26 Jan 2023
 | 26 Jan 2023

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12-km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions: First, how well are the 3- and 24-hr precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the NCEP/EMC 4-km Stage IV and PRISM data on 3-hr and 24-hr timescales, respectively. Our analysis suggests that the 12-km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3- and 24-hr precipitation maximum over the CONUS, in contrast to ERA5 which generally underestimates these quantities mainly over eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3- and 24-hr annual and seasonal maximum precipitation. WRF exhibits seasonally-dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year-round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics, but is susceptible to common regional climate model biases. Consequently, if used for operational purposes, we suggest moderate bias-correction be applied to the dynamically downscaled product.

Abhishekh Kumar Srivastava et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1382', Anonymous Referee #1, 13 Mar 2023
  • RC2: 'Comment on egusphere-2022-1382', Anonymous Referee #2, 17 Mar 2023

Abhishekh Kumar Srivastava et al.

Abhishekh Kumar Srivastava et al.


Total article views: 267 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
168 88 11 267 3 5
  • HTML: 168
  • PDF: 88
  • XML: 11
  • Total: 267
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 26 Jan 2023)
Cumulative views and downloads (calculated since 26 Jan 2023)

Viewed (geographical distribution)

Total article views: 248 (including HTML, PDF, and XML) Thereof 248 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 31 Mar 2023
Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability, mountain snowpack, etc. This study examines 3- and 24-hr historical precipitation over the contiguous United States in the 12-km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.