Extreme precipitation and temperature over the U.S. Pacific Northwest: A comparison between observations, reanalysis data, and regional models

Valérie R. Duliè, Yongxin Zhang, Eric P. Salathé

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

Extreme precipitation and temperature indices in reanalysis data and regional climate models are compared to station observations. The regional models represent most indices of extreme temperature well. For extreme precipitation, finer grid spacing considerably improves the match to observations. Three regional models, the Weather Research and Forecasting (WRF) at 12- and 36-km grid spacing and the Hadley Centre Regional Model (HadRM) at 25-km grid spacing, are forced with global reanalysis fields over the U.S. Pacific Northwest during 2003-07. The reanalysis data represent the timing of rain-bearing storms over the Pacific Northwest well; however, the reanalysis has the worst performance at simulating both extreme precipitation indices and extreme temperature indices when compared to the WRF and HadRM simulations. These results suggest that the reanalysis data and, by extension, global climate model simulations are not sufficient for examining local extreme precipitations and temperatures owing to their coarse resolutions. Nevertheless, the large-scale forcing is adequately represented by the reanalysis so that regional models may simulate the terrain interactions and mesoscale processes that generate the observed local extremes and frequencies of extreme temperature and precipitation.

Original languageEnglish
Pages (from-to)1950-1964
Number of pages15
JournalJournal of Climate
Volume24
Issue number7
DOIs
StatePublished - Apr 2011

Keywords

  • Extreme events
  • Mesoscale processes
  • Orographic effects
  • Precipitation
  • Regional models
  • Temperature

Fingerprint

Dive into the research topics of 'Extreme precipitation and temperature over the U.S. Pacific Northwest: A comparison between observations, reanalysis data, and regional models'. Together they form a unique fingerprint.

Cite this