Streamlining the graphical forecast process
Andrew Just A and Michael Foley B CA National Weather Service, Kansas City, United States of America.
B Bureau of Meteorology, GPO Box 1289, Melbourne Vic. 3001, Australia.
C Corresponding author. Email: michael.foley@bom.gov.au
Journal of Southern Hemisphere Earth Systems Science 70(1) 108-113 https://doi.org/10.1071/ES19047
Submitted: 1 July 2020 Accepted: 27 August 2020 Published: 29 October 2020
Journal Compilation © BoM 2020 Open Access CC BY-NC-ND
Abstract
The national meteorological services of Australia and the United States have followed similar paths in modernising production of their public weather forecasts during the past two decades. Both have adopted grid-based forecasts constructed by forecasters using a graphical forecast process. As gridded forecasting has matured, both have worked to achieve a more streamlined and standardised forecast process, so as to free up forecaster time for other activities such as decision support and a focus on high-impact weather, while increasing consistency in the gridded forecasts. We will describe the paths followed in Australia and the U.S., specifically in the U.S. National Weather Service Central Region, towards a more streamlined graphical forecast process. Although the journeys have been rather different, they have converged on similar solutions. A variety of lessons have been learned regarding how to achieve effective change in weather forecast production, through grassroots engagement and management support.
Keywords: Australian Bureau of Meteorology, BoM, forecast process, GFE, Graphical Forecast Editor, National Blend of Models, NWS, NWP, Operational Consensus Forecast, Regional Forecast Centres, streamlining, U.S. National Weather Service.
References
Abrams, E. (2004). Implementation and refinement of digital forecasting databases. Bull. Amer. Meteor. Soc. 85, 1667–1672.| Implementation and refinement of digital forecasting databases.Crossref | GoogleScholarGoogle Scholar |
Benjamin, S. G., Stanley, G., Brown, J. M., Brunet, G., Lynch, P., Saito, K., and Schlatter, T. W. (2018). 100 years of progress in forecasting and NWP applications. Meteor. Monogr. 59, 13.1–13.67.
| 100 years of progress in forecasting and NWP applications.Crossref | GoogleScholarGoogle Scholar |
Craven, J. P., Rudack, D. E., and Shafer, P. E. (2020). National Blend of Models: a statistically post-processed multi-model ensemble. J. Operational Meteor. 8, 1–14.
| National Blend of Models: a statistically post-processed multi-model ensemble.Crossref | GoogleScholarGoogle Scholar |
Engel, C., and Ebert, E. (2012). Gridded operational consensus forecasts of 2-m temperature over Australia. Wea. Forecasting 27, 301–322.
| Gridded operational consensus forecasts of 2-m temperature over Australia.Crossref | GoogleScholarGoogle Scholar |
Glahn, H. R., and Ruth, D. P. (2003). The new digital forecast database of the National Weather Service. Bull. Amer. Meteor. Soc. 84, 195–202.
| The new digital forecast database of the National Weather Service.Crossref | GoogleScholarGoogle Scholar |
Glowacki, T., Yi, X., and Steinle, P. (2012). Mesoscale surface analysis system for the Australian domain: design issues, development status, and system validation. Wea. Forecasting 27, 141–157.
| Mesoscale surface analysis system for the Australian domain: design issues, development status, and system validation.Crossref | GoogleScholarGoogle Scholar |
Griffiths, D., Jack, H., Foley, M., Ioannou, I. and Liu, M. (2017). Advice for automation of forecasts: a framework. Bureau Research Report 21. Available at http://www.bom.gov.au/research/publications/researchreports/BRR-021.pdf
Griffiths, D., Foley, M., Ioannou, I., and Leeuwenburg, T. (2019). Flip-flop index: quantifying revision stability for fixed-event forecasts. Meteorol. Appl. 26, 30–35.
| Flip-flop index: quantifying revision stability for fixed-event forecasts.Crossref | GoogleScholarGoogle Scholar |
Griffiths, D., and Park, J. (2012). Evaluation of medium range weather forecasts based on short term forecasts CAWCR. Res. Lett. 9, 10–13.
Hart, T., (2019). The Road Taken: An account, with lessons learned, of a 15 year journey in translating advances in science and technology into streamlining the weather forecast process and enhancing Bureau services to the Australian community. Bureau of Meteorology, Melbourne, Australia. Available at www.bom.gov.au/research/publications/otherreports/FSEP_to_NexGenFWS.pdf
Mass, C., and Baars, J. A. (2005). Performance of National Weather Service forecasts compared to operational, consensus, and weighted model output statistics. Wea. Forecasting 20, 1034–1047.
| Performance of National Weather Service forecasts compared to operational, consensus, and weighted model output statistics.Crossref | GoogleScholarGoogle Scholar |
Robbins, C. C., and Cortinas, J. V. (2002). Local and synoptic environments associated with freezing rain in the contiguous United States. Wea. Forecasting 17, 47–65.
| Local and synoptic environments associated with freezing rain in the contiguous United States.Crossref | GoogleScholarGoogle Scholar |
Sturrock, J., and Griffiths, D. (2020). The changing role of operational meteorologists. J. South. Hemisph. Earth Syst. Sci. , .
| The changing role of operational meteorologists.Crossref | GoogleScholarGoogle Scholar |
Woodcock, F., and Engel, C. (2005). Operational Consensus Forecasts. Wea. Forecasting 20, 101–111.
| Operational Consensus Forecasts.Crossref | GoogleScholarGoogle Scholar |