Value of real-time vegetation fraction to forecasts of severe convection in high-resolution models

Kenneth A. James, David J. Stensrud, Nusrat Yussouf

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Near-real-time values of vegetation fraction are incorporated into a 2-km nested version of the Advanced Research Weather Research and Forecasting (ARW) model and compared to forecasts from a control run that uses climatological values of vegetation fraction for eight severe weather events during 2004. It is hypothesized that an improved partitioning of surface sensible and latent heat fluxes occurs when incorporating near-real-time values of the vegetation fraction into models, which may result in improved forecasts of the low-level environmental conditions that support convection and perhaps even lead to improved explicit convective forecasts. Five of the severe weather events occur in association with weak synoptic-scale forcing, while three of the events occur in association with moderate or strong synoptic-scale forcing. Results show that using the near-real-time values of the vegetation fraction alters the values and structure of low-level temperature and dewpoint temperature fields compared to the forecasts using climatological vegetation fractions. The environmental forecasts that result from using the real-time vegetation fraction are more thermodynamically supportive of convection, including stronger and deeper frontogenetic circulations, and statistically significant improvements of most unstable CAPE forecasts compared to the control run. However, despite the improved environmental forecasts, the explicit convective forecasts using real-time vegetation fractions show little to no improvement over the control forecasts. The convective forecasts are generally poor under weak synoptic-scale forcing and generally good under strong synoptic-scale forcing. These results suggest that operational forecasters can best use high-resolution forecasts to help diagnose environmental conditions within an ingredients-based forecasting approach.

Original languageEnglish (US)
Pages (from-to)187-210
Number of pages24
JournalWeather and Forecasting
Volume24
Issue number1
DOIs
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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