Advice for Automation of Forecasts: A Framework

Griffiths, Deryn; Jack, Harry; Foley, Michael; Ioanna, Ioanna; Liu, Maoyuan

doi:10.22499/4.0021

Cited by 6 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This both ensures a consistent comparison between model guidance products of different spatial resolutions, and an assessment of how the official forecast compares to the model guidance products as they actually appear to forecasters in the GFE. This is the standard approach the BoM takes when comparing the performance of the official forecast to unedited model guidance (e.g., Griffiths et al 2017).…”

Section: A Datamentioning

confidence: 99%

“…Illustration of the method for calculating the difference of absolute errors (DAE) in the diurnal signals of an unedited model guidance dataset, and the human edited official forecast dataset, when compared with automatic weather station (AWS) observations, at an example time of 1200 UTC. estimating confidence in DAE is based on a method proposed by Griffiths et al (2017). Time series formed from the DAE values at a particular time, say 0000 UTC, across the 3-month time period, are treated as an independent sample of a random variable E. The sampling distribution for each DAE can be modeled by a Student's t distribution, and from this we calculate the probability that E is positive, denoted Pr(E .…”

Section: B Assessing Diurnal Variabilitymentioning

confidence: 99%

“…Confidence scores are calculated for the city and coastal station groups in the same way as for the individual airport stations, treating the spatially averaged data as a single time series. This provides a conservative way to deal with spatial correlation between the stations in each group (Griffiths et al 2017).…”

Section: B Assessing Diurnal Variabilitymentioning

confidence: 99%

See 2 more Smart Citations

Verifying Operational Forecasts of Land–Sea-Breeze and Boundary Layer Mixing Processes

Short

2020

Weather and Forecasting

View full text Add to dashboard Cite

Forecasters working for Australia’s Bureau of Meteorology (BoM) produce a seven day forecast in two key steps: first they choose a model guidance dataset to base the forecast on, then they use graphical software to manually edit this data. Two types of edits are commonly made to the wind fields that aim to improve how the influences of boundary layer mixing and land-sea breeze processes are represented in the forecast. In this study the diurnally varying component of the BoM’s official wind forecast is compared with that of station observations and unedited model guidance datasets. Coastal locations across Australia over June, July and August 2018 are considered, with data aggregated over three spatial scales. The edited forecast produces a lower mean absolute error than model guidance at the coarsest spatial scale (over fifty thousand square kilometres), and achieves lower seasonal biases over all spatial scales. However, the edited forecast only reduces errors or biases at particular times and locations, and rarely produces lower errors or biases than all model guidance products simultaneously. To better understand physical reasons for biases in the mean diurnal wind cycles, modified ellipses are fitted to the seasonally averaged diurnal wind temporal hodographs. Biases in the official forecast diurnal cycle vary with location for multiple reasons, including biases in the directions sea-breezes approach coastlines, amplitude biases, and disagreement in the relative contribution of sea-breeze and boundary layer mixing processes to the mean diurnal cycle.

show abstract

Section: A Datamentioning

confidence: 99%

Section: B Assessing Diurnal Variabilitymentioning

confidence: 99%

See 1 more Smart Citation

Verifying Operational Forecasts of Land–Sea-Breeze and Boundary Layer Mixing Processes

Short

2020

Weather and Forecasting

View full text Add to dashboard Cite

show abstract

“…Verification was used to explore the quality of the official forecasts produced from the GFE to see where the forecaster was adding value relative to the forecast guidance, taking the output of FirstCutForecast as a benchmark. The verification methodologies used were as presented by Griffiths et al (2017) for precipitation but extended to a wider range of elements. The picture that emerged was that the GOCF-based FirstCutForecast outputs were by and large of comparable quality to the officially produced forecasts.…”

Section: In the Australian Bommentioning

confidence: 99%

Streamlining the graphical forecast process

Just

Foley

2020

J. South. Hemisph. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…We will present results for a three-month period, comparing forecasts to observations at automatic weather stations in Southern Australia. The data and main verification techniques are described in Griffiths et al, 2017. Our verification is motivated by a need to assess the suitability of the ensemble-based output to replace the official forecast to deliver the public service. As such, our verification is based on definitions of the service and this informs choices made in conducting the verification.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Methods: Nowcasting to Climate Change - Abstracts of the Bureau of Meteorology Annual R and D Workshop, 26th November to 30th November 2018, Melbourne, Australia

Smith¹

2018

View full text Add to dashboard Cite

The Bureau of Meteorology advise that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law and the Bureau of Meteorology (including each of its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

show abstract

Advice for Automation of Forecasts: A Framework

Cited by 6 publications

References 3 publications

Verifying Operational Forecasts of Land–Sea-Breeze and Boundary Layer Mixing Processes

Verifying Operational Forecasts of Land–Sea-Breeze and Boundary Layer Mixing Processes

Streamlining the graphical forecast process

Ensemble Methods: Nowcasting to Climate Change - Abstracts of the Bureau of Meteorology Annual R and D Workshop, 26th November to 30th November 2018, Melbourne, Australia

Contact Info

Product

Resources

About