Mesoscale Modeling of the Atmosphere

166 Pages·1994·31.461 MB·English

by  Roger A. Pielke, Robert P. Pearce (eds.)

MESOSCALE MODELING
OF THE ATMOSPHERE
Edited by
Roger A. Pielke
Robert P. Pearce
Published by the American Meteorological Society
METEOROLOGICAL MONOGRAPHS
Editor Technical Editor Senior Copy Editor
DONALD R. JOHNSON KENNETH O. WILSON PATRICIA ENGLISH
University of Wisconsin American Meteorological Society American Meteorological Society
Copy Editors
JENNIFER L. BINFORD
B. DENISE TAYLOR
American Meteorological Society
Associate Editors
ROBERT C. BEARDSLEY JAMES R. HOLTON
Woods Hole Oceanographic Institution University of Washington
RICHARD E. ORVILLE JESSE J. STEPHENS
Texas A&M University Florida State University
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METEOROLOGICAL MONOGRAPHS
VOLUME 25 NOVEMBER 1994 NUMBER 47
MESOSCALE MODELING OF THE ATMOSPHERE
Edited by
Roger A. Pielke
Robert P. Pearce
American Meteorological Society
45 Beacon Street, Boston, Massachusetts 02108
© Copyright 1994 by the American Meteorological Society. Per
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ISSN 0065-940 I
ISBN 978-1-878220-15-8 ISBN 978-1-935704-12-6 (eBook)
DOI 10.1007/978-1-935704-12-6
Softcover reprint of the hardcover 1st edition 1994
Published by the American Meteorological Society
45 Beacon St., Boston, MA 02108
This monograph is dedicated to the memory of
Professor Jehuda Neumann
(1915-1993)
in recognition both of his outstanding pioneering contributions to mesoscale modeling
of the atmosphere and his most valuable services to meteorological research in the
U.S. Army as a member of its Advisory Panel in Mesoscale Meteorology for over
20 years.
TABLE OF CONTENTS
Preface Vll
Part I. Aspects of Mesoscale Modeling
Chapter 1. Initial Conditions and Boundary Conditions
-KEITH D. SASHEGYI AND RANGARAO V. MADALA
Chapter 2. Subgrid-Scale Parameterizations
-MAREK UUASZ .............................. 13
Chapter 3. Modeling of Surface Effects on the Mesoscale
-THOMAS T. WARNER ......................... 21
Chapter 4. Representing Moisture Processes in Mesoscale Numerical Models
-JERRY M. STRAKA............................ 29
Chapter 5. Inclusion of Radiation in Mesoscale Models
-BURKHARDT ROCKEL AND EHRHARD RASCHKE ... 39
Chapter 6. Mesoscale Meteorological Model Evaluation Techniques with Emphasis on Needs
of Air Quality Models
-STEVEN R. HANNA 47
Part II. The Mesoscale Model Comparison Project
Chapter 7. Introduction
-ROBERT P. PEARCE ........................... 61
Chapter 8. Overview of the Project WIND Data
-RONALD M. CIONCO .......................... 63
Chapter 9. Project WIND Numerical Simulations with FITNAH
-GONTER GROSS .............................. 73
Chapter 10. Project WIND Numerical Simulations with the Tel Aviv Model: PSU-NCAR
Model Run at Tel Aviv University
-Po ALPERT AND M. TSIDULKO .................. 81
Chapter 11. Simulations of Project WIND Cases with RAMS
-ROBERT L. WALKO AND ROGER A. PIELKE ....... 97
Chapter 12. HOTMAC: Model Performance Evaluation by Using Project WIND Phase I and II
Data
- TETSUJI YAMADA AND TEIZI HENMI 123
Chapter 13. Statistical Evaluation of the Mesoscale Model Results
-GONTER GROSS .............................. 137
Chapter 14. Comments on Statistical Results
-NIELS E. BUSCH, WERNER KLUG,
ROBERT P. PEARCE, AND PETER WHITE 155
References 159
VOL. 25, NO. 47 METEOROLOGICAL MONOGRAPHS
PREFACE
The past few years have seen an upsurge in mesoscale mospheric Science Laboratory (ASL), ARL's earlier
meteorology and, in particular, in the development of designation, recognized in the mid-1980s that the
mesoscale models. Such models, many incorporating models it was developing-ranging from the mesobeta
highly detailed physics, are now used extensively in (100 km) to the microscale (10 m)-needed an ob
experiments to improve understanding of mesoscale servational database of appropriate resolution if they
phenomena such as fronts, severe local storms, land were to be properly tested. It therefore designed for this
and sea breezes (one of their earliest applications), purpose and carried out during 1985-87, under the
tropical cyclones, flow around hills (including so-called direction of Mr. Ron Cionco, a field observation pro
large-eddy simulations), and associated effects such as gram in northern California called Project WIND. The
the spread of pollutants. These models are also being observations were concentrated in four periods, each
used increasingly in operational forecasting, some as of about two weeks and centered on a different season
"stand-alone" models, others nested within synoptic of the annual cycle. The vast quantity of data obtained
(or even global-) scale models. An example of the latter has now been almost completely quality assured and
processed into an accessible form on computer tape or
is the "unified model" currently in operational use at
disk. Details of the field program and dataset are given
the U.K. Meteorological Office; another is the tropical
in chapter 8.
cyclone prediction model at the National Hurricane
The army's Mesomet Panel suggested in 1990 that
Research Center, Miami, Florida.
advantage should be taken of the availability of pro
The American Meteorological Society book Meso
cessed subsets of the Project WIND dataset to invite
scale Meteorology and Forecasting, edited by Peter Ray
modeling groups in the United States and Europe to
and published in 1986, is perhaps the most up-to-date
take part in a model comparison project using data
comprehensive account of mesoscale meteorology
from the summer (phase I) and winter (phase II) pe
currently available. It not only covers the basic theory
riods to initialize the models and carry out 24-h fore
of the whole range of mesoscale phenomena but also
casts on a 41 X 41 horizontal (5 km) grid. Details of
includes substantial sections on observing systems,
the project implementation and of the requests made
mesoscale modeling, and short-range forecasting tech
by the modelers are given in chapter 7.
niques. However, during the eight years that have
Four groups participated and forwarded their results
elapsed since its publication, modeling developments
to ASL for analysis and presentation at a mesoscale
have led to a focusing of attention on those particular
modeling workshop held in El Paso, Texas, in June
aspects of mesoscale systems that models, particularly
1992. A selection of the results from each of the models
those in operational use, fail adequately to predict.
is presented in chapters 9-12. Finally, a statistical
It therefore seems appropriate to publish a new analysis of some of the results is provided in chapter
monograph devoted to an up-to-date review of these 13, and a commentary on the performance of the four
topics. Also, since a comparison was recently carried models is offered in chapter 14.
out of the performances of four models using an ob Publication of this monograph is supported under
servational dataset-the United States Army's Project Contract DAJA 45-90-C-0009 awarded to the Univer
WIND (Wind in Non-uniform Domains)-performed sity of Reading, United Kingdom by the U.S. Army
specifically for the purpose of model validation, a brief European Research Office. The editors also wish to
account of the results of these comparisons will be pre acknowledge with gratitude the support provided by
sented to illustrate the nature of the problems that me the staff of the Atmospheric Research Laboratory,
soscale modelers still need to address. White Sands, New Mexico, without which the extensive
Chapters 1-6 of this monograph provide an overview data analysis involved in the mesoscale model com
of the status of several components of mesoscale mod parison project described in chapters 7-14 would not
eling that are of particular importance to the accurate have been carried out. Thanks are due, in particular,
simulation of the Project WIND observations. to Mr. Ron Meyers of ARL for also undertaking the
The United States Army's Atmospheric Research task of collating the descriptions of the four models
Laboratory (ARL) at White Sands Missile Range, New involved in the project and which constitute the ap
Mexico, has been involved in mesoscale model appli pendices of chapters 9-12.
cations for many years. The army requires such models Roger A. Pielke
to provide weather forecasts in any part of the world Robert P. Pearce
in which it is engaged in military operations. The At- Editors
vii
I
PART
Aspects of Mesoscale Modeling
CHAPTER 1 SASHEGYI AND MADALA
Chapter 1
Initial Conditions and Boundary Conditions
KEITH D. SASHEGYI AND RANGARAO V. MADALA
Naval Research Laboratory, Washington, D.C.
1.1. Introduction are too sparse. Objective analysis describes the methods
used for such interpolation. Errors in the analysis ( due
High-resolution limited-area models have been suc to observational errors and unresolvable scales of mo
cessfully used for both the simulation and prediction tion) and inaccuracies in the model physics give rise
of regional and local mesoscale weather events (Anthes to imbalances between the wind and mass fields. Such
et al. 1989). This success can depend on accurately imbalances are removed during numerical integrations
specifying the initial state of the atmosphere and its of the model by inertia-gravity wave oscillations. The
variation in time along the lateral boundaries of the purpose of initialization prior to integrating the model
model domain. Synoptic-scale global or regional anal is to prevent such gravity wave noise contaminating
yses produced at operational weather centers do not the forecast particularly in the first 12 h of the forecast
provide sufficient resolution for the initial conditions of the mesoscale model. When insufficient data are
required by a mesoscale model. The observations available on the scale required by the numerical model,
themselves must be used to enhance a regional analysis the model can be used to provide the time continuity
or to correct an initial-guess field produced by the me between the observation times and generate the spatial
soscale model itself (e.g., by integrating the model from variability on the scales resolved by the model. Assim
an earlier operational regional analysis). Often the data ilation describes the process by which the data are in
themselves are of insufficient resolution to effectively troduced into the model to create a continuous de
enhance a global-or regional-scale analysis alone, and scription of the atmosphere, which has been updated
the data must be used in combination with the high by the data. During a period of assimilation, the model
resolution mesoscale model (Anthes et al. 1989). To can spin up mesoscale features forced by variable ter
provide the lateral boundary conditions for the me rain and surface heat distributions. Our interest in this
soscale model, forecasts from an operational global or paper is in generating more accurate initial conditions
regional model are usually used. For such an opera for starting a subsequent forecast with the model by
tional forecasting system, the forecasts are often too assimilating the data during a period of time prior to
infrequent to provide sufficiently accurate lateral
the start of the forecast run.
boundary values for the high-resolution mesoscale An excellent description of the above aspects of the
model. In this case it is useful to nest the high-resolution initial conditions can be found in Daley's ( 1991 ) recent
mesoscale model inside a limited-area model. This book and its review by Harms et al. (1992a). In this
provides a link between the mesoscale model and the
paper, we will discuss the above issues as they relate
sparse data and infrequent boundary values provided
in particular to high-resolution limited-area modeling.
by forecasts from a global or regional model. We will discuss in the following sections each of the
The components required to produce the initial four components listed above for the initial conditions.
conditions for a mesoscale model can be classified as The influence of the methods for prescribing the lateral
(i) quality control, (ii) objective analysis, (iii) initial
boundary conditions for high-resolution limited-area
ization, and (iv) assimilation. To utilize the data, some
models will also be discussed in section 1.6 but in less
form of quality control is initially required. Any un
detail. Conclusions with some discussion of possible
reliable data that may be inconsistent with some initial
future areas of research will follow at the end of the
guess field, are not supported by neighboring obser
chapter.
vations, or are of a scale too small to be resolved by
the analysis scheme (and can produce aliasing into the
1.2. Quality control
larger scales) need to be removed. To provide the initial
conditions for a mesoscale model, the observed data The data available for use in regional and mesoscale
must then be interpolated to the model grid points or models are of varied quality and some form of verifi
some lower-resolution analysis grid if the observed data cation of the data is required before use. The upper-
1
2 METEOROLOGICAL MONOGRAPHS VOL. 25, No. 47
air soundings from the rawinsonde network archived a climatological mean field or a forecast field produced
at national centers are not without problems (Schwartz from an earlier analysis. The method can also be re
and Doswell 1991). At operational weather centers, peated in an iterative fashion, so that the background
automated methods have been developed for quality field is updated by the latest analysis after each iteration
control of the data (see, e.g., DiMego 1988). Initially, or pass; that is,
the soundings such as those from the rawinsondes are
m
checked for hydrostatic consistency and possibly <Pa(n + 1) = <Pa(n) + L w;[<Po,; - <Pa,i(n)], (1.2)
smoothed or interpolated in the vertical to be more
;=1
representative of the model vertical resolution. In some
current methods of quality control at operational cen where <Pa( n) is the value of the analysis at the grid
ters, errors in the geopotential and temperature in the point and <Pa,; (n) is its value at the observation location
soundings from many sources are able to be corrected after the nth iteration.
at this stage (Collins and Gandin 1990). The obser The large-scale atmospheric flow is largely in ( quasi)
vations are then compared to an initial background geostrophic balance, and the analysis scheme can be
guess field that is a forecast field generated by an earlier generalized to include such a dependency between the
run of an operational model. In a "gross" check, ob meteorological variables. In such a multivariate
servations with large deviations from the first guess are scheme, the <Pa, <Po,i, and <Pb are then vectors repre
removed, and in a "buddy check," observations not senting several variables (such as the horizontal wind
agreeing with neighboring observations are removed. components and the geopotential), and the weight
Close observations are replaced by an average "super" coefficients become matrices of coefficients linking the
observation (hereafter superobs) in order to reduce the variables. Further dynamical constraints can also be
processing load on the analysis scheme and to keep satisfied by applying variational methods, as introduced
some analysis schemes stable. Isolated observations that by Sasaki (1958, 1970a). A concise review of these
cannot be verified by the above procedure are also usu objective analysis methods and others can be found in
ally eliminated. An important result of the above pro Schlatter ( 1988). Further details can be found in Thie
cedures is that observations of mesoscale features that baux and Pedder ( 1987) and Daley ( 1991 ). The com
are at scales not resolved by the analysis grid are re mon linear methods above can be divided into those
moved to prevent their aliasing to the larger scales. In methods where the weights are empirically prescribed
regions of little data, bogus data may be added to in and those where they are derived by statistical consid
clude weather features missed by the observations or erations.
to merge with a larger-scale analysis outside of the ob
servations of interest. Further details on quality control
a. Empirical interpolation
methods may be found in the excellent review by Gan
din ( 1988) and the sources mentioned therein. More In the empirical methods, the weights are prescribed
theoretical aspects of quality control are described by empirical functions that decrease with the distance of
Lorenc and Hammon ( 1988). the observation from the grid point. Only those data
that fall within a certain critical distance of the grid
point (called the radius of influence) are used. The
1.3. Objective analysis
weights are usually normalized by the sum of the in
The most commonly used methods to interpolate dividual weights used for each of the observations in
the meteorological observations to a model grid or a the interpolation at the grid point. Usually, the iterative
separate analysis grid are based on using a linear com method, which is commonly called the method of suc
bination of the observational data to obtain values at cessive corrections, is used. The successive correction
each grid point. Usually the deviations of the obser method was first introduced by Bergthorsson and Doos
vational data from an initial guess or background field ( 1955), and different versions have been developed by
are interpolated instead of the data themselves. If <Pa is Cressman (1959), Barnes (1973), and others. Various
the interpolated (analyzed) value at the grid point and derivatives of these earlier methods have been widely
<Pb is the value of the background or first guess at the used for the analysis of mesoscale systems in the re
grid point, we can write search community (e.g., Koch et al. 1983; Maddox
1980; Benjamin and Seaman 1985; Achtemeier 1989).
m
+ L By using repeated passes in these methods, successively
<Pa = <Pb w;(<Po,; - <Pb,;), (1.1)
smaller scales of the motion are analyzed with each
;=1
increasing iteration [see, e.g., chapter 3 in Daley
where <Po,; are the observations, W; are the weights for ( 1991 )]. In these successive correction methods, the
each observation, <Pb,; is the value of the background interpolated values at the data locations will eventually
at the observation point (which is usually derived by converge to the data values themselves. Since the data
a polynomial interpolation from <Pb), and m is the have some error, the number of iterations used is lim
number of observations. The background field can be ited by some criterion, such as analyzing for only those