Asymmetric Convective Model Version Research Articles

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.

Simulating Meteorological and Water Wave Characteristics of Cyclone Shaheen

The Bay of Bengal and Arabian Sea are annually exposed to severe tropical cyclones, which impose massive infrastructure damages and cause the loss of life in coastal regions. Cyclone Shaheen originally generated in the Bay of Bengal in 2021 and translated a rare east-to-west path toward the Arabian Sea. Although the cyclone’s wind field can be obtained from reanalysis datasets such as ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts), the wind speed cannot be reproduced with realistic details in the regions close to the center of cyclone due to spatial resolution. In this study, to address this problem, the high-resolution advanced Weather Research and Forecasting (WRF) model is used for simulation of Shaheen’s wind field. As a critical part of the study, the sensitivity of the results to the planetary boundary layer (PBL) parameterization in terms of the track, intensity, strength and structure of the cyclone Shaheen is investigated. Five experiments are considered with five PBL schemes: Yonsei University (YSU); Mellor–Yamada–Janjić (MYJ); Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN); Asymmetric Convective Model version 2 (ACM2); Quasi-Normal Scale Elimination (QNSE). The track, intensity, and strength of the experiments are compared with the wind fields obtained from the Joint Typhoon Warning Centre (JTWC) dataset. The results imply the high dependency of the track, intensity, and strength of the cyclone to the PBL parameterization. Simulated tracks with non-local PBL schemes (YSU and ACM2) outperformed those of the local PBL schemes (MYJ, MYNN, and QNSE), especially during the rapid intensification phase of Shaheen before landfall. The YSU produced highly intensified storm, while the ACM2 results are in better agreement with the JTWC data. The most accurate track was obtained from the ERA5 data; however, this dataset overestimated the spatial size and underestimated the wind speed. The WRF model using either YSU or ACM2 overestimated the wind speed compared to that of the altimeter data. The YSU and ACM2 schemes were able to reproduce the observed increase in wind speed and pressure drop at in situ stations. The wind data from EAR5 and cyclone parametric model were applied to the SWAN model to simulate the wave regime in the Arabian Sea during the time that Shaheen was translating across the region. Janssen formulation for wind input and whitecapping dissipation source terms in combination with both ERA5 and hybrid wind were used and the minimum combined error in the prediction of significant wave height (Hs) and zero up-crossing wave period (Tz) was examined. The maximum significant wave height for hybrid wind is higher than that of ERA5, while the cyclone development was successfully inferred from the wave field of the hybrid data.

Assessment of WRF planetary boundary layer schemes in the simulation of fog events over Hungary

Accurate depiction of meteorological conditions, especially within the planetary boundary layer (PBL), is essential for fog forecasting. This study examines the sensitivity of the performance of the Weather Research and Forecast (WRF) model to the use of four different PBL schemes [Yonsei University (YSU), asymmetric convective model version 2 (ACM2), quasinormal scale elimination (QNSE), and Mellor-Yamada-Nakanishi-Niino version 3.0 (MYNN3)]. For this case study we have taken the fog event occurred in November 23-24, 2020. Surface observed temperature and relative humidity, furthermore, sounding data are compared with the output of the 36 hours, high-resolution weather forecast. The horizontal extension of the simulated fog is compared with satellite observations. The visibility is calculated from the prognostic variables of drop number concentration and mixing ratio. The simulated visibility and fog duration are validated by the visibility and fog duration evaluated by ceilometer observations. Validation of thermodynamical values such as 2-m temperature and relative humidity reveals, that during most of the simulation time, the bias is significant between the simulated and observed data. Results show that the PBL parameterization scheme significantly impacts fog microphysics also. The QNSE scheme results in unrealistic early formation of the fog, and too large liquid water content. YSU and ACM2 simulated the duration of fog to be rather short comparing with the other two PBL schemes. The best fitting with observed data is found in the case of MYNN3 PBL schemes.

Development and Evaluation of a New Urban Parameterization in the Weather Research and Forecasting (WRF) Model

AbstractIn mesoscale Weather Research and Forecasting (WRF) model, several urban‐modeling options exist, such as Single‐layer Urban Canopy Model, Building Effect Parameterization, and Building Energy Model. However, these models have limitations in terms of the choice of land surface models (LSMs) and planetary boundary layer (PBL) schemes, the associated computational expenses, and other constraints. Recently, Dy et al. (2019, https://doi.org/10.1029/2018JD029333) included the urban‐momentum drag effect for wind‐speed modeling by developing a new multilayer model based on a modified nonlocal Asymmetric Convective Model version‐2 (ACM2) PBL scheme. This urban‐based ACM2 (UACM) has demonstrated a significantly improved ability over the base‐ACM2 (BACM) to predict wind speed near the urban ground surface and shown an inflection point at roof level in the vertical profile of horizontal wind. In this study, urban thermal and moisture components are introduced into the Pleim‐Xiu (PX) LSM and coupled with UACM. The diurnal variation in street, walls, and roof‐surface temperatures is modeled using the two‐layer force‐restore algorithm. Simple radiation treatment is considered to account for shadowing on streets based on the solar zenith angle and building morphology. Heat and moisture flux evolution are considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The idealized and real urban data case WRF simulations are performed over the Pearl River Delta region in southern China to test the new UACM. The computationally efficient UACM is expected to perform faster for operational forecasting runs.

Performance Evaluation of Planetary Boundary Layer Schemes in Simulating Structures of Wintertime Lower Troposphere in Seoul Using One-Hour Interval Radiosonde Observation

We investigated the structures of the wintertime lower troposphere in Seoul, South Korea on 17 and 18 January 2017 by performing 1 h interval radiosonde observation and numerical simulations. In the daytime on 17 January, the height of the convective boundary layer (CBL) sharply and quickly increased when the residual layer became a part of the CBL. From the afternoon on 17 January, moist air with clouds began to substantially intrude in the lower troposphere in Seoul, and radiative heating/cooling weakened. As a result, the mixing of air in the lower troposphere was inhibited and the vertical gradients of potential temperature and water vapor mixing ratio changed little on 18 January. We evaluated the performance of four planetary boundary layer (PBL) parameterization schemes (the Yonsei University (YSU), Mellor–Yamada–Janjić (MYJ), Mellor–Yamada–Nakanishi–Niino (MYNN), and Asymmetric Convective Model version 2 (ACM2) schemes) coupled with the Weather Research and Forecasting model in simulating the structures of the lower troposphere against 1 h interval radiosonde observation. The general tendencies of the air temperature and wind speed in the lower troposphere were well-reproduced in the four simulations. However, the sharp increase in the CBL height did not appear in the four simulations, implying that the process of the residual layer becoming a part of the CBL in the daytime is not well-parameterized. Additionally, the simulated water vapor mixing ratio near the surface was smaller compared with the observation. We found that small-scale turbulence in the CBL, which mixes advected air and pre-existing air, was not reproduced well by the PBL parameterization schemes. Compared with the other simulations, the most accurate air temperature and wind speed were reproduced in the simulation with the MYJ scheme, while the CBL development and moisture advection were reproduced relatively well in the simulation with the MYNN scheme.

Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China

The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes.

Sensitivity Analysis of PBL Physics Schemes of WRF-ARW Model in Simulating the Tropical Cyclone ‘TITLI’ (2018) Over the Bay of Bengal

Tropical Cyclone (TC) is the most destructive weather phenomenon in the Indian sub-continent. To mitigate the destruction due to TC better prediction is needed. So, the study of sensitivity of different physical schemes in WRF-ARW model with intensification and track of TC is important. In this study, sensitivity of Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Bougeault-Lacarrere (Boulac), Medium-Range Forecast (MRF), Mellor-Yamada Nakanishi and Niino Level 2.5 (MYNN2.5) and Level 3 (MYNN3) Planetary Boundary Layer (PBL) schemes are used to simulate the TC ‘Titli’ which made land fall near Palasa in North Andrha Pradesh and South Odhisha coasts at 0000 UTC of 11th October. National center for environmental prediction Global Final Reanalysis (FNL) data have been used as an initial and lateral boundary conditions. Variation of heat flux, latent heat flux and moisture flux with time for these schemes are shown which are responsible to intensify the TC. Model simulated intensity i.e., minimum central pressure, maximum sustained wind speed at the surface (10 m) and track are compared with the India Meteorological Department (IMD) estimated value. It can be specified that the Boulac, MYNN2.5 and MYNN3 schemes simulate the better intensity and track of TC ‘Titli’.

Impact of the planetary boundary layer on air quality simulations over the Yangtze River Delta region, China

The city clusters in eastern coastal China have suffered from serious air pollution in the past decades, which is partially related to the complex local topography and meteorological conditions. The planetary boundary layer (PBL) scheme is a critical parameter for accurate meteorology simulations and air quality predictions. In this study, we analyze the impact of four typical PBL schemes, namely, Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino Level 2.5 (MYNN), Asymmetric Convective Model version 2 (ACM2), and Mellor-Yamada-Janjic (MYJ) PBL, within the Weather Research and Forecasting (WRF) model to assess their impacts on the simulations of air pollutant concentrations based on the Comprehensive Air Quality Model with Extensions (CAMx) for the Yangtze River Delta (YRD) region, one of the most developed city clusters in eastern China. The results indicate that the MYNN scheme performs best in terms of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) simulations, with mean bias of 4.8 μg m−3 and 9.3 μg m−3 in summer and 11.7 μg m−3 and 5.4 μg m−3 in winter, respectively. The YSU scheme performs best for ozone (O3) prediction, with better simulation results in summer than in winter. Notably, some discrepancies among different PBL schemes in the prediction of air pollution are directly associated with the complex topography. For the prediction of PM2.5, the MYJ and YSU schemes tend to overestimate the concentrations in the plains of Jiangsu and northern Anhui while underestimating PM2.5 in the hilly areas compared with the MYNN scheme. Meanwhile, the performance of the ACM2 scheme is opposite to that of the MYJ and YSU schemes. For O3 predictions, MYJ overestimates O3 in the eastern coastal area and underestimates O3 in the inland areas in summer compared with the YSU scheme, while the ACM2 scheme is the opposite of the MYJ scheme, and the MYNN scheme consistently overestimates O3.

Segregation of Forecast Errors in the Planetary Boundary Layer Parameterization Over the State of Odisha and Neighboring Regions in India During Summer Monsoon Season

Planetary boundary layer parametrization (PBL) is a key factor influencing weather forecast skills. This article provides a quantitative illustration of the segregation of forecast errors and their growth arising from different PBLs of the Weather Research and Forecasting (WRF) model. The systematic (root mean square) components of forecast errors arising from four different PBLs of the WRF over the state of Odisha (India) and its surrounding regions are elucidated. Error characterizations are carried out for the forecast lead time up to 96 h (day-4) for two contrasting monsoon seasons, i.e., 2013 (normal) and 2014 (deficit). A total of 1112 simulations are carried out for each initial condition, i.e., May 15 to September 29 for both monsoon seasons using four PBL schemes, i.e., Yonsei University (YSU), Mellor–Yamada–Nakanishi-Niino (MYNN), Asymmetric Convective Model version 2 (ACM2), and medium-range forecast (MRF). The overall results suggest the errors in thermodynamical variables (i.e., temperature and relative humidity) are large compared to the dynamical variable (i.e., wind). For the normal monsoon year (i.e., 2013), the MRF and MYNN exhibit the lowest root mean square error (RMSE) of temperature compared to ACM2 and YSU, whereas MRF (MYNN) shows the lowest (highest) error growth at 24–48 h (72–96 h). The deficit year (2014) has a higher temperature error compared to that of the normal monsoon year (2013), which might be due to frequent monsoon break periods. The spatial distribution of wind exhibits the lowest systematic error for MRF with lead time up to 48 h. The subsequent decrease (increase) of convergence (divergence) of error flux over northern Odisha and increase (decrease) of the same over southern Odisha suggests that the error propagation occurs from north to south. In general, both the convergence and divergence of error energy are found to be weak in MRF and MYNN, attributable to the lower error growth rate and hence the smaller systematic errors compared to ACM2 and YSU for both these monsoon seasons. It is also found that the systematic component of the linear (nonlinear) error growth rate is contributed by the physics (dynamics) components of the model. These findings will provide guidance for the model community and operational agencies to make an optimal choice of PBL parameterizations, particularly for the monsoon forecast.

Systematic errors in the WRF model planetary boundary layer schemes for two contrasting monsoon seasons over the state of Odisha and its neighborhood region

This study evaluates the characteristics of systematic errors (up to day 4) in the planetary boundary layer (PBL) parameterization schemes over the state of Odisha and its neighboring regions for two contrasting monsoon seasons, i.e., 2013 (normal) and 2014 (deficit), using the Weather Research and Forecasting (WRF) model. A total of 1112 numerical experiments are carried out, each with initial conditions from May 28 to September 29 using four PBL schemes, i.e., Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino (MYNN), Asymmetric Convective Model version 2 (ACM2), and Medium Range Forecast (MRF) with horizontal resolutions of 27 and 9 km with a lead time up to 96 h. It is found that the PBL parameterizations significantly impact the basic atmospheric variables (i.e., wind, temperature, moisture, and rainfall) and associated convective processes such as moist static energy. The wind and rainfall forecast skill of MRF showed better agreement with European Re-Analysis (ERAI). However, YSU has better skills for 2-m temperature and specific humidity compared to others. All PBL schemes produced a moist bias at the surface except MRF. The principle cause of this moist bias with mid tropospheric warming is attributed to inherent tendency of the model to overestimate moist static energy leading to intense vertical motion and convective instability. The magnitude of errors for wind forecast is higher for the deficit year compared to the normal monsoon year. However, the strength of the cross equatorial flow is found to be weaker in all the experiments for both the seasons. In general, the spread and magnitude of errors are large for 2013 (normal) as compared to 2014 (deficit) year.

Influences of planetary boundary layer mixing parameterization on summertime surface ozone concentration and dry deposition over North China

We present a regional modeling study that analyzes how planetary boundary layer (PBL) and surface layer parameterizations influence surface ozone concentrations and dry deposition fluxes over the Beijing-Tianjin-Hebei region in summer (July 2015). We use the Weather Research and Forecasting Model coupled to Chemistry (WRF-Chem) to simulate surface ozone concentration and dry deposition, and examine three PBL schemes: the Yonsei University (YSU), Mellor–Yamada–Janjić (MYJ), and Asymmetric Convective Model version 2 (ACM2) schemes. The model sensitivity to surface layer schemes is also tested by coupling the ACM2 PBL scheme with either the revised MM5-similarity scheme or the Pleim-Xiu surface layer scheme. Key physical and chemical factors for ozone dry deposition parameterization are analyzed to explore the root causes of model discrepancies. We find that all simulations overestimate the summertime daily mean ozone concentrations over North China (42 ppbv in observations vs. 43–50 ppbv in model results for July 2015) that are caused by high biases in daytime ozone and partly compensated by low biases in nighttime ozone. The YSU scheme has the largest overestimate in daily mean ozone concentration, but best reproduces the ozone diurnal cycle. The ACM2 scheme shows the largest underestimates of surface ozone over North China during nighttime, which can be explained by its weakest vertical mixing leading to high NOx concentrations and strong ozone titration near surface. The choices of PBL and surface layer schemes lead to over 20% differences in ozone dry deposition fluxes due to differences in simulated surface ozone concentrations and dry deposition velocities. We find the differences in dry deposition velocity are mainly caused by differences in Monin-Obukhov length during nighttime and surface temperature during daytime. Our study emphasizes the needs to better understand these key PBL and surface factors for reducing the uncertainties in model simulation of surface ozone concentration and dry deposition.

Momentum Drag Effect Over Urbanized Areas in the ACM2 PBL Component of the WRF model

AbstractMesoscale meteorology models, such as the Weather Research and Forecasting (WRF) model, typically use land use categorization data to specify surface characteristics by grid cells. In commonly used land use schemes, such as the 24‐class U.S. Geological Survey land use classification system or the Moderate Resolution Imaging Spectroradiometer landcover product, cities are placed in single urban categories that are characterized by a high roughness length. Monin‐Obunkov similarity theory is commonly used to simulate boundary layer dynamics based on surface energy and momentum fluxes which resulted in an overprediction of wind speed in weather forecasts for urban areas. It is thus crucial to include the momentum drag effects of the buildings, including walls and roofs, in the prediction models. The WRF model contains several momentum flux parameterizations, including the single‐layer urban canopy model, building effect parameterization, and building energy model. The building energy model and building effect parameterization schemes are limited to the Mellor‐Yamada‐Janjic or BouLac planetary boundary layer models and are computationally costly. In this study, a realistic multilayer urban approach is introduced into the Asymmetric Convective Model version 2 planetary boundary layer component of the WRF model. Sensitivity tests are applied to a range of urban morphological parameters in idealized simulations, and the impact of building morphology on atmospheric conditions is examined. The modified Asymmetric Convective Model version 2 is implemented in two real cases in the forecast mode, resulting in significant improvements in wind speed prediction.

Evaluation of WRF planetary boundary layer parameterization schemes for simulation of monsoon depressions over India

This study evaluates the fidelity of five planetary boundary layer (PBL) parameterization schemes in the advanced weather research and forecasting model for simulating monsoon depressions (MDs) over India. Five PBL schemes include; nonlocal first-order medium-range forecasting (MRF) and Yonsei University (YSU); hybrid first-order Asymmetric Convective Model version 2 (ACM2), and local one-and-a-half-order Bougeault–Lacarrere (BouLac) and Mellor–Yamada–Nakanishi–Niino (MYNN2). PBL schemes show significant impact on rainfall along with dynamical and thermodynamical parameters associated with MDs at the surface as well as at the upper levels. MRF simulates a relatively shallower, warmer and drier boundary layer compared to others. Results reveal that strong upper-level divergence and high moisture content within the lower levels are favorable for the occurrence of heavy rain associated with MDs. However, stronger wind shear within the mid-troposphere weakens the system and reduces the rain intensity. Based on the results and keeping the rainfall product in view, it is found that nonlocal PBL schemes (MRF and ACM2) have better forecast skills score than local PBL schemes (BouLac and MYNN2) over the Indian region.

Customization of the Advanced Research Weather Research and Forecasting model over the Singapore region: impact of planetary boundary layer schemes, land use, land cover and model horizontal grid resolution

Accurate simulations of surface meteorological variables are important for the transport and dispersion of air pollutants and air quality in the lower atmosphere. In the present study, the surface meteorological variables over Singapore were simulated using the Advanced Research Weather Research and Forecasting (WRF‐ARW) mesoscale model. The sensitivity tests were conducted with six different planetary boundary layer (PBL) schemes (Yonsei University, Mellor–Yamada–Janjic, University of Washington, Asymmetric Convective Model version 2 [ACM2], Bougeault–Lacarrère, Quasi‐Normal Scale Elimination), four different horizontal grid resolutions (27, 9, 3 and 1 km) and two different land use, land cover datasets (US Geological Survey [USGS] and Moderate Resolution Imaging Spectroradiometer [MODIS]). Eight days (January 20–28, 2015) were selected for the WRF‐ARW model simulations for simulating surface meteorological variables. The model results were validated with available observations over the Singapore region. A lower mean bias, mean absolute error and root mean square error and good correlation were found in calculating surface meteorological variables with increase in the WRF‐ARW model horizontal resolution up to 3 km. Further, MODIS land use, land cover datasets considerably improved the prediction of surface meteorological variables compared to the USGS for all PBL schemes and horizontal grid resolutions. Qualitative and quantitative analyses of the surface meteorological variables simulated using the ACM2 PBL scheme with 3 km horizontal grid resolution with MODIS land use, land cover data are in better agreement with observations with less error and a good correlation coefficient. The better performance by ACM2 could be due to non‐local turbulence closure during unstable conditions and local closure during the stable and neutral conditions formulated in this scheme. Overall, this study indicates possibilities to improve regional level air quality monitoring and prediction capabilities over Singapore.

Evaluation of WRF SCM Simulations of Stratocumulus‐Topped Marine and Coastal Boundary Layers and Improvements to Turbulence and Entrainment Parameterizations

AbstractStratocumulus‐topped boundary layers (STBLs) are notoriously difficult to parameterize in single‐column models due to the strong inversion layer across which entrainment mixing plays an important role in modulating the boundary layer mass, energy, and moisture balances. We compare three different WRF planetary boundary layer (PBL) schemes (Yonsei University, YSU; Asymmetric Convective Model version 2, ACM2; Mellor‐Yamada‐Nakanishi‐Niino, MYNN) against large eddy simulations (LES) to find out that they underestimate entrainment flux in stratocumulus over both ocean and coastal land. Hence, the PBL schemes produce a cooler, moister STBL with higher liquid water content. In order to improve the entrainment parameterization, we propose a modification to the YSU scheme that takes into account the in‐cloud turbulence flux contribution to cloud top entrainment through the formulation of a velocity scale based on the in‐cloud buoyancy flux. A revised top‐down mixing profile is also implemented to model mixing due to turbulence generated by longwave cooling at the cloud top. The modified YSU simulates stronger entrainment flux, resulting in a STBL that matches LES results. Similar modifications were made to ACM2 in addition to implementing explicit entrainment, and while the results also showed good agreement with LES, discretization issues and conflicts with its original design prevent immediate implementation, as the contribution from the modifications and the original scheme are difficult to correctly modulate.

Evaluation of Surface Fluxes in the WRF Model: Case Study for Farmland in Rolling Terrain

The partitioning of available energy into surface sensible and latent heat fluxes impacts the accuracy of simulated near surface temperature and humidity in numerical weather prediction models. This case study evaluates the performance of the Weather Research and Forecasting (WRF) model on the simulation of surface heat fluxes using field observations collected from a surface flux tower in Oregon, USA. Further, WRF-modeled heat flux sensitivities to North American Mesoscale (NAM) and North American Regional Reanalysis (NARR) large-scale input forcing datasets; U.S. Geological Survey (USGS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) land use datasets; Pleim-Xiu (PX) and Noah land surface models (LSM); Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) schemes using the Noah LSM; and Asymmetric Convective Model version 2 (ACM2) PBL scheme using PX LSM are investigated. The errors for simulating 2-m temperature, 2-m humidity, and 10-m wind speed were reduced on average when using NAM compared with NARR. Simulated friction velocity had a positive bias on average, with the YSU PBL scheme producing the largest overestimation in the innermost domain (0.5 km horizontal grid resolution). The simulated surface sensible heat flux had a similar temporal behavior as the observations but with a larger magnitude. The PX LSM produced lower and more reliable sensible heat fluxes compared with the Noah LSM. However, Noah latent heat fluxes were improved with a lower RMSE compared to PX, when NARR forcing data was used. Overall, these results suggest that there is not one WRF configuration that performs best for all the simulated variables (surface heat fluxes and meteorological variables) and situations (day and night).

Performance of WRF for Simulation of Mesoscale Meteorological Characteristics for Air Quality Assessment over Tropical Coastal City, Chennai

The land–sea breezes (LSBs) play an important role in transporting air pollution from urban areas on the coast. In this study, the Advanced Research WRF (ARW) mesoscale model is used for predicting boundary layer features to understand the transport of pollution in different seasons over the coastal region of Chennai in Southern India. Sensitivity experiments are conducted with two non-local [Yonsei University (YSU) and Asymmetric Convective Model version 2 (ACM2)] and three turbulence kinetic energy (TKE) closure [Mellor–Yamada–Nakanishi and Niino Level 2.5 (MYNN2) and Mellor–Yamada–Janjic (MYJ) and quasi-normal scale elimination (QNSE)], planetary boundary layer (PBL) parameterization schemes for simulating the thermodynamic structure, and low-level atmospheric flow in different seasons. Comparison of simulations with observations from a global positioning system (GPS) radiosonde, meteorological tower, automated weather stations, and Doppler weather radar (DWR)-derived wind data reveals that the characteristics of LSBs vary widely in different seasons and are more prominent during the pre-monsoon and monsoon seasons (March–September) with large horizontal and vertical extents compared to the post-monsoon and winter seasons. The qualitative and quantitative results indicate that simulations with ACM2 followed by MYNN2 and YSU produced various features of the LSBs, boundary layer parameters and the thermo-dynamical structure in better agreement with observations than other tested physical parameterization schemes. Simulations revealed seasonal variation of onset time, vertical extent of LSBs, and mixed layer depth, which would influence the air pollution dispersion in different seasons over the study region.

Study of the Thermal Internal Boundary Layer in Sea Breeze Conditions Using Different Parameterizations: Application of the WRF Model in the Greater Vitória Region

Abstract In the present study, the physical parameterizations of the Weather Research and Forecasting (WRF) model are verified for making accurate inferences about the dynamics of the Thermal Internal Boundary Layer (TIBL) generated by sea breeze in an urban center with an island in a bay along a coastal region with rugged topography. The simulations were performed using parameterizations from Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ) and Asymmetric Convective Model version 2 (ACM2) for the atmospheric boundary layer (ABL) and Noah and Rapid Update Cycle (RUC) for the Land Surface Model (LSM). The data inferred by the WRF model were compared with those obtained by a Surface Meteorological Station (SMS) and by measurements generated using Light Detection and Ranging (LIDAR), Sonic Detection and Ranging (SODAR) and radiosonde. The simulations showed that although the object of this research was a region with high geographical complexity, the YSU parameterization set (non-local closure) for the ABL and the Noah parameterization for the LSM presented satisfactory results in determining ABL height generated by the sea breeze on the day in question.

Evaluation of weather research and forecasting model parameterizations under sea-breeze conditions in a North Sea coastal environment

Three atmospheric boundary layer (ABL) schemes and two land surface models that are used in the Weather Research and Forecasting (WRF) model, version 3.4.1, were evaluated with numerical simulations by using data from the north coast of France (Dunkerque). The ABL schemes YSU (Yonsei University), ACM2 (Asymmetric Convective Model version 2), and MYJ (Mellor–Yamada–Janjic) were combined with two land surface models, Noah and RUC (Rapid Update Cycle), in order to determine the performances under sea-breeze conditions. Particular attention is given in the determination of the thermal internal boundary layer (TIBL), which is very important in air pollution scenarios. The other physics parameterizations used in the model were consistent for all simulations. The predictions of the sea-breeze dynamics output from the WRF model were compared with observations taken from sonic detection and ranging, light detection and ranging systems and a meteorological surface station to verify that the model had reasonable accuracy in predicting the behavior of local circulations. The temporal comparisons of the vertical and horizontal wind speeds and wind directions predicted by the WRF model showed that all runs detected the passage of the sea-breeze front. However, except for the combination of MYJ and Noah, all runs had a time delay compared with the frontal passage measured by the instruments. The proposed study shows that the synoptic wind attenuated the intensity and penetration of the sea breeze. This provided changes in the vertical mixing in a short period of time and on soil temperature that could not be detected by the WRF model simulations with the computational grid used. Additionally, among the tested schemes, the combination of the localclosure MYJ scheme with the land surface Noah scheme was able to produce the most accurate ABL height compared with observations, and it was also able to capture the TIBL.

Large vertical gradient of reactive nitrogen oxides in the boundary layer: Modeling analysis of DISCOVER‐AQ 2011 observations

AbstractAn often used assumption in air pollution studies is a well‐mixed boundary layer (BL), where pollutants are evenly distributed. Because of the difficulty in obtaining vertically resolved measurements, the validity of the assumption has not been thoroughly evaluated. In this study, we use more than 200 vertical profiles observed in the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER‐AQ) aircraft campaign in July 2011 to examine the vertical distributions of pollutants over the Washington‐Baltimore area. While many long‐lived species are well mixed in daytime, the observed average vertical profile of NOx shows a large negative gradient with increasing altitude in the BL. Our analysis suggests that the magnitude of the NOx gradient is highly sensitive to atmospheric stability. We investigate how parameterizations of the BL and land‐surface processes impact vertical profiles in a 1‐D chemical transport model, using three BL schemes (Asymmetric Convective Model version 2 (ACM2), Yonsei University (YSU), and Mellor‐Yamada‐Janjic (MYJ)) and two land‐surface schemes (Noah and Rapid Update Cycle (RUC)). The model reasonably reproduces the median vertical profiles of NOx under different BL stability conditions within 30% of observations, classified based on potential temperature gradient and BL height. Comparisons with NOx observations for individual vertical profiles reveal that while YSU performs better in the turbulent and deep BL case, in general, ACM2 (RMSE = 2.0 ppbv) outperforms YSU (RMSE = 2.5 ppbv) and MYJ (RMSE = 2.2 ppbv). Results also indicate that the land‐surface schemes in the Weather Research and Forecasting (WRF) model have a small impact on the NOx gradient. Using model simulations, we analyze the impact of BL NOx gradient on the calculation of the ozone production rate and satellite NO2 retrieval. We show that using surface measurements and the well‐mixed BL assumption causes a ~45% high bias in the estimated BL ozone production rate and that the variability of NO2 vertical profiles is responsible for 5–10% variability in the retrieved NO2 tropospheric vertical columns.