Asset Details
- Identifier:
- grl61706-fig-0002
- Description:
- Waveforms for two −CG pulses in panels (a and b) and two −IC pulses in panels (c and d). In each row, one pulse was correctly classified by our model and the other was misclassified. The CAMMA‐reported peak current, source altitude, and probability of correct prediction for each type (CG and IC) are also given for each pulse. The atmospheric electricity sign convention is used.
- License:
- Rights Managed
- Rights Holder:
- John Wiley & Sons, Inc.
- License Rights Holder:
- © 2020. American Geophysical Union. All Rights Reserved.
- Asset Type:
- Image
- Asset Subtype:
- Chart/Graph
- Image Orientation:
- Portrait
- Image Dimensions:
- 2128 x 2167
- Image File Size:
- 651 KB
- Creator:
- Yanan Zhu, Phillip Bitzer, Vladimir Rakov, Ziqin Ding
- Credit:
- Zhu, Y., Bitzer, P., Rakov, V., & Ding, Z. (2021). A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning. Geophysical Research Letters, 48(1), n/a. https://doi.org/10.1029/2020GL091148.
- Collection:
- Keywords:
- Restrictions:
- Property Release:
- No
- Model Release:
- No
- Purchasable:
- Yes
- Sensitive Materials:
- No
- Article Authors:
- Yanan Zhu, Phillip Bitzer, Vladimir Rakov, Ziqin Ding
- Article Copyright Year:
- 2021
- Publication Title:
- Geophysical Research Letters
- Publication Volume:
- 48
- Publication Issue:
- 1
- Publication Date:
- 01/16/2021
- DOI:
- https://doi.org/10.1029/2020GL091148
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