Planetary Boundary Layer Schemes Research Articles

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method.

Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to Physics Options over the Loess Plateau

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The “best” configuration depends on the criteria.

Study of boundary layer parameterization simulation uncertainties of sand-dust storm windfield using high-resolution three-dimensional Doppler wind lidar data

The processes of dust emission, the development, and dissipation of sand and dust storms all exhibit dynamic and rapidly changing characteristics, posing significant challenges for us to accurately represent their complex behaviors in numerical simulations. In the numerical weather prediction model, the selection and optimization of planetary boundary layer (PBL) parameterization schemes have a direct impact on the simulation results of PBL wind fields, primarily due to the significant uncertainty in the fine-grained simulation of PBL wind field structure. To gain a deeper understanding of the sources of uncertainty in simulating sand-dust storms through vertical wind field structure simulations of the PBL, this study utilizes high-resolution 3D Doppler wind lidar (DWL) data. Focusing on sand-dust storm wind fields with distinctive regional characteristics in Inner Mongolia, China, it examines the turbulent momentum equation and assumptions of the PBL. A comprehensive and systematic evaluation was conducted on the characteristics of horizontal and vertical wind fields, wind shear, as well as the dynamic and thermodynamic differences that arise when simulating typical sand-dust storm wind fields using the Weather Research and Forecasting (WRF) Model with various PBL schemes. This evaluation focused on simulating the typical wind field of sand-dust storms during various stages, including dust generation, occurrence, development, and dissipation. The findings indicate that non-adiabatic heating plays a significant role in the sustainability and evolution of sand-dust storms. The wind shear index holds particular importance as it embodies the dynamic factor in the model. The PBL schemes such as Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2), A new version of asymmetric convective model (ACM2), and Grenier-Bretherton-McCaa (GBM), which factor in buoyancy effects, demonstrate more accurate wind shear simulations in sand-dust storms. Furthermore, the ACM2 scheme effectively replicates the fundamental variation characteristics of meteorological elements throughout the sand-dust storm lifecycle.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Sensitivity of simulation of rapidly intensified tropical cyclones to local planetary boundary layer scheme

Sensitivity of simulation of rapidly intensified tropical cyclones to local planetary boundary layer scheme

The influence of dry deposition on surface ozone simulations under different planetary boundary layer schemes over eastern China

The influence of dry deposition on surface ozone simulations under different planetary boundary layer schemes over eastern China

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

Abstract To reduce hurricane intensity bias, the NCEP Global Forecast System (GFS) planetary boundary layer (PBL) and convection schemes have been updated with a new parameterization for environmental wind shear and enhanced entrainment and detrainment rates with increasing PBL or subcloud mean turbulent kinetic energy (TKE) in their updraft and downdraft mass-flux schemes. Tests with the GFS show that the updated schemes significantly reduce the hurricane intensity bias by reducing the momentum transport in the mass-flux schemes. Along with the reduced intensity bias, the hurricane intensity and track errors have also been reduced. On the other hand, to reduce the PBL overgrowth over areas with a higher vegetation fraction or larger surface roughness, the entrainment rate in the PBL mass-flux scheme has also been increased with increasing vegetation fraction or increasing surface roughness. This entrainment rate increase has increased near-surface moisture, and as a result, helped to increase the underestimated convective available potential energy (CAPE) forecasts over the continental United States.

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part II: Sensitivity of Surface Wind Speeds to Model Resolution and Physics

Abstract Strong wind events can cause severe economic loss and societal impacts. Peak winds over Wyoming and Colorado occur during the wintertime months, and in Part I of this two-part series, it was shown that the High Resolution Rapid Refresh (HRRR) model displays large negative biases with respect to strong wind events. In Part II of this study, we address two questions: 1) does increasing the horizontal resolution improve the representation of strong wind events over this region, and 2) are the biases in HRRR-forecasted winds related to the selected planetary boundary layer (PBL), surface layer (SL), and/or land surface model (LSM) parameterizations? We conduct Weather Research and Forecasting (WRF) simulations to address these two main questions. Increasing the horizontal resolution leads to a small improvement in the simulation of strong wind speeds over the complex terrain of Wyoming and Colorado. In general, changes in the PBL, SL, and LSM parameterizations show much larger changes in simulated wind speeds compared to increasing the model resolution alone. Specifically, changing from the Mellor-Yamada-Nakanishi-Niino scheme to the Mellor-Yamada-Janjić PBL and SL schemes results in nearly no change in the r2 values, but there is a decrease in the magnitude of the strong wind speed bias (−12.52 m s−1 to −10.16 m s−1). We attribute these differences to differences in the diagnosis of surface wind speeds and mixing in the boundary layer. Further analysis is conducted to determine the value of 1-km forecasts of strong winds compared with wind speed diagnostics commonly used by the National Weather Service.

Univariate Flux Partition Functions for Planetary Boundary Layer Schemes at Gray Zone Resolutions

Abstract When the horizontal grid spacing of a numerical weather prediction model approaches kilometer scale, the so-called gray zone range, turbulent fluxes in the convective boundary layer (CBL) are partially resolved and partially subgrid scale (SGS). Knowledge of the partition between resolved and SGS turbulent fluxes is key to building scale-adaptive planetary boundary layer (PBL) schemes that are capable of regulating the SGS fluxes with varying grid spacing. However, flux partition depends not only on horizontal grid spacing, but also on local height, bulk stability of the boundary layer, and the particular turbulent flux. Such multivariate functions are difficult to construct analytically, so their implementations in scale-adaptive PBL schemes always involve certain levels of approximation that can lead to inaccuracies. This study introduces a physically based perspective for the flux partition functions that greatly simplifies their implementation with high accuracy. By introducing an appropriate scaling length λ that accounts for both height and bulk stability dependencies, the dimensionality of the partition functions is reduced to a single dimensionless group. Based on the analysis of a comprehensive large-eddy simulation dataset of the CBL, it is further shown that λ’s height and bulk stability dependencies can be separately represented by a similarity length scale and a stability coefficient. The resulting univariate partition functions are incorporated into a traditional first-order PBL scheme as a proof of concept. Our results show that the augmented scheme well-reproduces the SGS fluxes at gray zone resolutions. Significance Statement Flux partition functions are a key component in most scale-adaptive planetary boundary layer (PBL) schemes developed for kilometer- and subkilometer-resolution numerical weather prediction models. They regulate the parameterized turbulent fluxes as a function of horizontal grid spacing, while they also depend on height and atmospheric stability. Such multivariate dependencies forbid simple analytical expressions, and as a result, partition functions implemented in scale-adaptive PBL schemes are generally simplified at the cost of accuracy in previous works. This study investigates the possibility of constructing partition functions that are both accurate and easy to parameterize. Utilizing a physically based length scale, univariate partition functions are built, evaluated, and put into a conventional PBL scheme to improve the gray zone turbulence parameterization.

Identifying Meteorological Drivers for Errors in Modeled Winds along the Northern California Coast

Abstract An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.

Improving Estimates of Land–Atmosphere Coupling Through a Novel Framework of Land Aridity Classification

AbstractThe evapotranspiration (ET) regime is an illustration of the water‐energy interactions at the land surface and is important for regional climate. In this study, we propose a revised framework for land aridity classification based on the ET regime and the conditional mutual information method. The proposed framework effectively captures the influences of short‐term water and energy supply on ET and highlights their potential roles in extreme events. This framework can be a supplement to the traditional classification schemes that rely on long‐term mean climate. Furthermore, we examine the sensitivity of this land aridity framework to different model physics parameterization schemes. The cumulus schemes are found to have the most important impact, followed by the radiation and microphysics schemes, while the planetary boundary layer schemes have the weakest impact. These findings provide valuable insights for the identification of land aridity and its relationship with land–atmosphere interactions.

The Influence of Terrain Smoothing on Simulated Convective Boundary-Layer Depths in Mountainous Terrain

Many applications rely on a correct estimation of the convective boundary layer (CBL) depth over mountainous terrain, but often these applications use numerical model simulations. Although models inevitably smooth terrain, the amount of smoothing depends on grid spacing. We investigate the behavior of the CBL in coarse- and fine-grid models applied to mountainous terrain by using output from an operational mesoscale modeling system and by performing quasi-idealized simulations. We investigate different areas in different climate zones using different CBL top derivation methods, grid spacing ratios, planetary boundary layer (PBL) schemes, and terrain smoothing. We find that when compared to fine-grid simulations, CBL depths are systematically larger in coarse domains over mountaintops, and to a lesser extent in valleys. On average, differences between coarse- and fine-domains over mountaintops could reach around 10%. In certain locations, differences could be as high as 25%. We attribute the result to terrain smoothing. Similarly, when using a coarse-grid CBL height (relative to mean sea level) interpolated using fine-grid terrain information, there is good agreement with fine-grid CBL depths over mountaintops and less agreement in valleys. Our results have implications for applications that use output from coarse model grids in mountainous terrain. These include inverse modeling studies (e.g., greenhouse gas budget estimations or integrated water vapor transport), PBL evaluation studies, climate research, air quality applications, planning and executing prescribed burns, and studies associated with precipitation over mountainous terrain.

Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event

The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.

Mesoscale structures in the Orinoco basin during an extreme precipitation event in the tropical Andes

During the night of March 31st, 2017, a severe precipitation event affected the city of Mocoa, in the tropical Andes. Total 24-h accumulated precipitation during that day was the fourth largest on record. Satellite data shows that the event was associated with a Mesoscale Convective System (MCS) that formed over the Amazon and moved westward, reaching the tropical Andes. Reanalysis data suggests that a rapid intensification of the Orinoco Low-Level Jet (OLLJ) traveling southwestward parallel to the Andes was a precursor that favored the zones of convergence for MCS formation. Upstream intensification of the OLLJ was evident 8 h prior to the Mocoa precipitation event. Given the lack of a dense network of observations in this understudied region, we use the Weather Research and Forecasting model (WRF) to explore the plausible mesoscale structures in the OLLJ region associated with the initiation and development of the MCS. We study an ensemble of simulations with different grid spacings (12, 4 and 1.3 km) and Planetary Boundary Layer (PBL) schemes (YSU, MYNN and QNSE). The more realistic MCSs were obtained with the QNSE and YSU schemes, given that the corresponding simulations included a density current in the lowest levels moving parallel to the Andes, with a sharp line of convergence and large vertical velocities over the leading edge of the mesoscale disturbance. In contrast, the MYNN scheme produced a weaker OLLJ and no density current. It is suggested that the stronger vertical mixing in the MYNN scheme was associated with the vertical dilution of the OLLJ, and with a much weaker low-level traveling perturbation via the upward radiation of energy by gravity waves. Our results help to better understand flood-producing extreme events over the poorly studied Andes-Amazon region and provide the groundwork for improved predictability of such storms.

Analyzing “Gray Zone” Turbulent Kinetic Energy Predictions in the Boundary Layer from Three WRF PBL Schemes over New York City and Comparison with Aircraft Measurements

Abstract We investigated the ability of three planetary boundary layer (PBL) schemes in the Weather Research and Forecasting (WRF) Model to simulate boundary layer turbulence in the “gray zone” (i.e., resolutions from 100 m to 1 km). The three schemes chosen are the well-established MYNN PBL scheme and the two newest PBL schemes added to WRF: the three-dimensional scale-adaptive turbulent kinetic energy scheme (SMS-3DTKE) and the E–ε parameterization scheme (EEPS). The SMS-3DTKE scheme is designed to be scale aware and avoid the double counting of TKE in simulations within the gray zone. We evaluated their performance using aircraft measurements obtained during three research flights immediately downwind of Manhattan, New York City, New York. The MYNN PBL scheme simulates TKE best, despite not being scale aware and slightly underestimating TKE from observations, whereas the SMS-3DTKE scheme appears to be overly scale aware for the three flights examined, in particular, when combined with the MM5 surface layer scheme. The EEPS scheme significantly underestimates TKE, mostly in the elevated layers of the boundary layer. In addition, we examined the impact of flow over tall buildings on observed TKE and found that only the windiest day showed a significant increase in TKE directly downwind of Manhattan. This impact was not reproduced by any of the model configurations, regardless of the land-use data selected, although the better resolved National Land Cover Database (NLCD) land use led to a slight improvement of the spatial distribution of TKE, implying that more explicit representation of the impact of tall buildings may be needed to fully capture their impact on boundary layer turbulence. Significance Statement Because the majority of the world’s population lives in cities, it is important to accurately simulate the atmosphere above and around these cities including the turbulence caused by tall buildings. This turbulence can significantly impact the mixing and dilution of air pollutants and other toxins in highly populated urban environments. The scale of cities often falls into what is known as the “gray zone” for turbulence modeling, which has been analyzed theoretically before but rarely in varied real-world conditions. Our analysis around New York City, New York, suggests that model turbulence schemes can match observations relatively well even at gray zone scales, although newer schemes require refinement, and all schemes tend to underestimate turbulence downwind of tall buildings.

Parameterizations of Boundary Layer Mass Fluxes in High-Wind Conditions for Tropical Cyclone Simulations

Abstract Development of accurate planetary boundary layer (PBL) parameterizations in high-wind conditions is crucial for improving tropical cyclone (TC) forecasts. Given that eddy-diffusivity mass-flux (EDMF)-type PBL schemes are designed for nonhurricane boundary layers, this study examines the uncertainty of MF parameterizations in hurricane conditions by performing three-dimensional idealized simulations. Results show that the surface-driven MF plays a dominant role in the nonlocal turbulent fluxes and is comparable to the magnitude of downgradient momentum fluxes in the middle portion of TC boundary layers outside the radius of maximum wind (RMW); in contrast, the stratocumulus-top-driven MF is comparably negligible and exerts a marginal impact on TC simulations. To represent the impact of vertical wind shear on damping rising thermal plumes, a new approach of tapering surface-driven MF based on the surface stability parameter is proposed, aiming to retain the surface-driven MF only in unstable boundary layers. Compared to a traditional approach of MF tapering based on 10-m wind speeds, the new approach is physically more appealing as both shear and buoyancy forcings are considered and the width of the effective zone responds to diurnal variations of surface buoyancy forcing. Compared to the experiments retaining the original MF components, using either approach of MF tapering can lead to a stronger and more compact inner core due to enhanced boundary layer inflow outside the RMW; nevertheless, the radius of gale-force wind and inflow layer depth are only notably reduced using the new approach. Comparison to observations and further discussions on MF parameterizations in high-wind conditions are provided.

Comprehensive sensitivity analysis of the WRF model for meteorological simulations in the Arctic

Accurate meteorological simulations are important in response to the rapid climate change and insufficient meteorological observations in the Arctic. In this study, we assessed the performance of the Weather Research and Forecasting (WRF) model in simulating meteorological parameters at two Arctic stations (Barrow and Summit) in April 2019, by using measurements and statistical parameters. Sensitivity tests for different planetary boundary layer (PBL) schemes, four-dimensional data assimilation (FDDA) and sea surface temperature (SST) were also performed to reveal their impacts on the accuracy of model simulations. The results demonstrated that the WRF model performs the best in predicting the surface pressure but the worst in predicting the winds at these two stations. The sensitivity tests showed that among the four tested PBL schemes (ACM2, MYJ, BL and YSU), the model equipping with the MYJ scheme behaves the best in predicting the meteorological parameters especially the winds at these two Arctic stations. Applying FDDA nudging methods can also significantly increase the accuracy of simulations. In addition, we found that updating a time-varying SST in the model may bring a two-sided influence on meteorological simulations in the Arctic, especially at coastal stations.

Impacts of different boundary layer parameterization schemes on simulation of meteorology over Himalaya

The boundary layer plays a vital role in governing the local atmospheric dynamics and meteorology, including the vertical transport of moisture, momentum, energy, and air pollutants. Hence, accurate parameterization of the boundary layer processes is essential in atmospheric models such as the regional climate models (RCMs). However, evaluations of different boundary layer schemes within the Weather Research and Forecasting (WRF) model remain nearly non-existing over complex terrains of the Himalayan region. In this regard, six different planetary boundary layer (PBL) schemes: Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3), Shin-Hong Scale-aware (SHSS), Mellor-Yamada-Janjić (MYJ), Asymmetric Convective Model version 2 (ACM2), and Quasi Normal Scale Elimination (QNSE) in the WRF model have been evaluated against observations during the Ganges Valley Aerosol Experiment (GVAX). The evaluation is carried out for clear-sky conditions during spring (March 22–27, 2012) against surface-based, balloon-borne, and radar wind profiler (RWP) observations. The MYJ, YSU, and SHSS performed well in simulating the temperature and specific humidity, whereas the ACM2 (MYNN3) shows lower (higher) bias in T2 and higher (lower) bias in Q2. Model performance is limited in reproducing wind field (r ∼ 0.3) with different PBL schemes over this region. Model performance is seen to vary significantly with the choice of PBL schemes; nevertheless, all schemes could typically capture the daytime maxima in boundary layer height with some overestimation. The competing effects of synoptic versus local circulation were found to control the boundary layer evolution over the Himalayas, affecting the model performance. The model better reproduced the boundary layer evolution during strong surface flow regimes, while a prominent nocturnal peak in boundary layer height is observed during the weak mean flow conditions associated with topography-induced local circulations. The model performance is limited for reproducing such mountain meteorological features in contrast to the homogeneous terrains where such nocturnal peaks are absent. This study may serve as a reference for selecting a suitable PBL scheme for regional climate modeling studies in the future.