A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Pelz, Maria-Theresia; Schartau, Markus; Somes, Christopher J.; Lampe, Vanessa; Slawig, Thomas

doi:https://doi.org/10.5194/gmd-16-6609-2023

Articles | Volume 16, issue 22

https://doi.org/10.5194/gmd-16-6609-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-6609-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 22

Methods for assessment of models

|

16 Nov 2023

Methods for assessment of models |

| 16 Nov 2023

A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig

Download

Final revised paper (published on 16 Nov 2023)
Preprint (discussion started on 13 Feb 2023)

Interactive discussion

Status: closed

RC1: 'Comment on gmd-2023-17', Anonymous Referee #1, 26 Jun 2023

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2023-17/gmd-2023-17-RC1-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2023-17-RC1
RC2: 'Comment on gmd-2023-17', Anonymous Referee #2, 21 Jul 2023

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2023-17/gmd-2023-17-RC2-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2023-17-RC2
AC1: 'Comment on gmd-2023-17', Maria-Theresia Pelz, 07 Sep 2023

Dear editor

Please find attached our answers to the review comments. The document is organized as followed: The first 15 pages discuss the points made by RC1, the following 9 those by RC2 and the final pages illustrate the proposed changes in the updated version of our manuscript with tracked changes.

Citation: https://doi.org/10.5194/gmd-2023-17-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Thomas Slawig on behalf of the Authors (13 Sep 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (18 Sep 2023) by Travis O'Brien

AR by Thomas Slawig on behalf of the Authors (06 Oct 2023) Author's response Manuscript

Short summary

Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.