CALPUFF

CALPUFF Modeling System. CALPUFF is an advanced non-steady-state meteorological and air quality modeling system developed by scientists at Exponent, Inc. It is maintained by the model developers and distributed by Exponent. The model has been listed by the U.S. Environmental Protection Agency (EPA) as an alternative model for assessing long range transport of pollutants and their impacts on Federal Class I areas and for certain near-field applications involving complex meteorological conditions when the selection and use occur in agreement with the appropriate reviewing authority and approval by the EPA Regional Office (see details on CALPUFF’s Regulatory Status). The modeling system consists of three main components and a set of preprocessing and post-processing programs. The main components of the modeling system are CALMET (a diagnostic three-dimensional meteorological model), CALPUFF (an air quality dispersion model), and CALPOST (a post-processing package). In addition to these components, there are numerous other processors that may be used to prepare geophysical (land use and terrain) data in many standard formats; meteorological data (surface, upper air, precipitation, and buoy data); and interfaces to other models such as the Penn State/NCAR Mesoscale Model (MM5), the National Centers for Environmental Prediction (NCEP) Eta/NAM and RUC models, the Weather Research and Forecasting (WRF) model and the RAMS model.


References in zbMATH (referenced in 11 articles )

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  1. Baltar, Marina; Abreu, Victor; Ribeiro, Glaydston; Bahiense, Laura: Multi-objective model for the problem of locating tows for incident servicing on expressways (2021)
  2. Sun, Xiaotong; Xu, Wei; Jiang, Hongxun; Wang, Qili: A deep multitask learning approach for air quality prediction (2021)
  3. Elsakov, S. M.; Drozin, D. A.; Herreinstein, A. V.; Krupnova, T. G.; Nitskaya, S. G.; Olenchikova, T. Yu.; Zamyshlyaeva, A. A.: Numerical study of the SUSUPLUME air pollution model (2020)
  4. Hähnel, Philipp; Mareček, Jakub; Monteil, Julien; O’Donncha, Fearghal: Using deep learning to extend the range of air pollution monitoring and forecasting (2020)
  5. Peng, Zhen; Liu, Wanquan; An, Senjian: Haze pollution causality mining and prediction based on multi-dimensional time series with PS-FCM (2020)
  6. Schramm, Juliana; Bodmann, Bardo E. J.: A chemical kinetics extension to the advection-diffusion equation by NO(_x) and SO(_2) (2019)
  7. Sharipov, Daler; Muradov, Farrukh; Akhmedov, Dilshot: Numerical modeling method for short-term air quality forecast in industrial regions (2019)
  8. Hosseini, Bamdad; Stockie, John M.: Estimating airborne particulate emissions using a finite-volume forward solver coupled with a Bayesian inversion approach (2017)
  9. Kim, Jong Ho; Kwak, Byoung Kyu; Shin, Chee Burm; Jeon, Won Jin; Park, Hyeon-Soo; Choi, Kyunghee; Yi, Jongheop: Spatial distribution multimedia fate model: numerical aspects and ability for spatial analysis (2010)
  10. Kovalets, Ivan V.; Tsiouri, Vasso; Andronopoulos, Spyros; Bartzis, John G.: Improvement of source and wind field input of atmospheric dispersion model by assimilation of concentration measurements: method and applications in idealized settings (2009)
  11. Chang, Joseph C.: Uncertainty and sensitivity of dispersion model results to meteorological inputs: two case studies (2002)