Automatic differentiation as a tool for sensitivity analysis of a convective storm in a 3-D cloud model
- Univ. of Oklahoma, Norman, OK (United States)
- Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
The ADIFOR automatic differentiation tool is applied to a 3-D storm-scale meteorological model to generate a sensitivity-enhanced code capable of providing derivatives of all model output variables and related diagnostic (derived) parameters as a function of specified control parameters. The tangent linear approximation, applied to a deep convective storm by the first of its kind using a full-physics compressible model, is valid up to 50 min for a 1% water vapor perturbations. The result is very encouraging considering the highly nonlinear and discontinuous properties of solutions. The ADIFOR-generated code has provided valuable sensitivity information on storm dynamics. Especially, it is very efficient and useful for investigating how a perturbation inserted at earlier time propagates through the model variables at later times. However, it is computationally very expensive to be applied to the variational data assimilation, especially for 3-D meteorological models, which potentially have a large number of input variables.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States); Oklahoma Univ., Norman, OK (United States). Center for Analysis and Prediction of Storms
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-31109-ENG-38
- OSTI ID:
- 392797
- Report Number(s):
- ANL/MCS-P-582-0496; CONF-960226-2; ON: DE96014864; CNN: Grant ATM91-20009; TRN: AHC29622%%33
- Resource Relation:
- Conference: 2. SIAM international conference on automatic differentiation, Santa Fe, NM (United States), 12-14 Feb 1996; Other Information: PBD: [1996]
- Country of Publication:
- United States
- Language:
- English
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