The NCAR Technical Notes Collection comprises over 500 scientific and technical reports, issued by NCAR divisions and programs, and consists of data compilations, theoretical and numerical investigations, and experimental results.
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A Threedemiensional Variational (3DVAR) Data Assimilation System for Use With MM5

This report describes the threedimensional variational (3DVAR) data assimilation system designed and built in the MMM Division of NCAR for use with the MM5 modeling system. This, and additional, documentation can be found online at the MM5 3DVAR website: http://www.mmm.ucar.edu/3dvar The 3DVAR system described here was also adopted in June 2001 as the starting point for 3DVAR development for the Weather Research Forecast (WRF) model. This version of the technical documentation focuses on the use of 3DVAR within the MM5 modeling environment.


A Tutorial for Using 'Rmpi' on the NCAR/Wyoming Supercomputer

The recent growth in the size and complexity of climate data as well as the availability of cluster and supercomputing resources has brought parallel programming to the attention of geostatisticans. The National Center for Atmospheric Research's (NCAR) supercomputer facility is an accessible and powerful resource for any scientist or statistician working in climaterelated fields. This technical report presents a tutorial for users of the statistical language R to quickly utilize the massively parallel computational resources provided by the NCAR Yellowstone supercomputer through the Rmpi package. We provide fully functional scripts on the Yellowstone system that serve as examples of flexible implementations of the capabilities of massively parallel R scripts. Additionally, the tutorial serves as a guide for running parallel R scripts on anycluster or supercomputer with MPI capabilities.


A User's Guide to Mauna Loa Solar Observatory's Coronal Data System

This report is intended to be a user's guide to those data products which are produced at the observing site at Mauna Loa, or those which have been stored at the sealevel base in Hilo, Hawaii. Combined with the description of the NCAR/HAO data system, given by Everts (1981), this manual completes the documentation of the Coronal Dynamics Project's scientific data products. This discussion is complete and comprehensive as of the date of the first draft, 12 June 1982, but development of the Hawaiian data system continues.


A User's Guide to PIKAIA 1.0

This user's guide, organized with a very brief review of the biological evolutionary process relevant to the understanding of genetic algorithms. While Chapter 2 is concerned with pragmatic issues including how to obtain, install and validate the code. Chapter 3 describes the various genetic operators and ecological strategies incorporated in PIKAIA fairly detailed discussions of specific implementation issues. Chapter 4 contains detailed descriptions of input parameters, calling sequence, additional routines required from the user, etc. Readers who favor the "hands on" approach to learning may start by going straight to chapter 4 upon successfully completing the installation, and only subsequently study the (important) material discussed in chapter 3. Chapter 5, presents and discusses examples of applications of the subroutine to a sequence of increasingly difficult data modeling problems, including code listings for example fitness functions. Chapter 6 provides additional information for users wanting to modify, expand and/or tailor the code itself, and includes an annotated bibliography pointing to what we think are good entry points in the genetic algorithm literature. We do attempt to discuss relevant background material and include practical tips and general guidelines useful in dealing with real life problems, wherever most appropriate in the text and in particular in Chapter 5.


A User's Guide to the Penn State/NCAR Mesoscale Modeling System

The MM4 system is a series of FORTRAN programs and accompanying Cshell scripts that runs under UNICOS on the Cray YMP8/864 at NCAR. Each of these shells is a standalone script which must be submitted in a specific sequence to the Cray. The MM4 package is a flexible modeling system, implying that a good deal of user interaction is required. Each of the major components of the modeling system has a section in this document describing its standard usage; the initial forecast for any particular case involves running them all. Chapters include TERRAIN, DATAGRID, RAWINS, INTERP, GRAPH, MM4, INIT, TRAJEC, VERIFY. Appendix A: Modeling System Data Formats; Appendix B: Modeling System Input Files; Appendix C: Glossary.


A User's Guide to the VEMAP Phase 1 Database

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The VEMAP exercise allows us to identify important commonalties and differences among model controls and responses. Where the models differ, the comparison highlights areas of uncertainty or error and identifies problems for future research. Intermodel differences also help to quantify the uncertainty in modeled responses to changing climate and other drivers. The completed Phase I of the project was structured as a sensitivity analysis, with factorial combinations of climate (current and projected under doubled CO2), atmospheric CO2, and mapped and modelgenerated vegetation distributions. The highly structured nature of the intercomparison allowed rigorous analysis of results, while constraining the range of questions explored. Maps of climate, climate change scenarios, soil properties, and potential natural vegetation were prepared as common boundary conditions and driving variables for the models (Kittel et al. 1995). As a consequence, differences in model results arose only from differences among model algorithms and their implementation rather than from differences in inputs. Results from VEMAP I are reported in VEMAP Members (1995) and selected files are available through UCAR's anonymous FTP server (see Section 2.3). Abstracts describing the six modeling groups participating in VEMAP Phase I can be found under the subdirectory /docs. The VEMAP input database for the Phase I model intercomparison is documented in this Technical Note. It includes compiled and modelgenerated datasets of longterm mean climate, soils, vegetation, and climate change scenarios for the conterminous United States. The data are on a 0.5? latitude/longitude grid. There are both daily and monthly representations of the mean climate. The climate data and climate change scenarios are presented in both gridded and timesequential format. We developed the timesequential, "site" file format to facilitate extractions of information for individual grid cells (Sections 4.3 and 12).


A description of the fifthgeneration Penn State/NCAR Mesoscale Model (MM5)

This technical report describes the fifth generation Penn State/NCAR Mesoscale Model, or MM5. It is intended to provide scientific and technical documentation of the model for users. Source code documentation is available as a separate Technical Note (NCAR/TN392) by Haagenson et al. (1994). The document structure is as follows: In section 2 we describe the governing equations, algorithms, and boundary conditions. This will include the finite difference algorithms and time splitting techniques of both the hydrostatic and the nonhydrostatic equations of motion (hydrostatic and nonhydrostatic solver). All subsequent sections will describe features common to both solvers. Section 3 discusses the meshrefinement scheme, section 4 the fourdimensional dataassimilation technique, and section 5 focuses on the various physics options.


A fiveyear plan for research related to the assimilation of meteorological data

This report provides scientific rationale and justification for a fiveyear research plan for the assimilation of meteorological observations into numerical weather prediction models. In the broadest sense, meteorological data assimilation is partly the incorporation of atmospheric measurements into computer models that predict atmospheric behavior and partly the accommodation of such models to a set of observations. The goal of data assimilation is to produce a regular, physically consistent, fourdimensional representation of the atmosphere from a heterogeneous array of in situ and remote instruments which sample imperfectly and irregularly in space and time. Models and observations are inextricably linked in building this representation. Though data assimilation seldom receives public notice, it is true that today's computer forecasts would be impossible without it. Moreover, improvements in data assimilation are just as important as improvements to the model itself for accurate forecasts. For example, it is estimated that at least half the error in a twoday forecast of wind, temperature, or pressure is due to errors in the initial analysis. Meteorological data assimilation addresses a number of problems that will be introduced here and treated more thoroughly in Sections 2 and 3.


A multiresolution Gaussian process model for the analysis of large spatial data sets

A multiresolution basis is developed to predict twodimensional spatial fields based on irregularly spaced observations. The basis functions at each level of resolution are constructed as radial basis functions using a Wendland compactly supported correlation function with the nodes arranged on a rectangular grid. The grid at each finer level increases by a factor of two and the basis functions are scaled to have a constant overlap. The coefficients associated with the basis functions at each level of resolution are distributed according to a Gaussian Markov random field (GMRF) and take advantage of the fact that the basis is organized as a lattice. Several numerical examples and analytical results establish that this scheme gives a good approximation to standard covariance functions such as the Matern and also has flexibility to fit more complicated shapes. The other important feature of this model is that it can be applied to statistical inference for large spatial datasets because key matrices in the computations are sparse. The computational efficiency applies to both the evaluation of the likelihood and spatial predictions. Although our framework has similarities to fixed rank Kriging, the model gives a better approximation to situations where the nugget variance is small and the spatial process is close to interpolating the observations.
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