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Article

Improvement of Stable Atmospheric Boundary Simulation with High-Spatiotemporal-Resolution Nudging over the North China Plain

by
Tingting Xu
1,2,*,
Zhuohao Peng
1,
Yan Wang
1,
Chaoyue Wan
1,
Shenlan Liu
1,
Shuqiao Jiang
1,
Xiaolu Tang
1 and
Xilin Zhao
1
1
College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, China
2
College of Architecture and Environment, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(3), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030277
Submission received: 23 January 2024 / Revised: 16 February 2024 / Accepted: 20 February 2024 / Published: 25 February 2024
(This article belongs to the Special Issue Challenges in Modelling and Observing Urban Environments: Recent Trends, Current Progress and Future Directions (2nd Edition))
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Abstract

:
The WRF model often struggles to accurately replicate specific characteristics of the atmospheric boundary layer, particularly under highly stable conditions. In this study, we reconstructed an OBS-nudging module using meteorological data with high spatiotemporal resolution, then coupled it in the WRF model (WRF-OBS) to improve stable boundary layer (SBL) simulation over the North China Plain (NCP). The results showed that WRF-OBS improved the simulation of SBL characteristics and reduced the deviation from observations significantly. The correlations (R2) between WRF-OBS simulations and observations of 2 m temperature, relative humidity, and 10 m wind speed at 460 stations across the NCP were 0.72, 0.56, and 0.75, respectively, which were much higher than the values for results from the unassimilated WRF model (WRF-BS). The simulated vertical profiles of temperature, relative humidity, and wind were generally consistent with observations at Pingyuan station. The meteorological factors which caused heavy air pollution was also investigated based on WRF-OBS simulation. The SBL characteristics obtained from WRF-OBS showed that light wind persisted over the NCP region during the period of heavy pollution, and Pingyuan was affected by warm and humid air. Vertically, the persistent temperature inversion at Pingyuan station was one of the main drivers of the heavy pollution. The WRF-OBS simulation captured the characteristics of the two temperature inversion layers very well. The two inversion layers covered the NCP, with a horizontal scale of approximately 200 km, and created very stable conditions, preventing the vertical diffusion of pollutants and maintaining high PM2.5 concentrations.

1. Introduction

Many boundary layer and meteorological models use Monin-Obukhov similarity theory (MOST) to obtain mean values for meteorological variables in the lower atmospheric boundary layer [1,2,3,4]. Under unstable and neutral atmospheric conditions, these models reproduce boundary turbulence and its characteristics acceptably. However, under stable conditions, MOST fails to realistically represent turbulence and stability functions and other boundary characteristics [5,6,7], because of their sensitivity to frequently observed sub-mesoscale phenomena such as gravity waves, meandering motions, radiative divergence, and intermittency [5,8]. The Weather Research and Forecasting (WRF) model, which uses MOST, has been widely used for weather forecasting and air quality research in the numerical modelling community [9,10,11,12]. However, for the reasons noted, this model has limitations in presenting boundary characteristics under stable conditions, which leads to discrepancies in the results of studies based on the WRF model [13,14]. Therefore, improving the simulation accuracy of this model under stable boundary conditions is imperative.
Nudging is a method commonly used to improve model performance. Observational nudging (OBS-nudging) is an option in the four-dimensional data assimilation system of the WRF. OBS-nudging, which involves keeping simulation results close to observations over the integration period, has been used to improve model performance in many studies [15,16,17,18,19]. Barna et al. [20] used OBS-nudging to improve ozone modelling in regions with complex terrain, illustrating the importance of applying OBS-nudging in a prognostic meteorological model when simulating air quality in regions with complex terrain. Mylonas et al. [21] used the real-time OBS-nudging capability of the WRF to enhance model performance for offshore wind applications. They found that model performance improved with OBS-nudging, showing a reduction in the root mean square error of up to 27%. Tomasi et al. [22] used the WRF coupled with OBS-nudging of upper air and surface meteorological observations, as well as an improved snow cover initialisation, to better characterise valley winds. Through the assimilation of meteorological data from surface and radiosonde observations in winter 2017 over eastern China, Jia et al. [23] found that the WRF model represented planetary boundary layer dynamics and wind fields well, in particular near ground surface, which substantially improved particle tracing in the Lagrangian particle dispersion model. Li et al. [24] improved the model performance for fine particulate matter (PM2.5) concentration at Xianghe station and accurately reproduced the spatial pattern of O3 concentration in 367 cities across China using WRF-CMAQ, which assimilated observational data.
However, the resolution of meteorological observations in the present OBS-nudging module is coarse; thus, even with OBS-nudging selected, the WRF model cannot reproduce the exact boundary layer characteristics under stable conditions. Stable boundary layer (SBL) simulation studies using the present OBS-nudging model have limitations that also affect studies based on the WRF model, such as WRF-Chem and WRF-CMAQ simulation studies.
With advancements in methods and equipment for atmospheric monitoring, meteorological observations have recently achieved higher resolutions, both spatial and temporal. These improvements in data availability present an opportunity to provide high-quality nudging fields that may reduce model bias and better reproduce observations. Reconstructing a high-spatiotemporal-resolution OBS-nudging module and coupling it to the WRF model to show the exact characteristics of a SBL are of important theoretical value and practical significance.
In this study, we reconstructed an OBS-nudging module using meteorological data with high spatiotemporal resolution and then coupled it to the WRF model to investigate the impact of high-spatiotemporal-resolution nudging on the results of model simulations for a SBL over North China, an area that has been affected by severe air pollution in recent years. The main motivations behind this study were to improve the simulation performance of the WRF model under SBL conditions and investigate the exact characteristics of such boundary layers.
This paper is structured as follows: Section 1 provides an introduction. Section 2 describes the observational data and the reconstruction of the OBS-nudging module. Section 3 contains the modelling results and discussion. Section 4 presents the conclusions.

2. Data and Methodology

2.1. Data

Surface meteorological observational data were obtained from the China Meteorological Administration. Hourly observed meteorological elements included 2 m temperature (T2), 2 m relative humidity (RH2), and 10 m wind speed and direction (WS10). A data set covering 460 stations in North China was used (Figure 1). The distance between stations was generally around 10 km, which is quite dense. Data quality control involved removing meteorological elements that did not match the surrounding sites, historical records, and climatology.
Radiosonde data collected at Pingyuan station were used for reconstruction of the vertical high-resolution nudging module. The radiosonde data was obtained using a small-ball radiosonde, and the meteorological elements included profiles of temperature, relative humidity, and wind. The resulting data sets were of very fine vertical resolution, on the order of a few metres. From 1000 to 700 hPa, more than 400 pressure zones were generally present. The temporal resolution of the radiosonde data was also very high, with an interval of 3 h and data collection at 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 (Beijing Time) daily. Approximately 0.08% of recorders were excluded during data quality control, including those that lacked temperature data and those in which geopotential height increased with decreasing pressure.
Boundary layer height (BLH) from the hourly ERA5 reanalysis product was compared to the BLH parameters obtained during model simulation. The ERA5 data are fifth-generation data produced by the European Centre for Medium-Range Weather Forecasts for atmospheric reanalysis of the global climate. The dataset has a 0.25° × 0.25° horizontal grid resolution, 37 pressure levels in the vertical direction, and hourly temporal resolution. ERA5 data were produced through the assimilation of satellite and radiosonde observation datasets [25] and can be obtained from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu, accessed on 20 December 2022.). The boundary layer height in ERA5 data is defined as the depth of air near the Earth’s surface, and its calculation is based on the Bulk Richardson Number. Researchers have demonstrated a high correlation between ERA5 boundary layer height data and radiosonde-derived boundary layer height, providing strong support for the reliability of this data in research studies [26,27].

2.2. Reconstruction of the High-Spatiotemporal-Resolution OBS-Nudging Module

OBS-nudging relaxes the simulated states in each grid toward the observations using the weighted average of differences from observations within a certain radius of influence and time window [28]. The OBS nudging method uses relaxation terms based on the model error at observational stations, and the relaxation is such as to reduce this error. Each observation has a radius of influence, a time window, and a relaxation time scale determined by user-specified input. These determine where, when, and how much it affects the model solution. Typical model grid points may be within the radius of influence of several observations, and their contributions are weighted according to the distance from the observations.
In observational nudging, the difference between the observations and the analysis values is computed by a nudging term (innovation term), which constitutes the Newtonian relaxation term. In WRF, this is implemented as:
∂ q μ ∂ t x , y , z , t = F q x , y , z , t + μ G q ∑ i = 1 N W q 2 ( i , x , y , z , t ) q 0 i − q m ( x i , y i , z i , t ) ∑ i = 1 N W q ( i , x , y , z , t )
where q is the quantity being nudged ( q represents temperature, relative humidity, and wind speed in the current study), μ is the dry hydrostatic pressure, F q represents the physical tendency terms of q , G q is the nudging strength for q , N is the total number of observations ( N = 460 in the current study, as there are 460 stations taken into account for the nudging), i is the index to the current observation, W q is the spatiotemporal weighting function based on the temporal and spatial separation between the observation and the current model location, q 0 is the observed value of q , and q m ( x i , y i , z i , t ) is the model value of q interpolated to the observation location. The quantity q 0 − q m is the innovation; the innovation associated with a given observation evolves with time (both before and after the time of the observation) as the model value ( q m ) evolves. Thus, as the model value approaches the observed value, the nudging tendency term decreases [21,29,30]. More details about observational nudging can be referenced through https://www2.mmm.ucar.edu/wrf/users/docs/ObsNudgingGuide.pdf (accessed on 6 March 2023.)
The original OBS-nudging data from the National Center for Atmospheric Research were of coarse temporal and spatial resolution, with only a few stations over the North China Plain (NCP), and surface meteorological data were only collected four times daily. Sounding data from the National Center for Atmospheric Research were measured twice daily with few levels in the vertical direction. To replace the coarse-resolution data in the base OBS-nudging function, data optimisation was included in WRF-OBS, with high-spatiotemporal-resolution in situ observational meteorological data sets read directly into the improved OBS-nudging module (Figure 2). Both hourly surface meteorological data at 460 stations and 3 h radiosonde data at Pingyuan station were nudged into the WRF inner domain. Moreover, optimisation involved constructing a module that directly generated an OBSDOMAIN file instead of the OBSGRID.exe calculation module in the base OBS-nudging function. The reconstructed OBS-nudging module included data optimisation and method optimisation processes that not only improved the spatial and temporal resolution of the input data, but also sped up data decoding and OBSGRID calculation.
For the computational cost, on the one hand, the reconstructed OBS-nudging module read high-spatiotemporal-resolution observational data with corresponding format directly, while data decoding and data format conversion are included in the base OBS-nudging module. On the other hand, the reconstructed OBS-nudging module directly generated an OBSDOMAIN file into WRF model, instead of the OBSGRID.exe calculation in the base OBS-nudging module. Overall, the reconstructed OBS-nudging module is more computationally efficient than the base OBS-nudging module.

2.3. Model Configuration and Settings

WRF version 4.1 was used in this study. Simulations were performed at 30 and 10 km horizontal resolutions in domain 1 and domain 2, respectively. Domain 2 covered the NCP and surrounding areas with 11,880 grid cells. In total, 40 vertical layers from the ground level to the top pressure of 50 hPa were used for all grids. The initial and boundary conditions for WRF were provided using the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) datasets with 6 h temporal resolution and 1° × 1° spatial resolution.
The reconstructed high-spatiotemporal-resolution nudging module was coupled with the WRF model to improve the simulation of meteorological conditions. The parameterization schemes of physical processes in WRF model in this study are showed in Table S1. In the northern region of China, where the Pingyuan locates, it usually snows in winter. The Purdue Lin scheme is a sophisticated scheme that has ice, snow and graupel processes, suitable for real-data high-resolution simulations. Grell 3D is an improved version of the Grell-Devenyi scheme that may also be used on high resolution. The Grell 3D scheme in combination with other microphysics scheme performed better for the simulations of air temperature and planetary boundary layer height [31]. Moreover, the Grell 3D scheme is more suitable for spatial resolutions less than 10 km, because it spreads subsidence effects to neighboring grid columns, which is different from other cumulus schemes [32,33]. For the radiation scheme, RRTMG scheme is a new version of Rapid Radiative Transfer Model, which is an accurate scheme using look-up tables for efficiency and it accounts for multiple bands and microphysics species. Direct and diffuse irradiance are calculated directly using RRTMG, RRTMG also outputs direct, clear sky normal irradiance [34,35]. Thompson et al. showed that radiation fluxes reaching ground with the RRTMG scheme are better matched with observations [36]. The Noah is used as the column land surface physics model, where the one-dimensional Noah model simulates soil moisture, soil temperature, skin temperature, snowpack depth, snowpack water equivalent, canopy water content, and the energy and water flux terms at the earth’s surface [37,38]. The Yonsei University scheme is a Non-local-K scheme with an explicit entrainment layer and parabolic K profile in the unstable mixed layer. The enhancement of nighttime vertical mixing in YSU has contributed to a stronger downward thermal flux and upward moisture flux in the lower atmosphere, which has led to higher temperatures and lower moisture in the simulations with the YSU scheme near the surface at nighttime, in better agreement with observations [39,40]. Banks et al. also found that the closest simulated wind speed was found with the YSU scheme [41].
To systemically evaluate model performance at simulating meteorological conditions, we used three statistical indices: mean bias (MB), root mean square error (RMSE), and the square of the Pearson correlation coefficient (R2). The calculation formulas are provided in the Supplemental Material (Equations (S1)–(S3)).

3. Results and Discussion

3.1. Meteorological Conditions and PM2.5 Pollution

The NCP was affected by heavy air pollution from 14 January to 20 January 2018. Daily PM2.5 concentrations exceeded 100 µg m−3, with a maximum of 255 µg m−3 observed on 19 January (Figure S1), which is much higher than the Chinese National Air Quality Standard (75 µg m−3).
One reason for this heavy pollution could be unfavourable meteorological conditions and an SBL. The results of interpolating observations from 460 surface meteorological stations across the NCP are shown in Figure 3. WS10 gradually decreased from north to south (Figure 3a). WS10 was less than 2 m/s in southern Beijing, and the minimum value was approximately 1 m/s in Anhui and Jiangsu. The entire north China region had light winds and calm weather. T2 gradually increased from north to south; the maximum was approximately 6 °C in Anhui and Jiangsu (Figure 3b). RH2 showed the same regional variation as T2 (Figure 3c). In Inner Mongolia and the mountainous areas north of Beijing, RH2 was around 40% to 50%, whereas in Anhui and Jiangsu, it was around 90%. Regionally, meteorological conditions were stable, with low wind, high temperature, and high humidity. Vertically, temperature inversion occurred every day (Figure 4), and double inversion layers sometimes formed. The inversion strength was around 4 °C and the inversion depth reached an average of 200 m during this period.
These meteorological conditions are conducive to the accumulation of atmospheric pollutants, largely because of the stability of the air. The temperature inversion inhibits the vertical diffusion of air pollutants, leading to heavy pollution. High relative humidity and light wind resulting from the temperature inversion exacerbate heterogeneous reactions, further increasing air pollution [42,43].

3.2. Evaluation of the Surface Meteorology Simulation

The simulation results demonstrated that the model performance was significantly improved by WRF-OBS (Table 1). When we compared the simulated and observed values of T2, RH2, and WS10 at 460 stations, the agreement between the WRF-OBS simulation and observations showed various degrees of improvement. R2 values for T2, RH2, and WS10 increased to 0.72, 0.56, and 0.75, which were higher than the corresponding values for the WRF-BS simulation. Although T2 was underestimated by 2.3 °C in the WRF-OBS simulation results for all 460 stations, the MB of RH2 was less than that obtained with WRF-BS. Furthermore, the MB and RMSE values of WS10 were significantly reduced in the WRF-OBS simulation. The MB of 0.076 m/s indicated that the WRF-OBS simulation only slightly overestimated WS10. Compared to the result obtained in Hu et al. [44], which indicated WRF version 3.4.1 with the updated YSU boundary layer scheme overestimated WS10 by 0.334 m/s, the WRF-OBS simulation results produced more reliable WS10. While the MB of T2 in Hu et al. [44] was lower than MB in this study, which indicates WRF version 3.4.1 with the updated the YSU boundary layer scheme performance better than WRF-OBS in simulating T2. Compared to the result obtained in Lysenko et al. [45], which was shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the RMSE of short-range forecasts of surface air temperature by 16–33%, the WRF-OBS didn’t performance quite well that the RMSE of T2 simulation slightly increased. Despite all this, compared to other studies on the general tendency of WRF models to overestimate WS10 and T2 and underestimate RH2 [46,47,48], the WRF-OBS simulation results produced more realistic and reliable meteorological fields during a period with an SBL.
In the regional simulation results (Figure 5a,d), WRF-BS simulations of T2 were nearly consistent with observations in the central part of the NCP, although T2 was overestimated by about 3 °C in the southern part and underestimated by about 2 °C in the northern part. However, aside from the slight underestimation of T2 in the northern mountains and overestimation of T2 at individual stations in the south, the WRF-OBS simulations of T2 showed good agreement with observations for the NCP. T2 simulated with WRF-OBS was generally lower than that simulated with WRF-BS. In the southern and northern regions of the NCP, the temperatures simulated by WRF-OBS were approximately 2 °C and 2–4 °C lower, respectively, than those simulated by WRF-BS. In general, the WRF-OBS simulation of T2 was more consistent with observations than that of WRF-BS.
The regional simulation results for RH2 contrast with the results for T2 (Figure 5b,e). The regional simulation results for RH2 contrast with the results for T2. Although the WRF-BS simulation of RH2 showed good agreement with observations in the eastern Shanxi, northern Hebei, and Beijing areas, RH2 tended to be underestimated by 10–30% in the central and southern parts of the NCP but overestimated by 10–20% in the western Taihang Mountains and northern mountains. This pattern is consistent with previous simulation studies [18,49], that reported an overestimation of relative humidity compared to the unassimilated model. The regional RH2 simulation results for WRF-OBS were much closer than the WRF-BS results to the observed values. Despite a slight overestimation by 10–20% in the northern mountains and northern part of the Taihang Mountains, the simulation of RH2 was generally consistent with observations in other regions of the NCP. The simulation results for southern Hebei, Henan, western Shandong, Anhui, and northern Jiangsu clearly showed the advantage of the WRF-OBS simulation, as in most regions, they were closer to the observed values than were the results of the WRF-BS simulation. The southern region of the NCP has low terrain and is near the Yellow Sea, and thus, water vapour from the ocean can be transported onto land through horizontal flow [43]. WRF-OBS has finer resolution data, which provides support in the horizontal direction and captures changes in relative humidity better than WRF-BS.
Finally, WRF-BS sharply overestimated WS10 on the NCP, in particular over the northern mountains and Taihang Mountains (Figure 5c). The observations were around 1–2 m/s, whereas the simulation results reached 4–5 m/s over those mountainous regions. This finding illustrates a common shortcoming of the WRF model, in which wind speed is generally overestimated under a SBL [50]. In the WRF-OBS simulation, WS10 over the north-western mountainous regions was about 2–4 m/s (Figure 5f). Although WRF-OBS also overestimated WS10, the difference between the simulation results and observations was less apparent than for WRF-BS. Simulated WS10 over southern Hebei, southeast Henan, northern Anhui, and northwest Jiangsu and Shandong provinces was nearly consistent with observations. During this period with a SBL, few areas had wind speeds greater than 5 m/s, and in most regions WS10 was maintained around 0–0.5 m/s. Overall, the simulated values from WRF-OBS were much closer than those from WRF-BS to the measured values. Compared to WRF-BS, WRF-OBS is a step toward more accurate simulation for wind resource assessment in SBL.

3.3. Evaluation of the Vertical Profiles of Meteorological Factors

Figure 6a–d shows the simulated and observed temperature profiles on 19 January 2018. There were some deviations between the simulated temperature profiles and observations at various heights. At 00:00 UTC, the WRF-BS simulation markedly overestimated the temperature below 950 hPa, with the deviation reaching 4 °C at 1000 hPa, and slightly underestimated it above 950 hPa, with an underestimation of approximately 1 °C. Meanwhile, the temperature was slightly underestimated from 1000 to 950 hPa in WRF-OBS, with a deviation of about 0.8 °C. The temperature profile from WRF-OBS aligned well with the observed values above 940 hPa. Moreover, the temperature inversion characteristics were reproduced well by WRF-OBS, as the inversion intensity and thickness were generally consistent with the observed values. At 06:00 UTC, the temperature was underestimated in the WRF-BS simulation from 1000 to 850 hPa, with a large temperature deviation of 1.5 °C from 950 to 850 hPa. Although the inversion thickness was slightly greater than the observed value at 950 hPa, the temperature profile simulated by WRF-OBS was generally consistent with observations from 1000 to 850 hPa. At 12:00 UTC, the temperature profile was generally consistent with observations below 900 hPa, but the inversion layer between 900 and 875 hPa was not reproduced by WRF-BS. By contrast, the temperature profile simulated by WRF-OBS was completely consistent with the observed values, and the inversion intensity and thickness were accurately reproduced. At 18:00 UTC, the temperature was overestimated below 975 hPa and the peaked overestimation was 2 °C at 1000 hPa, whereas it was underestimated between 975 and 830 hPa by about 1–2 °C in the WRF-BS results. The WRF-OBS-simulated temperature profile was generally consistent with the observed values from 1000 to 800 hPa, and the temperature inversion at 1000 hPa was reproduced, with accurate simulation of the inversion intensity and thickness.
The simulated and observed relative humidity profiles are presented in Figure 6e–h. At 00:00 UTC, although the simulated and observed profiles were generally consistent above 940 hPa, WRF-BS underestimated relative humidity below 940 hPa, with a maximum deviation of 70% at 1000 hPa. The relative humidity in the WRF-OBS simulation results was slightly overestimated from 1000 to 970 hPa, with a deviation of about 6%, whereas above 970 hPa, the simulated values showed good agreement with the observations. At 06:00 UTC, WRF-BS underestimated relative humidity below 930 hPa and overestimated it from 920 to 850 hPa, with deviations of 30% and 5%, respectively. Aside from a 5% overestimation below 950 hPa, the relative humidity profile of the WRF-OBS simulation was consistent with observations. At 12:00 UTC, relative humidity was underestimated below 925 hPa in WRF-BS, with a maximum deviation of 35%. WRF-OBS slightly overestimated relative humidity below 900 hPa, with a deviation of less than 5%, and the relative humidity profile was generally consistent with observations. At 18:00 UTC, WRF-BS overestimated the relative humidity below 950 hPa and underestimated from 950 to 850 hPa. The WRF-OBS simulation results were consistent with the ob-served values from 1000 to 850 hPa.
The WRF model typically exhibits severe prediction bias at low to moderate wind speed, mainly due to limitations in physical parameterization techniques, differences in resolution, and inaccuracies in terrain data [48,51]. Besides, in the case of complex terrain, the model faces challenges in accurately simulating local wind speed [52]. The WRF-BS-simulated wind profiles deviated quite a bit from the observed values, but the WRF-OBS simulations had much smaller deviations (Figure 6i–l). At 00:00 UTC, the wind speed profile obtained from WRF-BS had deviations at multiple heights, with a maximum deviation of 8 m/s. In the WRF-OBS simulation, the wind speed profile was generally consistent with the observed values, and the maximum deviation was only 1.5 to 2 m/s. At 06:00 UTC, wind speed was markedly overestimated in WRF-BS below 925 hPa, with a maximum deviation of 4 m/s. The wind speed profiles simulated by WRF-OBS were accurately reproduced, with a maximum deviation of 1.5 m/s. At 12:00 UTC, wind speed was underestimated below 975 hPa and overestimated between 975 and 850 hPa in WRF-BS, with maximum deviations of 5 to 7 m/s. Aside from a slight underestimation at 925 hPa, the wind profile produced using WRF-OBS was consistent with the observations. At 18:00 UTC, wind speed was underestimated below 960 hPa and overestimated from 960 to 900 hPa in WRF-BS, with an underestimation of 1.5 m/s at 1000 hPa and an average overestimation of 2 m/s. The WRF-OBS-simulated wind speed was slightly underestimated below 990 hPa and overestimated from 990 to 900 hPa, with deviations less than 1 m/s.
Compared to WRF-BS, the WRF-OBS-simulated temperature profiles deviated significantly less from the observed values during this SBL period (Figure S2), and WRF-OBS accurately simulated the temperature inversion intensity and thickness in the SBL with strong reproducibility. While there were some deviations between the WRF-OBS simulation results and the observations (Figure S3), but they were much smaller than the deviations of WRF-BS. Compared to WRF-BS, WRF-OBS effectively improved the accuracy of humidity profile simulation. Furthermore, the WRF-OBS simulation produced wind profiles with significantly smaller deviations from the observed values during this SBL period (Figure S4), demonstrating effective improvement of the accuracy of wind profile simulation.

3.4. Evaluation of BLH Simulation

The atmospheric boundary layer, which is the lowest layer of the troposphere, directly controls the exchange and transport of matter and energy between land and the free atmosphere [53,54]. BLH is one of the most important physical parameters of the atmosphere, as it is closely related to the formation and evolution of air pollution as well as to climate change [55,56]. Figure 7 shows the temporal variation in simulated and ERA5 BLH at four stations. BLH showed significant diurnal variation, rising higher in the daytime and falling lower at night. At Pingyuan station, the BLH values produced by both WRF-BS and WRF-OBS were slightly overestimated around 14:00 Beijing Time on 19 January and earlier; thereafter, the simulated BLH was generally consistent with ERA5 BLH. Both WRF-BS and WRF-OBS-simulated BLH values were underestimated, with a maximum deviation of 1 km, at Beijing station. At Xingtai station, WRF-BS-simulated BLH and ERA5 values were generally consistent in the daytime before January 17, with deviations of 0.1–0.2 km, but were underestimated at noon on January 19 and overestimated at noon on 20 January, with deviations of 0.4 to 0.5 km. Aside from a slight overestimation during the daytime of 17 and 20 January, WRF-OBS-simulated BLH values were consistent with ERA5 BLH. At Handan station, WRF-BS markedly overestimated BLH during the daytime of January 17th and at noon on January 19th. WRF-OBS-simulated BLH was generally consistent with ERA5 BLH, except for a slight overestimation during the day of 17 January and night of 18 January, with deviations of 0.15 km. Compared to WRF-BS, WRF-OBS effectively improved the accuracy of BLH simulation under SBL conditions, and the correlation coefficient between WRF-OBS-simulated BLH and ERA5 BLH was greater than that for WRF-BS.

3.5. Meteorological Conditions Triggered Heavy Pollution Based on the WRF-OBS Simulation

Meteorological conditions and boundary layer characteristics are some of the most important drivers of heavy pollution events. The WRF-OBS simulation reflected the SBL meteorological conditions more accurately than WRF-BS, which suggests that the WRF-OBS simulation results are more reasonable for analysing the meteorological mechanisms underlying heavy pollution. The northern portion of the NCP was dominated by easterly and north-easterly winds, and near-surface pollutants were mainly affected by warm and humid air flowing over the Bohai Sea (Figure 8). This warm and humid environment was conducive to increasing pollutant concentrations, and low wind speed favoured the accumulation of pollutants. Pingyuan station is located in the middle of the NCP to the north of Mount Tai. In this unique geographic location, the Taihang Mountains block warm and humid air from the sea, causing the air mass to the south of Pingyuan station to remain in place and supporting the accumulation of pollutants near the surface. At 850 hPa, Pingyuan station was dominated by north-easterly wind and south-easterly wind prevailed over the south of Taihang Mountain under the control of an anticyclonic circulation (Figure S5) that carried warm air from the south to the Pingyuan region. Warm advection in the upper layer overlying cold air dammed near the surface contributed to a decrease in the vertical temperature lapse rate, enhancing atmospheric stability [57], which further inhibited the diffusion of pollutants in the vertical direction.
Vertically, temperature and relative humidity increase with height (Figure 9). On the one hand, the increase in relative humidity is conducive to heterogeneous reactions among particles and the formation of new particles. Liu et al. [58] found that when relative humidity increases from 50–60% to 60–70%, the concentration of secondary particulate matter doubles. The nighttime persistence of temperature inversion enhances atmospheric stability. Stability in the boundary layer is not conducive to the vertical diffusion of particles, resulting in the accumulation of pollutants in the boundary layer [59]. On the other hand, vertical variation in wind speed and direction can affect air pollution and the vertical transport of air pollutants [60,61]. Vertical wind speed is significantly higher during the day than at night, with predominantly north-westerly and south-easterly winds during the day and predominantly north-easterly and south-westerly winds at night (Figure 10). After 12:00 UTC, the near-surface wind speed rapidly decreases and the SBL leads to weakened near-surface turbulence, facilitating the near-surface accumulation of PM2.5. Although mechanical turbulence caused by wind shear favours vertical mixing and alters the vertical structures of air pollutants [62], the enhanced wind shear in the near-surface atmospheric boundary layer under weak wind conditions is conducive to increasing PM2.5 [63].
Temperature inversion is another important factor that leads to heavy pollution [64,65,66]. Temperature profiles based on sounding data indicated that temperature inversion was persistent and multiple inversion layers were present at some times during this period of heavy pollution (Figure 4). A marked double-level temperature inversion occurred at 18:00 UTC on January 20. The WRF-OBS simulation captured the characteristics of the double temperature inversion layer very well (Figure S6), with one temperature inversion layer from the surface with an intensity of 1 °C and depth of 190 m and another at 900 hPa with an intensity of 4 °C and depth of 340 m. The surface-based inversion mainly resulted from gradual weakening of solar heating and strengthening of radiative cooling at sunset [67], and the higher inversion layer may have been driven primarily by airflow subsidence [68,69,70]. A subsiding air mass rarely moves downward to the surface, as turbulent mixing is always present, which allows the air below the inversion to remain cooler, and thus, subsidence plays a very minor role near the ground [71]. The dynamic properties of the SBL in the vertical direction at Pingyuan station were also analysed based on vertical velocity. Positive vertical velocity represents sinking movement of air, as illustrated in Figure 11. Significant subsiding motion from 800 hPa down to approximately 850 hPa at a rate of 0.3 Pa/s indicated upper air divergence, whereas ascending motion from 950 to 850 hPa at a rate of 0.2 Pa/s indicated lower air convergence. At an altitude of about 850 hPa, the vertical velocity was very small, nearly zero, which suggests that a buffer zone was present between the regions of divergence and convergence and that this buffer zone corresponded to the upper temperature inversion layer. Similarly, divergence and convergence occurred at altitudes from 1000 to 975 hPa, which corresponded to the surface-based temperature inversion layer. The two buffer zones showed little turbulence, which indicates great stability. The upper inversion layer itself was also very stable, although the layers above and below it was generally quite unstable [69]. These conditions can result in vigorous convection and sufficient mixing of pollutants due to the strong turbulence induced by significant ascending motion below the inversion layer, allowing heavy pollution to accumulate.
The temperature difference between the two adjacent layers indicates that the two inversion layers covered the NCP with a horizontal extent of approximately 200 km during the period of heavy pollution (Figure 12). The two temperature inversion layers were so strong and deep that it would have been extremely difficult for them to dissipate. The strong vertical gradients of 0.52 °C per 100 m and 1.1 °C per 100 m in the inversion layers indicate highly stable conditions, and thus, relatively weak turbulence and diffusion. The strong atmospheric stability created by these temperature inversions impeded the horizontal transmission and vertical mixing of pollutants, which limited the vertical diffusion of pollutants and trapped them in a shallow layer, promoting the maintenance of high surface PM2.5 concentrations [71,72,73]. Shao et al. [74] confirmed that multiple temperature inversion layers impact near-surface PM2.5 concentrations more strongly than other temperature inversion patterns.

4. Conclusions

In this study, we reconstructed an OBS-nudging module, which directly generates OBSDOMAIN files for the WRF model, significantly improving WRF performance under the stable atmospheric conditions of the NCP. The meteorological mechanism driving heavy pollution was discussed based on WRF-OBS results.
WRF-OBS showed better model performance than WRF-BS in terms of T2, RH2, and WS10 on the NCP. The correlations (R2) between the WRF-OBS simulations and observations of T2, RH2, and WS10 at 460 stations over the NCP were 0.72, 0.56, and 0.75, respectively, which were much higher than the R2 values for the WRF-BS simulation. Regionally, the WRF-BS simulation underestimated T2 in the northern NCP and RH2 in the central and southern parts of the NCP. Moreover, WRF-BS overestimated T2 in the south as well as RH2 and WS10 in the northern mountains and Taihang Mountains. Compared to WRF-BS, the T2, RH2, and WS10 results produced by WRF-OBS were much closer to observations across the NCP, and WRF-OBS reflected the surface meteorological conditions more reasonably. Vertically, the WRF-BS simulation indicated an unstable boundary layer and scarce temperature inversion. The profiles of temperature, relative humidity, and wind speed simulated using WRF-OBS showed good agreement with the observed values. The temperature profiles produced through WRF-OBS assimilation were reproducible, and the temperature inversion strength and thickness were accurately simulated. Compared to WRF-BS, WRF-OBS showed improved simulation performance in terms of BLH characteristics. Overall, the WRF-OBS simulation method effectively improved on the accuracy of WRF-BS in both the horizontal and vertical directions.
The WRF-OBS simulation results accurately reflected the meteorological conditions of the SBL, supporting analyses of the formation mechanism that triggered the heavy pollution event. During the period of heavy pollution, the NCP experienced light wind. Pingyuan station was mainly affected by warm and humid air flowing in from the sea. This high humidity led to increased aerosol moisture absorption, and the low wind speed was not conducive to the diffusion of pollutants. The Taihang Mountains, located to the south of the plains containing the station, block warm and humid air from the sea, allowing for the accumulation of pollution to the south of Pingyuan station. At 850 hPa, the region south of Mount Tai is controlled by a south-easterly wind that carries warm air from the south to Pingyuan. Temperature inversion was another crucial factor contributing to the heavy pollution event. Two temperature inversion layers occurred during this heavy pollution period, with inversion intensities of 1 °C and 4 °C and inversion depths of 190 and 340 m, respectively. The two inversion layers covered the NCP, with a horizontal extent of approximately 200 km. Thus, the boundary layer was very stable and diffusion among layers was relatively weak because of the double-layered temperature inversion structure, which prevented the horizontal transmission and vertical mixing of pollutants and promoted the maintenance of high PM2.5 concentrations.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/atmos15030277/s1. Table S1: WRF model configuration options and settings; Figure S1: Daily concentration of PM2.5 and its components at Pingyuan station from 14 to 21 January 2018; Figure S2: Temperature bias between WRF-BS (a) and WRF-OBS (b) and observed values during heavy pollution. The shaded part represents the difference of 25 and 75 quantiles; Figure S3: Relative humidity bias between WRF-BS (a) and WRF-OBS (b) and observed values during heavy pollution. The shaded part represents the difference of 25 and 75 quantiles; Figure S4: Wind speed bais between WRF-BS (a) and WRF-OBS (b) and observed values during heavy pollution. The shaded part represents the difference of 25 and 75 quantiles; Figure S5: Spatial distributions of temperature (a), relative humidity (b) (contour) and wind (black vectors) at 850 hPa during heavy polluted period; Figure S6: simulated and observed vertical temperature profile at 18:00 UTC in January 20, 2018 at Pingyuan station; Table S2: List of abbreviations and corresponding full forms.

Author Contributions

Conceptualization, T.X.; Data curation, Z.P., Y.W. and C.W.; methodology, Y.W.; validation, T.X.; formal analysis, T.X., Z.P., Y.W. and S.J.; investigation, Z.P., Y.W., C.W., S.L., S.J., X.T. and X.Z.; resources, C.W. and S.L.; data curation, Z.P., Y.W., C.W. and S.L.; writing—original draft preparation, T.X.; writing—review and editing, T.X., Z.P. and C.W.; visualization, Z.P.; supervision, T.X. and X.Z.; project administration, T.X.; funding acquisition, T.X. and X.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Project of Science and Technology Department of Sichuan Province (2023NSFSC0746) and the Everest Scientific Research Program of Chengdu University of Technology (80000-2022ZF11410).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

ERA5 data are openly available in a public repository: https://0-doi-org.brum.beds.ac.uk/10.24381/cds.adbb2d47. The source code for WRF v4.1 and the code for the reconstructed observation nudging module are available at https://0-doi-org.brum.beds.ac.uk/10.5281/zenodo.8223496. Observational and simulation data are available at https://0-doi-org.brum.beds.ac.uk/10.5281/zenodo.8229421.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Meteorological station locations over the North China Plain (the black dot shows the Pingyuan station location).
Figure 1. Meteorological station locations over the North China Plain (the black dot shows the Pingyuan station location).
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Figure 2. The calculation steps of base OBS-nudging (up) and high-resolution OBS-nudging (down).
Figure 2. The calculation steps of base OBS-nudging (up) and high-resolution OBS-nudging (down).
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Figure 3. The interpolation results of WS10 (a), T2 (b), and RH2 (c) of 460 observation stations in the NCP from 14 to 20 January 2018.
Figure 3. The interpolation results of WS10 (a), T2 (b), and RH2 (c) of 460 observation stations in the NCP from 14 to 20 January 2018.
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Figure 4. Temperature profiles of observation data at 00 (a), 06 (b), 12 (c), and 18 (d) at Pianyuan station from 14 to 21 January 2018.
Figure 4. Temperature profiles of observation data at 00 (a), 06 (b), 12 (c), and 18 (d) at Pianyuan station from 14 to 21 January 2018.
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Figure 5. Spatial distribution of observations (dots) and simulations (contours) of T2, RH2, and WS10 in WRF-BS (a–c) and WRF-OBS simulation (d–f).
Figure 5. Spatial distribution of observations (dots) and simulations (contours) of T2, RH2, and WS10 in WRF-BS (a–c) and WRF-OBS simulation (d–f).
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Figure 6. Simulated and observed temperature (a–d), humidity (e–h), and wind profile (i–l) on 00:00, 06:00, 12:00, and 18:00 UTC at Pingyuan station.
Figure 6. Simulated and observed temperature (a–d), humidity (e–h), and wind profile (i–l) on 00:00, 06:00, 12:00, and 18:00 UTC at Pingyuan station.
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Figure 7. Comparison of WRF simulation values and ERA5 reanalysis boundary layer time series at Pingyuan (a), Beijing (b), Xingtai (c), and Handan (d).
Figure 7. Comparison of WRF simulation values and ERA5 reanalysis boundary layer time series at Pingyuan (a), Beijing (b), Xingtai (c), and Handan (d).
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Figure 8. Spatial distributions of T2 (a), RH2 (b) (contour), and wind (black vectors) during a heavy polluted period.
Figure 8. Spatial distributions of T2 (a), RH2 (b) (contour), and wind (black vectors) during a heavy polluted period.
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Figure 9. Temperature and relative humidity profiles at Pingyuan station from 14 to 21 January 2018.
Figure 9. Temperature and relative humidity profiles at Pingyuan station from 14 to 21 January 2018.
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Figure 10. Temperature and wind profiles at Pingyuan station on January 19 (a) and 20 (b), in 2018.
Figure 10. Temperature and wind profiles at Pingyuan station on January 19 (a) and 20 (b), in 2018.
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Figure 11. Vertical velocity on vertical cross section at 18:00 UTC, 20 February 2018.
Figure 11. Vertical velocity on vertical cross section at 18:00 UTC, 20 February 2018.
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Figure 12. Temperature difference vertical cross section at 18:00 UTC, 20 February 2018.
Figure 12. Temperature difference vertical cross section at 18:00 UTC, 20 February 2018.
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Table 1. Comparison of MB, RMSE, and R2 at 460 stations.
Table 1. Comparison of MB, RMSE, and R2 at 460 stations.
IndexT2RH2WS10
WRF-BSWRF-OBSWRF-BSWRF-OBSWRF-BSWRF-OBS
MB1.5−2.3−9.27.51.50.076
RMSE3.24.320.215.72.20.63
R20.630.720.40.560.50.75
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Xu, T.; Peng, Z.; Wang, Y.; Wan, C.; Liu, S.; Jiang, S.; Tang, X.; Zhao, X. Improvement of Stable Atmospheric Boundary Simulation with High-Spatiotemporal-Resolution Nudging over the North China Plain. Atmosphere 2024, 15, 277. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030277

AMA Style

Xu T, Peng Z, Wang Y, Wan C, Liu S, Jiang S, Tang X, Zhao X. Improvement of Stable Atmospheric Boundary Simulation with High-Spatiotemporal-Resolution Nudging over the North China Plain. Atmosphere. 2024; 15(3):277. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030277

Chicago/Turabian Style

Xu, Tingting, Zhuohao Peng, Yan Wang, Chaoyue Wan, Shenlan Liu, Shuqiao Jiang, Xiaolu Tang, and Xilin Zhao. 2024. "Improvement of Stable Atmospheric Boundary Simulation with High-Spatiotemporal-Resolution Nudging over the North China Plain" Atmosphere 15, no. 3: 277. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030277

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