveld, gert-jan.steeneveld@wur.nl

Supplementary Materials

    • Supplemental Materials (PDF 12.4 MB)
Save
  • Anderson, L. W. , and B. S. Bloom , 2001: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman, 352 pp.

    • Search Google Scholar
    • Export Citation
  • Apple, J. , J. Lemus, and S. Semken , 2014: Teaching geoscience in the context of culture and place. J. Geosci. Educ., 62, 14, https://doi.org/10.5408/1089-9995-62.1.1.

    • Search Google Scholar
    • Export Citation
  • Bauer, P. , A. Thorpe, and G. Brunet , 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L. , and Coauthors, 2017: The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP system. Mon. Wea. Rev., 145, 19191935, https://doi.org/10.1175/MWR-D-16-0417.1.

    • Search Google Scholar
    • Export Citation
  • Bond, N. A. , and C. F. Mass , 2009: Development of skill by students enrolled in a weather forecasting laboratory. Wea. Forecasting, 24, 11411148, https://doi.org/10.1175/2009WAF2222214.1.

    • Search Google Scholar
    • Export Citation
  • Chickering, A. W. , and Z. F. Gamson , 1987: Seven principles for good practice in undergraduate education. AAHE Bull., 39 ( 3), 37.

    • Search Google Scholar
    • Export Citation
  • Cohen, A. E. , and Coauthors, 2018: Bridging operational meteorology and academia through experiential education: The storm prediction center in the University of Oklahoma classroom. Bull. Amer. Meteor. Soc., 99, 269279, https://doi.org/10.1175/BAMS-D-16-0307.1.

    • Search Google Scholar
    • Export Citation
  • Conley, D. T. , and E. M. French , 2014: Student ownership of learning as a key component of college readiness. Amer. Behav. Sci., 58, 10181034, https://doi.org/10.1177/0002764213515232.

    • Search Google Scholar
    • Export Citation
  • de Vos, L. W. , A. M. Droste, M. J. Zander, A. Overeem, H. Leijnse, B. G. Heusinkveld, G. J. Steeneveld, and R. Uijlenhoet , 2020: Hydrometeorological monitoring using opportunistic sensing networks in the Amsterdam metropolitan area. Bull. Amer. Meteor. Soc., 101, E167E185, https://doi.org/10.1175/BAMS-D-19-0091.1.

    • Search Google Scholar
    • Export Citation
  • Doms, G. , and M. Baldauf , 2013: A description of the nonhydrostatic regional COSMO-model: Part I: Dynamics and numerics. Consortium for Small-Scale Modeling Rep., 166 pp., www.cosmo-model.org/content/model/documentation/core/cosmo_dynamics_5.00.pdf.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J. , D. Gill, K. Manning, W. Wang, and C. Bruyere , 2000: PSU/NCAR mesoscale modeling system tutorial class notes and user’s guide: MM5 modeling system version 3. National Center for Atmospheric Research Tech. Rep., 138 pp.

    • Search Google Scholar
    • Export Citation
  • Eden, P., 2011: From observations to forecasts—Part 14: Communicating forecasts. Weather, 66, 325327, https://doi.org/10.1002/wea.872.

    • Search Google Scholar
    • Export Citation
  • Ellrod, G. P. , and D. I. Knapp , 1992: An objective clear-air turbulence forecasting technique: Verification and operational use. Wea. Forecasting, 7, 150165, https://doi.org/10.1175/1520-0434(1992)007<0150:AOCATF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Etherton, B. J. , S. C. Arms, L. Oolman, G. M. Lackmann, and M. K. Ramamurthy , 2011: Using operational and experimental observations in geoscience education. Bull. Amer. Meteor. Soc., 92, 477480, https://doi.org/10.1175/2010BAMS3045.1.

    • Search Google Scholar
    • Export Citation
  • Garbanzo-Salas, M. , and D. Jimenez-Robles , 2020: Online education program in operational meteorology and a case study about a product for decision making. ISPRS Int. J. Geo-Inf., 9, 175, https://doi.org/10.3390/ijgi9030175.

    • Search Google Scholar
    • Export Citation
  • Griffiths, D. , M. Foley, I. Ioannou, and T. Leeuwenburg , 2019: Flip-flop index: Quantifying revision stability for fixed-event forecasts. Meteor. Appl., 26, 3035, https://doi.org/10.1002/met.1732.

    • Search Google Scholar
    • Export Citation
  • Grundstein, A. , J. Durkee, J. Frye, T. Andersen, and J. Lieberman , 2011: A severe weather laboratory exercise for an introductory weather and climate class using active learning techniques. J. Geosci. Educ., 59, 2230, https://doi.org/10.5408/1.3543917.

    • Search Google Scholar
    • Export Citation
  • Hintz, K. S. , and Coauthors, 2019: Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–5 December 2018. Atmos. Sci. Lett., 20, e921, https://doi.org/10.1002/asl.921.

    • Search Google Scholar
    • Export Citation
  • Inness, P. M. , and S. Dorling , 2013: Operational Weather Forecasting. John Wiley and Sons, 248 pp.

  • Leask, B., 2015: Internationalizing the Curriculum. Routledge, 198 pp.

  • Lee, L., 2013: A Climatological Study of Clear Air Turbulence over the North Atlantic. Uppsala University, 49 pp.

  • Mandement, M. , and O. Caumont , 2020: Contribution of personal weather stations to the observation of deep-convection features near the ground. Nat. Hazards Earth Syst. Sci., 20, 299322, https://doi.org/10.5194/nhess-20-299-2020.

    • Search Google Scholar
    • Export Citation
  • Napoly, A. , T. Grassmann, F. Meier, and D. Fenner , 2018: Development and application of a statistically-based quality control for crowdsourced air temperature data. Front. Earth Sci., 6, 118, https://doi.org/10.3389/feart.2018.00118.

    • Search Google Scholar
    • Export Citation
  • Overeem, A., 2002: Verification of clear-air turbulence forecasts. KNMI Tech Rep., 76 pp.

  • Pandya, R. E. , and Coauthors, 2009: A summary of the 16th symposium on education. Bull. Amer. Meteor. Soc., 90, 861866, https://doi.org/10.1175/2009BAMS2510.1.

    • Search Google Scholar
    • Export Citation
  • Pandya, R. E. , D. Charlevoix, E. Cordero, D. Smith, and S. Yalda , 2012: Trends in the AMS education symposium and highlights from 2012. Bull. Amer. Meteor. Soc., 93, 19171920, https://doi.org/10.1175/BAMS-D-12-00166.1.

    • Search Google Scholar
    • Export Citation
  • Powers, J. G. , and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2005: Bridging the gap between theory and applications: An inquiry into atmospheric science teaching. Bull. Amer. Meteor. Soc., 86, 507518, https://doi.org/10.1175/BAMS-86-4-507.

    • Search Google Scholar
    • Export Citation
  • Schultz, D. M. , S. Anderson, J. G. Fairman, D. Lowe, G. McFiggans, E. Lee, and R. Seo-Zindy , 2015: ManUniCast: A real-time weather and air-quality forecasting portal and app for teaching. Weather, 70, 180186, https://doi.org/10.1002/wea.2468.

    • Search Google Scholar
    • Export Citation
  • Steeneveld, G. J. , and J. Vilà-Guerau de Arellano , 2019: Teaching atmospheric modelling at the graduate level: 15 years of using mesoscale models as educational tools in an active learning environment. Bull. Amer. Meteor. Soc., 100, 21572174, https://doi.org/10.1175/BAMS-D-17-0166.1.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. , and P. Hobbs , 2006: Atmospheric Science: An Introductory Survey. Academic Press, 504 pp.

  • WMO, 2012: Manual on the implementation of education and training standards in meteorology and hydrology: Volume I—Meteorology. WMO Rep. 1083, 42 pp.

    • Search Google Scholar
    • Export Citation
  • WMO, 2019: BIP-M and BIP-MT compliance. WMO, https://community.wmo.int/bip-m-and-bip-mt-compliance.

  • Yarger, D. N. , W. A. Gallus, M. Taber, J. P. Boysen, and P. Castleberry , 2000: A forecasting activity for a large introductory meteorology course. Bull. Amer. Meteor. Soc., 81, 3139, https://doi.org/10.1175/1520-0477(2000)081<0031:AFAFAL>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zsoter, E. , R. Buizza, and D. Richardson , 2009: “Jumpiness” of the ECMWF and Met Office EPS control and ensemble-mean forecasts. Mon. Wea. Rev., 137, 38233836, https://doi.org/10.1175/2009MWR2960.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 14 0 0
Full Text Views 7988 5220 292
PDF Downloads 2634 352 68