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ICDC
CENIntegrated Climate Data Center ICDC

Photo: getty images/L. Calvetti

Forward Operators

Forward operators translate the state of the atmosphere in the model into virtual measurements, which can be directly compared to real measurements. This validation path has emerged as an exciting development at the interface between models and data and become a formidable alternative to comparing retrieved atmospheric state variables with their model counter-parts. A similar development in data assimilation (assimilation of observations instead of retrieved state variables) was responsible for the final acceptance of satellite observations for weather prediction. Forward operators are, however, often more ambiguous than they appear, as critical information influencing the real measurement may not exist in the model space. E.g. three-dimensional radiative effects on top-of-atmosphere radiances (Venema et al., 2010) cannot be computed solely based on model variables, but may be important for the structure of observed radiances. Another example is radar reflectivity, which depends approximately on the sixth moment of the droplet size spectrum. This  moment cannot be directly predicted by current numerical models and must thus be inferred with the aid of additional assumptions (Haase and Crewell, 2000; Battaglia et al., 2010). Past studies involving forward operators suffered in large-scale models from the coarse model resolution or in cloud-resolving models from the idealizations in the simulations. The Forward Operators, listed here are used in the HD(CP)² project. Here the FO's will provide a framework which exploits the full potential of forward operators for a range of resolutions.

References

  • Battaglia, A., S. Tanelli, S. Kobayashi, D. Zrnic, R. Hogan, and C. Simmer, 2010: Multiple-scattering in radar systems: A review. J. Quant. Spec. Rad. Transf., 111, 917–947, doi:10.1016/j.jqsrt.2009.11.024.
  • Haase, G. and S. Crewell, 2000: Simulation of radar reflectivities using a mesoscale weather forecast model. Water Resour. Res., 36, 2221–2231.
  • Venema, V., F. Ament, and C. Simmer, 2006: A stochastic iterative amplitude adjusted fourier transform algorithm with improved accuracy. Nonlinear Processes in Geophysics, 13, 321–328.