diff --git a/physics/docs/ccpp_doxyfile b/physics/docs/ccpp_doxyfile index 9d7140566..1b6423813 100644 --- a/physics/docs/ccpp_doxyfile +++ b/physics/docs/ccpp_doxyfile @@ -943,6 +943,7 @@ WARN_LOGFILE = # Note: If this tag is empty the current directory is searched. INPUT = pdftxt/mainpage.txt \ + pdftxt/ccppv7_phy_updates.txt \ pdftxt/all_schemes_list.txt \ pdftxt/GFS_v16_suite.txt \ pdftxt/GFS_v17_HR3_suite.txt \ @@ -968,6 +969,7 @@ INPUT = pdftxt/mainpage.txt \ pdftxt/GFS_OZPHYS.txt \ pdftxt/GFS_H2OPHYS.txt \ pdftxt/GFS_SAMFdeep.txt \ + pdftxt/GFS_CAUTOMATA.txt \ pdftxt/GFS_SAMFshal.txt \ pdftxt/GFDL_cloud.txt \ pdftxt/NSSLMICRO.txt \ diff --git a/physics/docs/library.bib b/physics/docs/library.bib index 0ca85a7b0..06d1c46b5 100644 --- a/physics/docs/library.bib +++ b/physics/docs/library.bib @@ -1,12 +1,65 @@ %% This BibTeX bibliography file was created using BibDesk. %% https://bibdesk.sourceforge.io/ -%% Created for Man Zhang at 2024-06-27 13:18:41 -0600 +%% Created for Man Zhang at 2024-07-02 13:44:09 -0600 %% Saved with string encoding Unicode (UTF-8) +@article{han_2021, + author = {J. Han, J. Peng, W. Li, W. Wang, Z. Zhang, F. Yang and W. Zheng}, + date-added = {2024-07-02 13:49:10 -0600}, + date-modified = {2024-07-02 13:49:10 -0600}, + doi = {10.25923/CYBH-W893}, + publisher = {National Centers for Environmental Prediction (U.S.)}, + title = {Updates in the NCEP GFS Cumulus Convection, Vertical Turbulent Mixing, and Surface Layer Physics}, + url = {https://repository.library.noaa.gov/view/noaa/33881}, + year = {2021}} + + + +@article{Han_2024, + author = {Han, Jongil and Peng, Jiayi and Li, Wei and Wang, Weiguo and Zhang, Zhan and Yang, Fanglin and Zheng, Weizhong}, + date-added = {2024-07-02 13:44:05 -0600}, + date-modified = {2024-07-02 13:44:05 -0600}, + doi = {10.1175/waf-d-23-0134.1}, + issn = {1520-0434}, + journal = {Weather and Forecasting}, + month = apr, + number = {4}, + pages = {679{\^a}€“688}, + publisher = {American Meteorological Society}, + title = {Updates in the NCEP GFS PBL and Convection Models with Environmental Wind Shear Effect and Modified Entrainment and Detrainment Rates and Their Impacts on the GFS Hurricane and CAPE Forecasts}, + url = {http://dx.doi.org/10.1175/WAF-D-23-0134.1}, + volume = {39}, + year = {2024}, + bdsk-url-1 = {http://dx.doi.org/10.1175/WAF-D-23-0134.1}} + +@article{Bengtsson_et_al_2020, + abstract = {Abstract In the atmosphere, convection can organize from smaller scale updrafts into more coherent structures on various scales. In bulk-plume cumulus convection parameterizations, this type of organization has to be represented in terms of how the resolved flow would ``feel'' convection if more coherent structures were present on the subgrid. This type of subgrid organization acts as building blocks for larger scale tropical convective organization known to modulate local and remote weather. In this work a parameterization for subgrid (and cross-grid) organization in a bulk-plume convection scheme is proposed using the stochastic, self-organizing, properties of cellular automata (CA). We investigate the effects of using a CA which can interact with three different components of the bulk-plume scheme that modulate convective activity: entrainment, triggering, and closure. The impacts of the revised schemes are studied in terms of the model's ability to organize convectively coupled equatorial waves (CCEWs). The differing impacts of adopting the stochastic CA scheme, as compared to the widely used Stochastically Perturbed Physics Tendency (SPPT) scheme, are also assessed. Results show that with the CA scheme, precipitation is more spatially and temporally organized, and there is a systematic shift in equatorial wave phase speed not seen with SPPT. Previous studies have noted a linear relationship between Gross Moist Stability (GMS) and Kelvin wave phase speed. Analysis of GMS in this study shows an increase in Kelvin wave phase speed and an increase in GMS with the CA scheme, which is tied to a shift from large-scale precipitation to convective precipitation.}, + author = {Bengtsson, Lisa and Dias, Juliana and Tulich, Stefan and Gehne, Maria and Bao, Jian-Wen}, + doi = {https://doi.org/10.1029/2020MS002260}, + eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002260}, + journal = {Journal of Advances in Modeling Earth Systems}, + keywords = {cellular automata, cumulus convection, convective organization, stochastic physics}, + note = {e2020MS002260 2020MS002260}, + number = {1}, + pages = {e2020MS002260}, + title = {A Stochastic Parameterization of Organized Tropical Convection Using Cellular Automata for Global Forecasts in NOAA's Unified Forecast System}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002260}, + volume = {13}, + year = {2021}, + bdsk-url-1 = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002260}, + bdsk-url-2 = {https://doi.org/10.1029/2020MS002260}} + +@article{Han_et_al_2022, + author = {J. Han, F. Yang, R. Montuoro, W. Li, R. Sun}, + date-added = {2024-07-02 11:17:39 -0600}, + date-modified = {2024-07-02 11:20:58 -0600}, + institution = {NCEP Office Note 506}, + title = {Implementation of a positive definite mass-flux scheme and a method for removing the negative tracers in the NCEP GFS planetary boundary layer and cumulus convection scheme}, + year = {2022}} @article{xu_and_randall_1996, author = {Xu, Kuan-Man and Randall, David A.}, diff --git a/physics/docs/pdftxt/GFS_CAUTOMATA.txt b/physics/docs/pdftxt/GFS_CAUTOMATA.txt new file mode 100644 index 000000000..7780612e2 --- /dev/null +++ b/physics/docs/pdftxt/GFS_CAUTOMATA.txt @@ -0,0 +1,143 @@ +/** +\page cellular_automata Cellular Automata Stochastic Convective Organization Scheme + +\b Scientific \b Background + +Cumulus clouds in the atmosphere can organize into a variety of sizes, ranging +from small fair‐weather cumulus clouds, rain showers and thunderstorms, to +larger scale weather systems. In weather and climate models, such organization +is traditionally not well-represented as the motions associated with cumulus +clouds are generally too small to be resolved by the numerical model. +In this scheme we use a stochastic cellular automaton (CA), a mathematical +model often used to describe self‐organizing behavior in physical systems to +represent the effects of convective organization. The scheme addresses the +effect of convective organization in a bulk-plume cumulus convection +parameterizations (saSAS), where this type of organization has to be +represented in terms of how the resolved flow would “feel” convection if +more coherent structures were present on the subgrid. + +In addition, for longer range forecasts (seasonal, decadal, climate), +the relevance of stochastic cumulus convection in numerical models can also +be discussed in terms of noise induced forcing. As an example, on the +time scale of organized convectively coupled waves, the small scale individual +convective plumes grow and decay so rapidly that they are not predictable +on time-scales longer than a few hours, whereas the organized larger scale +convectively coupled wave envelope can have a deterministic limit of +predictability of about two weeks. Thus, for longer range forecasts, +individual convective plumes can be viewed as stochastic noise - they can +have an impact on the convectively coupled waves (due to noise forcing), +but they are not predictable on their own. By providing the CA with a +stochastic initialization, the effect of stochastic cumulus convection +is also represented by the scheme. + +The scientific motivation for the scheme, the CA rulesets explored, and +the impact on convectively coupled equatorial waves can be found in the +following references; Bengtsson et al. 2011 \cite Bengtsson_2011, +Bengtsson et al. 2013 \cite bengtsson_et_al_2013, +Bengtsson and Kornich (2016) \cite bengtsson_and_kornich_2016, +Bengtsson et al 2019 \cite Bengtsson_2019, +and Bengtsson et al. 2021 \cite bengtsson_et_al_2021. + +\b Technical \b remarks + +The CA source code is located in the stochastic physics submodule in +the ufs-weather-model: https://github.com/noaa-psd/stochastic_physics . +In the UFS Weather Model, the main call to the CA routines are made +from FV3/stochastic_physics_driver.F90. + +There are currently two options to evolve the CA (can be done simultaneously); +(\p ca_global) a large scale global pattern which evolves the ruleset according +to game of life with cell history, or (\p ca_sgs) a sub-grid scale pattern +which is conditioned on a forcing from the atmospheric model. The two options +are controlled by namelist and are evolved in cellular_automata_globa.F90 +and cellular_automata_sgs.F90 respectively. Both approaches use the main +CA module update_ca.F90 to evolve the CA in time. Since the CA needs to know +about its neighborhood it uses the halo information to gather the state +in adjacent MPI domains and/or adjacent cube sphere interfaces. + +\b The \p ca_sgs \b option - \b Coupled \b to \b saSAS \b cumulus \b convection \b scheme + +The evolution of the CA is an extension to the automaton family known as “Generations,” +which in turn is based on the “Game of Life”(Chopard & Droz, 1998 \cite Chopard_1998) + but adds cell history to the rule set. It is a deterministic CA ruleset, initialized +with Gaussian white noise. Thus, when used in an ensemble system, each ensemble +member can provide a different seed to the random number generator governing +the initial state to then generate a different evolution for each member. +By cell history we refer to newborn cells being given a “lifetime,”τ, +that is incrementally reduced by 1 each time step where the rules are not met, + in contrast to going directly from 1 to 0. The CA is conditioned on a +forcing from the host model through the lifetime variable τ such that: + +\f[ +\tau =N\left( \frac{\int_{l=1}^{l=top}E\frac{dp }{g} }{\max\left( \int_{l=1}^{l=top} E\frac{d p}{g}\right)} \right) +\f] + +here, N is an integer that when multiplied by the model time-step represents +a physical time scale, such that τ is longer in regions where the forcing is larger, +E is the vertically integrated convective rain evaporation from the +saSAS cumulus convection scheme stored in Coupling%condition. The denominator is +the maximum value of the forcing in the global domain. While the grid-scale +forcing in practice could be any two-dimensional field, we choose here +to set it as the vertically integrated subgrid rain evaporation amount, +serving as an indicator of geographical regions where enhanced subgrid +organization may arise through convective cold-pools. + +The CA is evolved on a finer grid than the numerical prediction +host model (size controlled by namelist), and can be either coarse +grained back to the host model grid as a fraction, or (in case of \p nca_plumes = .true.) +give back the maximum number of connected “plumes” (represented by +connected CA cells), and their associated size within each numerical +prediction host model grid-box. nca_plumes is default true and the +maximum cluster size is passed to the saSAS cumulus convection scheme +in the Coupling%ca_deep container. + +Depending on the activated namelist options, the CA can feed back to +the saSAS convection scheme via the entrainment (\p ca_entr), closure +(\p ca_closure) or convective initiation (\p ca_trigger) in the following way: + +- Entrainment (\p ca_entr): In entraining plume model bulk mass-flux schemes, +the upward mass-flux is typically parameterized as a function of environmental +air being entrained into the rising plume (as well as parcel properties at +cloud base). The fractional entrainment is described as a function of the +plume radius. Larger thermals (plumes) have smaller fractional entrainment, +which is a consequence of the fact that larger areas have relatively smaller +perimeters. In this scheme, the assumption is that subgrid organization will +lead to a few larger plumes rather than several smaller plumes, such that +the grid-box average fractional entrainment is reduced. Thus, after +the CA is updated, we count the number of plumes, and their associated +size within each NWP grid-box (\p nca_plumes = true). If the largest +cluster of cells found on the subgrid is larger than a set radius, then the +fractional entrainment rate is reduced at that grid-point by 30% +(selected based on experimentation) + +- Triggering (\p ca_trigger): In NWP models physical processes are parameterized +in columns, and the horizontal interaction between physical processes takes +place only through advection and diffusion. As the CA can organize clusters +across adjacent NWP model grid-boxes, the method offers a novel approach to +enhance the probability of triggering of convection in nearby areas, +representing subgrid fluctuations in temperature and humidity, and triggering +in premoistened regions if convection is triggered in a cluster. The +stochastic nature of the CA may enhance organization in different +directions within the grid-box, and across grid-boxes, depending on the +initial seed. If the model is run as an ensemble, the convection scheme's +stochastic triggering function can help to improve uncertainty estimates +associated with subgrid fluctuations of temperature and humidity and +randomness in organization. In this work, model grid boxes in which the +CA's largest connecting plume exceeds a given threshold will be considered +as candidates for convective activation, in addition to saSAS’s current +triggering criteria. + +- Closure (\p ca_closure): We assume that convection that organizes into +plumes with larger radii tends to cover a larger area fraction of the +grid-box and thereby acts to enhance the cloud base mass flux. In this +coupling strategy, we again count the number of plumes (represented by +connected cellular automaton cells), and their associated size within +each NWP grid-box. If the largest cluster of cells found on the subgrid +is larger than a set radius, then the cloud base mass-flux is enhanced in +that grid-box by 25% (selected based on experimentation). This option is +being revisited by reformulating the entire closure using a prognostic +evolution of the updraft area fraction, and is in its current formulation +not recommended. + + +*/ diff --git a/physics/docs/pdftxt/GFS_SAMFdeep.txt b/physics/docs/pdftxt/GFS_SAMFdeep.txt index 1112cb05c..99ea4cb13 100644 --- a/physics/docs/pdftxt/GFS_SAMFdeep.txt +++ b/physics/docs/pdftxt/GFS_SAMFdeep.txt @@ -28,7 +28,7 @@ downdrafts and only one cloud type (the deepest possible), rather than a spectrum based on cloud top heights or assumed entrainment rates. The scheme was implemented for the GFS in 1995 by Pan and Wu (1995) \cite pan_and_wu_1995, - with further modifications discussed in Han and Pan (2011) \cite han_and_pan_2011 , including the calculation + with further modifications discussed in Han and Pan (2011) \cite han_and_pan_2011, including the calculation of cloud top, a greater CFL-criterion-based maximum cloud base mass flux, updated cloud model entrainment and detrainment, improved convective transport of horizontal momentum, a more general triggering function, @@ -50,7 +50,10 @@ cloud condensate in the updraft. The lateral entrainment is also enhanced to more strongly suppress convection in a drier environment. - In further update for FY19 GFS implementation, interaction with turbulent + +\subsection gfsv16updates GFSv16 Updates + + In further update for FY19 GFSv16 implementation, interaction with turbulent kinetic energy (TKE), which is a prognostic variable used in a scale-aware TKE-based moist EDMF vertical turbulent mixing scheme, is included. Entrainment rates in updrafts and downdrafts are proportional to sub-cloud @@ -58,167 +61,24 @@ cumulus convection is deduced from cumulus mass flux. On the other hand, tracers such as ozone and aerosol are also transported by cumulus convection. - Occasional model crashes have been occurred when stochastic physics is on, - due to too much convective cooling and heating tendencies near the cumulus - top which are amplified by stochastic physics. To reduce too much convective - cooling at the cloud top, the convection schemes have been modified for the + Occasional model crashes occurred when stochastic physics is on, + due to too strong convective cooling and heating tendencies near the cumulus + top which are amplified by stochastic physics. In order to alleviate this, + the convection schemes were modified for the rain conversion rate, entrainment and detrainment rates, overshooting layers, and maximum allowable cloudbase mass flux (as of June 2018). -\subsection ca_page Cellular Automata Stochastic Convective Organization Scheme - -\b Scientific \b Background - -Cumulus clouds in the atmosphere can organize into a variety of sizes, ranging -from small fair‐weather cumulus clouds, rain showers and thunderstorms, to -larger scale weather systems. In weather and climate models, such organization -is traditionally not well-represented as the motions associated with cumulus -clouds are generally too small to be resolved by the numerical model. -In this scheme we use a stochastic cellular automaton (CA), a mathematical -model often used to describe self‐organizing behavior in physical systems to -represent the effects of convective organization. The scheme addresses the -effect of convective organization in a bulk-plume cumulus convection -parameterizations (saSAS), where this type of organization has to be -represented in terms of how the resolved flow would “feel” convection if -more coherent structures were present on the subgrid. - -In addition, for longer range forecasts (seasonal, decadal, climate), -the relevance of stochastic cumulus convection in numerical models can also -be discussed in terms of noise induced forcing. As an example, on the -time scale of organized convectively coupled waves, the small scale individual -convective plumes grow and decay so rapidly that they are not predictable -on time-scales longer than a few hours, whereas the organized larger scale -convectively coupled wave envelope can have a deterministic limit of -predictability of about two weeks. Thus, for longer range forecasts, -individual convective plumes can be viewed as stochastic noise - they can -have an impact on the convectively coupled waves (due to noise forcing), -but they are not predictable on their own. By providing the CA with a -stochastic initialization, the effect of stochastic cumulus convection -is also represented by the scheme. - -The scientific motivation for the scheme, the CA rulesets explored, and -the impact on convectively coupled equatorial waves can be found in the -following references; Bengtsson et al. 2011 \cite Bengtsson_2011, -Bengtsson et al. 2013 \cite bengtsson_et_al_2013, -Bengtsson and Kornich (2016) \cite bengtsson_and_kornich_2016, -Bengtsson et al 2019 \cite Bengtsson_2019, -and Bengtsson et al. 2021 \cite bengtsson_et_al_2021. - -\b Technical \b remarks - -The CA source code is located in the stochastic physics submodule in -the ufs-weather-model: https://github.com/noaa-psd/stochastic_physics . -In the UFS Weather Model, the main call to the CA routines are made -from FV3/stochastic_physics_driver.F90. - -There are currently two options to evolve the CA (can be done simultaneously); -(\p ca_global) a large scale global pattern which evolves the ruleset according -to game of life with cell history, or (\p ca_sgs) a sub-grid scale pattern -which is conditioned on a forcing from the atmospheric model. The two options -are controlled by namelist and are evolved in cellular_automata_globa.F90 -and cellular_automata_sgs.F90 respectively. Both approaches use the main -CA module update_ca.F90 to evolve the CA in time. Since the CA needs to know -about its neighborhood it uses the halo information to gather the state -in adjacent MPI domains and/or adjacent cube sphere interfaces. - -\b The \p ca_sgs \b option - \b Coupled \b to \b saSAS \b cumulus \b convection \b scheme - -The evolution of the CA is an extension to the automaton family known as “Generations,” -which in turn is based on the “Game of Life”(Chopard & Droz, 1998 \cite Chopard_1998) - but adds cell history to the rule set. It is a deterministic CA ruleset, initialized -with Gaussian white noise. Thus, when used in an ensemble system, each ensemble -member can provide a different seed to the random number generator governing -the initial state to then generate a different evolution for each member. -By cell history we refer to newborn cells being given a “lifetime,”τ, -that is incrementally reduced by 1 each time step where the rules are not met, - in contrast to going directly from 1 to 0. The CA is conditioned on a -forcing from the host model through the lifetime variable τ such that: - -\f[ -\tau =N\left( \frac{\int_{l=1}^{l=top}E\frac{dp }{g} }{\max\left( \int_{l=1}^{l=top} E\frac{d p}{g}\right)} \right) -\f] - -here, N is an integer that when multiplied by the model time-step represents -a physical time scale, such that τ is longer in regions where the forcing is larger, -E is the vertically integrated convective rain evaporation from the -saSAS cumulus convection scheme stored in Coupling%condition. The denominator is -the maximum value of the forcing in the global domain. While the grid-scale -forcing in practice could be any two-dimensional field, we choose here -to set it as the vertically integrated subgrid rain evaporation amount, -serving as an indicator of geographical regions where enhanced subgrid -organization may arise through convective cold-pools. - -The CA is evolved on a finer grid than the numerical prediction -host model (size controlled by namelist), and can be either coarse -grained back to the host model grid as a fraction, or (in case of \p nca_plumes = .true.) -give back the maximum number of connected “plumes” (represented by -connected CA cells), and their associated size within each numerical -prediction host model grid-box. nca_plumes is default true and the -maximum cluster size is passed to the saSAS cumulus convection scheme -in the Coupling%ca_deep container. - -Depending on the activated namelist options, the CA can feed back to -the saSAS convection scheme via the entrainment (\p ca_entr), closure -(\p ca_closure) or convective initiation (\p ca_trigger) in the following way: - -- Entrainment (\p ca_entr): In entraining plume model bulk mass-flux schemes, -the upward mass-flux is typically parameterized as a function of environmental -air being entrained into the rising plume (as well as parcel properties at -cloud base). The fractional entrainment is described as a function of the -plume radius. Larger thermals (plumes) have smaller fractional entrainment, -which is a consequence of the fact that larger areas have relatively smaller -perimeters. In this scheme, the assumption is that subgrid organization will -lead to a few larger plumes rather than several smaller plumes, such that -the grid-box average fractional entrainment is reduced. Thus, after -the CA is updated, we count the number of plumes, and their associated -size within each NWP grid-box (\p nca_plumes = true). If the largest -cluster of cells found on the subgrid is larger than a set radius, then the -fractional entrainment rate is reduced at that grid-point by 30% -(selected based on experimentation) - -- Triggering (\p ca_trigger): In NWP models physical processes are parameterized -in columns, and the horizontal interaction between physical processes takes -place only through advection and diffusion. As the CA can organize clusters -across adjacent NWP model grid-boxes, the method offers a novel approach to -enhance the probability of triggering of convection in nearby areas, -representing subgrid fluctuations in temperature and humidity, and triggering -in premoistened regions if convection is triggered in a cluster. The -stochastic nature of the CA may enhance organization in different -directions within the grid-box, and across grid-boxes, depending on the -initial seed. If the model is run as an ensemble, the convection scheme's -stochastic triggering function can help to improve uncertainty estimates -associated with subgrid fluctuations of temperature and humidity and -randomness in organization. In this work, model grid boxes in which the -CA's largest connecting plume exceeds a given threshold will be considered -as candidates for convective activation, in addition to saSAS’s current -triggering criteria. - -- Closure (\p ca_closure): We assume that convection that organizes into -plumes with larger radii tends to cover a larger area fraction of the -grid-box and thereby acts to enhance the cloud base mass flux. In this -coupling strategy, we again count the number of plumes (represented by -connected cellular automaton cells), and their associated size within -each NWP grid-box. If the largest cluster of cells found on the subgrid -is larger than a set radius, then the cloud base mass-flux is enhanced in -that grid-box by 25% (selected based on experimentation). This option is -being revisited by reformulating the entire closure using a prognostic -evolution of the updraft area fraction, and is in its current formulation -not recommended. +\subsection gfsv17updates_samf GFS saSAS Scheme Updates in GFSv17 + The updates to the SAMF parameterization described above, between GFSv16 and GFSv17 + are carefully outlined in Bengtsson and Han (2004)(submitted to WAF). The main updates include: -\subsection gen_enh Physics Updates in GFS Cumulus Convection + - Implementation of a positive definition mass-flux scheme and a method for removing the negative tracers (Han et al. 2022 \cite Han_et_al_2022) + - Introduction of a new closure based on a prognostic evolution of the convective updraft area fraction in both shallow and deep convection (Bengtsson et al. 2022 \cite Bengtsson_2022) + - Introduction of 3D effects of cold-pool dynamics and stochastic initiation using self-organizing \ref cellular_automata (Bengtsson et al. 2021 \cite bengtsson_et_al_2021) + - Introduction of environmental wind shear and TKE dependence in convection, to seek improvements in hurricane forecast prediction (Han et al. 2024 \cite Han_2024) + - Stricter convective initiation criteria to allow for more CAPE to build up to address a low CAPE bias in GFSv16 (Han et al. 2021 \cite han_2021) + - Reduction of convective rain evaporation rate to address a systematic cold bias near the surface in GFSv16 (Han et al. 2021 \cite han_2021) -- To enhance the surface-based convective available potential energy (CAPE), -more strict convection trigger conditions are applied. -- Enhanced downdraft detrainments start from 60 mb above the ground surface -rather than from the cloud base. -- Reduced rain evaporation with the removal of wind shear dependency, which -helps to reduce cold bias in tropospheric temperature profile especially over Tropics. -- Separation cloud depth of deep and shallow convection is -increased to 200 hPa from 150 hPa. -- Updraft entrainment rates for moisture, hydrometeors, and tracers are -increased by about 30%. -- A positive definite TVD (Total Variation Diminishing) mass-flux transport -scheme for moisture, hydrometeors and tracers and a method for removing negative tracer mixing ratio values have been implemented. \sa NCEP Office Note 505 \cite https://doi.org/10.25923/cybh-w893 and 506 \cite https://doi.org/10.25923/5051-3r70 \section intra_deep Intraphysics Communication diff --git a/physics/docs/pdftxt/RRFS_v1_suite.txt b/physics/docs/pdftxt/RRFS_v1_suite.txt index f44321b3f..568bc4872 100644 --- a/physics/docs/pdftxt/RRFS_v1_suite.txt +++ b/physics/docs/pdftxt/RRFS_v1_suite.txt @@ -6,7 +6,7 @@ The RRFS_v1 suite is one of the candidates for the future operational implementation of the Rapid Refresh Forecast System (RRFS), which can be configured using the UFS SRW App. -The RRFS_v1beta suite uses the parameterizations in the following order: +The RRFS_v1 suite uses the parameterizations in the following order: - \ref SGSCLOUD_page - \ref GFS_RRTMG_page - \ref SFC_MYNNSFL diff --git a/physics/docs/pdftxt/all_schemes_list.txt b/physics/docs/pdftxt/all_schemes_list.txt index aa28e26ce..82d081e38 100644 --- a/physics/docs/pdftxt/all_schemes_list.txt +++ b/physics/docs/pdftxt/all_schemes_list.txt @@ -24,7 +24,7 @@ which facilitates model development and code maintenance. While some individual \b Cumulus \b Parameterizations - \subpage GFS_SAMFdeep - - \ref ca_page + - \subpage cellular_automata - \subpage GFS_SAMFshal - \subpage CU_GF diff --git a/physics/docs/pdftxt/ccppv7_phy_updates.txt b/physics/docs/pdftxt/ccppv7_phy_updates.txt new file mode 100644 index 000000000..661ff2ff6 --- /dev/null +++ b/physics/docs/pdftxt/ccppv7_phy_updates.txt @@ -0,0 +1,77 @@ +/** +\page ccppv7_phy Physics Update Summary + +\b General \b Changes: + Add GFS_v17, GFS_v16_RRTMGP, and RRFS_v1 suites as new supported suite +- New the Community Lake Model \ref CLM_LAKE_model in RRFS_v1 suite +- New \ref GFS_ugwpv1_gsldrag in GFS_v17 suite +- New \ref GFS_RRTMGP_page in GFS_v16_RRTMGP suite +- New RRFS SD scheme in RRFS_v1 suite +- rename GSL drag suite to \todo gsldrag + +\b GFS \b Scale-aware \b TKE-EDMF \b PBL \b and \b Cumulus \b Schemes: + +The updates between GFSv16 and GFSv17 are carefully outlined in Bengtsson and Han (2004)(submitted to WAF). The main updates include: + +- Implementation of a positive definition mass-flux scheme and a method for removing the negative tracers (Han et al. 2022 \cite Han_et_al_2022) +- Introduction of a new closure based on a prognostic evolution of the convective updraft area fraction in both shallow and deep convection (Bengtsson et al. 2022 \cite Bengtsson_2022) +- Introduction of 3D effects of cold-pool dynamics and stochastic initiation using self-organizing \ref cellular_automata (Bengtsson et al. 2021 \cite bengtsson_et_al_2021) +- Introduction of environmental wind shear and TKE dependence in convection, to seek improvements in hurricane forecast prediction (Han et al. 2024 \cite Han_2024) +- Stricter convective initiation criteria to allow for more CAPE to build up to address a low CAPE bias in GFSv16 (Han et al. 2021 \cite han_2021) +- Reduction of convective rain evaporation rate to address a systematic cold bias near the surface in GFSv16 (Han et al. 2021 \cite han_2021) + +\b Thompson \b Microphysics \b Scheme: +- Ice generation supersaturation requirement reduced from 0.25 to 0.15 to generate more ice at the upper +levels and reduce the outgoing longwave radiation bias +- Cloud number concentration divided into two parts (over land and others). Number concentration over +ocean reduced to a smaller number (50/L) from its previous default (100/L). Both changes were made to reduce +excessive surface downward shortwave radiative flux off coastal regions including the Southeast Pacific +- Small fixes to the minimum size of snow and collision constants + +\note The above improvements were tested with the non-aerosol option (in GFS_v17 suite), so results with the aerosol-aware +Thompson (in RRFS_v1 suite) may vary. + +\b NoahMP \b Land \b Surface \b Model: +- Option for using the unified frozen precipitation fraction in NoahMP +- Diagnostic 2-meter temperature and humidity now based on vegetation and bare-ground tiles (new namelist option \a iopt_diag) +- Bug fixes for GFS-based thermal roughness length scheme +- New soil color dataset introduced to improve soil albedo to reduce the large warm bias found in the Sahel desert +- Wet leaf contribution factor is included +- Leaf-area index now depends on momentum roughness length + +\b RUC \b Land \b Surface \b Model: +- Initialization of land and ice emissivity and albedo with consideration of partial snow cover +- Initialization of water vapor mixing ratio over land ice +- Initialization of fractions of soil and vegetation types in a grid cell +- Changes in the computation of a flag for sea ice: set to true only if \a flag_ice=.false. (atmosphere uncoupled from the sea ice model) +- Separate variable for sea ice, for example: \a snowfallac is replaced with \a snowfallac_ice +- Solar angle dependence of albedo for snow-free land +- Stochastic physics perturbations (SPP) introduced for emissivity, albedo and vegetation fraction +- Coefficient in soil resistance formulation (Sakaguchi and Zeng, 2009 \cite sakaguchi_and_zeng_2009) raised from 0.7 to 1.0 to increase soil resistance to evaporation +- Computation of snow cover fraction and snow thermal conductivity updated + + +\b MYNN-EDMF \b PBL \b Scheme: +- Small increase of buoyancy length scale in convective environment +- Patch for ensuring non-zero cloud fractions for all grid cells where cloud mixing ratio is greater than 1e-6 or ice mixing ratio is greater than 1e-9 + +\b Subgrid-scale \b (SGS) \b Clouds \b Scheme: +- Bug fix for cloud condensate input into RRTMG radiation +- New code section for use with SAS cumulus scheme +- Cloud fraction now computed as a mix between the area-dependent form and the modified Chaboureau and Bechtold (2005) \cite Chaboureau_2005 form +- Adjusted limit for the boundary flux functions + +\b MYNN \b Surface-layer \b Scheme +- Reintroduce friction velocity averaging over water to reduce noise in 10-m winds in the hurricane regime + +\b Grell-Freitas \b Scale \b and \b Aerosol \b Aware \b Convection \b Scheme: +- Update for aerosol-awareness (experimental) +- Scale-awareness turned off when explicit microphysics is not active anywhere in the column +- Convection is completely suppressed at grid points where the MYNN PBL sheme produces shallow convection +- Radar reflectivity considers mass flux PDF as well as whether scale-awareness is turned on at the gird point in equation + +\b Unified \b Gravity \b Wave \b Physics \b Scheme: +\todo UGWP updates for HR4 + + +*/ diff --git a/physics/docs/pdftxt/mainpage.txt b/physics/docs/pdftxt/mainpage.txt index 6a5d0250b..b6314d4d4 100644 --- a/physics/docs/pdftxt/mainpage.txt +++ b/physics/docs/pdftxt/mainpage.txt @@ -23,12 +23,6 @@ by the Development Testbed Center (DTC), supports suites: - \ref RRFS_v1_page - \ref WoFS_v0_page - -New schemes and capability highlights in this release: -- \ref GFS_ugwpv1_gsldrag -- \ref GFS_RRTMGP_page -- \ref CLM_LAKE_model - In this website you will find documentation on various aspects of each parameterization, including a high-level overview of its function, the input/output argument list, and a description of the algorithm.