Automatic differentiation, tangent linear models, and (pseudo) adjoints
This paper provides a brief introduction to automatic differentiation and relates it to the tangent linear model and adjoint approaches commonly used in meteorology. After a brief review of the forward and reverse mode of automatic differentiation, the ADIFOR automatic differentiation tool is introduced, and initial results of a sensitivity-enhanced version of the MM5 PSU/NCAR mesoscale weather model are presented. We also present a novel approach to the computation of gradients that uses a reverse mode approach at the time loop level and a forward mode approach at every time step. The resulting ``pseudoadjoint`` shares the characteristic of an adjoint code that the ratio of gradient to function evaluation does not depend on the number of independent variables. In contrast to a true adjoint approach, however, the nonlinearity of the model plays no role in the complexity of the derivative code.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-31-109-ENG-38
- OSTI ID:
- 93547
- Report Number(s):
- ANL/MCS/CP-85787; CONF-9306285-2; ON: DE95013715
- Resource Relation:
- Conference: North Atlantic Treaty Organization/Advanced Study Institute (NATO/ASI) workshop in high performance computing in geosciences, Chamouix (France), 21-25 Jun 1993; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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