Pleim Xiu Research Articles

Assessment of Land Surface Schemes from the WRF-Chem for Atmospheric Modeling in the Andean Region of Ecuador

Surface interactions occur near the land–atmosphere interface, thus affecting the temperature, convection, boundary layer, and stability of the atmosphere. A proper representation of surface interactions is a crucial component for numerical atmospheric and air quality modeling. We assessed four land surface schemes—1. 5–layer thermal diffusion scheme (1 5-Layer); 2. unified Noah land surface model (2 Noah); 3. rapid update cycle (3 RUC) land surface model; and 4. Pleim–Xiu land surface model (4 Pleim–Xiu)—from the Weather Research and Forecasting with Chemistry (WRF-Chem V3.2) model for the purposes of atmospheric modeling in Cuenca, which is a region with a complex topography and land use configuration and which is located in the Southern Andean region, in Ecuador. For this purpose, we modeled the meteorological and air quality variables during September 2014. It was found that the meteorological and short-term air quality variables were better modeled through the 2 Noah scheme. Long-term (mean monthly) air quality variables were better modeled by the 1 5–Layer and 3 RUC options. On average, the 2 Noah scheme was better at modeling meteorology and air quality. In addition, we assessed the 2 Noah scheme combined with the urban canopy model (UCM) (5 Noah UCM), which was developed as an option to represent the urban effects at a subgrid-scale. Results indicated that the performance of the 5 Noah UCM scheme was not better at modeling than the 2 Noah scheme alone. Moreover, the 5 Noah UCM scheme notably decreased the modeling performance for carbon monoxide and fine particulate matter. These results complement previous assessments of other schemes, allowing us to recommend a basic configuration of parameters for atmospheric modeling in the Andean region of Ecuador.

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.

Improving simulation of the fog life cycle with high-resolution land data assimilation: A case study from WiFEX

The present study highlights the role of high-resolution land data assimilation in improving the prediction of the radiation fog and near-surface meteorological variables. The performance of the Weather Research and Forecasting (WRF) model coupled with the High-Resolution Land Data Assimilation System (HRLDAS) is evaluated for a dense fog event that occurred on 24–25 January 2018 using detailed observations from the Winter Fog EXperiment (WiFEX) over the Delhi (India) region. The Noah-MP Land-Surface Model (LSM) based HRLDAS framework was executed in uncoupled mode to develop fine-grid soil states for soil moisture (SM) and soil temperature (ST) covering the Indo Gangetic Plain (IGP) region at 2 km horizontal grid resolution. The quality of soil states (SM/ST) from the HRLDAS and reanalysis (CNTL) dataset was verified with observed SM/ST at the Indira Gandhi International (IGI) airport, New Delhi, during 2017–18 winter months (December–January). It was found that the soil state in the CNTL dataset is moist, despite the actual soil condition being dryer at the observation site. HRLDAS simulated SM reasonably agrees with observations at IGI by reducing wet mean bias by about 56%. Subsequently, four sensitive experiments were carried out using Noah-MP (NM) and Pleim-Xiu (PX) land-surface parameterisations in the WRF model initialised with CNTL and HRLDAS soil states. We found that the bias in micro-meteorological variables (T2, RH2, WS10) and Turbulent Kinetic Energy (TKE) during the fog event was significantly improved with Pleim-Xiu (PX) land-surface parameterisations and HRLDAS soil states (HRLDAS_PX). Statistical performance of the micro-meteorological variables (T2, RH2 and WS10) exhibited low variance (0.13°C, 0.10% and 0.75 m s−1), high correlation (0.93, 0.92 and 0.82) and high index of agreement (0.92, 0.87 and 0.78). As a result, the error in fog onset timing was notably reduced to 02 h, and the vertical representation of fog was skillfully demonstrated in the HRLDAS_PX simulation. The sensitivity experiments revealed that there is a need to revisit soil states to improve the skill of the fog forecast.

Simulating summer mixing heights in California's San Joaquin Valley using the WRF meteorological model with three land surface modules

During the 1990 SARMAP/AUSPEX study a network of rawinsondes was flown at 3- and 6-h intervals to evaluate the meteorological conditions conducive high ozone concentrations in the Central Valley of California. Vertical profiles of air temperature, humidity, and pressure from 9 of the sites were used to determine maximum daily mixing heights in California’s San Joaquin Valley (SJV) during the period August 3–6, 1990 and to evaluate their spatial distribution. The analysis showed a general pattern of increasing mixing heights from north to south within the SJV. But, it also showed that mixing heights were lower in areas in proximity to irrigated agriculture. The WRF prognostic meteorological model was used to simulate mixing heights during this period using three land surface modules (LSM): thermal diffusion (TD), Noah, and Pleim-Xiu (PXU). The maximum daily mixing heights simulated using the TD LSM were generally negatively biased; while those simulated using the Noah and PXU LSMs were positively biased. The differences in simulated mixing heights were attributed to differences in how crop land irrigation water is represented within each LSM. The TD LSM effectively assumes a fixed soil water content, but of sufficient magnitude to reflect latent heat fluxes associated with crop land irrigation. The PXU and Noah LSMs include comprehensive descriptions of the soil water budget. However, soil water may be depleted in the arid summer climate and these LSMs provide no direct means of accounting for water added through irrigation. The LSMs were also sensitive to estimates of ground cover in irrigated areas. For all 9 sites, the root mean square error (RMSE) for maximum daily mixing heights for the TD LSM, the Noah LSM, and the PXU LSM was 269, 350, and 366 m, respectively; and the biases for the 9 sites were − 161, 355, and 437 m, respectively. Using the assumption of well-managed irrigation and leaf area index (LAI) estimates from AVHRR remote sensing data sets, adjustments to the WRF input files were made to update soil water contents and ground cover for irrigated crop land use. Using the Noah LSM with the adjusted files, site-specific mixing height biases were reduced up to a factor of 4 and, for all sites, the RMSE for maximum daily mixing heights were reduced from 350 to 260 m; for the PXU LSM RMSEs were reduced from 366 to 238 m; and for the TD LSM from 269 to 231 m. This analysis illustrates the importance of accounting for the effects of applied irrigation water and ground cover in the simulation of mixing heights during the arid summer within the SJV. Irrigation can influence actual mixing heights and their distribution within a domain. It also illustrates that the choice of LSM is of less importance than setting LSM parametrizations to values appropriate for the modeling domain.

Evaluation of the CAM and PX Surface Layer Parameterization Schemes for Momentum and Sensible Heat Fluxes Using Observations

In this study, the performances of the Community Atmosphere Model (CAM) and Pleim–Xiu (PX) surface layer parameterization schemes are investigated by using field observations. The parameterization schemes are evaluated against continuous momentum and sensible heat flux observations measured at two flat and homogeneous grassland sites in the suburb of Nanjing, eastern China. The observations were conducted from 30 December 2014 to 18 April 2017 at Jiangxinzhou and from 9 February 2015 to 26 March 2018 at Jiangning. It is found that the momentum flux is overall in good agreement with the observation, and the sensible heat flux is overestimated. The parameterizations of the momentum and sensible heat fluxes well capture the diurnal and seasonal patterns seen in the observations at the two sites. At Jiangxinzhou, the PX parameterization underestimates the momentum flux throughout the day and the CAM parameterization slightly overestimates it around the noon, while they underestimate the momentum flux throughout the year. The two parameterizations overestimate the sensible heat flux in the daytime as well as over the entire year. At Jiangning, the two parameterizations overestimate the momentum flux throughout the day and the sensible heat flux in the daytime, and overestimate both of them over the entire year. The two parameterizations are not significantly different from each other in reproducing the turbulent fluxes at the same site, while they perform differently at the two sites in terms of statistics. In addition, the parameterized fluxes increase with increased roughness length.

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).

Parallel Construction of the WRF Pleim-Xiu Land Surface Scheme With Intel Many Integrated Core (MIC) Architecture

The weather research and forecast model (WRF), a simulation model, is built for the needs of both research and operational weather forecast and research in atmospheric science. The land-surface model (LSM), which describes one physical process in atmosphere, supplies the heat and moisture fluxes over land points and sea-ice points. Among several schemes of LSM that have been developed and incorporated into WRF, Pleim-Xiu (PX) scheme is one popular scheme in LSM. Processing the WRF simulation codes for weather prediction has acquired the benefits of dramatically increasing computing power with the advent of large-scale parallelism. Several merits such as vectorization essence, efficient parallelization, and multiprocessor computer structure of Intel Many Integrated Core (MIC) architecture allow us to accelerate the computation performance of the modeling code of the PX scheme. This paper demonstrates that our results of the optimized MIC-based PX scheme executing on Xeon Phi coprocessor 7120P enhances the computing performance by 2.3× and 11.7×, respectively, in comparison to the initial CPU-based code running on one CPU socket (eight cores) and on one single CPU core with Intel Xeon E5-2670.

Effects of Land Surface Schemes on WRF-Simulated Geopotential Heights over China in Summer 2003*

Abstract To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

Sensitivity of a regional climate model to land surface parameterization schemes for East Asian summer monsoon simulation

Land surface processes play an important role in the East Asian Summer Monsoon (EASM) system. Parameterization schemes of land surface processes may cause uncertainties in regional climate model (RCM) studies for the EASM. In this paper, we investigate the sensitivity of a RCM to land surface parameterization (LSP) schemes for long-term simulation of the EASM. The Weather Research and Forecasting (WRF) Model coupled with four different LSP schemes (Noah-MP, CLM4, Pleim–Xiu and SSiB), hereafter referred to as Sim-Noah, Sim-CLM, Sim-PX and Sim-SSiB respectively, have been applied for 22-summer EASM simulations. The 22-summer averaged spatial distributions and strengths of downscaled large-scale circulation, 2-m temperature and precipitation are comprehensively compared with ERA-Interim reanalysis and dense station observations in China. Results show that the downscaling ability of RCM for the EASM is sensitive to LSP schemes. Furthermore, this study confirms that RCM does add more information to the EASM compared to reanalysis that imposes the lateral boundary conditions (LBC) because it provides 2-m temperature and precipitation that are with higher resolution and more realistic compared to LBC. For 2-m temperature and monsoon precipitation, Sim-PX and Sim-SSiB simulations are more consistent with observation than simulations of Sim-Noah and Sim-CLM. To further explore the physical and dynamic mechanisms behind the RCM sensitivity to LSP schemes, differences in the surface energy budget between simulations of Ens-Noah-CLM (ensemble mean averaging Sim-Noah and Sim-CLM) and Ens-PX-SSiB (ensemble mean averaging Sim-PX and Sim-SSiB) are investigated and their subsequent impacts on the atmospheric circulation are analyzed. It is found that the intensity of simulated sensible heat flux over Asian continent in Ens-Noah-CLM is stronger than that in Ens-PX-SSiB, which induces a higher tropospheric temperature in Ens-Noah-CLM than in Ens-PX-SSiB over land. The adaptive modulation of geopotential height gradients affects wind field (through geostrophic balance) simulation especially at lower levels, which subsequently affects the simulation of large-scale circulation, 2-m temperature and monsoon precipitation as well as RCM’s downscaling ability.

Application of the Weather Research and Forecasting Model for Air Quality Modeling in the San Francisco Bay Area

AbstractThe Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim–Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.

Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the WRF-ARW

Abstract The Pleim–Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model (version 2) are now options in version 3.0 of the Weather Research and Forecasting model (WRF) Advanced Research WRF (ARW) core. These physics parameterizations were developed for the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and have been used extensively by the air quality modeling community, so there was a need based on several factors to extend these parameterizations to WRF. Simulations executed with the new WRF physics are compared with simulations produced with the MM5 and another WRF configuration with a focus on the replication of near-surface meteorological conditions and key planetary boundary layer features. The new physics in WRF is recommended for retrospective simulations, in particular, those used to drive air quality simulations. In the summer, the error of all variables analyzed was slightly lower across the domain in the WRF simulation that used the new physics than in the similar MM5 configuration. This simulation had an even lower error than the other more common WRF configuration. For the cold season case, the model simulation was not as accurate as the other simulations overall, but did well in terms of lower 2-m temperature error in the western part of the model domain (plains and Rocky Mountains) and most of the Northeast. Both MM5 and the other WRF configuration had lower errors across much of the southern and eastern United States in the winter. The 2-m water vapor mixing ratio and 10-m wind were generally well simulated by the new physics suite in WRF when contrasted with the other simulations and modeling studies. Simulated planetary boundary layer features were compared with both wind profiler and aircraft observations, and the new WRF physics results in a more precise wind and temperature structure not only in the stable boundary layer, but also within most of the convective boundary layer. These results suggest that the WRF performance is now at or above the level of MM5. It is thus recommended to drive future air quality applications.

Improving ozone modeling in complex terrain at a fine grid resolution: Part I – examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model

Meteorological variables such as temperature, wind speed, wind directions, and Planetary Boundary Layer (PBL) heights have critical implications for air quality simulations. Sensitivity simulations with five different PBL schemes associated with three different Land Surface Models (LSMs) were conducted to examine the impact of meteorological variables on the predicted ozone concentrations using the Community Multiscale Air Quality (CMAQ) version 4.5 with local perspective. Additionally, the nudging analysis for winds was adopted with three different coefficients to improve the wind fields in the complex terrain at 4-km grid resolution. The simulations focus on complex terrain having valley and mountain areas at 4-km grid resolution. The ETA M–Y (Mellor–Yamada) and G–S (Gayno–Seaman) PBL schemes are identified as favorite options and promote O 3 formation causing the higher temperature, slower winds, and lower mixing height among sensitivity simulations in the area of study. It is found that PX (Pleim–Xiu) simulation does not always give optimal meteorological model performance. We also note that the PBL scheme plays a more important role in predicting daily maximum 8-h O 3 than land surface models. The results of nudging analysis for winds with three different increased coefficients' values (2.5, 4.5, and 6.0 × 10 −4 s −1) over seven sensitivity simulations show that the meteorological model performance was enhanced due to improved wind fields, indicating the FDDA nudging analysis can improve model performance considerably at 4-km grid resolution. Specifically, the sensitivity simulations with the coefficient value (6.0 × 10 −4) yielded more substantial improvements than with the other values (2.5 and 4.5 × 10 −4). Hence, choosing the nudging coefficient of 6.0 × 10 −4 s −1 for winds in MM5 may be the best choice to improve wind fields as an input, as well as, better model performance of CMAQ in the complex terrain area. As a result, a finer grid resolution is necessary to evaluate and access of CMAQ results for giving a detailed representation of meteorological and chemical processes in the regulatory modeling. A recommendation of optimal scheme options for simulating meteorological variables in the complex terrain area is made.

An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim–Xiu Land Surface Model

Abstract The Pleim–Xiu land surface model (PX LSM) has been improved by the addition of a second indirect data assimilation scheme. The first, which was described previously, is a technique in which soil moisture is nudged according to the biases in 2-m air temperature and relative humidity between the model- and observation-based analyses. The new technique involves nudging the deep soil temperature in the soil temperature force–restore (FR) model according to model bias in 2-m air temperature only during nighttime. While the FR technique is computationally efficient and very accurate for the special conditions for which it was derived, it is very dependent on the deep soil temperature that drives the restoration term of the surface soil temperature equation. Thus, adjustment of the deep soil temperature to optimize the 2-m air temperature during the night, when surface forcing is minimal, provides significant advantages over other methods of deep soil moisture initialization. Simulations of the Weather Research and Forecasting Model (WRF) using the PX LSM with and without the new deep soil temperature nudging scheme demonstrate substantial benefits of the new scheme for reducing error and bias of the 2-m air temperature. The effects of the new nudging scheme are most pronounced in the winter (January 2006) during which the model’s cold bias is greatly reduced. Air temperature error and bias are also reduced in a summer simulation (August 2006) with the greatest benefits in less vegetated and more arid regions. Thus, the deep temperature nudging scheme complements the soil moisture nudging scheme because it is most effective for conditions in which the soil moisture scheme is least effective, that is, when evapotranspiration is not important (winter and arid climates).

Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models

In this study, MM5 predictions with five PBL parameterizations in combination with three land-surface models (LSMs) are intercompared and evaluated by using a wide variety of observations derived from WMO routine surface weather stations, TRACE-P aircraft experiments, intense radiosonde soundings and satellite measurements. Six scenarios with various PBL schemes and LSMs are designed to investigate the similarities and differences in model predictions. For near-surface variables, all scenarios yield good correlation between prediction and observation for 2 m-temperature (T2) and 2 m-water vapor mixing ratio (Q2), and relatively poor ones for wind fields. On average, T2 was consistently underpredicted by all scenarios, whereas Q2 was overpredicted by five of the six scenarios. It is found that the application of Noah land-surface model instead of the five-layer soil model is able to enhance the prediction accuracy of Q2. For 10 m-wind speed, the GSE scenario (Gayno–Seaman scheme with the five-layer soil model) produces somewhat smaller correlation, but better consistency in magnitude than those of the other scenarios. Model predictions are more consistent for upper air as a result of using FDDA reanalysis nudging and the reducing influence of underlying surface with altitude. All scenarios show the tendencies to underpredict temperature and to overpredict wind speed at altitudes <1 km and to underpredict wind speed at altitudes >3 km. The correlations for water vapor mixing ratio are much smaller at altitudes >3 km than that in the boundary layer. The differences in predicted PBL height among scenarios are large. GSE scenario performs best for phase (correlation), whereas PCX scenario (Pleim–Chang scheme with Pleim–Xiu LSM) produces the best statistics for magnitude of PBL height. Diurnal variation of PBL height over the western Pacific region during the study period is characterized by the typical day and night cycling superimposed by occasional expansion associated with cold front passage. The scenarios similarly predict precipitation spatial variability, but the difference in absolute magnitude is comparatively large among them, even for the scenarios using the same LSM, suggesting the important impact of PBL turbulence parameterization on precipitation. All scenarios predict larger precipitation amount averaged over the entire domain with the exception of GSE scenario. The statistics for satellite estimates are better than those for model predictions, but satellite data tends to underpredict observation by a similar range to that of GSE scenario. In the study period, latent heat fluxes are about twice the sensible heat fluxes over the western Pacific region, with maximum being 300–600 and 100–300 W m −2, respectively. The differences in surface fluxes among scenarios are mainly determined by various LSMs rather than different PBL schemes applied.