WRF Model Physics Options and References



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Micro Physics Options (mp_physics)

Kessler Scheme option 1 Kessler, E., 1969: On the distribution and continuity of water substance in atmoshperic circulations. Meteor. Monogr., 32, Amer. Meteor. Soc.
Lin et al. Scheme option 2 Lin, Yuh–Lang, Richard D. Farley, and Harold D. Orville, 1983: Bulk Parameterization of the Snow Field in a Cloud Model. J.Climate Appl. Met., 22, 1065–1092.
WRF Single–moment 3–class and 5–class Schemes options 3 & 4 Hong, Song–You, Jimy Dudhia, and Shu–Hua Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120.
Eta (Ferrier) Scheme option 5 NOAA, cited 2001: National Oceanic and Atmospheric Administration Changes to the NCEP Meso Eta Analysis and Forecast System: Increase in resolution, new cloud microphysics, modified precipitation assimilation, modified 3DVAR analysis. [Available online at http://www.emc.ncep.noaa.gov/mmb/mmbpll/eta12tpb/.]
WRF Single–moment 6–class Scheme option 6 Hong, S.–Y., and J.–O. J. Lim, 2006: The WRF single–moment 6–class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.
Goddard Scheme option 7 Tao, Wei–Kuo, Joanne Simpson, Michael McCumber, 1989: An Ice–Water Saturation Adjustment. Mon. Wea. Rev., 117, 231–235.
Thompson Scheme option 8 Thompson, Gregory, Paul R. Field, Roy M. Rasmussen, William D. Hall, 2008: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Mon. Wea. Rev., 136, 5095–5115.
Milbrandt–Yau Double Moment Scheme option 9 Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051–3064.

Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part II: A proposed three–moment closure and scheme description. J. Atmos. Sci., 62, 3065–3081.
Morrison 2–moment Scheme option 10 Morrison, H., G. Thompson, V. Tatarskii, 2009: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One– and Two–Moment Schemes. Mon. Wea. Rev., 137, 991–1007.
CAM V5.1 2–moment 5–class Scheme option 11 Eaton, Brian. "User’s Guide to the Community Atmosphere Model CAM-5.1." NCAR. URL http://www.cesm.ucar.edu/models/cesm1.0/cam (2011).
Stony–Brook University Scheme option 13 Lin, Yanluan, and Brian A. Colle, 2011: A new bulk microphysical scheme that includes riming intensity and temperature–dependent ice characteristics. Mon. Wea. Rev., 139, 1013–1035.
WRF Double Moment 5–class and 6–class Schemes options 14 & 16 Lim, K.–S. S., and S.–Y. Hong, 2010: Development of an effective double–moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612.
NSSL 2–moment Scheme and 2–moment Scheme with CCN Prediction options 17 & 18 Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two–moment bulk microphysics. J. Atmos. Sci., 67, 171–194.
NSSL 1–moment 7–class Scheme option 19 This is a single–moment version of the NSSL 2–moment scheme (see above). No paper is available yet for this scheme.
NSSL 1–moment 6–class Scheme option 21 Gilmore, Matthew S., Jerry M. Straka, and Erik N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 2610–2627.
Aerosol–aware Thompson Scheme option 28 Thompson, Gregory, and Trude Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71.10, 3636-3658.
HUJI SBM (Fast) option 30 Khain, A., B. Lynn, and J. Dudhia, 2010: Aerosol effects on intensity of landfalling hurricanes as seen from simulations with the WRF model with spectral bin microphysics. J. Atmos. Sci., 67, 365–384.
HUJI SBM (Full) option 32 Khain, A., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips, 2004: Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: model description and possible applications. J. Atmos. Sci., 61, 2963–2982.


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Planetary Boundary Layer (PBL) Physics Options (bl_pbl_physics)

Yonsei University Scheme (YSU) option 1 Hong, Song–You, Yign Noh, Jimy Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.
Mellor–Yamada–Janjic Scheme (MYJ) option 2 Janjic, Zavisa I., 1994: The Step–Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.
NCEP Global Forecast System Scheme option 3  
Quasi–normal Scale Elimination (QNSE) Scheme option 4 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. Bound.–Layer Meteor., 117, 231–257.
Mellor–Yamada Nakanishi Niino (MYNN) Level 2.5 and Level 3 Schemes options 5 & 6 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. Bound. Layer Meteor. 119, 397–407.

Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895–912.
Asymmetric Convection Model 2 Scheme (ACM2) option 7 Pleim, Jonathan E., 2007: A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing. J. Appl. Meteor. Climatol., 46, 1383–1395.
Bougeault–Lacarrere Scheme (BouLac) option 8 Bougeault, P., P. Lacarrere, 1989: Parameterization of Orography–Induced Turbulence in a Mesobeta––Scale Model. Mon. Wea. Rev., 117, 1872–1890.
University of Washington (TKE) Boundary Layer Scheme option 9 Bretherton, Christopher S., and Sungsu Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. J. Climate, 22, 3422–3448.
TEMF Surface Layer Scheme option 10 Angevine, Wayne M., Hongli Jiang, and Thorsten Mauritsen, 2010: Performance of an eddy diffusivity–mass flux scheme for shallow cumulus boundary layers. Mon. Wea. Rev., 138, 2895–2912.
Shin-Hong Scale–aware Scheme option 11 Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250-271.
Grenier–Bretherton–McCaa Scheme option 12 Grenier, Herve, and Christopher S. Bretherton, 2001: A moist PBL parameterization for large–scale models and its application to subtropical cloud–topped marine boundary layers. Mon. Wea. Rev., 129, 357–377.
MRF Scheme option 99 Hong, S.–Y., and H.–L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium–range forecast model. Mon. Wea. Rev., 124, 2322–2339.
Gravity Wave Drag gwd_opt = 1 Hong, Song–You, Jung Choi, Eun-Chul Chang, Hoon Park, and Young-Joon Kim, 2008: Lower-tropospheric enhancement of gravity wave drag in a global spectral atmospheric forecast model. Wea. Forecasting, 23, 523–531.

Kim, Young-Joon, and Akio Arakawa, 1995: Improvement of orographic gravity wave parameterization using a mesoscale gravity wave model. J. Atmos. Sci., 52, 1875–1902.

Choi H, Hong S, 2015: An updated subgrid orographic parameterization for global atmospheric forecast models. J. Geophys. Res., 120
doi:10.1002/2015JD024230
Wind–farm (drag) Surface Layer Parameterization Scheme windturbine_spec Fitch, Anna C., Joseph B. Olson, Julie K. Lundquist, Jimy Dudhia, Alok K. Gupta, John Michalakes, and Idar Barstad, 2012: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Wea. Rev., 140, 3017–3038.
FogDES Scheme
(details)
grav_settling = 2 Katata, G. (2014), Fogwater deposition modeling for terrestrial ecosystems: A review of developments and measurements, J. Geophys. Res. Atmos., 119, 8137–8159


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Cumulus Parameterization Options (cu_physics)

Kain–Fritsch Scheme option 1 Kain, John S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181.
Moisture–advection–based Trigger for Kain–Fritsch Cumulus Scheme kfeta_trigger = 2 Ma, Lei–Ming, and Zhe–Min Tan, 2009: Improving the behavior of the cumulus parameterization for tropical cyclone prediction: Convection trigger. Atmos. Res., 92, 190–211.
RH–dependent Additional Perturbation to option 1 for the Kain-Fritsch Scheme kfeta_trigger = 3
Betts–Miller–Janjic Scheme option 2 Janjic, Zavisa I., 1994: The Step–Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.
Grell–Freitas Ensemble Scheme option 3 Grell, G. A. and Freitas, S. R., 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233-5250, doi:10.5194/acp-14-5233-2014.
Old Simplified Arakawa–Schubert Scheme option 4 Pan, H. L., and W. S. Wu., 1995: Implementing a mass flux convective parameterization package for the NMC medium range forecast model. NMC office note, 409.40, 20–233.
Grell 3D Ensemble Scheme option 5 Grell, Georg A., 1993: Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations. Mon. Wea. Rev., 121, 764–787.

Grell, G. A, D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693
Tiedtke Scheme option 6 Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large–scale models. Mon. Wea. Rev., 117, 1779–1800.

Zhang, Chunxi, Yuqing Wang, and Kevin Hamilton, 2011: Improved representation of boundary layer clouds over the southeast pacific in ARW–WRF using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 3489–3513.
Zhang–McFarlane Scheme option 7 Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.– Ocean, 33, 407–446.
Multi–scale Kain–Fritsch Scheme option 11 Zheng, Yue, K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev.,117-3, 833-860.
New Simplified Arakawa–Schubert Scheme (Standard and for HWRF) options 14 & 84 Han, Jongil and Hua–Lu Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520–533.
Grell–Devenyi (GD) Ensemble Scheme option 93 Grell, G. A., and D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29(14).
Old Kain–Fritsch Scheme option 99 Kain, John S., and J. Michael Fritsch, 1990: A one–dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802.


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Shortwave (ra_sw_physics) and Longwave (ra_lw_physics) Options

Dudhia Shortwave Scheme option 1 Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two–dimensional model. J. Atmos. Sci., 46, 3077–3107.
RRTM Longwave Scheme option 1 Mlawer, Eli. J., Steven. J. Taubman, Patrick. D. Brown, M. J. Iacono, and S. A. Clough (1997), Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. J. Geophys. Res., 102, 16663–16682.
Goddard Shortwave Scheme option 2 Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Memo. 104606, 15, 40 pp.

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, 68 pp.
CAM Shortwave and Longwave Schemes option 3 Collins, William D., et al., 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN–464+STR. 214 pp.
RRTMG Shortwave and Longwave Schemes option 4 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. J. Geophys. Res., 113, D13103.
New Goddard Shortwave and Longwave Schemes option 5 Chou, Ming–Dah, and Max J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Memo 104606 40.
Fu–Liou–Gu Shortwave and Longwave Schemes option 7 Gu, Y., Liou, K. N., Ou, S. C., and Fovell, R., 2011: Cirrus cloud simulations using WRF with improved radiation parameterization and increased vertical resolution. J. Geophys. Res., 116, D06119.

Fu, Qiang, and K. N. Liou, 1992: On the correlated k–distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 2139–2156.
Held–Suarez Relaxation Longwave Scheme option 31
GFDL Shortwave and Longwave Schemes option 99 Fels, Stephen. B., and M. D. Schwarzkopf, 1981, An efficient, accurate algorithm for calculating CO2 15 ?m band cooling rates. J. Geophys. Res., 86, 1205–1232.


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Land Surface Options (sf_surface_physics)

5–layer Thermal Diffusion Scheme option 1 Dudhia, Jimy, 1996: A multi-layer soil temperature model for MM5. the Sixth PSU/NCAR Mesoscale Model Users' Workshop.
Unified Noah Land Surface Model option 2 Tewari, M., F. Chen, W. Wang, J. Dudhia, M. A. LeMone, K. Mitchell, M. Ek, G. Gayno, J. Wegiel, and R. H. Cuenca, 2004: Implementation and verification of the unified NOAH land surface model in the WRF model. 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, pp. 11–15.
RUC Land Surface Model option 3 Benjamin, Stanley G., Georg A. Grell, John M. Brown, and Tatiana G. Smirnova, 2004: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Wea. Rev., 132, 473-494.
Noah–MP Land Surface Model option 4 Niu, Guo–Yue, Zong–Liang Yang, Kenneth E. Mitchell, Fei Chen, Michael B. Ek, Michael Barlage, Anil Kumar, Kevin Manning, Dev Niyogi, Enrique Rosero, Mukul Tewari, Youlong Xia, 2011: The community Noah land surface model with multiparameterization options (Noah–MP): 1. Model description and evaluation with local–scale measurements. J. Geophys. Res., 116, D12109.

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. J. Geophys. Res., 116, D12110.
Community Land Model Version 4 (CLM4) option 5 Oleson, Keith W., et al., 2010: Technical description of version 4 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN–478+STR. 266 pp.

Lawrence, D. M., et al., 2011: Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001.

An earlier version of CLM has been quantitatively evaluated within WRF, referenced here:

Jin, J., and L. Wen, 2012: Evaluation of snowmelt simulation in the Weather Research and Forecasting model. J. Geophys. Res., 117, D10110.

Lu, Y., and L. M. Kueppers, 2012: Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model (Weather Research and Forecasting Model 3–Community Land Model 3.5). J. Geophys. Res., 117, D06111.

Subin, Z. M., W. J. Riley, J. Jin, D. S. Christianson, M. S. Torn, and L. M. Kueppers, 2011: Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land surface model (WRF3–CLM3.5). Earth Interac., 15, 15.
Pleim–Xiu Land Surface Model option 7 Noilan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.

Pleim, J. E., and A. Xiu, 1995: Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. J. Appl. Meteor., 34, 16-32.

Xiu, Aijun, and J. E. Pleim, 2001: Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model. J. Appl. Meteor., 40, 192–209.

Pleim, J. E., and A. Xiu, 2003: Development of a land surface model. Part II: Data assimilation. J. Appl. Meteor., 42, 1811-1822.

Pleim, J. E., and R. Gilliam, 2009: An indirect data assimilation scheme for deep soil temperature in the Pleim-Xiu land surface model. J. Appl. Meteor. Climatol., 48, 1362-1376.

Gilliam, R. C., and J. E. Pleim, 2010: Performance assessment of new land-surface and planetary boundary layer physics in the WRF-ARW. J. App. Meteor. Climatol., 49(4), 760-774.
Simplified Simple Biosphere (SSiB) Land Surface Model option 8 Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. J. Climate, 4, 345–364.

Sun, S., and Y. Xue, 2001: Implementing a new snow scheme in Simplified Simple Biosphere Model (SSiB), Adv. Atmos. Sci., 18, 335–354.
University of Arizona Snow Physics for Noah ua_phys = .true. Wang, Z., X. Zeng, and M. Decker, 2010: Improving snow processes in the Noah land model, J. Geophys. Res., 115, D20108.
Sub-tiling option for Noah LSM sf_surface_mosaic = 1 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. J. Geophys. Res., 118,11,918-11,11,935. DOI: 10.1002/2013JD02065


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Shallow Cumulus Options (shcu_physics)

University of Washington Scheme option 2 Park, Sungsu, and Christopher S. Bretherton, 2009: The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the Community Atmosphere Model. J. Climate, 22, 3449–3469.
Global/Regional Integrated Modeling System (GRIMS) Scheme option 3 This paper is still in progress.


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Surface Layer Options (sf_sfclay_physics)

MM5 Similarity Scheme option 1 Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteor., 9, 857–861.

Dyer, A. J., and B. B. Hicks, 1970: Flux–gradient relationships in the constant flux layer. Quart. J. Roy. Meteor. Soc., 96, 715–721.

Webb, E. K., 1970: Profile relationships: The log-linear range, and extension to strong stability. Quart. J. Roy. Meteor. Soc., 96, 67–90.

Beljaars, A.C.M., 1994: The parameterization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255–270.

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. J. Appl. Meteor., 21, 1594–1609.
Eta Similarity Scheme option 2 Monin A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib Geophys Inst Acad Sci USSR 151:163–187 (in Russian)

Janjic, Z. I., 1994: The step-mountain Eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.

Janjic, Z. I., 1996: The surface layer in the NCEP Eta Model. Eleventh conference on numerical weather prediction, Norfolk, VA, 19–23 August 1996. Amer Meteor Soc, Boston, MA, pp 354–355.

Janjic, Z. I., 2002: Nonsingular implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model. NCEP Office Note No. 437, 61 pp.
NCEP Global Forecast System Scheme option 3
QNSE Scheme option 4
MYNN Scheme option 5
Pleim–Xiu Scheme option 7 Pleim, J. E., 2006: A simple, efficient solution of flux-profile relationships in the atmospheric surface layer, J. Appl. Meteor. and Clim., 45, 341–347.
TEMF Scheme option 10
Revised MM5 Scheme option 11 Jimenez, Pedro A., Jimy Dudhia, J. Fidel Gonzalez–Rouco, Jorge Navarro, Juan P. Montavez, and Elena Garcia–Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898–918.
topo_wind: Topographic Correction for Surface Winds to Represent Extra Drag from Sub–grid Topography and Enhanced Flow at Hill Tops topo_wind = 1 or 2 Jimenez, Pedro A., and Jimy Dudhia, 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J. Appl. Meteor. Climatol., 51, 300–316.
Chen–Zhang Modified Zilitinkevich Thermal Roughness Length iz0tlnd Chen, F. and Y. Zhang, 2009: On the coupling strength between the land surface and the atmosphere: From viewpoint of surface exchange coefficients. Geophys. Res. Lett., 36, L10404.


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Urban Surface Options (sf_urban_physics)

Urban Canopy Model option 1 Chen, F., H. Kusaka, R. Bornstain, J. Ching, C.S.B. Grimmond, S. Grossman-Clarke, T. Loridan, K. Manning, A. Martilli, S. Miao, D. Sailor, F. Salamanca, H. Taha, M. Tewari, X. Wang, A. Wyszogrodzki, and C. Zhang, 2011: The integrated WRF/urban modeling system: development, evaluation, and applications to urban environmental problems. International Journal of Climatology, 31, 273-288. DOI: 10.1002/joc.2158.
Building Environment Parameterization (BEP) and Building Energy Model (BEM) Schemes options 2 & 3 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. Theor. Appl. Climatol., 99, 345–356.

Martilli A, Clappier A, and Rotach M.W., 2002: An urban surface exchange parameterization for mesoscale models. Bound.-Layer Meteorol., 104, 261–304.


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Ocean Model Options (sf_ocean_physics)

Simple Mixed–layer Ocean Model option 1 Pollard, R. T., P. B. Rhines, and R. O. R. Y. Thompson, 1973: The deepening of the wind–mixed layer. Geophys. Fluid. Dyn., 3, 381–404.
Price–Weller–Pinkel 3–D Ocean Model option 2 Price, J. F., 1981: Upper Ocean Response to a Hurricane. J. Phy. Oceanogr., 11, 153–175.

Price, J. F., T. B. Sanford, and G. Z. Forristall, 1994: Forced stage response to a moving hurricane. J. Phy. Oceanogr., 24, 233–260.

Chia-Ying Lee and Shuyi S. Chen, 2012: Symmetric and asymmetric structures of hurricane boundary layer in coupled atmosphere–wave–ocean models and observations. J. Atmos. Sci., 69, 3576–3594. doi: http://dx.doi.org/10.1175/JAS-D-12-046.1


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Other Model Options

Analysis Nudging (FDDA) grid_fdda = 1 Stauffer D. R., and N. L. Seaman, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor., 33, 416–434.

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. J. Appl. Meteor. Clim., 47, 1077–1092.
Spectral Nudging grid_fdda = 2 Miguez-Macho, G., G. L. Stenchikov, and A. Robock, 2004: Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J. Geophys. Res., 109, D13104.
Observational Nudging obs_nudge_opt=1 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. J. Appl. Meteor. Clim., 47, 1077–1092.

Stauffer D. R., and N. L. Seaman, 1994: Multiscale four–dimensional data assimilation. J. Appl. Meteor., 33, 416–434.

Xu, M., Y. Liu, C. Davis and T. Warner, 2002: Sensitivity study on nudging parameters for a mesoscale FDDA system. 19th Conference on Weather Analysis and Forecasting, and 15th Conference on Numerical Weather Prediction, 12–16 August, 2002, San Antonio, Texas, Amer. Meteror. Soc., 4B.4.
Stochastic–energy Backscatter Scheme stoch_force_opt = 1 Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q.J.R. Meteorol. Soc., 131, 3079–3102.

Berner, J., S.–Y. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 1972–1995.
Zaengl Radiation/Topography (Slope/Shadowing) slope_rad = 1 & topo_shading = 1 Zangl, Gunther, 2002: An Improved Method for Computing Horizontal Diffusion in a Sigma–Coordinate Model and Its Application to Simulations over Mountainous Topography. Mon. Wea. Rev., 130, 1423–1432.




LES References

Moeng, C.-H., J. Dudhia, J. B. Klemp, and P. Sullivan, 2007: Examining two-way grid nesting for large eddy simulation of the PBL using the WRF Model. Mon. Wea. Rev., 135, 2295–2311. doi: http://dx.doi.org/10.1175/MWR3406.1

Mirocha, J. D., J. K. Lundquist, and B. Kosović, 2010: Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the Advanced Research WRF Model. Mon. Wea. Rev., 138, 4212–4228. doi: http://dx.doi.org/10.1175/2010MWR3286.1




WRF Specialty Systems

WRF-Hydro Gochis, D.J., W. Yu, D.N. Yates, 2015: The WRF-Hydro Model Technical Description and User's Guide, Version 3.0. NCAR Tech. Document. 120 pp. Available online at: http://www.ral.ucar.edu/projects/wrf_hydro/
WRF-Fire Coen J. L., M. Cameron, J. Michalakes, E. G. Patton, P. J. Riggan, and K. M. Yedinak, 2013: WRF-Fire: Coupled Weather–Wildland Fire Modeling with the Weather Research and Forecasting Model. J. Appl. Meteor. Climatol., 52, 16–38. doi: http://dx.doi.org/10.1175/JAMC-D-12-023.1


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