Corrigendum

Soubhik Biswas aInstitute of Innovation, Science and Sustainability, Federation University, Ballarat, Victoria, Australia

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https://orcid.org/0000-0002-0069-0107
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Savin S. Chand aInstitute of Innovation, Science and Sustainability, Federation University, Ballarat, Victoria, Australia

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Andrew J. Dowdy bBureau of Meteorology, Melbourne, Victoria, Australia

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Wendy Wright cFuture Regions Research Centre, Federation University, Gippsland, Victoria, Australia

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Cameron Foale aInstitute of Innovation, Science and Sustainability, Federation University, Ballarat, Victoria, Australia

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Xiaohui Zhao aInstitute of Innovation, Science and Sustainability, Federation University, Ballarat, Victoria, Australia

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Anil Deo aInstitute of Innovation, Science and Sustainability, Federation University, Ballarat, Victoria, Australia

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Free access

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

Corresponding author: Soubhik Biswas, soubhikbiswas@students.federation.edu.au

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

Corresponding author: Soubhik Biswas, soubhikbiswas@students.federation.edu.au

A subset of the results based on the temperature in Biswas et al. (2022) used an incorrect latitude, and corrected results are provided in the figures and tables presented herein. This correction does not change the interpretation of results or summary of overall findings, as detailed below.

Biswas et al. (2022) presented results in their section 4. That analysis included several different methods for bias correction applied to each of the four input variables used for a fire weather index (wind speed, relative humidity, rainfall, and temperature), as shown in their sections 4a and 4b, with the bias correction methods then applied to the fire weather index in their section 4c. The issue was present only for some of the initial results that were based on temperature and was not present for any subsequent processing, such as for the fire weather index itself. As such, the corrections presented here are only relevant for temperature-specific results in sections 4a and 4b of Biswas et al. (2022).

The third, or right center, columns (pertaining to temperature) of Figs. 1, C2, D1, and E1 from Biswas et al. (2022) are now corrected, and the corrected columns are combined in Fig. 1 of this corrigendum. The mean absolute error (MAE) results for temperature that were presented in the bottom-left panels of Figs. 2, C3, D2, and E2 from Biswas et al. (2022) are now corrected, and the corrected plots are combined in Fig. 2 of this corrigendum. The cross-validated MAE results for temperature that were presented in the bottom-left panels of Figs. 3, C4, D3, and E3 from Biswas et al. (2022) are now corrected, and the corrected plots are combined in Fig. 3 of this corrigendum. The temperature sections of Tables B1, C1, D1, and E1 from Biswas et al. (2022) are now corrected and are shown here combined in Table B1. Similarly, the sections of Tables B2, C2, D2, and E2 from Biswas et al. (2022) that contain the cross-validated temperature results are now corrected and are shown here combined in Table B2. All other results from Biswas et al. (2022) are correct, including the spatial maps for temperature shown in Figs. 4–6 of that paper.

Fig. 1.
Fig. 1.

Comparison of kernel density graphs of bias-corrected temperature for the calculation of McArthur forest fire danger index using each of the methods from 1975 to 2014 at Melbourne (37.80°S, 144.95°E), over Murray Basin, over entire Australia, and at Yarragundry (35.10°S, 147.20°E). This is shown for the raw 20CRv2c data prior to calibration (blue), for the calibrated data (black), and for the observation-based reference data (orange).

Citation: Journal of Applied Meteorology and Climatology 62, 3; 10.1175/JAMC-D-23-0001.1

Fig. 2.
Fig. 2.

MAE for each probability interval (i.e., deciles 1–10) at Melbourne (37.80°S, 144.95°E), over Murray Basin, over entire Australia, and at Yarragundry (35.10°S, 147.20°E). This is presented for temperature (°C), shown for the six different calibration methods as well as the uncorrected 20CRv2c data.

Citation: Journal of Applied Meteorology and Climatology 62, 3; 10.1175/JAMC-D-23-0001.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for cross-validated results.

Citation: Journal of Applied Meteorology and Climatology 62, 3; 10.1175/JAMC-D-23-0001.1

Table B1.

MAE for each probability interval (i.e., deciles 1–10) at Melbourne (37.80°S, 144.95°E), over Murray Basin, over entire Australia and at Yarragundry (35.10°S, 147.20°E). This is presented for temperature (°C), shown for the six different calibration methods.

Table B1.
Table B2.

As in Table B1, but for cross-validated results.

Table B2.

The corrected results for temperature presented here in Figs. 13 show that the spline method performs well, together with some of the other methods, with the text from Biswas et al. (2022) that describes the results for temperature therefore still being valid (i.e., paragraphs 4 and 7 of their section 4a and paragraph 2 of their section 4b). In addition, still valid is the summary of results presented in section 5 of Biswas et al. (2022) that is based on considering the overall findings, including the various input variables of the fire weather index and the fire weather index itself. This includes the key statements, such as in the first paragraph of their section 5: “Our findings demonstrate the feasibility of constructing a long-term historical daily fire weather dataset throughout Australia, with very low relative error in relation to observation-based data.” At the end of their section 5, within the the final paragraph of that paper, this key summary of findings is also still valid:

[The results suggest that] monotonic piecewise Hermite cubic spline as a [probability density function] in quantile–quantile matching is a good option as a bias-correction approach for 20CRv2c reanalysis data for fire weather measures, with no other method examined here showing results that are consistently better than the spline method (noting that some show results that are broadly similar in some respects). Application of this spline-based method to long-term reanalysis data successfully reproduces climatological features of an observation-based fire weather dataset, including spatial variations and magnitudes of values, covering mean values as well as for the higher percentile values that are more relevant for the risk of dangerous wildfires.

Acknowledgments.

Thanks are given to Dr. Chris Lucas from the Bureau of Meteorology, Australia, for bringing this error to our attention.

REFERENCE

Biswas, S., S. S. Chand, A. J. Dowdy, W. Wright, C. Foale, X. Zhao, and A. Deo, 2022: Statistical calibration of long-term reanalysis data for Australian fire weather conditions. J. Appl. Meteor. Climatol., 61, 729758, https://doi.org/10.1175/JAMC-D-21-0034.1.

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  • Biswas, S., S. S. Chand, A. J. Dowdy, W. Wright, C. Foale, X. Zhao, and A. Deo, 2022: Statistical calibration of long-term reanalysis data for Australian fire weather conditions. J. Appl. Meteor. Climatol., 61, 729758, https://doi.org/10.1175/JAMC-D-21-0034.1.

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  • Fig. 1.

    Comparison of kernel density graphs of bias-corrected temperature for the calculation of McArthur forest fire danger index using each of the methods from 1975 to 2014 at Melbourne (37.80°S, 144.95°E), over Murray Basin, over entire Australia, and at Yarragundry (35.10°S, 147.20°E). This is shown for the raw 20CRv2c data prior to calibration (blue), for the calibrated data (black), and for the observation-based reference data (orange).

  • Fig. 2.

    MAE for each probability interval (i.e., deciles 1–10) at Melbourne (37.80°S, 144.95°E), over Murray Basin, over entire Australia, and at Yarragundry (35.10°S, 147.20°E). This is presented for temperature (°C), shown for the six different calibration methods as well as the uncorrected 20CRv2c data.

  • Fig. 3.

    As in Fig. 2, but for cross-validated results.

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