Updated physics chapter and references to WRF Version 4.3.

description.bbl

@@ -37,9 +37,11 @@ Baek, S., 2017: A revised radiation package of G-packed McICA and two-stream app
 Performance evaluation in a global weather forecasting model. 
 {\em J. Adv. Model. Earth Syst.}, {\bf 9}, 1--13, doi:10.1002/2017MS000994.
 
+\bibitem[Beljaars et al.(2004)]{Beljaars-et-al-2004}%
+Beljaars A.C.M., Brown A.R., Wood N. (2004) A new parametrization of turbulent orographic form drag. {\em Q. J. R. Meteorol Soc.}, {\bf 130}, 1327–1347. doi:10.1256/qj.03.73.
+
 \bibitem[Ban et al.(2017)]{ban17}%
-Ban, J., Z. Liu, X. Zhang, X.-Y. Huang, and H. Wang, 2017: Precipitation data assimilation in WRFDA 4D-Var: implementation and
- application to convection-permitting forecasts over United States. 
+Ban, J., Z. Liu, X. Zhang, X.-Y. Huang, and H. Wang, 2017: Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States. 
  {\em Tellus A: Dynamic Meteorology and Oceanography}, {\bf 69:1}, 1368310, DOI: 10.1080/16000870.2017.1368310.
 
 \bibitem[Barker et al.(2003)]{barker03}%
@@ -454,8 +456,7 @@ Janjic, Z. I., 2002: Nonsingular Implementation of the Mellor--Yamada Level 2.5
   {\em NCEP Office Note}, {\bf No. 437}, 61 pp.
 
 \bibitem[Jensen et al.(2017)]{jensen17}
-Jensen, A. A., J. Y. Harrington,  H. Morrison, and J. A. Milbrandt, 2017: Predicting ice shape evolution in a bulk microphysics model. 
-{\em J. Atmos. Sci.}, {\bf 74}, 2081--2104, https://doi.org/10.1175/JAS-D-16-0350.1.
+Jensen, A. A., J. Y. Harrington,  H. Morrison, and J. A. Milbrandt, 2017: Predicting ice shape evolution in a bulk microphysics model. {\em J. Atmos. Sci.}, {\bf 74}, 2081--2104, https://doi.org/10.1175/JAS-D-16-0350.1.
 
 \bibitem[Jimenez et al.(2012)]{jimenez12}%
 Jimenez, P., J. Dudhia, J. F. Gonzalez-Ruoco, J. Navarro, J. P. Montavez,
@@ -546,6 +547,9 @@ Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the
 Lang, S., W.-K. Tao, J.-D. Chern, D. Wu, and X. Li, 2014: Benefits of a 4th ice class in the simulated radar reflectivities of convective systems using a bulk microphysics scheme. 
 {\em J. Atmos. Sci.}, {\bf 71}, 3583--3612, https://doi.org/10.1175/JAS-D-13-0330.1
 
+\bibitem[Langland et al.(1996)]{langland96}%
+Langland, R. H., and C. S. Liou, 1996: Implementation of an E–$\epsilon$ parameterization of vertical subgrid-scale mixing in a re- gional model. {\em Mon. Wea. Rev.}, {\bf 124}, 905–918, https://doi.org/ 10.1175/1520-0493(1996)124,0905:IOAPOV.2.0.CO;2.
+
 \bibitem[Laprise(1992)]{laprise92}%
 Laprise R., 1992: The Euler Equations of motion with hydrostatic pressure
   as as independent variable,
@@ -1029,10 +1033,18 @@ Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and p
 {\em J. Atmos. Sci.}, {\bf 71}, 3636--3658.
 
 \bibitem[Tong et al.(2016)]{tong16}%
-Tong, W., G. Li, J. Sun, X. Tang and Y. Zhang, 2016: Design Strategies of an Hourly Update 3DVAR Data Assimilation System for 
-Improved Convective Forecasting. 
+Tong, W., G. Li, J. Sun, X. Tang and Y. Zhang, 2016: Design Strategies of an Hourly Update 3DVAR 
+Data Assimilation System for Improved Convective Forecasting. 
  {\em Weather and Forecasting}, {\bf 31}, 1673--1695.
 
+\bibitem[Tsai and Chen(2020)]{tsai20}%
+Tsai, T.C. and J.P. Chen, 2020: Multi-moment ice bulk microphysics scheme with consideration for 
+particle shape and apparent density. Part I: Methodology and idealized simulation. 
+{\em J. Atmos. Sci.}, {\bf 77}, 1821–1850, doi:10.1175/JAS-D-19-0125.1.
+
+\bibitem[Tsiringakis et al.(2017)]{Tsiringakis-et-al-2017}%
+Tsiringakis, A., Steeneveld, G. J., Holtslag, A. A. M., 2017: Small-scale orographic gravity wave drag in stable boundary layers and its impact on synoptic systems and near-surface meteorology, {\em Q. J. R. Meteorol. Soc.}, {\bf 143}, 704, https://doi.org/10.1002/qj.3021.
+
 \bibitem[Vendrasco et al.(2016)]{ven16}%
 Vendrasco, E. P., J. Sun, D. L. Herdies, and C. F. de Angelis, 2016: Constraining a 3DVAR Radar Data Assimilation System with 
  Large-Scale Analysis to Improve Short-Range Precipitation Forecasts. 
@@ -1049,8 +1061,7 @@ Advanced Research WRF model using large-eddy simulations of aerosol-cloud intera
  {\em Mon. Wea. Rev.}, {\bf 137}, 2547--2558.
 
 \bibitem[Wang et al.(2013a)]{wang13a}%
-Wang, H., J. Sun, S. Fan, and X.-Y. Huang, 2013: Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of
- Four Summertime Convective Events. 
+Wang, H., J. Sun, S. Fan, and X.-Y. Huang, 2013: Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events. 
  {\em J. Appl. Meteor. Climatol.}, {\bf 52}, 889--902.
 
 \bibitem[Wang et al.(2013b)]{wang13b}%
@@ -1196,19 +1207,21 @@ The community Noah land surface model with multiparameterization options (Noah-M
 {\em J. Geophys. Res.}, {\bf 116}, D12110.
 
 \bibitem[Zalesak (1979)]{Zalesak-1979}%
-Zalesak, S. T., 1979:
-Fully multidimensional flux-corrected transport algorithms for fluids.
-  {\em J. Comp. Phys.}, {\bf 31}, 335–362.
+Zalesak, S. T., 1979: Fully multidimensional flux-corrected transport algorithms for fluids.
+ {\em J. Comp. Phys.}, {\bf 31}, 335–362.
 
 \bibitem[Zhang and Wang(2011)]{zhangc11}%
-Zhang, Chunxi, Yuqing Wang, and Kevin Hamilton, 2011: Improved representation of boundary layer clouds over 
+Zhang, C,, Y. 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. 
   {\em Mon. Wea. Rev.}, {\bf 139}, 3489–3513. doi:10.1175/MWR-D-10-05091.1.
 
 \bibitem[Zhang and Wang(2017)]{zhangc17}%
-Zhang, C., and Y. Wang, 2017: Projected Future Changes of Tropical Cyclone Activity over the Western North and 
+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. {\em J. Climate}, {\bf 30}, 5923-5941. doi:10.1175/JCLI-D-16-0597.1.
 
+\bibitem[Zhang et al.(2020)]{zhangc20}%
+Zhang, C., Wang, Y., and Xue, M., 2020: Evaluation of an E–$\epsilon$ and Three Other Boundary Layer Parameterization Schemes in the WRF Model over the Southeast Pacific and the Southern Great Plains, {\em Mon. Wea. Rev.}, {\bf 148(3)}, 1121-1145. doi.org/10.1175/MWR-D-19-0084.1.
+
 \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. 
@@ -1224,20 +1237,20 @@ Zhang, X., X.-Y. Huang, and N. Pan., 2013: Development of the Upgraded Tangent L
 {\em J. Atmos. Oceanic Technol.} {\bf 30}, 1180--1188.
 
 \bibitem[Zhang et al.(2014a)]{zhang14a}%
-Zhang, X., , X.-Y. Huang, J. Liu, J. Poterjoy, Y. Weng, F. Zhang, and H. Wang, 2014: Development of an Efficient Regional 
-Four-Dimensional Variational Data Assimilation System for WRF. 
+Zhang, X., , X.-Y. Huang, J. Liu, J. Poterjoy, Y. Weng, F. Zhang, and H. Wang, 2014: Development of an Efficient Regional Four-Dimensional Variational Data Assimilation System for WRF. 
 {\em J. Atmos. Oceanic Technol.}, {\bf 31}, 2777--2794.
 
 \bibitem[Zhang et al.(2014b)]{zhang14b}%
-Zhang, X.,Y.-H. Kuo, S.-Y. Chen, X.-Y. Huang, and L.F. Hsiao, 2014: Parallelization Strategies for the GPS Radio Occultation Data
- Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model. 
+Zhang, X.,Y.-H. Kuo, S.-Y. Chen, X.-Y. Huang, and L.F. Hsiao, 2014: Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model. 
  {\em J. Atmos. Oceanic Technol.}, {\bf 31}, 2008--2014.
 
 \bibitem[Zhang et al.(2015)]{zhang15}%
-Zhang, X., H. Wang, X.-Y. Huang, F. Gao, and N. A. Jacobs, 2015: Using Adjoint-Based Forecast Sensitivity Method to Evaluate TAMDAR 
-  Data Impacts on Regional Forecasts. 
+Zhang, X., H. Wang, X.-Y. Huang, F. Gao, and N. A. Jacobs, 2015: Using Adjoint-Based Forecast Sensitivity Method to Evaluate TAMDAR Data Impacts on Regional Forecasts. 
   {\em Adv. Meteorol.}, {\bf 2015}, doi:10.1155/2015/427616.
 
+\bibitem[Zhang et al.(2018)]{zhangbao18}%
+Zhang, X., Bao, J., Chen, B., and Grell, E., 2018: A Three-Dimensional Scale-Adaptive Turbulent Kinetic Energy Scheme in the WRF-ARW Model. {\em Mon. Wea. Rev.}, {\bf 146(7)}, 2023-2045, https://doi.org/10.1175/MWR-D-17-0356.1.
+
 \bibitem[Zheng et al.(2016)]{zheng16}%
 Zheng, Y., 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. 

physics.tex

@@ -234,10 +234,10 @@ \subsection{Goddard Cumulus Ensemble Model scheme}
 allow the ice-particle properties to evolve rather than imposing categories like snow, graupel and hail.
 
 \subsection {Jensen ISHMAEL microphysics}
-The ISHMAEL (Ice-Spheroids Habit Model with Aspect-ratio EvoLution) scheme \citep{jensen17} can predict ice-particle habits
-that are defined by their aspect ratios and volumes and how these develop through deposition and riming. Up to three independent
-habits are carried which contain generalized particles that represent the whole evolution from ice to snow to graupel together with their numbers
-per unit dry air mass.
+The ISHMAEL (Ice-Spheroids Habit Model with Aspect-ratio EvoLution) scheme \citep{jensen17} can predict ice-particle habits that are defined by their aspect ratios and volumes and how these develop through deposition and riming. Up to three independent habits are carried which contain generalized particles that represent the whole evolution from ice to snow to graupel together with their numbers per unit dry air mass.
+
+\subsection {National Taiwan University (NTU) microphysics scheme}
+The NTU scheme is a multi-moment microphysics scheme \citep{tsai20}. It is double-moment for liquid phase and triple moment for ice phase hydrometeors with additional consideration of ice crystal shape and density variations. The condensation nuclei and ice nuclei are tracked separately in the processes of cloud/rain activation and ice deposition-nucleation using predicted supersaturation. The triple-moment (the zeroth, second and third moments) closure is applied to the evolution of ice particle's spectrum. The classification for solid-phase hydrometeors (pristine ice, snow aggregate, rimed ice and hailstone) is redefined according to their key formation mechanisms, while the shape and apparent density of ice crystals are allowed to evolve gradually according to the growth conditions. The fall speed of each frozen particles depends on its shape and density.
 
 
 \section{Cumulus Parameterization}
@@ -938,6 +938,12 @@ \subsection{Grenier-Bretherton-McCaa (GBM) PBL}
 It handles vertical diffusion with an implicit local scheme, and it is based 
 on local $Ri$ in the free atmosphere.
 
+\subsection {3DTKE PBL}
+This scheme is a scale-aware, three-dimensional TKE subgrid mixing scheme \citep{zhangbao18}. It extends the original 1.5-order TKE-closure subgrid model from LES (km\_opt = 2) to mesoscale. In the LES limit, this option is the same as km\_opt = 2. Going towards the mesoscale limit, the horizontal diffusion transitions to the first-order 2D Smagorinsky and a strengthening non-local term, following Shin-Hong, is added to the vertical diffusion, which is also made implicit to allow for longer time steps and thin model layers.
+
+\subsection {E-$\epsilon$ PBL}
+This scheme \citep{zhangc20} predicts TKE (E) as well as TKE dissipation rate ($\epsilon$) with a 1.5-order closure. It follows \citet{langland96} with some modification and improvements. It uses different coefficients for the epsilon equation. The scheme chooses maximum of shear production versus the sum of shear and buoyancy productions in epsilon equation to avoid oscillation, and enhances the buoyancy term in both equations when clouds are present. It also includes TKE dissipative rate as an additional heat source. A nonlocal term is considered for potential temperature and moisture in vertical mixing.
+
 \subsection {Gravity Wave Drag}
 
 For grid sizes that exceed about 10 km and for longer simulations that include significant orography,
@@ -947,6 +953,9 @@ \subsection{Grenier-Bretherton-McCaa (GBM) PBL}
 sub-grid orographic data provided by {\em geogrid}. The sub-grid information includes direction-sensitive
 statistics related to the orientation of the orography.
 
+\subsection {GSL Gravity Wave Drag}
+This is an extended and scale-aware gravity-wave drag scheme developed by Global Systems Laboratory NOAA. In addition to the traditional gravity wave drag effect due to unresolved topography and low-level blocking that remain similar to the older scheme, this scheme has two parameterizations for grid sizes down to 1 km. One of these is the turbulent orographic form drag based on \citet{Beljaars-et-al-2004}, due to pressure perturbations and shape of the orography (note that this is not gravity wave drag despite being included in this option). The other parameterization is for small-scale gravity wave drag in the stable PBL \citep{Tsiringakis-et-al-2017} which allows vertical propagation of gravity waves at smaller scales. The scheme also considers ramping down the large scale gravity wave drag as the grid sizes decrease to 5 km, and the smaller scale drags are turned off at 1 km. This option uses a different set of sub-grid orographic data based on GMTED provided by {\em geogrid}.
+
 
 \section{Atmospheric Radiation}
 

preface.tex

@@ -15,6 +15,6 @@ \chapter*{Preface}
 air quality modeling, atmosphere-ocean coupling, and idealized- atmosphere studies.
 
 \vskip 10pt
-This particular version of the Tech Note covers ARW releases up to Version 4.1.
+This particular version of the Tech Note covers ARW releases up to Version 4.3.
 This document will be updated as new releases become available and new features are added to the model.