Articles | Volume 14, issue 11
Data description paper
11 Nov 2022
Data description paper |  | 11 Nov 2022

Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Utkarsh Mital, Dipankar Dwivedi, James B. Brown, and Carl I. Steefel


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-67', Anonymous Referee #1, 19 Jun 2022
  • RC2: 'Comment on essd-2022-67', Anonymous Referee #2, 05 Aug 2022
  • AC1: 'Comment on essd-2022-67', Utkarsh Mital, 09 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Utkarsh Mital on behalf of the Authors (09 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (11 Sep 2022) by Conrad Jackisch
RR by Anonymous Referee #2 (02 Oct 2022)
ED: Publish as is (06 Oct 2022) by Conrad Jackisch
AR by Utkarsh Mital on behalf of the Authors (12 Oct 2022)  Manuscript 
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
Final-revised paper