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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.
doi:10.1007/978-1-935704-36-2_1
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| Purdue Lin Scheme |
option 2 |
Chen, S.-H. and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan., 80(1), 99–118.
doi:10.2151/jmsj.80.99
PDF |
| 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.
doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
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| 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.
Hong and Lim, 2006
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| Goddard Scheme |
option 7 |
Tao, Wei–Kuo, Joanne Simpson, Michael McCumber, 1989: An Ice–Water Saturation Adjustment. Mon. Wea. Rev., 117, 231–235.
doi:10.1175/1520-0493(1989)117<0231:AIWSA>2.0.CO;2
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Tao, W.-K., D. Wu, S. Lang, J.-D. Chern, C. Peters-Lidard, A. Fridlind, and T. Matsui, 2016: High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and obser- vations. J. Geophys. Res. Atmos., 121, 1278–1305.
doi:10.1002/2015JD023986
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| 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.
doi:10.1175/2008MWR2387.1
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| 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.
doi:10.1175/JAS3534.1
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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.
doi:10.1175/JAS3535.1
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| 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.
doi:10.1175/2008MWR2556.1
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| 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.
doi:10.1175/2010MWR3293.1
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| 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.doi:10.1175/2009MWR2968.1
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| 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.
doi:10.1175/2009JAS2965.1
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| 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.
doi:10.1175/MWR2810.1
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WRF Single Moment and Double Moment 7-class Schemes |
options 24 & 26 |
Bae, S.Y., Hong, SY. & Tao, WK., 2018: Development of a single-moment cloud microphysics scheme with prognostic hail for the Weather Research and Forecasting (WRF) model. Asia-Pac. J. Atmos. Sci.
doi:10.1007%2Fs13143-018-0066-3
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| Aerosol–aware & Hail/Graupel/Aerosol Thompson Schemes |
options 28 & 38 |
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.
doi:10.1175/JAS-D-13-0305.1
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| 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.
doi:10.1175/2009JAS3210.1
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| 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.
doi:10.1175/JAS-3350.1
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| P3 |
options 50, 51, 52 |
Morrison, Hugh, and Jason A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287-311. doi:10.1175/JAS-D-14-0065.1
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| Jensen ISHMAEL Scheme |
option 55 |
Jensen, A. A., J. Y. Harrington, H. Morrison, and J. A. Milbrandt, 2017: Predict- ing ice shape evolution in a bulk microphysics model. J. Atmos. Sci., 74, 2081–2104.
doi:10.1175/JAS-D-16-0350.1
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| National Taiwan University (NTU) Scheme |
option 56 |
Tsai, Tzu-Chin, and Jen-Ping Chen: Multimoment ice bulk microphysics scheme with consideration for particle shape and apparent density. Part I: Methodology and idealized simulation J. Atmos. Sci., 77-5, 1821-1850.
doi:10.1175/JAS-D-19-0125.1
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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. doi:10.1175/MWR3199.1
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| 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. doi:10.1175/1520-0493(1994)122%3c0927:TSMECM%3e2.0.CO;2
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Mesinger, F., 1993: Forecasting upper tropospheric turbulence within the framework of the Mellor-Yamada 2.5 closure. Res. Activ. in Atmos. and Ocean. Mod., WMO, Geneva, CAS/JSC WGNE Rep. No. 18, 4.28-4.29.
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| NCEP Global Forecast System Scheme |
option 3 |
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. doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2.
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| 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. doi:10.1007/s10546-004-6848-4
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| 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. doi:10.1007/s10546-005-9030-8
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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. doi:10.2151/jmsj.87.895
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Olson, Joseph B., Jaymes S. Kenyon, Wayne M. Angevine, John M . Brown, Mariusz Pagowski, and Kay Sušelj, 2019: A Description of the MYNN-EDMF Scheme and the Coupling to Other Components in WRF–ARW. NOAA Technical Memorandum OAR GSD, 61, pp. 37.
doi:10.25923/n9wm-be49
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| 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. doi:10.1175/JAM2539.1
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| 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. doi:10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2
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| 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. doi:10.1175/2008JCLI2556.1
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| TEMF 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. doi:10.1175/2010MWR3142.1
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| 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. doi:10.1175/MWR-D-14-00116.1
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| 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. doi:10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2
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| TKE (E)-TKE Dissipation Rate (Epsilon) (EEPS) |
option 16 |
Zhang, C., Y. Wang and M. Xue, 2020: Evaluation of an E–ε and Three Other Boundary Layer Parameterization Schemes in the WRF Model over the Southeast Pacific and the Southern Great Plains. Mon. Wea. Rev.,148, 1121–1145. https://doi.org/10.1175/MWR-D-19-0084.1 |
| K-epsilon-theta^2 (KEPS) |
option 17 |
Zonato, Andrea, A. Martilli, P. A. Jimenez, J. Dudhia, D. Zardi, and L. Giovannini, A new K-epsilon turbulence parameterization for mesoscale meteorological models. Mon. Wea. Rev., 150, 2157–2174. doi:10.1175/MWR-D-21-0299.1
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| 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. doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
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| 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. doi:10.1175/2007WAF2007030.1
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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. doi:10.1175/1520-0469(1995)052<1875:IOOGWP>2.0.CO;2
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Choi H, Hong S, 2015: An updated subgrid orographic parameterization for global atmospheric forecast models. J. Geophys. Res., 120 doi:10.1002/2015JD024230
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|
| 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. doi:10.1175/MWR-D-11-00352.1
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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 doi:10.1002/2014JD021669
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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.
doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
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| 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.
doi:10.1016/j.atmosres.2008.09.022
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| 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.
doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
PDF |
| 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.
Grell and Freitas, 2014
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|
| 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.
Pan et al., 1995
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| Grell 3D Ensemble Scheme |
option 5 |
Grell, Georg A., 1993: Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations. Mon. Wea. Rev., 121, 764–787.
doi:10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2
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Grell, G. A, D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693. doi:10.1029/2002GL015311
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| 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.
doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2
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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.
doi:10.1175/MWR-D-10-05091.1
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| 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. doi:10.1080/07055900.1995.9649539
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Kain–Fritsch
Cumulus Potential Scheme |
option 10 |
Berg, L.K., W.I. Gustafson, E.I. Kassianov, E.I., and L. Deng, 2013: Evaluation of a modified scheme for shallow convection: Implementation of CuP and case studies. Mon. Wea. Rev., 141, 134-147.
doi:10.1175/MWR-D-12-000136.1
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| 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.
doi:10.1175/MWR-D-15-0005.1
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Glotfelty, T., K. Alapaty, J. He, P. Hawbecker, X. Song, and G. Zhang, 2019: The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI): Development, evaluation, and initial application. Mon. Wea. Rev. , 147, 1491-1511.
doi:10.1175/MWR-D-18-0267.1
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| New Simplified Arakawa–Schubert Scheme (for Basic WRF) |
option 14 |
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. doi:10.1175/WAF-D-10-05038.1
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After V4.0
Kwon, Y.-C. and S.-Y. Hong, 2017: A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon. Wea. Rev. 145, 585-598.
doi:10.1175/MWR-D-16-0034.1
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| New Tiedtke Scheme |
option 16 |
Zhang, C. and Y. Wang, 2017: Projected Future Changes of Tropical Cyclone Activity over the Western North and South Pacific in a 20-km-Mesh Regional Climate Model. J. Climate, 30, 5923-5941.
doi:10.1175/JCLI-D-16-0597.1
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| New Simplified Arakawa-Schubert Scheme (for HWRF) |
option 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. doi:10.1175/WAF-D-10-05038.1
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| 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).
doi:10.1029/2002GL015311
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| 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.
doi:10.1175/1520-0469(1990)047<2785:AODEPM>2.0.CO.2;
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Shortwave (ra_sw_physics) and Longwave (ra_lw_physics) Options
| Dudhia Shortwave Scheme |
sw 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. doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
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| RRTM Longwave Scheme |
lw 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. doi:10.1029/97JD00237
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| Goddard Shortwave Scheme |
sw option 2 |
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.
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Matsui, T., S. Q. Zhang, W.-K. Tao, S. Lang, C. Ichoku, and C. Peters-Lidard, 2018: Impact of radiation frequency, precipitation radiative forcing, and radiation column aggregation on convection-permitting West African Monsoon simulations. Clim. Dyn., 1-21.
doi:10.1007/s00382-018-4187-2
|
| CAM Shortwave and Longwave Schemes |
lw/sw 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.
PDF |
| RRTMG Shortwave and Longwave Schemes |
lw/sw 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. doi:10.1029/2008JD009944
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| New Goddard Shortwave and Longwave Schemes |
lw/sw option 5
|
Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Memo. 104606, 15, 40 pp.
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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.
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| Fu–Liou–Gu Shortwave and Longwave Schemes |
lw/sw 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.
doi:10.1029/2010JD014574
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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.
doi:10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2
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| RRTMG–K Shortwave and Longwave Schemes |
lw/sw option 14
|
Baek, Sunghye, 2017, A revised radiation package of G–packed McICA and two–stream approximation: Performance evaluation in a global weather forecasting model. J. Adv. Model. Earth Syst., 9. doi:10.1002/2017MS000994
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| Held–Suarez Relaxation Longwave Scheme |
sw option 31 |
|
| GFDL Shortwave and Longwave Schemes |
lw/sw option 99
|
Fels, Stephen. B., and M. D. Schwarzkopf, 1981, An efficient, accurate algorithm for calculating CO2 15 µm band cooling rates. J. Geophys. Res., 86, 1205–1232. doi:10.1029/JC086iC02p01205
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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.
PDF |
| 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.
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| 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.
doi:10.1175/1520-0493(2004)132<0473:MWPWTF>2.0.CO;2
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https://doi.org/10.25923/55x8-cy36 |
| 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. doi:10.1029/2010JD015139
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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. doi:10.1029/2010JD015140
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He et al., 2023: The community Noah–MP land surface modeleling system technical description Version 5.0 No. NCAR/TN-575+STR.
doi:10.5065/ew8g-yr95
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| 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.
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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. doi:10.1029/2011MS00045
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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.
doi:10.1029/2011JD016980
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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. doi:10.1029/2011JD016991
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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.
doi:10.1175/2010EI331.1
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| 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.
doi:10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2
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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.
doi:10.1175/1520-0450-34.1.16
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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.
doi:10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2
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Pleim, J. E., and A. Xiu, 2003: Development of a land surface model. Part II: Data assimilation. J. Appl. Meteor., 42, 1811-1822. doi:10.1175/1520-0450(2003)042<1811:DOALSM>2.0.CO;2
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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. doi:10.1175/2009JAMC2053.1
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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. doi:10.1175/2009JAMC2126.1
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| 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. doi:10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2
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Sun, S., and Y. Xue, 2001: Implementing a new snow scheme in Simplified Simple Biosphere Model (SSiB), Adv. Atmos. Sci., 18, 335–354.
doi:10.1007/BF02919314
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| 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. doi:10.1029/2009JD013761
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| 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.
doi:10.1175/2008JCLI2557.1
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| Global/Regional Integrated Modeling System (GRIMS) Scheme |
option 3 |
Hong, SY. & JH Jang, 2018: Impacts of shallow convection processes on a simulated Boreal summer climatology in a global atmospheric model. J. Asia-Pacific J Atmos Sci (2018) 54(Suppl 1): 361.
doi:10.1007/s13143-018-0013-3
|
| Deng Scheme |
option 5 |
Deng, A., N. L. Seaman, and J. S. Kain, 2003: A shallow-convection parameterization for mesoscale models. Part I: Submodel description and preliminary applications. J. Atmos. Sci., 60, 3456.
doi:10.1175/1520-0469(2003)060<0034:ASCPFM>2.0.CO;2
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Surface Layer Options (sf_sfclay_physics)
| Revised MM5 Scheme |
option 1 |
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. doi:10.1175/MWR-D-11-00056.1
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| 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)
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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. doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
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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.
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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.
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| NCEP Global Forecast System Scheme |
option 3 |
|
| QNSE Scheme |
option 4 |
|
| MYNN Scheme |
option 5 |
Olson et al., 2021
|
| 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.
doi:10.1175/JAM2339.1
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| TEMF 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. doi:10.1175/2010MWR3142.1
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| MM5 Similarity Scheme |
option 91 |
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.
doi:10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2
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Dyer, A. J., and B. B. Hicks, 1970: Flux–gradient relationships in the constant flux layer. Quart.
J. Roy. Meteor. Soc., 96, 715–721. doi:10.1002/qj.49709641012
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Webb, E. K., 1970: Profile relationships: The log-linear range, and extension to strong stability. Quart. J. Roy. Meteor. Soc., 96, 67–90.
doi:10.1002/qj.49709640708
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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.
doi:10.1002/qj.49712152203
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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. doi:10.1175/1520-0450(1982)021<1594:AHRMOT>2.0.CO;2
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| 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.
doi:10.1175/JAMC-D-11-084.1
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| 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. doi:10.1029/2009GL037980
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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.
doi:10.1002/joc.2158
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| 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.
doi:10.1007/s00704-009-0143-8
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Martilli A, Clappier A, and Rotach M.W., 2002: An urban surface exchange parameterization for mesoscale models. Bound.-Layer Meteorol., 104, 261–304.
doi:10.1023/A:1016099921195
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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. doi:10.1080/03091927208236105 |
| 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.
doi:10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2
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Price, J. F., T. B. Sanford, and G. Z. Forristall, 1994: Forced stage response to a moving
hurricane. J. Phy. Oceanogr., 24, 233–260. doi:10.1175/1520-0485(1994)024<0233:FSRTAM>2.0.CO;2
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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 doi: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.
doi:10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2
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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.
doi:10.1175/2007JAMC1653.1
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| 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.
doi:10.1029/2003JD004495
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| 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.
doi:10.1175/2007JAMC1653.1
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Stauffer D. R., and N. L. Seaman, 1994: Multiscale four–dimensional data assimilation. J. Appl. Meteor., 33, 416–434.
doi:10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2
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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.
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| 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.
doi:10.1256/qj.04.106
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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. doi:10.1175/2010MWR3595.1
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| 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.
doi:10.1175/1520-0493(2002)130<1423:AIMFCH>2.0.CO;2
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| CLM4.5 Lake Model |
sf_lake_physics = 1 |
Subin, Z. M., W. J. Riley, and D. Mironov, 2012: An improved lake model for climate simulations: Model structure, evaluation, and sensitivity analyses in CESM1. J. Adv. Model. Earth Syst., 4, M02001, doi:10.1029/2011MS000072
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| Fast All-sky Radiation Model for Solar applications (FARMS) |
swint_opt = 2 |
Xie, Y., M. Sengupta and J. Dudhia, 2016: A Fast All-sky Radiation Model for Solar applicatins (FARMS): Algorithm and performance evaluation. Solar Energy, 135, 435 - 445. |
| PR92 Lightning Option |
lightning_option = 1, 2, or 11 |
Price, C. and D. Rind, 1992: Simple lightning parameterization for calculating global lightning distributions. J. Geophys. Res., 97(D9), 9919-9933. doi:10.1029/92JD00719
Wong, J., M. Barth, and D. Noone, 2012: Evaluating a Lightning parameterization at resolutions with partially-resolved convection, GMDD, in preparation. Geosci. Model Dev., 6, 429-443. doi:10.5194/gmd-6-429-2013
|
| Yair et al. Lightning Option |
lightning_option = 3 |
Yair, Y. et al., 2010: Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res., 115, D04205, doi:10.1029/2008JD010868
Lynn, B., et al., 2012: Predicting cloud-to-ground and intracloud lightning in weather forecast models. Wea. Forecasting, 27:6, 1470-1488. doi:10.1175/WAF-D-11-00144.1
|
| SMS-3DTKE Subgrid Mixing |
km_opt = 5 |
Zhang et al., 2018
|
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
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| 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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