The physics of space weather/solar-terrestrial physics (STP): what we know now and what the current and future challenges are

Tsurutani, Bruce T.; Lakhina, Gurbax S.; Hajra, Rajkumar

doi:https://doi.org/10.5194/npg-27-75-2020

Articles | Volume 27, issue 1

https://doi.org/10.5194/npg-27-75-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-27-75-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 1

Review article

|

25 Feb 2020

Review article |

| 25 Feb 2020

The physics of space weather/solar-terrestrial physics (STP): what we know now and what the current and future challenges are

Bruce T. Tsurutani, Gurbax S. Lakhina, and Rajkumar Hajra

Download

Final revised paper (published on 25 Feb 2020)
Preprint (discussion started on 01 Aug 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Full report', Anonymous Referee #1, 21 Aug 2019
- AC1: 'Reply to the comments by Referee #1', Bruce Tsurutani, 15 Nov 2019
RC2: 'Referee comments on “Space weather forecasting: what we know now and what are the current and future challenges?” by B. Tsurutani et al.', Anonymous Referee #2, 02 Sep 2019
- AC2: 'Reply to the comments by Referee #2', Bruce Tsurutani, 15 Nov 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Bruce Tsurutani on behalf of the Authors (15 Nov 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (18 Nov 2019) by Giovanni Lapenta (deceased)

RR by Anonymous Referee #2 (29 Nov 2019)

Suggestions for revision or reasons for rejection

Major comments:

We thank the authors' great effort to improve their manuscript, by taking account of the referees' comments. It became clear that the authors focus on the physical causes of phenomena, rather than forecasts in this paper. They changed the title, and it became more suitable to the topic of this paper. However, if the authors focus on physics, it may be more suitable to use "Solar-Terrestrial Physics" (STP) for the title.

I agree with the authors' idea that the application of weather forecasting will help us improve the predictability of space weather. In the point of view of space weather forecasts, it is more important to show how much we can predict phenomena even if they are chaotic or which phenomena can be predicted. Recently, machine-learning techniques have been applied to solar flare predictions, and they succeeded in improving prediction accuracy a lot.

I also agree that prediction models should be evaluated by the third parties, but in the same operational settings. Recently, operational models have been compared in CCMC/NASA and other small benchmark workshops. I feel that there is no standard evaluation method discussed in our community and it is a problem.

I read through the revised parts in the text, colored in blue. I have additional comments on them, and I think that the current manuscript is still not enough for publication in NPG. I hope that my comments are useful for the authors to improve their manuscript.

Detailed comments:

1) [p.2, section 1.1, 1st para., line 1] The sentence "Space Weather is a new term for a topic that actually began over a century and a half ago." is confusing. The term "space weather forecasting" first appeared in 1988 (Nagai 1988 JGR, Marubashi 1989 SSRv), and US national space weather program started in 1993. Please modify the sentence for readers to avoid misunderstanding.

2) [p.9, 2nd para, line 3-4] In the sentence "Only very few CMEs have speeds >2,000 km/s... (personal communications with referee, 2019)", it is better to cite some references (e.g. Yashiro et al.).

3) [p.16, 2nd para, line 3-4] Is it correctly "y is in the direction of the Earth's south magnetic pole, (Omega x X)/|Omega x X|, where Omega is the angular velocity"?

4) [p.20, section 3.2.1, 1st para. & Fig. 7, caption] Correctly, "the Soft X-ray Telescope (SXT)" onboard Yohkoh.

5) [p.47, 1st para, line 2 and Fig. 22] It is hard to see the characters in Fig. 22 (c)-(d). Please cite the reference of this figure in the caption.

6) [p.47, 1st para, the last two sentences] It is better to remove the sentence "this solar flare example is... no transfer of energy to interplanetary space and then to the magnetosphere.", because there is no evidence to show no energy transfer to the interplanetary space and the magnetosphere.

7) [p.47, 2nd para, line 1-2] The sentence "Some future space weather problems are to understand if the solar flare photon energy spectrum varies often" is misleading. Previous observations have shown that the energy spectra of solar flares differ in time and events. It is due to particle acceleration in flares, and the problem has been challenged by lots of solar physicists. I also recommend to use the term "solar energy spectrum" rather than "photon spectrum".

8) [p.48, line 1-2] The sentence "Solar flare data... would be useful to understand the details of flare spectral differences but solar physicists need to explain what the causes are" is also strange to solar physicists. The mechanisms of thermal and nonthermal emissions are qualitatively understood. For space weather forecasting, it is more important to predict X-ray/UV emissions from solar flares quantitatively. In addition, the solar energy spectrum is not observed by SORCE and TIMED, but by RHESSI and EVE/SDO.

9) [p.62, Final comments, 1st para, line 7-10] "Meng et al. (2019b) have tested ...indices were used." In this sentence, "Meng et al." is not accepted now. What are the names of the two well-known ionospheric codes to predict TEC? The representation "not so good" is subjective, and the evaluation method is not explained at all. It is better to remove this sentence.

10) [p.63, 3rd para] Who are the "atmospheric weather forecasters"? NOAA? Please cite references. As for the idea of ensemble forecast, it has already been included in machine-learning algorithms. So, it is useful mainly for scientists using models of physics-based numerical simulations.

Hide

ED: Publish subject to minor revisions (review by editor) (29 Nov 2019) by Giovanni Lapenta (deceased)

AR by Bruce Tsurutani on behalf of the Authors (06 Dec 2019) Manuscript

ED: Publish as is (10 Dec 2019) by Giovanni Lapenta (deceased)

AR by Bruce Tsurutani on behalf of the Authors (23 Jan 2020) Author's response Manuscript