This paper proposes a revised vertical diffusion package with a nonlocal turbulent mixing coefficient in the planetary boundary layer (PBL). Based on the study of Noh et al. and accumulated results of the behavior of the Hong and Pan algorithm, a revised vertical diffusion algorithm that is suitable for weather forecasting and climate prediction models is developed. The major ingredient of the revision is the inclusion of an explicit treatment of entrainment processes at the top of the PBL. The new diffusion package is called the Yonsei University PBL (YSU PBL). In a one-dimensional offline test framework, the revised scheme is found to improve several features compared with the Hong and Pan implementation. The YSU PBL increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime, which alleviates the well-known problems in the Medium-Range Forecast (MRF) PBL. Excessive mixing in the mixed layer in the presence of strong winds is resolved. Overly rapid growth of the PBL in the case of the Hong and Pan is also rectified. The scheme has been successfully implemented in the Weather Research and Forecast model producing a more realistic structure of the PBL and its development. In a case study of a frontal tornado outbreak, it is found that some systematic biases of the large-scale features such as an afternoon cold bias at 850 hPa in the MRF PBL are resolved. Consequently, the new scheme does a better job in reproducing the convective inhibition. Because the convective inhibition is accurately predicted, widespread light precipitation ahead of a front, in the case of the MRF PBL, is reduced. In the frontal region, the YSU PBL scheme improves some characteristics, such as a double line of intense convection. This is because the boundary layer from the YSU PBL scheme remains less diluted by entrainment leaving more fuel for severe convection when the front triggers it.

This paper addresses and documents a number of issues related to the implementation of an advanced land surface–hydrology model in the Penn State–NCAR fifth-generation Mesoscale Model (MM5). The concept adopted here is that the land surface model should be able to provide not only reasonable diurnal variations of surface heat fluxes as surface boundary conditions for coupled models, but also correct seasonal evolutions of soil moisture in the context of a long-term data assimilation system. In a similar way to that in which the modified Oregon State University land surface model (LSM) has been used in the NCEP global and regional forecast models, it is implemented in MM5 to facilitate the initialization of soil moisture. Also, 1-km resolution vegetation and soil texture maps are introduced in the coupled MM5–LSM system to help identify vegetation/water/soil characteristics at fine scales and capture the feedback of these land surface forcings. A monthly varying climatological 0.15° × 0.15° green ... Abstract This paper addresses and documents a number of issues related to the implementation of an advanced land surface–hydrology model in the Penn State–NCAR fifth-generation Mesoscale Model (MM5). The concept adopted here is that the land surface model should be able to provide not only reasonable diurnal variations of surface heat fluxes as surface boundary conditions for coupled models, but also correct seasonal evolutions of soil moisture in the context of a long-term data assimilation system. In a similar way to that in which the modified Oregon State University land surface model (LSM) has been used in the NCEP global and regional forecast models, it is implemented in MM5 to facilitate the initialization of soil moisture. Also, 1-km resolution vegetation and soil texture maps are introduced in the coupled MM5–LSM system to help identify vegetation/water/soil characteristics at fine scales and capture the feedback of these land surface forcings. A monthly varying climatological 0.15° × 0.15° green ...

This study summarizes the revision performed on the surface layer formulation of the Weather Research and Forecasting (WRF) model. A first set of modifications are introduced to provide more suitable similarity functions to simulate the surface layer evolution under strong stable/unstable conditions. A second set of changes are incorporated to reduce or suppress the limits that are imposed on certain variables in order to avoid undesired effects (e.g., a lower limit in u*). The changes introduced lead to a more consistent surface layer formulation that covers the full range of atmospheric stabilities. The turbulent fluxes are more (less) efficient during the day (night) in the revised scheme and produce a sharper afternoon transition that shows the largest impacts in the planetary boundary layer meteorological variables. The most important impacts in the near-surface diagnostic variables are analyzed and compared with observations from a mesoscale network.

A nonhydrostatic extension to the Pennsylvania State University-NCAR Mesoscale Model is presented. This new version employs reference pressure as the basis for a terrain-following vertical coordinate and the fully compressible system of equations. In combination with the existing initialization techniques and physics of the current hydrostatic model, this provides a model capable of real-data simulations on any scale, limited only by data resolution and quality and by computer resources. The model uses pressure perturbation and temperature as prognostic variables as well as a B-grid staggering in contrast to most current nonhydrostatic models. The compressible equations are solved with a split-time- step approach where sound waves are treated semi-implicitly on the shorter step. Numerical techniques and finite differencing are described. Two-dimensional tests of flow over a bell-shaped hill on a range of scales were carded out with the hydrostatic and nonhydrostatic models to contrast the two and... Abstract A nonhydrostatic extension to the Pennsylvania State University-NCAR Mesoscale Model is presented. This new version employs reference pressure as the basis for a terrain-following vertical coordinate and the fully compressible system of equations. In combination with the existing initialization techniques and physics of the current hydrostatic model, this provides a model capable of real-data simulations on any scale, limited only by data resolution and quality and by computer resources. The model uses pressure perturbation and temperature as prognostic variables as well as a B-grid staggering in contrast to most current nonhydrostatic models. The compressible equations are solved with a split-time- step approach where sound waves are treated semi-implicitly on the shorter step. Numerical techniques and finite differencing are described. Two-dimensional tests of flow over a bell-shaped hill on a range of scales were carded out with the hydrostatic and nonhydrostatic models to contrast the two and...

A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

Climate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enhanced snowfall on the order of 10%–25% over the Colorado Headwaters region, with the enhancement being less in the core headwaters region due to the topographic reduction of precipitation upstream of the region (rain-shadow effect). The main climate change impacts are in the enhanced melting at the lower-elevation bound of the snowpack and the increased snowfall at higher elevations. The changes in peak snow mass are generally near zero due to these two compensating effects, and simulated wintertime total runoff is above current levels. The 1 April snow water equivalent (SWE) is reduced by 25% in the warmer climate, and the date of maximum SWE occurs 2–17 days prior to current climate results, consistent with previous studies.