Meteorology and Atmospheric Physics

, Volume 107, Issue 1–2, pp 51–64

Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model

  • Thomas Nehrkorn
  • Janusz Eluszkiewicz
  • Steven C. Wofsy
  • John C. Lin
  • Christoph Gerbig
  • Marcos Longo
  • Saulo Freitas
Original Paper

DOI: 10.1007/s00703-010-0068-x

Cite this article as:
Nehrkorn, T., Eluszkiewicz, J., Wofsy, S.C. et al. Meteorol Atmos Phys (2010) 107: 51. doi:10.1007/s00703-010-0068-x

Abstract

This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top–down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF–STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Thomas Nehrkorn
    • 1
  • Janusz Eluszkiewicz
    • 1
  • Steven C. Wofsy
    • 2
  • John C. Lin
    • 3
  • Christoph Gerbig
    • 4
  • Marcos Longo
    • 2
  • Saulo Freitas
    • 5
  1. 1.Atmospheric and Environmental Research, Inc.LexingtonUSA
  2. 2.Harvard UniversityCambridgeUSA
  3. 3.University of WaterlooWaterlooCanada
  4. 4.Max-Planck-Institut für BiogeochemieJenaGermany
  5. 5.Center for Weather Forecasts and Climate Studies (CPTEC), INPECachoeira PaulistaBrazil

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