From a7a9f56e28e89adc2aaacf84d88b35fd118a7584 Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Thu, 27 Dec 2018 12:26:05 -0700 Subject: [PATCH 1/7] add NoahMP --- description.bbl | 10 ++++++++++ physics.tex | 10 +++++++++- 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/description.bbl b/description.bbl index 1386e36..79c18e1 100644 --- a/description.bbl +++ b/description.bbl @@ -560,6 +560,11 @@ simulated squall line: Comparison of one- and two-moment schemes. {\em Mon. Wea. Rev.} , {\bf 137}, 991--1007. +\bibitem[Niu et al.(2011)]{niu11}% +Niu, G.-Y, Z.-L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, E. Rosero, M. Tewari, Y. Xia, 2011: +The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. +{\em J. Geophys. Res.}, {\bf116}, D12109. + \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. @@ -907,6 +912,11 @@ on the analysis and forecast of Hurricane Sandy with a limited-area data assimil Yang, C., Z. Liu, F. Gao, P. P. Childs, and J. Min, 2017: Impact of assimilating GOES imager clear-sky radiance with a rapid refresh assimilation system for convection-permitting forecast over Mexico. {\em J. Geophys. Res. Atmos.}, {\bf 122}, 5472--5490. +\bibitem[Yang et al.(2011)]{yang11}% +Yang, Z.-L., G.-Y. Niu, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, L. Longuevergne, K. Manning, D. Niyogi, M. Tewari, and Y. Xia, 2011: +The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. +{\em J. Geophys. Res.}, {\bf 116}, D12110. + \bibitem[Zhang and Anthes(1982)]{zhanganthes82}% Zhang, D.-L., and R.A. Anthes, 1982: A high-resolution model of the planetary boundary layer--sensitivity tests and comparisons with SESAME--79 data. diff --git a/physics.tex b/physics.tex index 88333cd..b3d8ec6 100644 --- a/physics.tex +++ b/physics.tex @@ -543,10 +543,18 @@ \subsection{Noah LSM} soil drainage, and runoff, taking into account vegetation categories, monthly vegetation fraction, and soil texture. The scheme provides sensible and latent heat fluxes to the boundary-layer scheme. The Noah LSM additionally predicts -soil ice, and fractional snow cover effects, has an improved urban treatment, +soil ice, and fractional snow cover effects, is linked to urban model options, and considers surface emissivity properties, which are all new since the OSU scheme. +\subsection{NoahMP LSM} + +This model follows on from the Noah LSM and is a large collaborative effort \citep{niu11, yang11} to allow for multiple parameterization options +for each part of the LSM physics making it a multi-parameterization (MP) scheme. The sub-options within this scheme +include dynamic vegetation, stomatal resistance, runoff/groundwater, soil permeability, radiative transfer, soil and snow +options and surface evaporation resistance options. There is also a crop model (GeCROS). NoahMP is linked to +all the urban options like Noah (UCM, BEP and BEM). + \subsection{Rapid Update Cycle (RUC) Model LSM} 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 From f7c483e95f7acbdef3f92ea3bb76eb19704ddf60 Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Thu, 27 Dec 2018 13:19:54 -0700 Subject: [PATCH 2/7] add CLM4 --- description.bbl | 7 +++++++ physics.tex | 14 +++++++++++--- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/description.bbl b/description.bbl index 79c18e1..eec32ea 100644 --- a/description.bbl +++ b/description.bbl @@ -415,6 +415,10 @@ Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the Laprise R., 1992: The Euler Equations of motion with hydrostatic pressure as as independent variable, {\em Mon. Wea. Rev.}, {\bf 120}, 197--207. + + \bibitem[Lawrence et al.(2011)]{lawrence11}% +Lawrence, D. M., et al., 2011: Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model. +{\em J. Adv. Model. Earth Syst.}, {\bf 3}, M03001. \bibitem[Lee et al.(2004)]{lee04}% Lee, M.-S., D. Barker, W. Huang and Y.-H. Kuo, 2004: First Guess at @@ -575,6 +579,9 @@ 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[Oleson et al.(2010)]{oleson10}% +Oleson, K. W., et al., 2010: Technical description of version 4 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR. 266 pp. + \bibitem[Ooyama(1990)]{ooyama90}% Ooyama K. V., 1990: A thermodynamic foundation for modeling the moist atmosphere, {\em J. Atmos. Sci.}, {\bf 47}, 2580--2593. diff --git a/physics.tex b/physics.tex index b3d8ec6..377ebb3 100644 --- a/physics.tex +++ b/physics.tex @@ -481,7 +481,7 @@ \subsection{TEMF surface layer} and consider unstable conditions similarly to neutral conditions. -\section{Land-Surface Model} +\section{Land-Surface Model and Other Surface Options} The land-surface models (LSMs) use atmospheric information from the surface layer scheme, radiative forcing from the radiation scheme, and precipitation forcing from the @@ -552,8 +552,8 @@ \subsection{NoahMP LSM} This model follows on from the Noah LSM and is a large collaborative effort \citep{niu11, yang11} to allow for multiple parameterization options for each part of the LSM physics making it a multi-parameterization (MP) scheme. The sub-options within this scheme include dynamic vegetation, stomatal resistance, runoff/groundwater, soil permeability, radiative transfer, soil and snow -options and surface evaporation resistance options. There is also a crop model (GeCROS). NoahMP is linked to -all the urban options like Noah (UCM, BEP and BEM). +options and surface evaporation resistance options. The scheme has 4 soil layers and 3 snow layers. +There are also a crop model options. NoahMP is linked to all the urban options like Noah (UCM, BEP and BEM). \subsection{Rapid Update Cycle (RUC) Model LSM} @@ -577,6 +577,14 @@ \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 top layer is taken to be 1 cm thick, and the lower layer is 99 cm. 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 quantities. +\subsection{Community Land Model (CLM4)} + +CLM4 is a version of the land component of the Coupled Earth System Model (CESM) that is used for climate modeling \citep{oleson10, lawrence11}. +The model has 10 soil layers and 5 snow layers. +The scheme allows for sub-grid tiling by 5 main categories (glacier, wetland, vegetated, lake and urban), and 4 vegetated sub-tiles of different "plant functional types" (PFTs). +This has a rather comprehensive set of physics related to land-surface, soil, hydrology, and vegetation processes, also including urban areas, glaciers, and a multi-layer lake +model that is also an option to run with other LSMs in WRF. + \subsection{Urban Canopy Model} This can be run as an option with the Noah LSM. From 16fa811fd54fc821a68110ed74b395398acb6873 Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Thu, 27 Dec 2018 14:55:39 -0700 Subject: [PATCH 3/7] add SSiB LSM --- description.bbl | 10 +++++++++- physics.tex | 11 +++++++++-- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/description.bbl b/description.bbl index eec32ea..d9ae956 100644 --- a/description.bbl +++ b/description.bbl @@ -766,6 +766,10 @@ over the U.S. Great Plains. Sun, J., H. Wang, W. Tong, Y. Zhang, C. Lin, and D. Xu, 2016: Comparison of the Impacts of Momentum Control Variables on High-Resolution Variational Data Assimilation and Precipitation Forecasting. {\em Mon. Wea. Rev.}, {\bf 144}, 149--169. + + \bibitem[Sun and Xue(2001)]{sun01}% + Sun, S., and Y. Xue, 2001: Implementing a new snow scheme in Simplified Simple Biosphere Model (SSiB), + {\em Adv. Atmos. Sci.}, {\bf 18}, 335--354. \bibitem[Tao and Simpson(1993)]{tao93}% Tao, W.-K., and J. Simpson, 1993: @@ -908,7 +912,11 @@ Xu, M., Y. Liu, C. Davis and T. Warner, 2002: \bibitem[Xue(2000)]{xue.2000} Xue, M., 2000: High-order monotonic numerical diffusion and smoothing. {\em - Mon.\ Wea.\ Rev.}, {\bf 128}, 2853--2864. + Mon. Wea. Rev.}, {\bf 128}, 2853--2864. + +\bibitem[Xue et al.(1991)]{xue91}% +Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. +{\em J. Climate}, {\bf 4}, 345--364. \bibitem[Yang et al.(2016)]{yang16} Yang, C., Z. Liu, J. Bresch, S.R.H. Rizvi, X.-Y. Huang, and J. Min, 2016: AMSR2 all-sky radiance assimilation and its impact diff --git a/physics.tex b/physics.tex index 377ebb3..3ad8aa0 100644 --- a/physics.tex +++ b/physics.tex @@ -545,7 +545,7 @@ \subsection{Noah LSM} heat fluxes to the boundary-layer scheme. The Noah LSM additionally predicts soil ice, and fractional snow cover effects, is linked to urban model options, and considers surface emissivity properties, which are all new since the OSU -scheme. +scheme. There is a sub-tiling (mosaic) option for this LSM. \subsection{NoahMP LSM} @@ -572,10 +572,12 @@ \subsection{Rapid Update Cycle (RUC) Model LSM} 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. +This option also has a sub-grid mosaic sub-option. \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 top layer is taken to be 1 cm thick, and the lower layer is 99 cm. 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 quantities. +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 top layer is taken to be 1 cm thick, and the lower layer is 99 cm. 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 quantities. The scheme also +allows for sub-grid variability by averaging grid-cell properties from sub-grid fractions. \subsection{Community Land Model (CLM4)} @@ -585,6 +587,11 @@ \subsection{Community Land Model (CLM4)} This has a rather comprehensive set of physics related to land-surface, soil, hydrology, and vegetation processes, also including urban areas, glaciers, and a multi-layer lake model that is also an option to run with other LSMs in WRF. +\subsection{Simplified Simple Biosphere Model (SSiB)} + +SSiB \citep{xue91, sun01} has been provided from the UCLA GCM as another climate option for land-surface physics. It has a 2-layer bulk soil temperature model, +vegetation effects at the surface, 3 layers of soil moisture with a root zone, and a 4-layer snow treatment. It also predicts canopy temperature and moisture. + \subsection{Urban Canopy Model} This can be run as an option with the Noah LSM. From 874bad13a636915624589d512d95555a03d88191 Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Fri, 28 Dec 2018 14:52:42 -0700 Subject: [PATCH 4/7] add LSM updates --- description.bbl | 24 ++++++++++++++++++++++++ physics.tex | 43 +++++++++++++++++++++++++++++++++++++------ 2 files changed, 61 insertions(+), 6 deletions(-) diff --git a/description.bbl b/description.bbl index d9ae956..8669566 100644 --- a/description.bbl +++ b/description.bbl @@ -420,6 +420,10 @@ Laprise R., 1992: The Euler Equations of motion with hydrostatic pressure Lawrence, D. M., et al., 2011: Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model. {\em J. Adv. Model. Earth Syst.}, {\bf 3}, M03001. +\bibitem[Lee and Chen(2012)]{lee12}% +Lee, C.-Y. and S. S. Chen, 2012: Symmetric and asymmetric structures of hurricane boundary layer in coupled atmosphere-wave-ocean models and observations. +{\em J. Atmos. Sci.}, {\bf 69}, 3576--3594. + \bibitem[Lee et al.(2004)]{lee04}% Lee, M.-S., D. Barker, W. Huang and Y.-H. Kuo, 2004: First Guess at Appropriate Time (FGAT) with WRF 3DVAR. {\em WRF/MM5 Users Workshop}, @@ -429,6 +433,10 @@ Appropriate Time (FGAT) with WRF 3DVAR. {\em WRF/MM5 Users Workshop}, Leutbecher, M., and Coauthors, 2017: Stochastic representations of model uncertainties at ECMWF: State of the art and future vision. \textit{Quart. J. Roy. Meteor. Soc.},{\bf 143}, 2315-2339. + +\bibitem[Li et al.(2013)]{li13}% +Li, D., E. Bou-Zeid, M. Barlage, F. Chen, and J. A. Smith, 2013: Development and Evaluation of a Mosaic Approach in the WRF-Noah Framework. +{\em J. Geophys. Res.}, {\bf 118}, 11918--11935. \bibitem[Li et al.(2015)]{li15}% Li, X., J. Ming, M. Xue, Y. Wang, and K. Zhao, 2015: Implementation of a dynamic equation constraint based on the steady state momentum @@ -494,6 +502,10 @@ Lynch, P., 1997: The Dolph-Chebyshev Window: A Simple Optimal Filter. \bibitem[Mansell et al.(2010)]{mansell10}% Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. {\em J. Atmos. Sci.}, {\bf 67}, 171--194. + +\bibitem[Martilli et al.(2002)]{martilli02}% +Martilli A, Clappier A, and Rotach M.W., 2002: An urban surface exchange parameterization for mesoscale models. +{\em Bound.-Layer Meteorol.}, {\bf 104}, 261--304. \bibitem[McCumber et al.(1991)]{mccumber91} McCumber, M., W.-K. Tao, J. Simpson, R. Penc, and S.-T. Soong, 1991: @@ -636,6 +648,10 @@ 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[Price et al.(1994)]{price94}% +Price, J. F., T. B. Sanford, and G. Z. Forristall, 1994: Forced stage response to a moving hurricane. +{\em J. Phy. Oceanogr.}, {\bf 24}, 233--260. + \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 @@ -672,6 +688,10 @@ Rutledge, S. A., and P. V. Hobbs, 1984: \bibitem[Ryan (1996)]{ryan96}% Ryan, B. F., 1996: On the global variation of precipitating layer clouds. {\em Bull. Amer. Meteor. Soc.}, {\bf 77}, 53--70. + + \bibitem[Salamanca and Martilli(2010)]{salamanca10}% + Salamanca, F., and A. Martilli, 2010: A new building energy model coupled with an urban canopy parameterization for urban climate simulations -- part II. Validation with one dimension off-line simulations. + {\em Theor. Appl. Climatol.}, {\bf 99}, 345--356. \bibitem[Sasamori et al.(1972)]{sasamori72}% Sasamori, T., J. London, and D. V. Hoyt, 1972: Radiation budget of the @@ -752,6 +772,10 @@ Stephens, G. L., 1978: Radiation profiles in extended water clouds. Part II: Parameterization schemes, {\em J. Atmos. Sci.}, {\bf 35}, 2123--2132. + \bibitem[Subin et al.(2012)]{subin12}% + Subin, Z.M., Riley, W.J. and Mironov, D. 2012. Improved lake model for climate simulations, + {\em J. Adv. Model. Earth Syst.}, {\bf 4}, M02001. + \bibitem[Sukoriansky et al.(2005)]{sukoriansky05}% Sukoriansky, S., B. Galperin, and V. Perov, 2005: Application of a new spectral model of stratified turbulence to the atmospheric boundary layer over sea ice. diff --git a/physics.tex b/physics.tex index 3ad8aa0..86717fe 100644 --- a/physics.tex +++ b/physics.tex @@ -545,7 +545,7 @@ \subsection{Noah LSM} heat fluxes to the boundary-layer scheme. The Noah LSM additionally predicts soil ice, and fractional snow cover effects, is linked to urban model options, and considers surface emissivity properties, which are all new since the OSU -scheme. There is a sub-tiling (mosaic) option for this LSM. +scheme. There is a sub-tiling (mosaic) option for this LSM \citep{li13}. \subsection{NoahMP LSM} @@ -594,7 +594,7 @@ \subsection{Simplified Simple Biosphere Model (SSiB)} \subsection{Urban Canopy Model} -This can be run as an option with the Noah LSM. +This can be run as an option with the Noah and NoahMP LSMs. 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 @@ -614,19 +614,48 @@ \subsection{Urban Canopy Model} 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. -If they are available, the UCM can take three dfferent densities +If they are available, the UCM can take three different densities of urban development using special land-use categories. In Version 3, an anthropogenic heating diurnal cycle was added as an option. +\subsection{Building Environment Parameterization (BEP)} + +This is an urban option that also aims to account for more of the dynamical effects of buildings on the flow \citep{martilli02}. +The option is available for the Noah and NoahMP LSMs. +The buildings are allowed to directly impact more than the lowest model layer with suitable choices of PBL schemes (MYJ and Bougeault-Lacarrere). +The scheme is also adaptable to more detailed urban morphology datasets such as NUDAPT and WUDAPT. + +\subsection{Building Energy Model (BEM)} + +An urban option that builds on BEP to include a building energy budget including heat transfer through walls, windows, floors, roofs, etc., and effects of air conditioning +in controlled environments and other anthropogenic internal heating +in addition to the external urban canyon representations that exist in other urban schemes \citep{salamanca10}. This model allows for a prediction of energy consumption. +This option is also available with the Noah and NoahMP LSMs. + \subsection{Ocean Mixed-Layer Model} -This can be selected with the 5-layer option, and is designed for +This can be selected with its own 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 on 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{3-D Ocean Model} + +This is a three-dimensional ocean model with configurable layers \citep{price94,lee12}, but is simple in the sense of having a fixed flat bathymetry so that all the layers are at fixed depths. +The model predicts temperature, salinity, and currents at each point along with advective effects. However, currents are typically initialized to zero and this model would +require additional data to be initialized from real ocean data. Its main use is as added sophistication for a mixed layer model that responds to the atmosphere and includes +3d dynamical ocean effects driven by the atmospheric stress which can improve over the 1d approach. + +\subsection{CLM4.5 Lake Model} + +This is a standalone option in WRF that can be used with LSMs other than CLM4. It is based on \citet{subin12} and has a +one-dimensional mass and energy balance scheme with 20-25 model layers, +including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil +layers on the lake bottom. The lake scheme is used with actual lake points and +lake depth data where available (WPS has a bathymetry dataset for many lakes), and it also can be used with user defined +lake points and lake depth in WRF. -\subsection{Specified Lower Boundary Conditions} +\subsection{Updating 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 @@ -729,7 +758,9 @@ \section{Atmospheric Radiation} that make up the solar spectrum. Hence, the only source is the Sun, but processes include absorption, reflection, and scattering in the atmosphere and at surfaces. For shortwave radiation, the upward flux is the reflection due to surface -albedo. Within the atmosphere the radiation responds to model-predicted +albedo. For higher resolutions, the shortwave schemes can represent slope effects +that modify the surface downward shortwave flux according to slope aspect angles. +Within the atmosphere the radiation responds to model-predicted cloud and water vapor distributions, as well as specified carbon dioxide, ozone, and (optionally) trace gas concentrations. All the radiation schemes in WRF currently are column (one-dimensional) schemes, so each column is From dcb8dc692b1b4f549e661e98451d3b3648f848ee Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Mon, 31 Dec 2018 15:07:44 -0700 Subject: [PATCH 5/7] add PBL sections and remove old tables --- description.bbl | 53 ++++++++- physics.tex | 283 ++++++++++++++++++++---------------------------- 2 files changed, 169 insertions(+), 167 deletions(-) diff --git a/description.bbl b/description.bbl index 8669566..68a5b72 100644 --- a/description.bbl +++ b/description.bbl @@ -77,12 +77,20 @@ Betts, A. K., 1986: A new convective adjustment scheme. Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, and arctic air-mass data sets. {\em Quart. J. Roy. Meteor. Soc.}, {\bf 112}, 693--709. + + \bibitem[Bougeault and Lacarrere(1989)]{bougeault89}% + Bougeault, P., and P. Lacarrere, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. + {\em Mon. Wea. Rev.}, {\bf 117}, 1872--1890. \bibitem[{Bowler et~al.(2008)Bowler, Arribas, Mylne, Robertson,, and Beare}]{Bo08} Bowler, N.~E., A.~Arribas, K.~R. Mylne, K.~B. Robertson, and S.~E. Beare, 2008: The {MOGREPS} short-range ensemble prediction system. \textit{Quart. J. Roy. Meteor. Soc.}, \textbf{134}, 703--722. + + \bibitem[Bretherton and Park(2009)]{bretherton09}% + Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. + {\em J. Climate}, {\bf 22}, 3422--3448. \bibitem[{Buizza et~al.(1999)Buizza, Miller,, and Palmer}]{Bu99} Buizza, R., M.~Miller, and T.~N. Palmer, 1999: Stochastic representation of @@ -120,7 +128,11 @@ Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. \bibitem[Chen et al.(2013)]{chen13}% Chen, Y., S. R. H. Rizvi, X.-Y. Huang, J. Min, and X. Zhang, 2013: Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions. - {\em Meteor. Atmos. Phys.}, {\bf 121}, 79?98. + {\em Meteor. Atmos. Phys.}, {\bf 121}, 79--98. + + \bibitem[Choi and Hong(2015)]{choi15}% + Choi H., and S. Hong, 2015: An updated subgrid orographic parameterization for global atmospheric forecast models. + {\em J. Geophys. Res.}, {\bf 120}, 12445--12457. \bibitem[Chou and Suarez(1994)]{chou94}% Chou M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation @@ -200,6 +212,11 @@ Fels, S. B. and M. D. Schwarzkopf, 1975: Fisher, M., 2003: Background error covariance modeling. {\em Seminar on Recent Development in Data Assimilation for Atmosphere and Ocean}, 45--63, ECMWF. + + \bibitem[Fitch et al.(2012)]{fitch12}% + Fitch, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad, 2012: Local and mesoscale impacts of wind farms as parameterized in a + mesoscale NWP model. + {\em Mon. Wea. Rev.}, {\bf 140}, 3017--3038. \bibitem[Fletcher(1962)]{fletcher62}% Fletcher, N. H., 1962: {\em The Physics Of Rain Clouds.} Cambridge University Press, @@ -229,6 +246,10 @@ Grell, G.A., S.E. Peckham, R. Schmitz, S.A. McKeen, G. Frost, W.C. Skamarock and B. Eder, 2005: Fully coupled online chemistry within the WRF model. {\em Atmos. Environ.}, {\bf 39}, 6957-6975. +\bibitem[Grenier and Bretherton(2001)]{grenier01}% +Grenier, H., and C. S. Bretherton, 2001: A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. +{\em Mon. Wea. Rev.}, {\em 129}, 357--377. + \bibitem[{Hacker et~al.(2011)Hacker, Snyder, Ha,, and Pocernich}]{Ha11a} Hacker, J.~P., C.~Snyder, S.-Y. Ha, and M.~Pocernich, 2011: Linear and nonlinear response to parameter variations in a mesoscale model. @@ -338,6 +359,10 @@ Jimenez, P., J. Dudhia, J. F. Gonzalez-Ruoco, J. Navarro, J. P. Montavez, and E. Garcia-Bustamente, 2012: A revised scheme for the WRF surface layer formulation. {\em Mon. Wea. Rev.,} {\bf 140,} 898--918. +\bibitem[Jimenez and Dudhia(2012)]{jimenez12a}% +Jimenez, P. A., and J. Dudhia, 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. +{\em J. Appl. Meteor. Climatol.}, {\bf 51}, 300--316. + \bibitem[Kain and Fritsch(1990)]{kain90}% Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/ detraining plume model and its application in convective parameterization, @@ -486,6 +511,10 @@ Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filte Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. {\em Quart. J. Roy. Meteor. Soc.}, {\bf 112}, 1177--1194. + \bibitem[Lorente-Plazas et al.(2016)]{lorente16}% + Lorente-Plazas, R., P. A. Jimenez, J. Dudhia, and J. P. Montavez, 2016: Evaluating and improving the impact of the atmospheric stability and orography on surface winds in the WRF model. +{\em Mon. Wea. Rev.}, {\bf 144}, 2685--2693. + \bibitem[Lynch and Huang(1992)]{lynchhuang92}%, Lynch, P., and X.-Y. Huang, 1992: Initialization of the HIRLAM Model Using a Digital Filter. {\em Mon. Wea. Rev.}, {\bf 120}, @@ -576,6 +605,14 @@ simulated squall line: Comparison of one- and two-moment schemes. {\em Mon. Wea. Rev.} , {\bf 137}, 991--1007. +\bibitem[Nakanishi and Niino(2006)]{nakanishi06}% +Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog. +{\em Bound. Layer Meteor.}, {\bf 119}, 397--407. + +\bibitem[Nakanishi and Niino(2009)]{nakanishi09}% +Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. +{\em J. Meteor. Soc. Japan}, {\bf 87}, 895--912. + \bibitem[Niu et al.(2011)]{niu11}% Niu, G.-Y, Z.-L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, E. Rosero, M. Tewari, Y. Xia, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. @@ -594,6 +631,9 @@ Noilhan, J., and S. Planton, 1989: \bibitem[Oleson et al.(2010)]{oleson10}% Oleson, K. W., et al., 2010: Technical description of version 4 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR. 266 pp. +\bibitem[Olson et al.(2019)]{olson19}% +J. B. Olson, J. S. Kenyon, W.. A. Angevine, J. M. Brown, M. Pagowski, and K. Su?elj, 2019: A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF-ARW. NOAA Technical Memorandum (in preparation). + \bibitem[Ooyama(1990)]{ooyama90}% Ooyama K. V., 1990: A thermodynamic foundation for modeling the moist atmosphere, {\em J. Atmos. Sci.}, {\bf 47}, 2580--2593. @@ -617,6 +657,9 @@ Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's Spe Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. {\em J. Appl. Meteor.}, {\bf 9}, 857--861. + +\bibitem[Pergaud et al.(2009)]{pergaud09}% +Pergaud J, V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry thermal and shallow cumuli for mesoscale numerical weather prediction. {\em Boundary-Layer Meteorol.}, {\bf 132}, 83--106. \bibitem[Pleim(2006)]{pleim06}% Pleim, J. E., 2006: @@ -710,6 +753,10 @@ Schwarzkopf, M. D., and S. B. Fels, 1985: Improvements to the algorithm for comp Schwarzkopf, M. D., and S. B. Fels, 1991: The simplified exchange method revisited --- An accurate, rapid method for computation of infrared cooling rates and fluxes. {\em J. Geophys. Res.}, {\bf 96} (D5), 9075--9096. + +\bibitem[Shin and Hong(2015)]{shin15}% +Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. +{\em Mon. Wea. Rev.}, {\bf 143}, 250--271. \bibitem[{Shutts(2005)}]{Sh05} Shutts, G.~J., 2005: {A kinetic energy backscatter algorithm for use in @@ -875,6 +922,10 @@ Webb, E. K., 1970: Profile relationships: The log-linear range, and extension to Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm. {\em J. Atmos. Sci.}, {\bf 52}, 2675--2703. + + \bibitem[Wilson and Fovell(2018)]{wilson18}% + Wilson, T. H., and R. G. Fovell, 2018: Modeling the evolution and life cycle of radiative cold pools and fog. + {\em Weather and Forecasting}. {\bf 33}, 203--220. \bibitem[Wu et al.(2002)]{wu02}% Wu, W. -S., R. J. Purser, and D. F. Parrish, 2002: Three-Dimensional Variational diff --git a/physics.tex b/physics.tex index 86717fe..7e6c2bb 100644 --- a/physics.tex +++ b/physics.tex @@ -86,29 +86,6 @@ \section{Microphysics} or icing situations. For coarser grids the added expense of these schemes is not worth it because riming is not likely to be well resolved. -\begin{table} -\caption{Microphysics Options} -\label{microphysics table} -$$\vbox -{\halign{#\hfil \qquad & \hfil#\hfil \qquad & -\hfil#\hfil \qquad & \hfil#\hfil \qquad & \hfil#\hfil \cr -\multispan4\hrulefill \cr -Scheme & Number of & Ice-Phase & Mixed-Phase \cr - & Variables & Processes & Processes \cr -\multispan4\hrulefill \cr -Kessler & 3 & N & N \cr -Purdue Lin & 6 & Y & Y \cr -WSM3 & 3 & Y & N \cr -WSM5 & 5 & Y & N \cr -WSM6 & 6 & Y & Y \cr -Eta GCP & 2 & Y & Y \cr -Thompson & 7 & Y & Y \cr -Goddard & 6 & Y & Y \cr -Morrison 2-Moment & 10 & Y & Y \cr -\multispan4\hrulefill \cr -}}$$ -\end{table} - \subsection{Kessler scheme} This scheme \citep{kessler69}, which was taken from the COMMAS model @@ -270,24 +247,6 @@ \section{Cumulus parameterization} Table \ref{cumulus table} summarizes the basic characteristics of the available cumulus parameterization options in the {\wrf}. -\begin{table} -\caption{Cumulus Parameterization Options} -\label{cumulus table} -$$\vbox -{\halign{#\hfil \qquad & \hfil#\hfil \qquad & -#\hfil \qquad & #\hfil \qquad & #\hfil \cr -\multispan4\hrulefill \cr -Scheme & Cloud & Type of scheme & Closure \cr - & Detrainment & & \cr -\multispan4\hrulefill \cr -Kain-Fritsch & Y & Mass flux & CAPE removal \cr -Betts-Miller-Janjic & N & Adjustment & Sounding adjustment \cr -Grell-Devenyi & Y & Mass flux & Various \cr -Grell-3 & Y & Mass flux & Various \cr -\multispan4\hrulefill \cr -}}$$ -\end{table} - \subsection{Kain-Fritsch scheme} The modified version of the Kain-Fritsch scheme \citep{kain04} is based on @@ -412,31 +371,12 @@ \section{Surface Layer} scalars. Diagnostic outputs of 2m and 10m quantities are consistently computed with the profiles of these schemes. -\subsection{Similarity theory (MM5)} - -This scheme is now superseded by the Revised MM5 similarity theory scheme -(see next subsection) but still available as an option. -This scheme uses stability functions from \citet{paulson70}, \citet{dyer70}, -and \citet{webb70} -to compute surface exchange coefficients for heat, moisture, and momentum. -A convective velocity following \citet{beljaars94} is used to enhance surface -fluxes of heat and moisture. A Charnock relation relates -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. 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{Revised MM5 similarity theory} \citet{jimenez12} revised the previous MM5 similarity theory by improving the consistency between Ri and z/L and removing limits by using new stability functions for stable and unstable conditions that also include the extra term -z$_0$/L. The scheme gives largely similar results to the old option but shows +$\psi(z_0/L)$. The scheme gives largely similar results to the old option but shows some improvement in the transition periods. Both the MM5-based schemes also have thermal roughness length options in addition to convective velocity. The thermal roughness length allows for a reduced roughness length in @@ -467,8 +407,13 @@ \subsection{QNSE similarity theory} mixing properties of stratified turbulence. The theory provides effective viscosity and diffusivity as a function of Richardson number that are used in the stable PBL regime and the surface layer similarity functions for stable conditions. The scheme is also -distinguished by have a Parndtl number of 0.7 instead of a value near 1. Unstable conditions -follow the MYJ functions, and much of the rest of the code is based on MYJ. +distinguished by have a Prandtl number of 0.7 instead of a value near 1. + +\subsection{MYNN surface layer} + +The MYNN surface-layer scheme includes several forms for stability functions with Dyer and Hicks +used by default. There are also several options for handling thermal roughness lengths and the +fluxes over water. \subsection{Similarity theory (PX)} @@ -480,6 +425,26 @@ \subsection{TEMF surface layer} \citep{angevine10}. The similarity functions used in this scheme are functions of Ri for stable conditions, and consider unstable conditions similarly to neutral conditions. +\subsection{Similarity theory (MM5) -- old version} + +This scheme is now superseded by the Revised MM5 similarity theory scheme +(see next subsection) but still available as an option. +This scheme uses stability functions from \citet{paulson70}, \citet{dyer70}, +and \citet{webb70} +to compute surface exchange coefficients for heat, moisture, and momentum. +A convective velocity following \citet{beljaars94} is used to enhance surface +fluxes of heat and moisture. A Charnock relation relates +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. 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. + + \section{Land-Surface Model and Other Surface Options} @@ -503,24 +468,6 @@ \section{Land-Surface Model and Other Surface Options} Table \ref{land table} summarizes the basic features of the land-surface treatments in {\wrf}. -\begin{table} -\caption{Land Surface Options} -\label{land table} -$$\vbox -{\halign{#\hfil \qquad & \hfil#\hfil \qquad & -#\hfil \qquad & #\hfil \qquad & #\hfil \cr -\multispan4\hrulefill \cr -Scheme & Vegetation & Soil & Snow \cr - & Processes & Variables (Layers) & Scheme \cr -\multispan4\hrulefill \cr -5-layer & N & Temperature (5) & none \cr -Noah & Y & Temperature, Water+Ice, Water (4) & 1-layer, fractional \cr -RUC & Y & Temperature, Ice, Water + Ice (6) & multi-layer \cr -Pleim-Xiu & Y & Temperature, Moisture (2) & input only \cr -\multispan4\hrulefill \cr -}}$$ -\end{table} - \subsection{5-layer thermal diffusion} This simple LSM is based on the MM5 5-layer soil temperature model. Layers are @@ -655,6 +602,14 @@ \subsection{CLM4.5 Lake Model} lake depth data where available (WPS has a bathymetry dataset for many lakes), and it also can be used with user defined lake points and lake depth in WRF. +\subsection{Sea-Ice Treatment} + +Most of the land models (CLM, Noah, NoahMP, RUC, PX) also consider sea-ice surfaces, and the model surface-layer schemes +can also consider fractional sea-ice cover within a grid-cell where the fluxes are combined with those of open water. +Usually the depth is considered fixed and the fraction may be updated with the sea-surface temperature periodically +during the simulation as the model has no prognostic equation for sea-ice amounts. +The sea ice in Noah and NoahMP considers 4 layers each 1 meter thick for the sea-ice energy budget. + \subsection{Updating Lower Boundary Conditions} For long simulation periods, in excess of about a week, as in applications such as @@ -691,40 +646,16 @@ \section{Planetary Boundary Layer} in these situations the scheme should be replaced by a fully three-dimensional local sub-grid turbulence scheme such as the TKE diffusion scheme (Section \ref{tke_section}.) -Table \ref{pbl table} summarizes the basic features of the PBL schemes -in {\wrf}. - - -\begin{table} -\caption{Planetary Boundary Layer Options} -\label{pbl table} -$$\vbox -{\halign{#\hfil \quad & #\hfil \quad & -#\hfil \quad & #\hfil \quad & #\hfil \cr -\multispan4\hrulefill \cr -Scheme & Unstable PBL & Entrainment & PBL Top \cr - & Mixing & treatment & \cr -\multispan4\hrulefill \cr -MRF & K profile + countergradient term & part of PBL mixing & from critical bulk $Ri$ \cr -YSU & K profile + countergradient term & explicit term & from buoyancy profile \cr -MYJ & K from prognostic TKE & part of PBL mixing & from TKE \cr -ACM2 & transilient mixing up, local K down & part of PBL mixing & from critical bulk $Ri$ \cr -\multispan4\hrulefill \cr -}}$$ -\end{table} - -\subsection {Medium Range Forecast Model (MRF) PBL} - -The scheme is described by \citet{hong96}. -This PBL scheme employs a so-called counter-gradient flux for heat and moisture -in unstable conditions. It uses enhanced vertical flux coefficients in the PBL, -and the PBL height is determined from a critical bulk Richardson number. -It handles vertical diffusion with an implicit local scheme, and it is based -on local $Ri$ in the free atmosphere. \subsection{Yonsei University (YSU) PBL} -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, in 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-layer profile, whereas there is a pronounced over-stable structure 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 between the boundary layer and precipitation physics. In version 3.0, an enhanced stable boundary-layer diffusion algorithm \citep{hong07} is also devised that allows deeper mixing in windier conditions. +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, in 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-layer profile, whereas there is a pronounced over-stable structure 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 between the boundary layer and precipitation physics. + +Topographic drag effects were added as an option to this PBL scheme by \citet{jimenez12a} and improved by \citet{lorente16} which modifies the +model drag according to sub-grid variance in terrain elevation and also resolved local variability. + + Top-down mixing was +added as an option \citep{wilson18} to allow for radiative-driven downward mixing that helps the life-cycle of stratocumulus clouds and fog. \subsection{Mellor-Yamada-Janjic (MYJ) PBL} @@ -741,11 +672,85 @@ \subsection{Mellor-Yamada-Janjic (MYJ) PBL} smaller than that corresponding to the regime of vanishing turbulence. The TKE production/dissipation differential equation is solved iteratively. The empirical constants have been revised as well \citep{janjic96,janjic02}. +This scheme is also enabled with fluxes from layers other than the surface for use with +the BEP and BEM urban models. + +\subsection{Quasi-Normal Scale Elimination (QNSE) scheme with EDMF} + +The QNSE scheme for the stable boundary is combined with an eddy-diffusivity mass-flux (EDMF) scheme for thermals in the unstable +boundary layer. The QNSE scheme \citep{sukoriansky05} is a theoretically derived scheme for the stably stratified boundary layer. +The scheme is a Mellor-Yamada TKE-based method that modifies the vertical diffusion with a function of the Richardson number +to incorporate the theory. For unstable conditions an eddy-diffusivity mass flux approach has +been adopted \citep{pergaud09} that considers non-local thermals which may include cumulus clouds in additional to local +TKE-based mixing. The shallow convective mass-flux scheme is called after the local TKE-based calculations in the QNSE scheme. + +\subsection{Mellor-Yamada-Nakanishi-Niino (MYNN) Levels 2.5 and 3} + +The MYNN2.5 and MYNN3 schemes \citep{nakanishi06,nakanishi09} are TKE-based schemes where Level 2.5 predicts +TKE as an extra prognostic variable, while Level 3 adds variances of potential temperature, moisture and their covariance. +However in both schemes only TKE is advected, but the TKE advection option is a unique aspect of this scheme. The scheme is +used operationally as part of the NOAA HRRR physics and has included many newer updates including shallow convection +and EDMF options \citep{olson19} as well as updated options for computing mixing length scales. The schemes can be used +with the MYNN or MM5 surface-layer schemes. + +An additional option related to the MYNN PBL scheme is a wind-farm parameterization \citep{fitch12} that accounts for +the additional drag and turbulence generation by wind-farm rotors.The scheme is customizable to different rotor characteristics +as a function of wind speed. \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. +\subsection{Bougeault-Lacarrere PBL} + +The BouLac PBL \citep{bougeault89} is a 1.5-order (level 2.5) scheme with a prognostic TKE equation and a method of calculating length scales +that defines both upwards and downwards lengths scales affected by the PBL top and ground and uses the lesser of these for a length scale. +The scheme has also been adapted for use with the BEP and BEM urban models that can represent buildings higher than the lowest model level +thickness. + +\subsection{University of Washington (UW) PBL} + +The scheme of \citet{bretherton09} is part of the CAM climate model physics suite. It is a TKE-based scheme that includes a +moist turbulence parameterization. The TKE is a diagnostic quantity. The scheme defines convectively mixed sets of layers +and includes a method for explicit entrainment related to the convective velocity. + +\subsection{Total Energy Mass Flux (TEMF) PBL} + +\citet{angevine10} use total energy rather than TKE as a prognostic variable. This includes potential energy in addition. +The scheme includes the effects of shallow cumulus convection and is also coupled with its own surface-layer scheme. + +\subsection{Shin-Hong PBL} + +\citet{shin15} have developed a scale-aware PBL option based on the YSU PBL scheme. At larger grid sizes it +resembles YSU, but as the grid size becomes much less than the PBL depth, the nonlocal term is reduced in +strength to allow the resolved scales to do a fraction of the transport consistent with resolution. + +\subsection{Grenier-Bretherton-McCaa (GBM) PBL} + +This is a moist PBL scheme \citet{grenier01} that can also represent cloud-topped boundary layers such as marine stratocumulus. +The scheme includes a TKE equation and the effect of cloud-top radiative cooling that modifies the entrainment +and TKE, an important process for stratocumulus clouds. + + +\subsection {Medium Range Forecast Model (MRF) PBL} + +The scheme is described by \citet{hong96}. +This PBL scheme employs a so-called counter-gradient flux for heat and moisture +in unstable conditions. It uses enhanced vertical flux coefficients in the PBL, +and the PBL height is determined from a critical bulk Richardson number. +It handles vertical diffusion with an implicit local scheme, and it is based +on local $Ri$ in the free atmosphere. + +\subsection {Gravity Wave Drag} + +For grid sizes that exceed about 10 km and for longer simulations that include significant orography, +gravity wave drag may be an important process to include. This accounts for the momentum flux +due to unresolved mountain waves that may affect jet-stream level winds, and mountains also +have a low-level flow-blocking effect. WRF has an option for these effects \citep{choi15} that uses +sub-grid orographic data provided by {\em geogrid}. The sub-grid information includes direction-sensitive +statistics related to the orientation of the orography. + + \section{Atmospheric Radiation} The radiation schemes provide atmospheric heating due to radiative @@ -772,27 +777,6 @@ \section{Atmospheric Radiation} Table \ref{radiation table} summarizes the basic features of the radiation schemes in the {\wrf}. -\begin{table} -\caption{Radiation Options} -\label{radiation table} -$$\vbox -{\halign{#\hfil \qquad & \hfil#\hfil \qquad & -\hfil#\hfil \qquad & #\hfil \qquad & #\hfil \cr -\multispan4\hrulefill \cr -Scheme & Longwave/ & Spectral & CO$_2$, O$_3$, clouds \cr - & Shortwave & Bands & \cr -\multispan4\hrulefill \cr -RRTM & LW & 16 & CO$_2$, O$_3$, clouds \cr -GFDL LW & LW & 14 & CO$_2$, O$_3$, clouds \cr -CAM3 LW & LW & 2 & CO$_2$, O$_3$, clouds \cr -GFDL SW & SW & 12 & CO$_2$, O$_3$, clouds \cr -MM5 SW & SW & 1 & clouds \cr -Goddard & SW & 11 & CO$_2$, O$_3$, clouds \cr -CAM3 SW & SW & 19 & CO$_2$, O$_3$, clouds \cr -\multispan4\hrulefill \cr -}}$$ -\end{table} - \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} This RRTM, which is taken from MM5, is based on \citet{mlawer97} @@ -858,39 +842,6 @@ \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} \section {Physics Interactions} -\begin{table} -\caption{Physics Interactions. Columns correspond to -model physical processes: radiation (Rad), -microphysics (MP), cumulus parameterization (CP), planetary boundary layer/vertical diffusion -(PBL), and surface physics (Sfc). Rows corresponds to -model variables where {\em i} and {\em o} indicate whether a variable is -input or output (updated) by a physical process.} -\label{physics interaction table} -$$\vbox -{\halign{#\hfil \qquad & #\hfil \qquad & \hfil#\hfil \qquad & \hfil#\hfil -\qquad & \hfil#\hfil \qquad & \hfil#\hfil \qquad & \hfil#\hfil \cr -\multispan7\hrulefill \cr - & & Rad & MP & CP & PBL & Sfc \cr -\multispan7\hrulefill \cr -Atmospheric & Momentum & & & i & io & \cr -State or & Pot. Temp. & io & io & io & io & \cr -Tendencies & Water Vapor & i & io & io & io & \cr - & Cloud & i & io & o & io & \cr - & Precip & i & io & o & & \cr -\multispan7\hrulefill \cr -Surface & Longwave Up & i & & & & o \cr -Fluxes & Longwave Down & o & & & & i \cr - & Shortwave Up & i & & & & o \cr - & Shortwave Down & o & & & & i \cr - & Sfc Convective Rain & & & o & & i \cr - & Sfc Resolved Rain & & o & & & i \cr - & Heat Flux & & & & i & o \cr - & Moisture Flux & & & & i & o \cr - & Surface Stress & & & & i & o \cr -\multispan7\hrulefill \cr -}}$$ -\end{table} - While the model physics parameterizations are categorized in a modular way, it should be noted that there are many interactions between them via the model state variables (potential temperature, moisture, wind, etc.) From 9baaa7baaf36282e0b0e6b59d721e45274d97cda Mon Sep 17 00:00:00 2001 From: Jimy Dudhia Date: Thu, 3 Jan 2019 13:30:50 -0700 Subject: [PATCH 6/7] add radiation sections --- description.bbl | 36 ++++++++++++++++-- physics.tex | 99 +++++++++++++++++++++++++++++++++---------------- 2 files changed, 100 insertions(+), 35 deletions(-) diff --git a/description.bbl b/description.bbl index 68a5b72..fb3bdbd 100644 --- a/description.bbl +++ b/description.bbl @@ -101,6 +101,11 @@ Carpenter, R. L., K. K. Droegemeier, P. R. Woodward, and C. E. Hane, 1990: Application of the Piecewise Parabolic Method (PPM) to meteorological modeling. {\em Mon. Wea. Rev.}, {\bf 118}, 586--612. + + \bibitem[Cavallo et al.(2011)]{cavallo11}% + Cavallo, S. M., J. Dudhia and C. Snyder, 2011: A multi-layer upper boundary condition for longwave +radiative flux to correct temperature biases in a mesoscale model. + {\em Mon. Wea. Rev.}, {\bf 139}, 1952--1959. \bibitem[Chen and Dudhia(2001)]{chendudhia01}% Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/ hydrology model @@ -139,6 +144,14 @@ Chou M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, 3, 85pp. +\bibitem[Chou and Suarez(1999)]{chou99}% +Chou M.-D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. + NASA Tech. Memo. 104606, 15, 40pp. + + \bibitem[Chou et al.(2001)]{chou01}% + Chou, M. D., M. J. Suarez, X. Z. Liang, and M. M. H. Yan, 2001: A thermal infrared radiation parameterization for atmospheric studies. + NASA Tech. Memo. 104606, 19, 68pp. + \bibitem[Collins et al.(2004)]{collins04}% Collins, W.D. et al., 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0), @@ -221,6 +234,10 @@ Fisher, M., 2003: Background error covariance modeling. \bibitem[Fletcher(1962)]{fletcher62}% Fletcher, N. H., 1962: {\em The Physics Of Rain Clouds.} Cambridge University Press, 386 pp. + +\bibitem[Fu and Liou(1992)]{fu92}% +Fu, Q., and K. N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. +{\em J. Atmos. Sci.}, {\bf 49}, 2139--2156. \bibitem[Gao et al.(2015)]{gao15}% Gao, F., X.-Y. Huang, N. A. Jacobs, and H. Wang, 2015: Assimilation of wind speed and direction observations: @@ -250,6 +267,10 @@ within the WRF model. {\em Atmos. Environ.}, {\bf 39}, 6957-6975. Grenier, H., and C. S. Bretherton, 2001: A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. {\em Mon. Wea. Rev.}, {\em 129}, 357--377. +\bibitem[Gu et al.(2011)]{gu11}% +Gu, Y., K. N. Liou, S. C. Ou, and R. Fovell, 2011: Cirrus cloud simulations using WRF with improved radiation parameterization and increased vertical resolution. +{\em J. Geophys. Res.}, {\bf 116}, D06119. + \bibitem[{Hacker et~al.(2011)Hacker, Snyder, Ha,, and Pocernich}]{Ha11a} Hacker, J.~P., C.~Snyder, S.-Y. Ha, and M.~Pocernich, 2011: Linear and nonlinear response to parameter variations in a mesoscale model. @@ -315,6 +336,11 @@ Huang, X.-Y., F. Gao, N. A. Jacobs, and H. Wang, 2013: Assimilation of wind spee and results from idealised experiments. {\em Tellus A}, {\bf 65}, 19936, doi:10.3402/tellusa.v65i0.19936. +\bibitem[Iacono et al.(2008)]{iacono08}% +Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: +Calculations with the AER radiative transfer models. +{\em J. Geophys. Res.}, {\bf 113}, D13103. + \bibitem[Ide et al.(1997)]{ide97}% Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. @@ -512,7 +538,8 @@ Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. {\em Quart. J. Roy. Meteor. Soc.}, {\bf 112}, 1177--1194. \bibitem[Lorente-Plazas et al.(2016)]{lorente16}% - Lorente-Plazas, R., P. A. Jimenez, J. Dudhia, and J. P. Montavez, 2016: Evaluating and improving the impact of the atmospheric stability and orography on surface winds in the WRF model. + Lorente-Plazas, R., P. A. Jimenez, J. Dudhia, and J. P. Montavez, 2016: Evaluating and improving the +impact of the atmospheric stability and orography on surface winds in the WRF model. {\em Mon. Wea. Rev.}, {\bf 144}, 2685--2693. \bibitem[Lynch and Huang(1992)]{lynchhuang92}%, @@ -632,7 +659,8 @@ Noilhan, J., and S. Planton, 1989: Oleson, K. W., et al., 2010: Technical description of version 4 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR. 266 pp. \bibitem[Olson et al.(2019)]{olson19}% -J. B. Olson, J. S. Kenyon, W.. A. Angevine, J. M. Brown, M. Pagowski, and K. Su?elj, 2019: A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF-ARW. NOAA Technical Memorandum (in preparation). +J. B. Olson, J. S. Kenyon, W.. A. Angevine, J. M. Brown, M. Pagowski, and K. Su?elj, 2019: A Description of the MYNN-EDMF Scheme and +the Coupling to Other Components in WRF-ARW. NOAA Technical Memorandum (in preparation). \bibitem[Ooyama(1990)]{ooyama90}% Ooyama K. V., 1990: A thermodynamic foundation for modeling the moist atmosphere, @@ -659,7 +687,9 @@ Paulson, C. A., 1970: The mathematical representation of wind speed and {\em J. Appl. Meteor.}, {\bf 9}, 857--861. \bibitem[Pergaud et al.(2009)]{pergaud09}% -Pergaud J, V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry thermal and shallow cumuli for mesoscale numerical weather prediction. {\em Boundary-Layer Meteorol.}, {\bf 132}, 83--106. +Pergaud J, V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry +thermal and shallow cumuli for mesoscale numerical weather prediction. +{\em Boundary-Layer Meteorol.}, {\bf 132}, 83--106. \bibitem[Pleim(2006)]{pleim06}% Pleim, J. E., 2006: diff --git a/physics.tex b/physics.tex index 7e6c2bb..7e77280 100644 --- a/physics.tex +++ b/physics.tex @@ -765,17 +765,24 @@ \section{Atmospheric Radiation} and at surfaces. For shortwave radiation, the upward flux is the reflection due to surface albedo. For higher resolutions, the shortwave schemes can represent slope effects that modify the surface downward shortwave flux according to slope aspect angles. +There are also diagnostic outputs of the diffuse and direct components of solar radiation +at the surface. Diagnostics for the RRTMG and CAM3 options also include +TOA/surface, longwave/shortwave, clearsky/allsky, upward/downward accumulated +fluxes for radiation budgets. + Within the atmosphere the radiation responds to model-predicted cloud and water vapor distributions, as well as specified carbon dioxide, ozone, and (optionally) trace gas concentrations. All the radiation schemes in WRF currently are column (one-dimensional) schemes, so each column is treated independently, and the fluxes correspond to those in -infinite horizontally uniform planes, which +infinite horizontally uniform planes with cloud fractions at leach layer, which is a good approximation if the vertical thickness of the model layers is much less than the horizontal grid length. This assumption would become less accurate at high horizontal resolution. -Table \ref{radiation table} summarizes the basic features of the radiation schemes -in the {\wrf}. + +In addition to radiative transfer schemes listed below, WRF also has idealized +temperature relaxation methods for the Held-Suarez global test case and the +idealized tropical cyclone case. \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} @@ -785,7 +792,36 @@ \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} to water vapor, ozone, CO$_2$, and trace gases (if present), as well as accounting for cloud optical depth. -\subsection {Eta Geophysical Fluid Dynamics Laboratory (GFDL) Longwave} +\subsection {CAM3 Longwave and Shortwave} + +These are spectral-band longwave and shortwave schemes used in the NCAR Community Atmosphere +Model (CAM 3.0) for climate simulations. It has the potential to handle +several trace gases including time variation as in climate-change scenarios. +It interacts with resolved clouds and cloud fractions, + and is documented fully by \citet{collins04}. 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. + + In more recent versions of CAM, this has been replaced by RRTMG radiation. + +\subsection {RRTMG Longwave and Shortwave} + +The Rapid Radiative Transfer Model for GCMs (RRTMG, \citet{iacono08}) is a state-of-the-art +widely used radiative model for weather and climate applications both globally +and regionally. The schemes use spectral bands and the k-distribution method of +integration with look-up tables for efficiency. For clouds with cloud fractions that vary vertically it uses the +Monte Carlo Independent Column Approximation (MCICA) together with a +maximum-random overlap assumption. It can also make use of effective radii +of cloud water, ice and snow if they come from the microphysics or it will use its +own assumptions if these are not provided. It includes the effect +of trace gases and has an option for their time variation for climate projections. +For ozone there is a global monthly climatology option that comes from the CAM3 data. +Aerosols can use a global monthly climatology (Tegen or Eidhammer/Thompson) or can come from +optical properties computed by WRF-Chem or can be specified/input in other ways. The longwave scheme has been +modified at the top-of-atmosphere to account for the significant downward flux originating from +above the model top \citep{cavallo11}. + +\subsection {Eta Geophysical Fluid Dynamics Laboratory (GFDL) Longwave and Shortwave} This longwave radiation scheme is from GFDL. It follows the simplified exchange method of \citet{fels75} and \citet{schwarzkopf91}, @@ -797,16 +833,7 @@ \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} 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} +The shortwave radiation is a GFDL version of the \citet{lacis74} parameterization. Effects of atmospheric water vapor, ozone \citep[both from][]{lacis74}, and carbon dioxide \citep{sasamori72} are employed. Clouds are randomly overlapped. Shortwave calculations are made @@ -815,30 +842,38 @@ \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} \subsection {MM5 (Dudhia) Shortwave} -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, +This scheme is based on \citet{dudhia89} and is taken from MM5. It has a simple +downward integration of solar flux, accounting for tunable clear-air scattering, as well as water vapor absorption \citep{lacis74}, and cloud albedo and absorption. -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. +It uses look-up tables for clouds from \citet{stephens78} but does not use +sub-grid cloud fractions, only uniformly clear or cloudy within a model layer. The scheme +has no ozone, so it should not be used with model tops in the mid-stratosphere. -\subsection {Goddard Shortwave} +\subsection {Old Goddard Shortwave} This scheme is based on \citet{chou94}. It has a total of 11 spectral bands and considers diffuse and direct solar radiation components in a two-stream 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. +with several climatological one-dimensional profiles available. The scheme can also take +aerosol optical properties provided by WRF-Chem. + +\subsection {New Goddard Longwave and Shortwave} + +These spectral-band schemes provided by NASA Goddard are advanced and relatively efficient while also being accurate. +The shortwave and longwave schemes are based on \citet{chou99} and \citet{chou01} +respectively. It does not interact with WRF-Chem but 2D aerosol optical depth +information can be used for aerosol effects. It includes trace gases and uses +ozone climatology profiles. Cloud fractions in layers are also accounted for with +low, middle and high layers maximally overlapped within these grouped layers and randomly between +them. + +\subsection {Fu-Liou-Gu (FLG) Longwave and Shortwave} + +Sophisticated spectral band schemes provided by UCLA \citep{gu11,fu92}. +Contains capabilities for fractional clouds and aerosols, but these are off by default, +so clouds are either present or absent in a layer. Ozone uses profiles similar to the +Goddard schemes. CO2 and trace gases are specified. +Correlated-k distribution method for longwave scheme. \section {Physics Interactions} From 533d1abbbb1207a8152a73be5b551fbe27000336 Mon Sep 17 00:00:00 2001 From: Wei Wang Date: Tue, 8 Jan 2019 16:14:16 -0700 Subject: [PATCH 7/7] make fdda a separate chapter --- description.tex | 1 + fdda.tex | 68 ++++++++++++++++++++++++++++++++++++++++++++++ physics.tex | 72 +------------------------------------------------ 3 files changed, 70 insertions(+), 71 deletions(-) create mode 100644 fdda.tex diff --git a/description.tex b/description.tex index e7f65d3..b13f04e 100644 --- a/description.tex +++ b/description.tex @@ -79,6 +79,7 @@ \include{nest} \include{physics} \include{stoch} +\include{fdda} % Next two lines MUST come right before the last chapter include %\addtocontents{toc}{\contentsline{chapter}{}{}{}} %\addtocontents{toc}{\contentsline{chapter}{Appendices}{}{}} diff --git a/fdda.tex b/fdda.tex new file mode 100644 index 0000000..0c2d01d --- /dev/null +++ b/fdda.tex @@ -0,0 +1,68 @@ +\chapter{Four-Dimensional Data Assimilation} +\label{fdda_chap} + +Four-dimensional data assimilation (FDDA), also known as nudging, is a method of keeping +simulations close to analyses and/or observations or forcing data 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. + +\section{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. + +\section{Observational or Station Nudging} + +The observation-nudging FDDA capability allows the model 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 follows: + +(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 wind 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 in 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 corresponding 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, the 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 has been added into WRF since the WRF V3.0 release. + diff --git a/physics.tex b/physics.tex index 7e77280..2eb2ecd 100644 --- a/physics.tex +++ b/physics.tex @@ -8,10 +8,7 @@ \chapter{Physics} (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}. 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. +is described in Chapter \ref{filter_chap}. 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 @@ -911,70 +908,3 @@ \subsection{Rapid Radiative Transfer Model (RRTM) Longwave} 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 the model 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 follows: - -(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 wind 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 in 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 corresponding 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, the 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 has been added into WRF since the WRF V3.0 release. -