<p><b>dudhia</b> 2008-05-12 19:09:49 -0600 (Mon, 12 May 2008)</p><p>mods for physics mostly, but also lateral boundaries, and references<br>
</p><hr noshade><pre><font color="gray">Modified: trunk/wrf/technote/description.bbl
===================================================================
--- trunk/wrf/technote/description.bbl 2008-05-12 23:50:13 UTC (rev 66)
+++ trunk/wrf/technote/description.bbl 2008-05-13 01:09:49 UTC (rev 67)
@@ -39,6 +39,13 @@
Part I: Model description and implementation.
{\em Mon. Wea. Rev.}, {\bf 129}, 569--585.
+\bibitem[Chen et al.(2006)]{chen06}%
+Chen, F., M. Tewari, H. Kusaka, and T. T. Warner, 2006:
+ Current status of urban modeling in the community Weather Research and Forecast (WRF) model.
+Joint with Sixth Symposium on the Urban Environment and AMS Forum: Managing our
+Physical and Natural Resources: Successes and Challenges, Atlanta, GA, USA, Amer.
+Meteor. Soc., CD-ROM. J1.4.
+
\bibitem[Chen and Sun(2002)]{chen02}%
Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model.
{\em J. Meteor. Soc. Japan}, {\bf 80}, 99--118.
@@ -48,6 +55,11 @@
parameterization for use in general circulation models.
NASA Tech. Memo. 104606, 3, 85pp.
+\bibitem[Collins et al.(2004)]{collins04}%
+Collins, W.D. et al., 2004:
+Description of the NCAR Community Atmosphere Model (CAM 3.0),
+NCAR Technical Note, NCAR/TN-464+STR, 226pp.
+
\bibitem[Cooper(1986)]{cooper86}%
Cooper, W. A., 1986: Ice initiation in natural clouds.
{\em Precipitation Enhancement --- A Scientific Challenge}, Meteor. Monogr., No. 43,
@@ -85,6 +97,11 @@
Dudhia, J., 1995: Reply,
{\em Mon. Wea. Rev.}, {\bf 123}, 2571--2575.
+\bibitem[Dudhia et al.(2008)]{dudhia08}%
+Dudhia, J., S.-Y. Hong, and K.-S. Lim, 2008:
+ A new method for representing mixed-phase particle fall speeds in bulk microphysics parameterizations.
+ {\em J. Met. Soc. Japan}, in press.
+
\bibitem[Durran and Klemp(1983)]{durran_klemp83}%
Durran, R. D., and J. B. Klemp, 1983: A compressible model for the simulation
of moist mountain waves,
@@ -127,6 +144,21 @@
Forecast Errors As Determined From Radiosonde Data. Part I: The Wind Field.
{\em Tellus}, {\bf 38A}, 111--136.
+\bibitem[Holtslag and Boville (1993)]{holtslag93}%
+Holtslag, A. A. M. and B. A. Boville, 1993:
+Local versus non-local boundary layer diffusion in a global climate model,
+ {\em J. Climate}, {\bf 6}, 1825--1842.
+
+\bibitem[Hong(2007)]{hong07}%
+Hong, S.-Y., 2007:
+ Stable Boundary Layer Mixing in a Vertical Diffusion Scheme.
+ The Korea Meteor. Soc., Fall conference, Seoul, Korea, Oct. 25-26.
+
+\bibitem[Hong and Lim(2006)]{honglim06}%
+Hong, S.-Y., and J.-O. J. Lim, 2006:
+ The WRF Single-Moment 6-Class Microphysics Scheme (WSM6),
+ {\em J. Korean Meteor. Soc.}, {\bf 42}, 129--151.
+
\bibitem[Hong and Pan(1996)]{hong96}%
Hong, S.-Y., and H.-L. Pan, 1996:
Nonlocal boundary layer vertical diffusion in a medium-range forecast model,
@@ -143,6 +175,11 @@
of Clouds and Precipitation,
{\em Mon. Wea. Rev.}, {\bf 132}, 103--120.
+\bibitem[Hong et al.(2006)]{hong06}%
+Hong, S.-Y., and Y. Noh, and J. Dudhia, 2006:
+A new vertical diffusion package with an explicit treatment of entrainment processes.
+ {\em Mon. Wea. Rev.}, {\bf 134}, 2318--2341.
+
\bibitem[Huang and Lynch(1993)]{huanglynch93}%
Huang, X.-Y. and P. Lynch, 1993:
Diabatic Digital-Filtering Initialization: Application to the HIRLAM Model.,
@@ -224,6 +261,17 @@
diffusion in the {WRF} {M}odel. {\em Mon.\ Wea.\ Rev.}, {\bf 135},
3808--3824.
+\bibitem[Kusaka and Kimura(2004)]{kusaka04}
+Kusaka, H. and F. Kimura, 2004:
+ Coupling a single-layer urban canopy model with a simple atmospheric model: Impact on urban heat island simulation for an idealized case.
+{\em J. Meteor. Soc. Japan}, {\bf 82}, 67--80.
+
+\bibitem[Kusaka et al.(2001)]{kusaka01}
+Kusaka, H., H. Kondo, Y. Kikegawa, and F. Kimura, 2001:
+ A simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab
+models.
+{\em Bound.-Layer Meteor.}, {\bf 101}, 329--358.
+
\bibitem[Lacis and Hansen(1974)]{lacis74}%
Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the
absorption of solar radiation in the earth's atmosphere.
@@ -249,6 +297,17 @@
semi-Lagrangian transport schemes. {\em Mon. Wea. Rev.}, {\bf 124},
2046--2070.
+\bibitem[Liu et al.(2008)]{liu08}%,
+Liu, Y., T.T. Warner, J. F. Bowers, L. P. Carson, F. Chen, C. A. Clough,
+C. A. Davis, C. H. Egeland, S. Halvorson, T.W. Huck Jr., L. Lachapelle,
+R.E. Malone, D. L. Rife, R.-S. Sheu, S. P. Swerdlin, and D.S. Weingarten,
+2008:
+ The operational mesogamma-scale analysis and forecast system of the
+U.S. Army Test and Evaluation Command. Part 1: Overview of the modeling
+system, the forecast products.
+{\em J. Appl. Meteor. Clim.}, {\bf 47}, 1077--1092.
+
+
\bibitem[Lorenc(1986)]{lorenc86}%
Lorenc, A. C., 1986: Analysis methods for numerical weather prediction.
{\em Quart. J. Roy. Meteor. Soc.}, {\bf 112}, 1177--1194.
@@ -266,6 +325,11 @@
Lynch, P., 1997: The Dolph-Chebyshev Window: A Simple Optimal Filter.
{\em Mon. Wea. Rev.}, {\bf 125}, 655--660.
+\bibitem[McCumber et al.(1991)]{mccumber91}
+McCumber, M., W.-K. Tao, J. Simpson, R. Penc, and S.-T. Soong, 1991:
+Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection,
+ {\em J. Appl. Meteor.}, {\bf 30}, 985--1004.
+
\bibitem[Mellor and Yamada(1982)]{melloryamada82}%
Mellor, G. L., and T. Yamada, 1982:
Development of a turbulence closure model for geophysical fluid problems.
@@ -294,6 +358,37 @@
of the atmosphere.
{\em Contrib. Geophys. Inst. Acad. Sci.}, USSR, {\bf (151)}, 163--187 (in Russian).
+\bibitem[Morrison et al.(2005)]{morrison05}%
+Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005:
+ A new double-moment
+microphysics parameterization for application in cloud and climate models, Part
+I: Description.
+ {\em J. Atmos. Sci.}, {\bf 62}, 1665--1677.
+
+\bibitem[Morrison and Pinto(2006)]{morrison06}%
+Morrison, H., and J. O. Pinto, 2006:
+ Intercomparison of bulk microphysics
+schemes in mesoscale simulations of springtime Arctic mixed-phase stratiform clouds.
+{\em Mon. Wea. Rev.}, {\bf 134}, 1880--1900.
+
+\bibitem[Morrison et al.(2008)]{morrison08}%
+Morrison, H., G. Thompson, and V. Tatarskii, 2008:
+ Impact of cloud
+micrpohysics on the development of trailing stratiform precipitation in a
+simulated squall line: Comparison of one- and two-moment
+schemes.
+Submitted to {\em Mon. Wea. Rev.}
+
+\bibitem[Noh et al.(2003)]{noh03}%
+Noh, Y., W.G. Cheon, S.-Y. Hong, and S. Raasch, 2003:
+ Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data.
+ {\em Bound.-Layer Meteor.}, {\bf 107}, 401--427.
+
+\bibitem[Noilhan and Planton(1989)]{noilhan89}%
+Noilhan, J., and S. Planton, 1989:
+ A simple parameterization of land surface processes for meteorological models.
+{\em Mon. Wea. Rev.}, {\bf 117}, 536--549.
+
\bibitem[Ooyama(1990)]{ooyama90}%
Ooyama K. V., 1990: A thermodynamic foundation for modeling the moist atmosphere,
{\em J. Atmos. Sci.}, {\bf 47}, 2580--2593.
@@ -308,6 +403,36 @@
temperature profiles in the unstable atmospheric surface layer.
{\em J. Appl. Meteor.}, {\bf 9}, 857--861.
+\bibitem[Pleim(2006)]{pleim06}%
+Pleim, J. E., 2006:
+ A simple, efficient solution of flux-profile relationships in the atmospheric surface layer,
+{\em J. Appl. Meteor. and Clim.}, {\bf 45}, 341--347
+
+\bibitem[Pleim(2007)]{pleim07}%
+Pleim, J. E., 2007:
+ A combined local and non-local closure model for the atmospheric boundary layer. Part 1: Model description and testing,
+{\em J. Appl. Meteor. and Clim.}, {\bf 46}, 1383--1395.
+
+\bibitem[Pleim and Gilliam(2008)]{pleim08}%
+Pleim, J.E. and R.C. Gilliam, 2008:
+ An indirect data assimilation scheme for deep soil temperature in the Pleim-Xiu land surface model.
+ Submitted to {\em J. Appl. Meteor. Climatol.}
+
+\bibitem[Pleim and Xiu(1995)]{pleim95}%
+Pleim, J. E. and A. Xiu, 1995:
+ Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models.
+ {\em J. Appl. Meteor.}, {\bf 34}, 16--32.
+
+\bibitem[Pleim and Xiu(2003)]{pleim03}%
+Pleim, J. E., and A. Xiu, 2003:
+ Development of a land surface model. Part II: Data Assimilation.
+{\em J. Appl. Meteor.}, {\bf 42}, 1811--1822.
+
+\bibitem[Pollard et al.(1973)]{pollard73}%
+Pollard, R. T., P. B. Rhines and R. O. R. Y. Thompson, 1973:
+ The deepening of the wind-mixed layer.
+{\em Geophys. Fluid Dyn.}, {\bf 3}, 381--404.
+
\bibitem[Purser et al.(2003)]{purser03}%
Purser, R. J., W. -S. Wu, D. F. Parrish, and N. M. Roberts, 2003: Numerical aspects of
the application of recursive filters to variational statistical analysis. Part I: Spatially
@@ -391,11 +516,32 @@
Smolarkiewicz, P. K., and G. A. Grell, 1990: A class of monotone interpolation schemes,
{\em J. Comp. Phys.}, {\bf 101}, 431--440.
+\bibitem[Stauffer and Seaman(1990)]{stauffer90}%
+Stauffer D. R., and N. L. Seaman, 1990:
+ Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data.
+ {\em Mon. Wea. Rev.}, {\bf 118}, 1250--1277.
+
+\bibitem[Stauffer and Seaman(1994)]{stauffer94}%
+Stauffer D. R., and N. L. Seaman, 1994:
+ Multiscale four-dimensional data assimilation.
+{\em J. Appl. Meteor.}, {\bf 33}, 416--434.
+
\bibitem[Stephens(1978)]{stephens78}%
Stephens, G. L., 1978: Radiation profiles in extended water clouds.
Part II: Parameterization schemes,
{\em J. Atmos. Sci.}, {\bf 35}, 2123--2132.
+\bibitem[Tao and Simpson(1993)]{tao93}%
+Tao, W.-K., and J. Simpson, 1993:
+ The Goddard cumulus ensemble model. Part I: Model description.
+{\em Terr. Atmos. Oceanic Sci.}, {\bf 4}, 35--72.
+
+\bibitem[Tao et al.(2003)]{tao03}%
+Tao, W.-K,, J. Simpson, D. Baker, S. Braun, M.-D. Chou, B. Ferrier, D. Johnson, A. Khain, S. Lang, B. Lynn, C.-L. Shie, D. Starr, C.-H. Sui, Y. Wang, and P. Wetzel, 2003:
+Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model.
+{\em Meteor. and Atmos. Phys.}, {\bf 82}, 97--137.
+
+
\bibitem[Tao et al.(1989)]{tao89}%
Tao, W.-K., J. Simpson, and M. McCumber 1989: An ice-water saturation adjustment,
{\em Mon. Wea. Rev.}, {\bf 117}, 231--235.
@@ -441,6 +587,18 @@
Doppler Velocities on Forecasts of a Heavy Rainfall Case.
{\em J. Appl. Met.}, {\bf 44(6)}, 768--788.
+\bibitem[Xiu and Pleim(2001)]{xiu01}
+Xiu, A. and J. E. Pleim, 2001:
+ Development of a land surface model part I: Application in a mesoscale meteorology model.
+{\em J. Appl. Meteor.}, {\bf 40}, 192--209
+
+\bibitem[Xu et al.(2002)]{xu02}
+Xu, M., Y. Liu, C. Davis and T. Warner, 2002:
+ Sensitivity of nudging parameters on the performance of a mesoscale FDDA
+ system: A case study.
+ 15th Conference on Numerical Weather
+ Prediction, 12-16 August, 2002, San Antonio, Texas, 127-130.
+
\bibitem[Xue(2000)]{xue.2000}
Xue, M., 2000: High-order monotonic numerical diffusion and smoothing. {\em
Mon.\ Wea.\ Rev.}, {\bf 128}, 2853--2864.
Modified: trunk/wrf/technote/discretization.tex
===================================================================
--- trunk/wrf/technote/discretization.tex 2008-05-12 23:50:13 UTC (rev 66)
+++ trunk/wrf/technote/discretization.tex 2008-05-13 01:09:49 UTC (rev 67)
@@ -1134,6 +1134,7 @@
\end{figure}
\subsection{Pole Conditions for the Global Latitude-Longitude Grid}
+\label{pole_condition}
The latitude-longitude grid has a singularity at the two poles where the
latitude $\psi = \pm 90^\circ$, as illustrated in Figure
Modified: trunk/wrf/technote/lbc.tex
===================================================================
--- trunk/wrf/technote/lbc.tex 2008-05-12 23:50:13 UTC (rev 66)
+++ trunk/wrf/technote/lbc.tex 2008-05-13 01:09:49 UTC (rev 67)
@@ -144,6 +144,12 @@
If the specified lateral boundary condition is selected for the coarse
grid, then all four grid
sides (west, east, north, and south) use specified lateral conditions.
+However, in tropical channel mode, where the domain wraps completely around
+the equator, it is possible to combine specified boundary conditions with
+periodic conditions in the $x$ direction. Note that care is needed in setting
+the domain up such that the points exactly match longitude at the east and west
+boundaries when periodic conditions are used in real-data cases. Also note that
+a Mercator projection is needed to make this possible.
The coarse grid specified lateral boundary is comprised of both a specified and
a relaxation zone as shown in Fig. \ref{figure:spec}).
@@ -186,6 +192,20 @@
$F_1$ and $F_2$ are
linear ramping functions with a maximum at the first relaxation row or column
nearest the coarse grid boundary (just inside of the specified zone).
+In Version 3.0 these linear functions can optionally be multiplied by an
+exponential function for smoother behavior when broad boundary zones are
+needed. This is achieved through an additional multiplier.
+\begin{equation}
+F_1 = {1 \over {10 \Delta t }} {{SpecZone + RelaxZone - n} \over {RelaxZone - 1}}
+\exp [ - SpecExp ( n - SpecZone - 1)]
+</font>
<font color="blue">otag
+\end{equation}
+\begin{equation}
+F_2 = {1 \over {50 \Delta t }} {{SpecZone + RelaxZone - n} \over {RelaxZone - 1}}
+\exp [ - SpecExp ( n - SpecZone - 1)]
+</font>
<font color="gray">otag
+\end{equation}
+where $SpecExp$ is an inverse length scale in grid lengths.
On the coarse grid, the specified boundary condition applies to
the horizontal wind components, potential temperature, $\phi'$, $\mu'_d$, and water vapor.
@@ -201,4 +221,10 @@
conditions applied in the specified zone. This boundary condition specifies zero on inflow
and zero-gradient on outflow. Since these boundary conditions require only information from
the interior of the grid, these variables are not in the specified boundary condition file.
+However, for nested boundaries, microphysical variables and scalars are also available
+from the parent domain, and use same relaxation towards interpolated variables as the
+dynamic variables.
+\section{Polar Conditions}
+See subsection \ref{pole_condition} for details on how the polar boundary
+condition is applied.
Modified: trunk/wrf/technote/physics.tex
===================================================================
--- trunk/wrf/technote/physics.tex 2008-05-12 23:50:13 UTC (rev 66)
+++ trunk/wrf/technote/physics.tex 2008-05-13 01:09:49 UTC (rev 67)
@@ -8,7 +8,10 @@
(2) cumulus parameterization, (3) planetary boundary layer (PBL),
(4) land-surface model, and (5) radiation. Diffusion, which
may also be considered part of the physics,
-is described in Chapter \ref{filter_chap}.
+is described in Chapter \ref{filter_chap}. The chapter will also
+address four-dimensional data assimilation (FDDA) methods that are
+available in ARW. These methods apply extra forcings to the model
+equations, and are internally treated similarly to physics.
The physics section is insulated from the rest of the dynamics solver by the
use of physics drivers. These are between solver-dependent routines: a
@@ -99,6 +102,8 @@
WSM6 & 6 & Y & Y \cr
Eta GCP & 2 & Y & Y \cr
Thompson & 7 & Y & Y \cr
+Goddard & 6 & Y & Y \cr
+Morrison 2-Moment & 10 & Y & Y \cr
\multispan4\hrulefill \cr
}}$$
\end{table}
@@ -124,43 +129,19 @@
\subsection{WRF Single-Moment 3-class (WSM3) scheme}
-This scheme follows \citet{hong04} including ice sedimentation and other
-new ice-phase parameterizations revised from the older NCEP3 scheme
-\citep{hong98} that was in WRF Version 1.
-A major difference from other schemes is that a diagnostic
-relation is used for ice number concentration that is based on ice mass
-content rather than temperature. Three categories of hydrometers are included:
-vapor, cloud water/ice, and rain/snow. As with \citet{dudhia89}, this is a
-so-called simple-ice scheme wherein the cloud ice and cloud water
-are counted as the same category. They are distinguished by temperature:
-namely, cloud ice can only exist when the temperature is less
-than or equal to the freezing point; otherwise, cloud water can exist.
-The same condition is applied to rain and snow. Though the ice
-phase is included, it is considered efficient enough for using in operational models.
+The WRF single-moment microphysics scheme follows \citet{hong04} including ice sedimentation and other new ice-phase parameterizations. A major difference from other approaches is that a diagnostic relation is used for ice number concentration that is based on ice mass content rather than temperature. The computational procedures for WRF single-moment microphysics scheme are described in \citet{honglim06}. As with WSM5 and WSM6, the freezing/melting processes are computed during the fall-term sub-steps to increase accuracy in the vertical heating profile of these processes. The order of the processes is also optimized to decrease the sensitivity of the scheme to the time step of the model. The WSM3 scheme predicts three categories of hydrometers: vapor, cloud water/ice, and rain/snow, which is a so-called simple-ice scheme as with \citet{dudhia89}'s method in separating warn and ice phase water substance. This scheme is efficient in mesoscale grids.
\subsection{WSM5 scheme}
-This scheme is similar to the WSM3 simple ice scheme.
-However, vapor, rain, snow,
-cloud ice, and cloud water are held in five different arrays.
-Thus, it allows supercooled
-water to exist, and a gradual melting of snow as it falls below the
-melting layer. Details can be found
-in \citet{hong04}. It replaces WRF Version 1's NCEP5 scheme \citep{hong98}.
+This scheme is similar to the WSM3 simple ice scheme. However, vapor, rain, snow, cloud ice,
+and cloud water are held in five different arrays. Thus, it allows supercooled water to exist, and
+a gradual melting of snow as it falls below the melting layer. Details can be found in
+\citet{hong04}, and \citet{honglim06}. As with WSM6, the saturation adjustment follows \citet{dudhia89} and \citet{hong98} in separately treating ice and water saturation processes, rather than a combined saturation such as the Purdue Lin (above) and Goddard \citep{tao89} schemes. This scheme is efficient in intermediate grids between the mesoscale and cloud-resolving grids.
\subsection{WSM6 scheme}
-The six-class scheme extends the WSM5 scheme to include graupel and its associated processes.
-Many of these processes are parameterized similarly to \citet{lin83}, but
-there are differences for the accretion calculation and in some other
-parameters. The freezing/melting processes are computed during the fall-term
-sub-steps to increase accuracy in the vertical heating profile of these processes.
-The order of the processes is also optimized to decrease the sensitivity of
-the scheme to the time step of the model.
-As with WSM3 and WSM5, saturation adjustment follows \citet{dudhia89} and \citet{hong98}
-in separately treating ice and water saturation processes, rather than a
-combined saturation such as the Purdue Lin (above) and Goddard
-\citep{tao89} schemes.
+The six-class scheme extends the WSM5 scheme to include graupel and its associated processes.
+Some of the graupel-related terms follows \citet{lin83}, but its behavior is much different due to the ice-phase microphysics of \citet{hong04}. A new method for representing mixed-phase particle fall speeds for the snow and graupel particles by assigning a single fallspeed to both that is weighted by the mixing ratios, and applying that fallspeed to both sedimentation and accretion processes is introduced \citep{dudhia08}. The behavior of the WSM3, WSM5, and WSM6 does not differ in mesoscale grid, but they work much differently in cloud-resolving grids. The WSM6 scheme is suitable for cloud-resolving grid, considering the efficiency and theoretical backgrounds \citep{honglim06}.
\subsection{Eta Grid-scale Cloud and Precipitation (2001) scheme}
@@ -213,7 +194,38 @@
thereby simulating the fall velocity of drizzle drops as well as raindrops.
\end{itemize}
+\subsection{Goddard Cumulus Ensemble Model scheme}
+The Goddard Cumulus Ensemble (GCE) models \citep{tao93} one-moment bulk microphysical schemes are mainly based on \citet{lin83} with additional processes from \citet{rutledge84}. However, the Goddard microphysics schemes have several modifications.
+
+First, there is an option to choose either graupel or hail as the third class of ice \citep{mccumber91}. Graupel has a relatively low density and a high intercept value (i.e., more numerous small particles). In contrast, hail has a relative high density and a low intercept value (i.e., more numerous large particles). These differences can affect not only the description of the hydrometeor population and formation of the anvil-stratiform region but also the relative importance of the microphysical-dynamical-radiative processes.
+
+Second, new saturation techniques \citep{tao89, tao03} were added. These saturation techniques are basically designed to ensure that super saturation (sub-saturation) cannot exist at a grid point that is clear (cloudy).
+
+Third, all microphysical processes that do not involve melting, evaporation or sublimation (i.e., transfer rates from one type of hydrometeor to another) are calculated based on one thermodynamic state. This ensures that all of these processes are treated equally.
+
+Fourth, the sum of all sink processes associated with one species will not exceed its mass. This ensures that the water budget will be balanced in the microphysical calculations .
+
+The Goddard microphysics has a third option, which is equivalent to a two-ice (2ICE) scheme having only cloud ice and snow. This option may be needed for coarse resolution simulations (i.e., $>$ 5 km grid size). The two-class ice scheme could be applied for winter and frontal convection.
+
+\subsection {Morrison et al. 2-Moment scheme}
+
+The \citet{morrison08} scheme is based on the two-moment bulk
+microphysics scheme of \citet{morrison05} and \citet{morrison06}. Six species of
+water are included: vapor, cloud droplets, cloud ice, rain, snow, and
+graupel/hail. The code has a user-specified switch to include either graupel or hail.
+Prognostic variables include number concentrations and mixing ratios
+of cloud ice, rain, snow, and graupel/hail, and mixing ratios of cloud
+droplets and water vapor (total of 10 variables). The prediction of two-moments (i.e., both number concentration and
+mixing ratio) allows for a more robust treatment of the particle size
+distributions, which are a key for calculating the microphysical process rates
+and cloud/precipitation evolution. Several liquid, ice, and mixed-phase
+processes are included. Particle size distributions are treated using gamma
+functions, with the associated intercept and slope parameters derived from the
+predicted mixing ratio and number concentration.
+The scheme has been extensively tested and compared with both
+idealized and real case studies covering a wide range of conditions.
+
\section{Cumulus parameterization}
These schemes are responsible for the sub-grid-scale effects of
@@ -252,11 +264,12 @@
Kain-Fritsch & Y & Mass flux & CAPE removal \cr
Betts-Miller-Janjic & N & Adjustment & Sounding adjustment \cr
Grell-Devenyi & Y & Mass flux & Various \cr
+Grell-3 & Y & Mass flux & Various \cr
\multispan4\hrulefill \cr
}}$$
\end{table}
-\subsection{Kain-Fritsch}
+\subsection{Kain-Fritsch scheme}
The modified version of the Kain-Fritsch scheme (KF-Eta) is based on
\citet{kain90} and \citet{kain93}, but has been modified based on
@@ -296,7 +309,7 @@
\end{itemize}
-\subsection{Betts-Miller-Janjic}
+\subsection{Betts-Miller-Janjic scheme}
The Betts-Miller-Janjic (BMJ) scheme \citep{janjic94,janjic00}
was derived from the Betts-Miller (BM) convective adjustment scheme
@@ -326,7 +339,7 @@
a prescribed positive threshold.
\end{itemize}
-\subsection{Grell-Devenyi ensemble}
+\subsection{Grell-Devenyi ensemble scheme}
\citet{grell02} introduced an ensemble cumulus scheme
in which effectively multiple cumulus schemes and variants are run
@@ -347,6 +360,17 @@
is the trigger, where the maximum cap strength that permits convection
can be varied. These controls typically provide ensembles of 144 members.
+\subsection{Grell-3 scheme}
+
+The Grell-3 scheme was introduced in Version 3. It shares a lot in
+common with the Grell-Devenyi in scheme, being based on an ensemble mean
+approach, but the quasi-equilibrium approach is no longer included
+among the ensemble members. The scheme is distinguished from other
+cumulus schemes by allowing subsidence effects to be spread to
+neighboring grid columns, making the method more suitable to grid sizes
+less than 10 km, while it can also be used at larger grid sizes where
+subsidence occurs within the same grid column as the updraft.
+
\section{Surface Layer}
The surface layer schemes calculate friction velocities and exchange
@@ -371,7 +395,12 @@
roughness length to friction velocity over water. There are four stability
regimes following \citet{zhanganthes82}.
This surface layer scheme must be run in conjunction with the MRF or
-YSU PBL schemes.
+YSU PBL schemes. In Version 3, there is an option to replace the Charnock
+relation for roughness length with a Donelan relation that has lower
+drag at hurricane-force wind speeds, and may be more suitable for hurricane
+simulations. Also for water points, the Beljaars formulation for convective
+velocity is replaced by one proportional only to the vertical thermal gradient
+to help in weak-wind situations.
\subsection{Similarity theory (Eta)}
@@ -388,6 +417,10 @@
(Mellor-Yamada-Janjic) PBL scheme, and is therefore sometimes referred to
as the MYJ surface scheme.
+\subsection{Similarity theory (PX)}
+
+The PX surface layer scheme \citep{pleim06} was developed as part of the PX LSM but can be used with any LSM or PBL model. This scheme is based on similarity theory and includes parameterizations of a viscous sub-layer in the form of a quasi-laminar boundary layer resistance accounting for differences in the diffusivity of heat, water vapor, and trace chemical species. The surface layer similarity functions are estimated by analytical approximations from state variables.
+
\section{Land-Surface Model}
The land-surface models (LSMs) use atmospheric information from the surface layer scheme,
@@ -423,6 +456,7 @@
5-layer & N & Temperature (5) & none \cr
Noah & Y & Temperature, Water+Ice, Water (4) & 1-layer, fractional \cr
RUC & Y & Temperature, Ice, Water + Ice (6) & multi-layer \cr
+Pleim-Xiu & Y & Temperature, Moisture (2) & input only \cr
\multispan4\hrulefill \cr
}}$$
\end{table}
@@ -454,11 +488,72 @@
\subsection{Rapid Update Cycle (RUC) Model LSM}
-This is a LSM with 6 sub-soil layers and up to two snow
-layers that is used operationally in the RUC model \citep{smirnova97, smirnova00}.
-The model considers frozen soil processes, patchy snow, with snow
-temperature and density variation, vegetation effects, and canopy water.
+The RUC LSM has a multi-level soil model (6 levels is default, could be 9 or more) with higher resolution in the top part of soil domain
+(0, 5, 20, 40, 160, 300 cm is default). The soil model solves heat diffusion and Richards moisture transfer equations, and in the cold season
+takes into account phase changes of soil water \citep{smirnova97, smirnova00}.
+The RUC LSM also has a multi-layer snow model with changing snow density, refreezing liquid water
+percolating through the snow pack, snow depth and temperature dependent albedo, melting algorithms applied at both
+snow-atmosphere interface and snow-soil interface, and simple parameterization of fractional snow cover with possibility of
+grid averaged skin temperature going above freezing. It also includes vegetation effects and canopy water.
+The RUC LSM has a layer approach to the solution of energy and moisture budgets.
+The layer spans the ground surface and includes half of the first atmospheric layer and half of the top soil layer with the
+corresponding properties (density, heat capacity, etc.) The residual of the incoming fluxes (net radiation, latent and sensible heat fluxes,
+soil heat flux, precipitation contribution into heat storage, etc.) modify the heat storage of this layer.
+An implicit technique is applied to the solution of these equations.
+Prognostic variables include soil temperature, volumetric liquid, frozen and total soil moisture contents,
+surface and sub-surface runoff, canopy moisture, evapotranspiration, latent, sensible and soil heat fluxes,
+heat of snow-water phase change, skin temperature, snow depth and density, and snow temperature.
+\subsection{Pleim-Xiu LSM}
+
+The PX LSM \citep{pleim95, xiu01}, originally based on the ISBA model \citet{noilhan89}, includes a 2-layer force-restore soil temperature and moisture model. The PX LSM features three pathways for moisture fluxes: evapotranspiration, soil evaporation, and evaporation from wet canopies. Evapotranspiration is controlled by bulk stomatal resistance that is dependent on root zone soil moisture, photosynthetically active radiation, air temperature, and the relative humidity at the leaf surface. Grid aggregate vegetation and soil parameters are derived from fractional coverages of land use categories and soil texture types. There are two indirect nudging schemes that correct biases in 2-m air temperature and RH by dynamic adjustment of soil moisture \citep{pleim03} and deep soil temperature \citep{pleim08}. Note that a small utility program (ipxwrf) can be used to propagate soil moisture and temperature between consecutive runs to create a continuous simulation of these qu!
antities.
+
+\subsection{Urban Canopy Model}
+
+This can be run as an option with the Noah LSM.
+In order to represent the city scale effects on the mesoscale,
+an urban canopy model (UCM) originally developed by \citet{kusaka01}
+and \citet{kusaka04} and later on modified
+by \citet{chen06}, is coupled to the WRF model via
+Noah Land surface model. In the UCM, all the urban effects in the
+vertical are assumed to be subgrid scale meaning that the urban
+processes are occurring below the lowest model level. The urban
+canopy model includes:
+\begin{itemize}\setlength{\parskip}{-4pt}
+\item (i) Parameterization of Street canyons to represent the urban
+geometry.
+\item (ii) Shadowing from building and radiation reflection.
+\item (iii) An exponential wind profile in the canopy layer
+\item (iv) multilayer heat equation from roof wall and road surfaces.
+\end{itemize}
+
+The urban canopy model estimates the surface temperature and heat
+fluxes from the roof, wall and road surface. It also calculates
+the momentum exchange between the urban surface and the atmosphere.
+In Version 3, an anthropogenic heating diurnal cycle was added as
+an option.
+
+\subsection{Ocean Mixed-Layer Model}
+
+This can be selected with the 5-layer option, and is designed for
+hurricane modeling in order to simulate the cooling of the ocean underneath
+hurricanes. The ocean mixed-layer model is based on that of \citet{pollard73}. Each column is independently coupled to the local atmospheric column, so the model is one-dimensional. The ocean part consists of a time-varying layer, representing the variable-depth mixed layer over a fixed layer acting as a reservoir of cooler water with a specified thermal lapse rate. In the mixed layer, the prognostic variables are its depth, vector horizontal current, and mean temperature taken to be the sea-surface temperature (SST). The hurricane winds drive the current, which in turn leads to mixing at the base of the mixed layer when the Richardson number becomes low enough. This mixing deepens and cools the mixed layer, and hence the cooler sea-surface temperature impacts the heat and moisture fluxes at the surface, and has a negative feedback on hurricane intensity. The model includes Coriolis effects on the current, which are important in determining the location of maximum cooling o!
n the right side of the hurricane track. It also includes a mixed-layer heat budget, but the surface fluxes and radiation have much less impact than the hurricane-induced deep mixing on the thermal balance at the time scales considered during a forecast. The ocean mixed-layer model is initialized using the observed SST for the mixed layer, and with a single depth representative of known conditions in the hurricane's vicinity that may be replaced with a map of the mixed-layer depth, if available . The initial current is set to zero, which is a reasonable assumption given that the hurricane-induced current is larger than pre-existing ones.
+
+
+\subsection{Specified Lower Boundary Conditions}
+
+For long simulation periods, in excess of about a week, as in applications such as
+regional climate, ARW has a capability to specify lower boundary conditions
+on non-prognostic fields as
+a function of time. Foremost among these is the specification of the
+sea-surface temperature during the simulation. The Noah, RUC and PX LSMs also
+need to consider variations in vegetation fraction and albedo with season, so
+monthly datasets are interpolated to also be read in with the lower boundary file.
+Sea-ice cover variation can also be specified by this method in Version 3.
+The lower boundary conditions are simply read in typically at the
+same frequency as the lateral boundary conditions, and the fields are
+updated with new current values at each read.
+
\section{Planetary Boundary Layer}
The planetary boundary layer (PBL) is responsible for vertical sub-grid-scale
@@ -498,6 +593,7 @@
MRF & K profile + countergradient term & part of PBL mixing & from critical bulk $Ri$ \cr
YSU & K profile + countergradient term & explicit term & from buoyancy profile \cr
MYJ & K from prognostic TKE & part of PBL mixing & from TKE \cr
+ACM2 & transilient mixing up, local K down & part of PBL mixing & from critical bulk $Ri$ \cr
\multispan4\hrulefill \cr
}}$$
\end{table}
@@ -513,15 +609,7 @@
\subsection{Yonsei University (YSU) PBL}
-The Yonsei University PBL is the next generation of the MRF PBL, also using the
-counter-gradient terms to represent fluxes due to non-local gradients.
-This adds to the MRF PBL an explicit treatment of the entrainment layer at
-the PBL top. The entrainment is made proportional to the surface buoyancy flux
-in line with results from studies with large-eddy models. The PBL top is
-defined using a critical bulk Richardson number of zero
-(compared to 0.5 in the MRF PBL), so is effectively only dependent on the
-buoyancy profile
-which, in general, lowers the calculated PBL top compared to MRF.
+The Yonsei University PBL \citep{hong06} is the next generation of the MRF PBL, also using the countergradient terms to represent fluxes due to non-local gradients. This adds to the MRF PBL \citep{hong96} an explicit treatment of the entrainment layer at the PBL top. The entrainment is made proportional to the surface buoyancy flux in line with results from studies with large-eddy models \citep{noh03}. The PBL top is defined using a critical bulk Richardson number of zero (compared to 0.5 in the MRF PBL), so is effectively dependent on the buoyancy profile, which the PBL top is defined at the maximum entrainment layer (compared to the layer at which the diffusivity becomes zero). A smaller magnitude of the counter-gradient mixing in the YSU PBL produces a well-mixed boundary profile, whereas overstable structure is pronounced in the upper part of the mixed layer in the case of the MRF PBL. Details are available in \citet{hong06}, including the analysis of the interaction be!
tween the boundary layer and precipitation physics. In version 3.0, an enhanced stable boundary-layer diffusion algorithm \citep{hong07} is also devised.
\subsection{Mellor-Yamada-Janjic (MYJ) PBL}
@@ -539,6 +627,10 @@
The TKE production/dissipation differential equation is solved iteratively.
The empirical constants have been revised as well \citep{janjic96,janjic02}.
+\subsection{Asymmetrical Convective Model version 2 (ACM2) PBL}
+
+The ACM2 \citep{pleim07} is a combination of the ACM, which is a simple transilient model that was originally a modification of the Blackadar convective model, and an eddy diffusion model. Thus, in convective conditions the ACM2 can simulate rapid upward transport in buoyant plumes and local shear induced turbulent diffusion. The partitioning between the local and non-local transport components is derived from the fraction of non-local heat flux according to the model of \citet{holtslag93}. The algorithm transitions smoothly from eddy diffusion in stable conditions to the combined local and non-local transport in unstable conditions. The ACM2 is particularly well suited for consistent PBL transport of any atmospheric quantity including both meteorological (u, v,$\theta$ , qv) and chemical trace species.
+
\section{Atmospheric Radiation}
The radiation schemes provide atmospheric heating due to radiative
@@ -575,9 +667,11 @@
\multispan4\hrulefill \cr
RRTM & LW & 16 & CO$_2$, O$_3$, clouds \cr
GFDL LW & LW & 14 & CO$_2$, O$_3$, clouds \cr
+CAM3 LW & LW & 2 & CO$_2$, O$_3$, clouds \cr
GFDL SW & SW & 12 & CO$_2$, O$_3$, clouds \cr
MM5 SW & SW & 1 & clouds \cr
Goddard & SW & 11 & CO$_2$, O$_3$, clouds \cr
+CAM3 SW & SW & 19 & CO$_2$, O$_3$, clouds \cr
\multispan4\hrulefill \cr
}}$$
\end{table}
@@ -602,6 +696,13 @@
Clouds are randomly overlapped.
This scheme is implemented to conduct comparisons with the operational Eta model.
+\subsection {CAM Longwave}
+
+A spectral-band scheme used in the NCAR Community Atmosphere
+Model (CAM 3.0) for climate simulations. It has the potential to handle
+several trace gases. It interacts with resolved clouds and cloud fractions,
+ and is documented fully by \citet{collins04}.
+
\subsection {Eta Geophysical Fluid Dynamics Laboratory (GFDL) Shortwave}
This shortwave radiation is a GFDL version of the \citet{lacis74}
@@ -616,7 +717,9 @@
This scheme is base on \citet{dudhia89} and is taken from MM5. It has a simple
downward integration of solar flux, accounting for clear-air scattering,
water vapor absorption \citep{lacis74}, and cloud albedo and absorption.
-It uses look-up tables for clouds from \citet{stephens78}.
+It uses look-up tables for clouds from \citet{stephens78}. In Version 3,
+the scheme has an option to account for terrain slope and shadowing effects
+on the surface solar flux.
\subsection {Goddard Shortwave}
@@ -625,6 +728,17 @@
approach that accounts for scattered and reflected components. Ozone is considered
with several climatological profiles available.
+\subsection {CAM Shortwave}
+
+A spectral-band scheme used in the NCAR Community Atmosphere
+Model (CAM 3.0) for climate simulations. It has the ability to handle optical properties of
+several aerosol types and trace gases. It uses cloud fractions and overlap assumptions
+in unsaturated regions, and has a monthly zonal ozone climatology. It is documented fully by
+\citet{collins04}.
+The CAM radiation scheme is especially suited for regional climate simulations by having a
+ozone distribution that varies during the simulation according
+to monthly zonal-mean climatological data.
+
\section {Physics Interactions}
\begin{table}
@@ -694,4 +808,70 @@
The boundary-layer scheme is necessarily after the land-surface scheme
because it requires the heat and moisture fluxes.
+\section{Four-Dimensional Data Assimilation}
+Four-dimensional data assimilation (FDDA), also known as nudging, is a method of keeping
+simulations close to analyses and/or observations over the course of an
+integration. There are two types of FDDA that can be used separately or in
+combination. Grid- or analysis-nudging simply forces the model simulation
+towards a series of analyses grid-point by grid-point. Observational- or station-nudging
+locally forces the simulation towards observational data. These methods
+provide a four-dimensional analysis that is somewhat balanced dynamically,
+and in terms of continuity,
+while allowing for complex local topographical or convective variations.
+Such datasets can cover long periods, and have particular value in driving
+off-line air quality or atmospheric chemistry models.
+
+\subsection{Grid Nudging or Analysis Nudging}
+
+Grid nudging is a major component of so-called Four-Dimensional Data Assimilation (FDDA). (Other components are surface-analysis nudging to be developed soon, and observational nudging, developed and released in WRF Version 2.2).
+
+The grid-nudging method is specifically three-dimensional analysis nudging, whereby the atmospheric model is nudged towards time- and space-interpolated analyses using a point-by-point relaxation term. \citet{stauffer90} originally developed the technique for MM5.
+
+The grid-nudging technique has several major uses.
+
+{\it a) Four-dimensional datasets.} The model is run with grid-nudging for long periods, e.g. months, to provide a four-dimensional meteorologically self-consistent dataset that also stays on track with the driving analyses. In this way, the model is used as an intelligent interpolator of analyses between times, and also accounting better for topographic and convective effects. As mentioned, the primary use for such datasets is in air quality where the wind fields may be used to drive off-line chemistry models.
+
+{\it b) Boundary conditions.} A nested simulation is run with the outer domain nudged towards analyses, and the nest running un-nudged. This provides better temporal detail at the nest boundary than driving it directly from linearly interpolated analyses, as it would be if it were the outer domain. This technique could also be used in forecasting, where an outer domain is nudged towards global forecast fields that are available in advance of the regional forecast.
+
+{\it c) Dynamic initialization.} A pre-forecast period (e.g., -6 hours to 0 hours) is run with nudging using analyses at those times that are already available. This is probably better than a cold-start using just the 0 hour analysis because it gives the model a chance to spin up. In particular, the model will have six hours to adjust to topography, and produce cloud fields by hour 0, whereas with a cold start there would be a spin-up phase where waves are produced and clouds are developed. This method could also be combined with 3D-Var techniques that may provide the hour 0 analysis.
+
+Grid nudging has been added to ARW using the same input analyses as the WPS pre-processing systems can provide. Since it works on multiple domains in a nesting configuration, it requires multiple time-periods of each nudged domain as input analyses. Given these analyses, the {\it real} program produces another input file which is read by the model as nudging is performed. This file contains the gridded analysis 3d fields of the times bracketing the current model time as the forecast proceeds. The four nudged fields are the two horizontal wind components (u and v), temperature, and specific humidity.
+
+The method is implemented through an extra tendency term in the nudged variable's equations, e.g.
+
+$$ {\partial \theta \over \partial t} = F(\theta) + G_{\theta} W_{\theta} ( \hat \theta_0 - \theta) $$
+where $F(\theta)$ represents the normal tendency terms due to physics, advection, etc., $G_{\theta}$ is a time-scale controlling the nudging strength, and $W_{\theta}$ is an additional weight in time or space to limit the nudging as described more below, while $\hat \theta_0$ is the time- and space-interpolated analysis field value towards which the nudging relaxes the solution.
+
+Several options are available to control the nudging.
+
+{\it a)} Nudging end-time and ramping. Nudging can be turned off during the simulation, as in dynamical initialization. Since turning nudging off suddenly can lead to noise, there is a capability for ramping the nudging down over a period, typically 1-2 hours to reduce the shock.
+
+{\it b)} Strength of nudging. The timescale for nudging can be controlled individually for winds, temperature and moisture. Typically the namelist value of 0.0003 s-1 is used, corresponding to a timescale of about 1 hour, but this may be reduced for moisture where there may be less confidence in the analysis versus the details in the model.
+
+{\it c)} Nudging in the boundary layer. Sometimes, since the analysis does not resolve the diurnal cycle, it is better not to nudge in the boundary layer to let the model PBL evolve properly, particularly the temperature and moisture fields. Each variable can therefore be selectively not nudged in the model boundary layer, the depth of which is given by the PBL physics.
+
+{\it d)} Nudging at low levels. Alternatively the nudging can be deactivated for any of the variables below a certain layer throughout the simulation. For example, the lowest ten layers can be free of the nudging term.
+
+{\it e)} Nudging and nesting. Each of these controls is independently set for each domain when nesting, except for the ramping function, which has one switch for all domains.
+
+\subsection{Observational or Station Nudging}
+
+The observation-nudging FDDA capability allows to effectively assimilate temperature, wind and moisture observations from all platforms, measured at any location within the model domains and any time within a given data assimilation periods. With the observation-nudging formulation, each observation directly interacts with the model equations and thus the scheme yields dynamically and diabatically initialized analyses to support the applications that need regional 4-D full-field weather and/or to start regional NWP with spun-up initial conditions. The observation-nudging scheme, which is an enhanced version of the standard MM5 observation-nudging scheme, was implemented into WRF-ARW and has been in the model since WRF Version 2.2.
+More details of the methods can be found in \citet{liu08}.
+The most significant modifications to the standard MM5 observation-nudging scheme \citep{stauffer94} that are included in the WRF observation-nudging scheme are summarized as following:
+
+(1) Added capability to incorporate all, conventional and non-conventional, synoptic and asynoptic data resources, including the twice daily radiosondes; hourly surface, ship and buoy observations, and special observations from GTS/WMO; NOAA/NESDIS satellite winds derived from cloud, water vapor and IR imageries; NOAA/FSL ACARS, AMDAR, TAMDAR and other aircraft reports; NOAA/FSL NPN (NOAA Profiler Network) and CAP (Corporative Agencies Profilers) profilers; the 3-hourly cloud-drifting winds and water-vapor-derived winds from NOAA/NESDIS; NASA Quikscat sea surface winds; and high-density, high-frequency observations from various mesonets of government agencies and private companies. In particular, special weights are assigned to the application-specific data, such as the SAMS network, special soundings and winder profilers located at and operated by the Army test ranges.
+
+(2) Added capability to assimilate multi-level upper-air observations, such as radiosondes, wind profilers and radiometers, in a vertically coherent way, which is contrast to the algorithm for single point observations such as aircraft reports and satellite derived winds.
+
+(3) Surface temperature (at 2 m AGL) and winds (at 10 m AGL) observations are first adjusted to the first model level according to the similarity theory that is built in the model surface-layer physics and the surface-layer stability state at the observation time. The adjusted temperature and wind innovations at the lowest model level are then used to correct the model through the mixing layer, with weights gradually reduced toward the PBL top.
+
+(4) Steep mountains and valleys severely limit the horizontal correlation distances. For example, weather variables on the upwind slope are not correlated with those on the downwind slope. To take account this effect, a terrain-dependent nudging weight correction is designed to eliminate the influence of an observation to a model grid point if the two sites are physically separated by a mountain ridge or a deep valley. More details about the scheme and numerical test results can be found in \citet{xu02}. Essentially, for a given observation and grid point, a terrain search is done along the line connecting the grid point and the observation site. If there is a terrain blockage or a valley (deeper than a given depth), the nudging weight for the observation at the given grid point is set to zero. Currently, this algorithm is applied for surface observations assimilation only.
+
+(5) The scheme was adjusted to accomplish data assimilation on multi-scale domains. Two adjustments among many are noteworthy. The first is an addition of grid-size-dependent horizontal nudging weight for each domain and the correspondent inflation with heights. The second is adding the capability of double-scans, a two-step observation-nudging relaxation. The idea is similar to the successive corrections: the first scan, with large influence radii and smaller weights, allows observations to correct large scale fields, while the second scan, with smaller influence radii and large weights, permits the observation to better define the smaller-scale feature.
+
+(6) Observation-nudging allows the observation correction to be propagated into the model state in a given time influence window. One technical difficulty with this is that the model state is not known at the observation time for computing the observation increment, or innovation, during the first half of the time window. In the \citet{stauffer94} scheme, at each time step within the time influence window, innovation is calculated (or approximated) by differing the observation from the model state at the time step. This obviously leads to dragging the future forecasts toward previous (observation) states. To reduce this error, the innovation calculation is kept the same as before up to the observation time (this is OK since the model state is gradually tacking toward the observation state), but the true innovation s kept and used during the later half of the time influence window.
+
+(7) An ability for users to set different nudging time-windows and influence radii for different (nested) domains is added into WRF since the WRF V3.0 release.
+
</font>
</pre>