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Title: Sensitivity analysis of numerically-simulated convective storms using direct and adjoint methods

Abstract

The goal of this project is to evaluate the sensitivity of numerically modeled convective storms to control parameters such as the initial conditions, boundary conditions, environment, and various physical and computational parameters. In other words, the authors seek the gradient of the solution vector with respect to specified parameters. One can use two approaches to accomplish this task. In the first or so-called brute force method, one uses a fully nonlinear model to generate a control forecast starting from a specified initial state. Then, a number of other forecasts are made in which chosen parameters (e.g., initial conditions) are systematically varied. The obvious drawback is that a large number of full model predictions are needed to examine the effects of only a single parameter. The authors describe herein an alternative, essentially automated method (ADIFOR, or Automatic DIfferentiation of FORtran) for obtaining the solution gradient that bypasses the adjoint altogether yet provides even more information about the gradient. (ADIFOR, like the adjoint technique, is constrained by the linearity assumption.) Applied to a 1-D moist cloud model, the authors assess the utility of ADIFOR relative to the brute force approach and evaluate the validity of the tangent linear approximation in the contextmore » of deep convection.« less

Authors:
;  [1]; ;  [2]
  1. Univ. of Oklahoma, Norman, OK (United States)
  2. Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
Publication Date:
Research Org.:
Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
Sponsoring Org.:
USDOE, Washington, DC (United States); National Science Foundation, Washington, DC (United States)
OSTI Identifier:
10159235
Report Number(s):
ANL/MCS/CP-82981; CONF-940769-4
ON: DE94013399; CNN: Grant ATM92-22576; Grant ATM88-09862; TRN: AHC29415%%12
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Technical Report
Resource Relation:
Conference: 6. conference on mesoscale processes,Portland, OR (United States),18-22 Jul 1994; Other Information: PBD: [1994]
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; STORMS; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS; ONE-DIMENSIONAL CALCULATIONS; CLOUDS; EXPERIMENTAL DATA; COMPUTERIZED SIMULATION; 540110; BASIC STUDIES

Citation Formats

Park, S K, Droegemeier, K K, Bischof, C, and Knauff, T. Sensitivity analysis of numerically-simulated convective storms using direct and adjoint methods. United States: N. p., 1994. Web. doi:10.2172/10159235.
Park, S K, Droegemeier, K K, Bischof, C, & Knauff, T. Sensitivity analysis of numerically-simulated convective storms using direct and adjoint methods. United States. https://doi.org/10.2172/10159235
Park, S K, Droegemeier, K K, Bischof, C, and Knauff, T. 1994. "Sensitivity analysis of numerically-simulated convective storms using direct and adjoint methods". United States. https://doi.org/10.2172/10159235. https://www.osti.gov/servlets/purl/10159235.
@article{osti_10159235,
title = {Sensitivity analysis of numerically-simulated convective storms using direct and adjoint methods},
author = {Park, S K and Droegemeier, K K and Bischof, C and Knauff, T},
abstractNote = {The goal of this project is to evaluate the sensitivity of numerically modeled convective storms to control parameters such as the initial conditions, boundary conditions, environment, and various physical and computational parameters. In other words, the authors seek the gradient of the solution vector with respect to specified parameters. One can use two approaches to accomplish this task. In the first or so-called brute force method, one uses a fully nonlinear model to generate a control forecast starting from a specified initial state. Then, a number of other forecasts are made in which chosen parameters (e.g., initial conditions) are systematically varied. The obvious drawback is that a large number of full model predictions are needed to examine the effects of only a single parameter. The authors describe herein an alternative, essentially automated method (ADIFOR, or Automatic DIfferentiation of FORtran) for obtaining the solution gradient that bypasses the adjoint altogether yet provides even more information about the gradient. (ADIFOR, like the adjoint technique, is constrained by the linearity assumption.) Applied to a 1-D moist cloud model, the authors assess the utility of ADIFOR relative to the brute force approach and evaluate the validity of the tangent linear approximation in the context of deep convection.},
doi = {10.2172/10159235},
url = {https://www.osti.gov/biblio/10159235}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jun 01 00:00:00 EDT 1994},
month = {Wed Jun 01 00:00:00 EDT 1994}
}