Ronald M. Errico

Bio: Ronald M. Errico is an academic researcher from National Center for Atmospheric Research. The author has contributed to research in topics: Normal mode & Initialization. The author has an hindex of 25, co-authored 59 publications receiving 3230 citations. Previous affiliations of Ronald M. Errico include Goddard Space Flight Center & University of Maryland, College Park.

A regional climate model for the western United States

Abstract: A numerical approach to modeling climate on a regional scale is developed whereby large-scale weather systems are simulated with a global climate model (GCM) and the GCM output is used to provide the boundary conditions needed for high-resolution mesoscale model simulations over the region of interest. In our example, we use the National Center for Atmospheric Research (NCAR) community climate model (CCM1) and the Pennsylvania State University (PSU)/NCAR Mesoscale Model version 4 (MM4) to apply this approach over the western United States (U.S.). The topography, as resolved by the 500-km mesh of the CCM1, is necessarily highly distorted, but with the 60-km mesh of the MM4 the major mountain ranges are distinguished. To obtain adequate and consistent representations of surface climate, we use the same radiation and land surface treatments in both models, the latter being the recently developed Biosphere-Atmosphere Transfer Scheme (BATS). Our analysis emphasizes the simulation at four CCM1 points surrounding Yucca Mountain, NV, because of the need to determine its climatology prior to certification as a high-level nuclear waste repository.

What is an adjoint model

Abstract: Adjoint models are powerful tools for many studies that require an estimate of sensitivity of model output (e.g., a forecast) with respect to input. Actual fields of sensitivity are produced directly and efficiently, which can then be used in a variety of applications, including data assimilation, parameter estimation, stability analysis, and synoptic studies. The use of adjoint models as tools for sensitivity analysis is described here using some simple mathematics. An example of sensitivity fields is presented along with a short description of adjoint applications. Limitations of the applications are discussed and some speculations about the future of adjoint models are offered.

Sensitivity Analysis Using an Adjoint of the PSU-NCAR Mesoseale Model

Abstract: An adjoint of the Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model has been developed for use in sensitivity analysis following Cacuci. Sensitivity analysis is defined as the determination of the potential impact on some quantitative measure of a forecast aspect due to arbitrary perturbations of the model dynamic fields at earlier times. Input to the adjoint operator is the gradient of the forecast-aspect measure with respect to the model fields at the verification time, and output is the corresponding gradients defined at earlier times. The adjoint is exactly determined from a tangent linear model, which is itself an approximation to the dry nonlinear model. This approximation is shown to be accurate even when evaluated with regard to the moist nonlinear model for periods up to 36 h, although this accuracy is necessarily case and perturbation dependent. The mathematics describing the scheme are applied to the model in its spatially and temporally ...

Singular-Vector Perturbation Growth in a Primitive Equation Model with Moist Physics

Abstract: Finite-time growth of perturbations in the presence of moist physics (specifically, precipitation) is investigated using singular vectors (SVs) in the context of a primitive equation regional model. Two difficulties appear in the explicit consideration of the effect of moist physics when studying such optimal growth. First, the tangent-linear description of moist physics may not be as straightforward and accurate as for dry-adiabatic processes; second, because of the consideration of moisture, the design of an appropriate measure of growth (i.e., norm) is subject to even more ambiguity than in the dry situation. In this study both of these problems are addressed in the context of the moist version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System, version 2, with emphasis on the second problem. Leading SVs are computed in an iterative fashion, using a Lanczos algorithm, for three norms over an optimization interval of 24 h; these norms are based on an expression re...

Predictability Experiments Using a High-Resolution Limited-Area Model

Abstract: Recently reported results indicate that limited-area mesoscale models with prescribed lateral boundaries do not exhibit the same predictability error growth as observed in large-scale (global) models. These results have been reanalyzed in greater detail. New methods of limited-area initialization and spectral analysis have been used. The new analyses indicate that several model properties act to restrict the growth of perturbation. These include: the Projection of initial perturbations onto gravity waves which interact only weakly with other, more significant motions; the “sweeping out” of errors by correct or perfect lateral boundaries; and the reduction of differences by subgrid dissipation. This last property suggests that there is a strong dynamical forcing of small scales by much larger scales, so that this forcing is only weakly affected by typical, small perturbations in this model. New experiments suggest that some quasi-geostrophic components of the forecasts, away from the inflow bounda...

Parameter estimation and inverse problems

Abstract: "Parameter Estimation and Inverse Problems, 2/e" provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical algorithms for solving inverse problems. The authors' treatment is appropriate for geoscience graduate students and advanced undergraduates with a basic working knowledge of calculus, linear algebra, and statistics. "Parameter Estimation and Inverse Problems, 2/e" introduces readers to both Classical and Bayesian approaches to linear and nonlinear problems with particular attention paid to computational, mathematical, and statistical issues related to their application to geophysical problems. The textbook includes Appendices covering essential linear algebra, statistics, and notation in the context of the subject. This book includes a companion website that features computational examples (including all examples contained in the textbook) and useful subroutines using MATLAB. It: includes appendices for review of needed concepts in linear, statistics, and vector calculus; features a companion website that contains comprehensive MATLAB code for all examples, which readers can reproduce, experiment with, and modify; offers an online instructor's guide that helps professors teach, customize exercises, and select homework problems; and, is accessible to students and professionals without a highly specialized mathematical background.

Atmospheric Modeling, Data Assimilation and Predictability

Abstract: This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

An Ensemble Adjustment Kalman Filter for Data Assimilation

Abstract: A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filter, and the more traditional implementation of the ensemble Kalman filter in which “perturbed observations” are used, are derived as Monte Carlo approximations to the nonlinear filter. Both ensemble Kalman filter methods produce assimilations with small ensemble mean errors while providing reasonable measures of uncertainty in the assimilated variables. The ensemble methods can assimilate observations with a nonlinear relation to model state variables and can also use observations to estimate the value of imprecisely known model parameters. These ensemble filter methods are shown to have significant advantag...

Atmospheric Modeling, Data Assimilation, and Predictability

Abstract: (2005). Atmospheric Modeling, Data Assimilation, and Predictability. Technometrics: Vol. 47, No. 4, pp. 521-521.