<p><b>weiwang</b> 2008-05-13 18:17:46 -0600 (Tue, 13 May 2008)</p><p>update intro<br>
</p><hr noshade><pre><font color="gray">Modified: trunk/wrf/technote/intro.tex
===================================================================
--- trunk/wrf/technote/intro.tex 2008-05-13 18:37:30 UTC (rev 69)
+++ trunk/wrf/technote/intro.tex 2008-05-14 00:17:46 UTC (rev 70)
@@ -1,44 +1,46 @@
\chapter{Introduction}
\label{introduction_chap}
The Weather Research and Forecasting (WRF) modeling system is
-a capability for the numerical generation of atmospheric simulations.
-These may be produced both prospectively, as in real-time forecasts,
-or retrospectively, as in case studies.
-The development of the WRF system has been a multi-agency effort
-to create a next-generation mesoscale forecast model
+a numerical weather prediction (NWP) capability designed for both
+research and operational applications. It can produce
+atmospheric simulations both retrospectively, as in case studies,
+and prospectively, as in real-time forecasts.
+The development of WRF has been a multi-agency effort
+to build a next-generation mesoscale forecast model
and data assimilation system to advance the understanding and prediction
of mesoscale weather and accelerate the transfer of research
-advances into operations. WRF was developed as a collaborative effort
+advances into operations. The WRF effort has been a collaborative one
among the National Center for Atmospheric Research's (NCAR)
Mesoscale and Microscale Meteorology (MMM) Division, the
National Oceanic and Atmospheric Administration's
(NOAA) National Centers for Environmental Prediction (NCEP) and
-Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather
-Agency (AFWA) and Naval Research Laboratory (NRL), the Center
-for Analysis and Prediction of Storms (CAPS) at the University
+Earth System Research Laboratory (ESRL), the Department of Defense's
+Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL),
+the Center for Analysis and Prediction of Storms (CAPS) at the University
of Oklahoma, and the Federal Aviation Administration (FAA),
with the participation of university scientists.
WRF reflects flexible, state-of-the-art, portable code that is
efficient in computing environments ranging from massively-parallel
-supercomputers to laptop systems.
-Its modular single-source code can be configured for both
+supercomputers to laptops.
+Its modular, single-source code can be configured for both
research and operational applications. Its spectrum of physics
and dynamics options reflects the experience and input of the
-broad scientfic community. Its variational data assimilation system (WRF-Var)
-allows it to ingest a host of observation types in pursuit of optimal
-initial conditions. WRF is maintained and supported as a community model to
-facilitate wide use internationally, for research, operations, and teaching.
-It is suitable for use in a broad span of applications across
-scales ranging from large-eddy to global scales. Such applications
-include real-time numerical weather prediction (NWP), data assimilation
+broad scientific community. Its variational data assimilation system (WRF-Var)
+allows it to ingest a host of observation types in pursuit of
+generating optimal initial conditions. WRF is maintained and
+supported as a community model to facilitate wide use internationally,
+for research, operations, and teaching.
+It is suitable for a broad span of applications across
+scales ranging from large-eddy to global simulations. Such applications
+include real-time NWP, data assimilation
development and studies, parameterized-physics research, regional
climate simulations, air quality modeling, atmosphere-ocean coupling, and
idealized simulations. Goals of having WRF as a common tool in the
university/research and operational communities are to promote
-closer ties between the groups and to shorten the path of research
+closer ties between these groups and to shorten the path of research
advances to operations. These aims have made the WRF endeavor
-noteworthy in the evolution of NWP. At the time of this writing,
+noteworthy in the evolution of NWP. As of this writing,
the WRF registered user community numbers over 6000, and WRF is in
operational and research use around the world.
@@ -79,16 +81,14 @@
component of any WRF configuration involving the NMM solver.
The following section highlights the major features of the
ARW, Version 3, and reflects elements of WRF Version 3,
-which was first released in March 2008.
+which was first released in April 2008.
This technical note focuses on the scientific and algorithmic approaches
in the ARW. Discussed are the solver, physics options,
initialization capabilities, boundary conditions, and grid-nesting techniques.
-The WSF provides the software infrastructure for all WRF configurations.
+The WSF provides the software infrastructure.
WRF-Var, a component of the broader WRF system, was
adapted from MM5 3DVAR \citep{barker04} and is encompassed within the ARW.
-This technical note summarizes the changes implemented to adapt
-this data assimilation capability from the MM5 to WRF.
For those seeking information on running the ARW system,
the {\wrf} User's Guide (update citation \citep{wang04})
has the details on its operation.
@@ -114,40 +114,37 @@
and cloud water/ice mixing ratio.
%
\item{$\bullet$} {\em Vertical Coordinate:}
-Terrain-following hydrostatic-pressure, with vertical grid stretching permitted.
+Terrain-following, dry hydrostatic-pressure, with vertical grid stretching permitted.
Top of the model is a constant pressure surface.
%
\item{$\bullet$} {\em Horizontal Grid:}
Arakawa C-grid staggering.
%
\item{$\bullet$} {\em Time Integration:}
-Time-split integration using a 3rd order Runge-Kutta scheme with
+Time-split integration using a 2dn- or 3rd-order Runge-Kutta scheme with
smaller time step for acoustic and gravity-wave modes.
Variable time step capability.
%
\item{$\bullet$} {\em Spatial Discretization:}
-2nd to 6th order advection options in horizontal and vertical.
+2nd- to 6th-order advection options in horizontal and vertical.
%
\item{$\bullet$} {\em Turbulent Mixing and Model Filters:} Sub-grid scale
turbulence formulation in both coordinate and physical space.
Divergence damping, external-mode filtering, vertically implicit
-acoustic step off-centering. Explicit filter option.
+acoustic step off-centering. Explicit filter option.
Digital filter initialization (DFI) capability.
%
\item{$\bullet$} {\em Initial Conditions:}
Three dimensional for real-data, and one-, two- and
-three-dimensional using idealized data.
+three-dimensional for idealized data.
A number of test cases are provided.
%
-\item{$\bullet$} {\em Nudging:}
-Grid (analysis) and observation nudging capabilities available.
-%
\item{$\bullet$} {\em Lateral Boundary Conditions:}
Periodic, open, symmetric, and specified options available.
%
\item{$\bullet$} {\em Top Boundary Conditions:}
-Gravity wave absorbing (diffusion or Rayleigh damping).
-$w = 0$ top boundary condition at constant pressure level.
+Gravity wave absorbing (diffusion, Rayleigh damping, or implicit Rayleigh damping
+for vertical velocity). $w = 0$ top boundary condition at constant pressure level.
%
\item{$\bullet$} {\em Bottom Boundary Conditions:}
Physical or free-slip.
@@ -156,13 +153,16 @@
Full Coriolis terms included.
%
\item{$\bullet$} {\em Mapping to Sphere:}
-Three map projections are supported for real-data simulation:
-polar stereographic, Lambert-conformal, and Mercator.
+Four map projections are supported for real-data simulation:
+polar stereographic, Lambert conformal, Mercator, and latitude-longitude.
Curvature terms included.
%
\item{$\bullet$} {\em Nesting:}
One-way interactive, two-way interactive, and moving nests.
%
+\item{$\bullet$} {\em Nudging:}
+Grid (analysis) and observation nudging capabilities available.
+%
\item{$\bullet$} {\em Global Grid:}
Global simulation capability.
\end{description}
@@ -174,11 +174,11 @@
\begin{description}
\setlength{\itemsep}{-5pt}
\item{$\bullet$} {\em Microphysics:} Schemes ranging from simplified
-physics suitable for mesoscale modeling to sophisticated mixed-phase
-physics suitable for cloud-resolving modeling.
+physics suitable for larger scales to sophisticated mixed-phase
+physics suitable for cloud-resolving scales.
%
\item{$\bullet$} {\em Cumulus parameterizations:}
-Adjustment and mass-flux schemes for mesoscale modeling including NWP.
+Adjustment and mass-flux schemes for mesoscale modeling.
%
\item{$\bullet$} {\em Surface physics:}
Multi-layer land surface models ranging from a simple thermal model to full
@@ -193,12 +193,46 @@
\end{description}
\vskip 12pt
+{</font>
<font color="blue">oindent\bf WRF-Var System}
+\vskip 12pt
+
+\begin{description}
+\setlength{\itemsep}{-5pt}
+\item{$\bullet$} WRF-Var merged into WRF software framework.
+%
+\item{$\bullet$} Incremental formulation of the model-space cost function.
+%
+\item{$\bullet$} Quasi-Newton or conjugate gradient minimization algorithms.
+%
+\item{$\bullet$} Analysis increments on un-staggered Arakawa-A grid.
+%
+\item{$\bullet$} Representation of the horizontal component of background error ${\bf B}$ via
+recursive filters (regional) or power spectra (global). The
+vertical component is applied through projection onto climatologically-averaged
+eigenvectors of vertical error. Horizontal/vertical errors are
+non-separable (horizontal scales vary with vertical eigenvector).
+%
+\item{$\bullet$} Background cost function ($J_b$) preconditioning
+via a control variable transform ${\rm U}$ defined as ${\bf B}={\rm U} {\rm U}^T$.
+%
+\item{$\bullet$} Flexible choice of background error model and control variables.
+%
+\item{$\bullet$} Climatological background error covariances estimated via either the
+NMC-method of averaged forecast differences or suitably averaged
+ensemble perturbations.
+%
+\item{$\bullet$} Unified 3D-Var (4D-Var under development), global
+and regional, multi-model capability.
+%
+\end{description}
+
+\vskip 12pt
{</font>
<font color="gray">oindent\bf WRF-Chem}
\vskip 12pt
\begin{description}
\setlength{\itemsep}{-5pt}
-\item{$\bullet$} Online (or ``inline'') model, in which the model is consistent,
+\item{$\bullet$} Online (or ``inline'') model, in which the model is consistent
with all transport done by the meteorology model.
%
\item{$\bullet$} Dry deposition, coupled with the soil/vegetation scheme.
@@ -232,43 +266,7 @@
deposition, etc., has been turned off.
\end{description}
-
-</font>
<font color="red">ewpage
\vskip 12pt
-{</font>
<font color="red">oindent\bf WRF-Var System}
-\vskip 12pt
-
-\begin{description}
-\setlength{\itemsep}{-5pt}
-\item{$\bullet$} WRF-Var merged into WRF software framework.
-%
-\item{$\bullet$} Incremental formulation of the model-space cost function.
-%
-\item{$\bullet$} Quasi-Newton or conjugate gradient minimization algorithms.
-%
-\item{$\bullet$} Analysis increments on un-staggered Arakawa-A grid.
-%
-\item{$\bullet$} Representation of the horizontal component of background error ${\bf B}$ via
-recursive filters (regional) or power spectra (global). The
-vertical component is applied through projection onto climatologically-averaged
-eigenvectors of vertical error. Horizontal/vertical errors are
-non-separable (horizontal scales vary with vertical eigenvector).
-%
-\item{$\bullet$} Background cost function ($J_b$) preconditioning
-via a control variable transform ${\rm U}$ defined as ${\bf B}={\rm U} {\rm U}^T$.
-%
-\item{$\bullet$} Flexible choice of background error model and control variables.
-%
-\item{$\bullet$} Climatological background error covariances estimated via either the
-NMC-method of averaged forecast differences or suitably averaged
-ensemble perturbations.
-%
-\item{$\bullet$} Unified 3D-Var (4D-Var under development), global
-and regional, multi-model capability.
-%
-\end{description}
-
-\vskip 12pt
{</font>
<font color="black">oindent\bf WRF Software Framework}
\vskip 12pt
</font>
</pre>