the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Feb 2025
Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand
Abstract. Accurate determination of the planetary boundary layer (PBL) height, mixing layer height (MLH), and aerosol layer top (ALT) is critical for air quality and climate studies, especially in regions with complex aerosol dynamics like Chiang Mai, northern Thailand. This study develops a novel LiDAR-based methodology that incorporates a temperature-based dynamic maximum analysis altitude (MAA) to decouple these layers, addressing the limitations of conventional methods such as the Haar Wavelet Covariance Transform (WCT). Traditional fixed-altitude approaches often misclassify the ALT as the PBL height, particularly during nighttime or transition periods, leading to significant overestimations. By dynamically adjusting the MAA based on surface temperature variations, the proposed approach effectively distinguishes the PBL from residual aerosol layers and cloud interference. Comparison against radiosonde data and WRF-Chem simulations demonstrates strong agreement, with LiDAR-derived PBL heights showing improved diurnal resolution and accuracy. However, model simulations tend to overestimate the PBL height during high aerosol events, highlighting the need for refined aerosol-radiation interaction parameterizations. This study underscores the importance of integrating thermodynamic and aerosol data for accurate boundary layer characterization and provides a robust framework for improving air quality and climate models in regions with high aerosol loading and complex topography. These findings have implications for enhancing pollutant transport analysis and advancing LiDAR-based remote sensing techniques in Southeast Asia.
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RC1: 'Comment on egusphere-2025-630', Anonymous Referee #1, 06 May 2025
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Reviewer comments on “Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand”
This study presents a timely and methodologically robust investigation into the critical challenge of distinguishing planetary boundary layer dynamics from aerosol layer top and mixing layer height using LiDAR observations. The authors demonstrate commendable innovation in integrating surface temperature variability to dynamically adjust the MAA, thereby improving the decoupling of PBL height from residual aerosol layers and cloud interference. However, in my opinion, there are still some issues that need to be addressed before publishing.
- The manuscript lacks a detailed description of the datasets used, especially the lidar data. Please specify the temporal and spatial resolution of the lidar measurements. Additionally, clarify whether there is any temporal or spatial overlap in the data, as this could impact the analysis.
- The physical basis and theoretical justification for the proposed MMA method are not clearly explained. Including a discussion of the underlying principles or physical assumptions would enhance the credibility and reproducibility of the method.
- In Line 115, the authors select a specific method as the reference for radiosonde (RS) observations. Please clarify why this method was chosen over others and explain its suitability for this purpose.
- In Figure 1, the MMA method appears to overestimate the planetary boundary layer height (PBLH) on January 27 between 12:00 and 18:00. The authors should analyze and discuss possible reasons for this discrepancy.
- Figures 2 and 3 are currently of low resolution, and the axis labels in the subplots are difficult to read. Please enhance the image clarity and consider adding subplot labels (e.g., (a), (b), (c)) for easier reference in the text.
- The study compares MMA-derived PBLH with RS values only at 07:00 local time. This limited time point may not be representative of the method’s overall performance. The authors are encouraged to incorporate additional time periods or alternative observational data to provide a more robust validation.
- The manuscript reports good agreement between MMA and WRF-derived PBLH. However, it is unclear whether the accuracy of the WRF simulations has been independently validated. Please provide supporting evidence or references to justify the reliability of the WRF-derived PBLH used in this comparison.
Citation: https://doi.org/10.5194/egusphere-2025-630-RC1 -
AC1: 'Reply on RC1: Author Responses on “Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand”', Ronald Macatangay, 16 May 2025
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Author Responses on “Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand”
Thank you for your comprehensive and insightful feedback. We appreciate the opportunity to clarify and strengthen our manuscript. Below are our responses to each point:1. Dataset Description: We acknowledge that a detailed description of our LiDAR datasets was insufficient. The LiDAR measurements were collected continuously over the study period (December 2023 – February 2024) with a vertical resolution of 30 meters. The instrument operated at a pulse frequency of 2500 Hz, with each profile having an averaging time of 30 seconds while being pointed at the same position in the sky—providing consistent spatiotemporal overlap. Each MiniMPL LiDAR profile was averaged over 30 seconds, and the data were grouped at 5-minute intervals for analysis. This approach aligns with standard practice in boundary layer studies and helps balance temporal resolution with noise reduction, as demonstrated in similar studies, including Solanki et al. (2019). The spatial coverage was centered at NARIT AstroPark in Chiang Mai, northern Thailand, with the instrument fixed at this location to ensure spatial consistency. There was significant temporal overlap in the data, allowing continuous monitoring and analysis of boundary layer variations. A full site description can be found in Solanki et al. (2019), which is already referenced in line 90: "A more comprehensive description of the study site is given in Solanki et al., 2019." This information will be added to the paragraph in line 85 as:
The study was conducted at the headquarters of the National Astronomical Research Institute of Thailand (NARIT), situated at the Princess Sirindhorn AstroPark in Chiang Mai, northern Thailand (18.85° N, 98.96° E, 332 mASL), a region known for its complex aerosol dynamics driven by mountainous topography, biomass burning (forest and agricultural fires), anthropogenic pollution, and biogenic emissions from forested areas. The site experiences significant seasonal variations in aerosol concentrations, influencing the vertical distribution of particulate matter. To monitor these dynamics, a Mini Micro Pulse LiDAR (MiniMPL) system was deployed at the site and remained fixed throughout the measurement campaign to ensure spatial consistency. The instrument operated at a pulse repetition frequency of 2500 Hz, with each vertical profile averaged over 30 seconds. For analysis, the data were grouped into 5-minute intervals to balance temporal resolution with noise reduction, in line with standard practices in boundary layer studies, including those at this site (Solanki et al., 2019). The LiDAR was continuously pointed at the zenith, ensuring consistent spatiotemporal overlap for robust monitoring of the vertical aerosol structure and planetary boundary layer (PBL) evolution. The study period, from December 2023 to February 2024, coincides with the beginning of the dry season in northern Thailand, when agricultural and forest fires begin to elevate aerosol concentrations (Bran et al., 2024), complicating the identification of the PBL and aerosol layers. This period provides a unique opportunity to evaluate the performance of the proposed LiDAR-based approach under high aerosol loading and variable meteorological conditions. A more comprehensive description of the study site is given in Solanki et al., 2019.2. Physical Basis of MAA Method: The MAA method is fundamentally grounded in the principle that surface temperature variations reflect thermodynamic conditions influencing atmospheric stability and boundary layer development. By dynamically adjusting the maximum analysis altitude based on surface temperature and its derivatives, the method accounts for thermally-driven boundary layer growth and residual layer stratification. The physical assumption is that higher surface temperatures correlate with a convectively active boundary layer, guiding the adjustment of the detection altitude. We will add a dedicated subsection elaborating on these principles and referencing relevant theoretical frameworks, such as boundary layer turbulence models, to enhance clarity and reproducibility in the paragraph in line 95:
The dynamic maximum analysis altitude (MAA) method introduced in this study is grounded in boundary layer thermodynamics and turbulence theory. It addresses a key limitation in conventional planetary boundary layer (PBL) detection approaches—namely, the use of a fixed maximum altitude for analysis regardless of prevailing atmospheric conditions. By leveraging real-time surface temperature variations, this method introduces a thermodynamically responsive upper boundary for LiDAR-based boundary layer retrievals. The physical rationale stems from the well-established relationship between surface heating, buoyant turbulence generation, and boundary layer growth. Under convective conditions, surface warming leads to rising thermals that entrain air and deepen the boundary layer (Stull, 1988). Conversely, cooler surface temperatures typically indicate stable stratification or residual layer conditions in the early morning or late evening (Seibert et al., 2000). These thermal variations strongly influence the height and structure of the PBL, as described in classic boundary layer turbulence models such as the Mixed Layer Model (Tennekes, 1973; Garratt, 1994) and first-order closure turbulence schemes implemented in models like WRF-Chem (Skamarock et al., 2008). This dynamic parameter, unlike the fixed altitudes used in conventional methods, is calculated using Equation (1):MAA(t) = LAA+HAA-LAAT(t) - min(T)maxT-min(T) (1)
where MAA(t) is the time-varying surface temperature-based maximum analysis altitude
LAA and HAA, represent the lowest and highest allowable maximum analysis altitudes. These are set to 0.5 and 2.5 km, respectively, based on Solanki et al., 2019
T(t) is the observed surface temperature (in °C)
min(T) is the minimum temperature of the day (or the previous day for
operational use)
max[ ] is the maximum of the expression inside the brackets
t is time, representing temporal variation for all T, with data recorded every 5 minutes. The normalization ensures that MAA is low under cooler conditions (e.g., early morning residual layer regimes) and higher under warmer, convectively unstable conditions typical of late morning and afternoon boundary layer growth. This dynamic framework enhances robustness when detecting the PBL top using the Haar wavelet covariance transform (WCT) method (Brooks, 2003), as implemented in the Ceilometer Layer Identification and Optimization (Ceilo) code. Following the WCT detection, a 6-hour moving average is applied to the raw PBL height time series to suppress high-frequency variability associated with short-lived turbulence bursts or instrumental noise. The resulting PBL height estimates are subsequently validated against radiosonde measurements and WRF-Chem model outputs to assess performance and reproducibility. This methodology contributes to a growing body of literature advocating adaptive and physically-informed PBL detection methods (Hennemuth & Lammert, 2006), particularly under complex aerosol and meteorological regimes like those encountered in Southeast Asia.
Key References:
- Stull, R. B. (1988). An Introduction to Boundary Layer Meteorology. Springer. https://doi.org/10.1007/978-94-009-3027-8
- Garratt, J. R. (1994). The Atmospheric Boundary Layer. Cambridge University Press.
- Tennekes, H. (1973). A model for the dynamics of the inversion above a convective boundary layer. Journal of the Atmospheric Sciences, 30(4), 558–567. https://doi.org/10.1175/1520-0469(1973)030<0558:AMFTDO>2.0.CO;2
- Brooks, I. M. (2003). Finding boundary layer top: Application of wavelet covariance transform to lidar backscatter profiles. Journal of Atmospheric and Oceanic Technology, 20(8), 1092–1105. https://doi.org/10.1175/1520-0426(2003)020<1092:FBLTAO>2.0.CO;2
- Solanki, R., Macatangay, R., Sakulsupich, V., Sonkaew, T., & Mahapatra, P. S. (2019). Mixing Layer Height Retrievals From MiniMPL Measurements in the Chiang Mai Valley: Implications for Particulate Matter Pollution. Frontiers in Earth Science, 7, 308. https://doi.org/10.3389/feart.2019.00308
- Skamarock, W. C., et al. (2008). A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN–475+STR. https://doi.org/10.5065/D68S4MVH
- Hennemuth, B., & Lammert, A. (2006). Determination of the atmospheric boundary layer height from radiosonde and lidar backscatter. Boundary-Layer Meteorology, 120, 181–200. https://doi.org/10.1007/s10546-005-9035-3
3. Choice of Radiosonde Method: We thank the reviewer for this insightful comment. The method proposed by Wang and Wang (2014) was chosen because it improves upon earlier approaches by integrating multiple meteorological variables—such as temperature, humidity, and cloud presence—into a unified framework for estimating the mixing layer height (MLH). Traditional methods often rely on a single variable, such as potential temperature gradients, specific humidity, or refractivity (e.g., Seibert et al., 2000; Liu and Liang, 2010; Zhang et al., 2010), which can lead to inconsistent or inaccurate PBL height estimates, particularly under complex atmospheric conditions. In contrast, the Wang and Wang method identifies the height at which sharp gradients in temperature and humidity align most consistently, taking into account the effects of cloud-capped layers and stable stratification. This integrative approach enhances the robustness of MLH estimates, especially during transition periods or in the presence of residual layers and variable moisture profiles—conditions common in our study region. As a result, it yields more reliable boundary layer estimates compared to single-variable or gradient-threshold methods. We have updated the manuscript (line 115) to include this rationale and added supporting citations as:
PBL heights were determined using the method of Wang and Wang (2014), which identifies the planetary boundary layer height by analyzing the first derivatives of key meteorological variables—specifically, temperature, wind speed, wind direction, potential temperature, dewpoint, and relative humidity. This approach considers both the maxima and minima in these gradients to detect significant atmospheric transitions associated with the top of the mixing layer. The final PBL height was computed as the average of the estimates derived from these parameters. This method was selected over traditional single-variable approaches because it integrates multiple physical parameters and accounts for cloud presence and stable stratification, providing more robust and consistent results under diverse atmospheric conditions. Earlier methods that rely solely on individual gradients (e.g., of potential temperature or humidity) are prone to inaccuracies, particularly in regions with residual layers, cloud-capped boundaries, or complex moisture profiles (Seibert et al., 2000; Liu and Liang, 2010). In contrast, the Wang and Wang method aligns discontinuities across multiple variables to better identify the true extent of turbulent mixing. Its integrative design makes it especially suitable for the complex atmospheric dynamics observed in this study over northern Thailand.PBL heights were determined using the maxima in the first derivatives of temperature, wind speed, wind direction, and potential temperature, as well as the minima in the first derivatives of dewpoint and relative humidity (Wang and Wang, 2014). The PBL height was computed as the average of estimates derived from these parameters. However, a significant limitation is that the radiosondes were launched only once daily at 7 AM local time (00 UTC), coinciding with the early morning minimum PBL height. This limitation means that diurnal variations in the PBL height, especially during its daytime growth and decay phases, cannot be captured, potentially reducing the representativeness of radiosonde-derived estimates for broader atmospheric analyses.
Suggested citations to add (with links):
- Seibert, P., et al. (2000): Review and intercomparison of operational methods for the determination of the mixing height, Atmos. Environ., 34, 1001–1027. https://doi.org/10.1016/S1352-2310(99)00349-0
- Liu, S., & Liang, X. Z. (2010): Observed diurnal cycle climatology of planetary boundary layer height, J. Clim., 23(21), 5790–5809. https://doi.org/10.1175/2010JCLI3552.1
4. Discrepancies in Figure 1 (January 27): We thank the reviewer for their observation. We would like to clarify that the gray line labeled as the Maximum Analysis Altitude (MAA) in Figure 1 does not represent the PBL height. Rather, it defines the maximum vertical extent up to which the Wavelet Covariance Transform (WCT) algorithm is applied to detect the PBL height. In other words, if the MAA is at 2.5 km AGL (above ground level) at a particular time, the WCT analysis is limited to searching for the PBL height only up to that altitude. The MAA is defined dynamically and is designed to follow the expected range of the convective boundary layer, informed by the diurnal cycle of surface temperature. This prevents overestimation of the PBL height, especially during nighttime or during the transition phases of growth and decay. On January 27, between 12:00 and 18:00, surface heating resulted in an elevated MAA, which simply means the algorithm had permission to search for PBL heights up to those altitudes below the MAA—but this does not imply that the actual PBL reached those levels. The actual refined PBL height, depicted by the red line, stays well below the MAA throughout this period, as expected. We have revised the figure caption and added a clarification in the main text to ensure that the distinction between MAA and PBL height is clearly understood (lines 140-150 ):
Figure 1 shows the NRB signal as a colored curtain plot, where the aerosol layer top (ALT) is marked as a white line, the time-varying maximum analysis altitude (MAA) as a gray line, and the refined PBL estimate as a red line. It is important to note that the MAA does not represent the PBL height, but rather defines the maximum vertical range within which the wavelet covariance transform (WCT) analysis is conducted to detect the PBL height. For instance, if the MAA is set at 2.5 km AGL, the WCT algorithm only searches for the PBL height below that altitude. This adaptive constraint prevents overestimation of the PBL height, particularly during nighttime or during the growth and decay phases of the boundary layer. The MAA is defined dynamically and follows the diurnal variation of surface temperature, providing a physically realistic ceiling for analysis that adjusts with expected atmospheric mixing. During 00:00–06:00 LT, conventional PBL detection methods often mischaracterize the PBL height by incorrectly identifying the residual layer top or aerosol layer top as the PBL. However, during the well-mixed part of the day and under cloud-free conditions (12:00–16:00 LT on January 27), the red PBL line closely aligns with the white ALT line. This alignment indicates a well-defined mixing layer, allowing for an accurate determination of the mixing layer height (MLH).In contrast, under partly cloudy conditions (12:00–16:00 LT on January 28), conventional algorithms misclassify the cloud base as the PBL height. Transitional periods, such as the morning PBL growth phase (06:00–12:00) and evening decay (16:00–00:00), also pose challenges due to aerosol accumulation in residual layers, which creates ambiguous gradients in the NRB signal. By incorporating the novel time-varying MAA and refining the PBL estimates, these limitations are mitigated. The results demonstrate improved PBL detection, as seen during the well-mixed hours on January 28, where the refined red PBL line separates from the white ALT and follows the expected diurnal development. This approach improves reliability in characterizing the boundary layer, especially in aerosol-rich and meteorologically complex environments.
Figure 1. The normalized relative backscatter (NRB) signal from the LiDAR is shown as a colored curtain plot, illustrating variations in aerosol number concentration over time. The aerosol layer top (ALT) is marked as a white line, the time-varying maximum analysis altitude (MAA) as a gray line, and the refined planetary boundary layer (PBL) estimate is shown in red. The MAA does not represent the PBL height; rather, it defines the maximum altitude (in km AGL) up to which the WCT algorithm is applied to detect the PBL height. The MAA is dynamically adjusted based on surface temperature to follow the expected diurnal evolution of the boundary layer and to avoid overestimating the PBL height, particularly during nighttime and transitional phases. Data shown here were collected on January 27–29, 2024.5. Figures Improvement: We will improve the resolution of Figures 2 and 3 and enlarge the axes labels for better clarity. Additionally, we will add subplot labels (e.g., (a), (b), (c)) to facilitate reference within the main text as:
(see figure in the supplement to this Author Responses on “Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand”)
Figure 2. Comparison of planetary boundary layer (PBL) height estimates derived from this study’s LiDAR retrievals (red curve), TMD radiosonde measurements at Chiang Mai Airport (black points), and aerosol layer top (ALT) heights calculated using commercial LiDAR software (green dashed line) for (a) December 2023, (b) January 2024, and (c) February 2024 at NARIT AstroPark, Chiang Mai, northern Thailand. The right panels show the correlations between LiDAR-based PBL estimates and radiosonde-derived heights, with Pearson correlation coefficients (r-values) and %RMSEs ranging from 0.85 to 0.94 and 7.2% to 19.6%, respectively, for each month.
(see figure in the supplement to this Author Responses on “Decoupling the PBL Height, the Mixing Layer Height, and the Aerosol Layer Top in LiDAR Measurements over Chiang Mai, Northern Thailand”)
Figure 3. Comparison of planetary boundary layer (PBL) height estimates from LiDAR retrievals (red line) and WRF-Chem forecasts (black line) at NARIT AstroPark, Chiang Mai, Thailand, for (a) December 2023, (b) January 2024, and (c) February 2024 (left column). The middle column shows time series of differences between WRF-Chem and LiDAR estimates (WRF – LiDAR), while the right column presents scatter plots with correlation coefficients (r), root mean square error (RMSE), percentage RMSE (%RMSE), and number of matched data points (n). The WRF-Chem model configuration used in this comparison has been previously validated under similar regional conditions in northern Thailand, supporting its use as a reference for PBL height estimation.6. Radiosonde Validation at Multiple Times: We agree that validation using radiosonde data limited to 07:00 local time provides only partial insight into the diurnal evolution of the planetary boundary layer (PBL). Unfortunately, the radiosonde launches are conducted by the Thai Meteorological Department, which, due to budgetary constraints, is currently limited to a single launch per day. This limitation has already been noted in the manuscript (line 115 and 230), but we can further clarify the text by explicitly stating:
"However, a significant limitation is that the radiosondes were launched only once daily at 07:00 local time (00 UTC), coinciding with the early morning minimum PBL height. This constraint—stemming from the operational limitations of the Thai Meteorological Department—means that diurnal variations in the PBL height, especially during its daytime growth and decay phases, cannot be captured, potentially reducing the representativeness of radiosonde-derived estimates for broader atmospheric analyses."Additionally, we have recently validated our method using aircraft observations collected during a measurement campaign over Chiang Mai Airport in April 2025. While the results are promising, this new dataset lies outside the scope of the current paper and will be presented in a separate publication.
7. Validation of WRF PBL Heights: The WRF-Chem simulations used in our comparison have been previously validated under similar regional conditions in several studies, including:
- Bran, S. H., Macatangay, R., Chotamonsak, C., Chantara, S., & Surapipith, V. (2024). Understanding the seasonal dynamics of surface PM₂.₅ mass distribution and source contributions over Thailand. Atmospheric Environment, 331, 120613. https://doi.org/10.1016/j.atmosenv.2024.120613
- Macatangay, R., Rattanasoon, S., Butterley, T., Bran, S. H., et al. (2024). Seeing and turbulence profile simulations over complex terrain at the Thai National Observatory using a chemistry-coupled regional forecasting model. Monthly Notices of the Royal Astronomical Society, 530(2), 1414–1423. https://doi.org/10.1093/mnras/stae727
- Bran, S. H., Macatangay, R., Surapipith, V., et al. (2022). Atmospheric Research, 277, 106303. https://doi.org/10.1016/j.atmosres.2022.106303
These studies demonstrate the model’s reliability in capturing air quality dynamics, optical turbulence, and regional transport processes over northern Thailand. Nonetheless, we will revise the manuscript to include a brief discussion confirming the WRF-Chem model’s validation status, along with appropriate citations, to clarify its suitability as a reference for comparison in this study (added to the abstract, introduction, methodology, Figure 3 caption, conclusion and references section):
Abstract:
Accurate determination of the planetary boundary layer (PBL) height, mixing layer height (MLH), and aerosol layer top (ALT) is critical for air quality and climate studies, particularly in regions with complex aerosol dynamics such as Chiang Mai, northern Thailand. This study presents a novel LiDAR-based methodology that incorporates a temperature-dependent, dynamic maximum analysis altitude (MAA) to decouple these layers, addressing the limitations of conventional algorithms like the Haar Wavelet Covariance Transform (WCT). Traditional fixed-altitude approaches often misclassify the ALT as the PBL height—especially during nighttime or transition periods—leading to significant overestimations. By dynamically adjusting the MAA based on surface temperature variations, the proposed approach more effectively distinguishes the PBL from residual aerosol layers and cloud interference. Validation using radiosonde data and comparison with WRF-Chem simulations demonstrate strong agreement, with LiDAR-derived PBL heights exhibiting improved diurnal resolution and accuracy. However, model simulations tend to overestimate the PBL height during periods of elevated aerosol loading, underscoring the need for improved aerosol–radiation interaction parameterizations. The WRF-Chem model used in this study has been previously validated for northern Thailand and provides a robust benchmark for PBL comparison. This analysis highlights seasonal variations in agreement metrics and supports the integration of thermodynamic and aerosol observations for enhanced boundary layer characterization. The framework developed here offers a reliable tool for advancing air quality forecasting, pollutant transport analysis, and LiDAR-based remote sensing applications across Southeast Asia.
Introduction (2nd paragraph):
This study focuses on enhancing LiDAR-based boundary layer characterization by refining the detection of PBL height, MLH, and ALT. Traditional algorithms, such as the Haar Wavelet Covariance Transform (WCT), often misclassify the ALT as the PBL height, especially at night or during transitional periods when aerosol gradients are less distinct. Clouds and other atmospheric complexities make these measurements more challenging. By integrating normalized relative backscatter (NRB) profiles with dynamic thermodynamic adjustments, this approach addresses ambiguities in traditional methods and improves the reliability of boundary layer determinations. The novel method developed in this study was validated using radiosonde measurements and compared against WRF-Chem simulations. To support model–observation comparisons, we use a WRF-Chem configuration that has been previously validated under similar regional conditions in northern Thailand for surface pollutant distributions, boundary layer dynamics, and optical turbulence (Bran et al., 2022; Macatangay et al., 2024; Bran et al., 2024), confirming its suitability as a benchmark.Methodology (end):
The WRF-Chem simulations used in this study have been previously validated under similar regional conditions in northern Thailand. Prior work has demonstrated the model's reliability in capturing key atmospheric dynamics, including surface PM₂.₅ distributions, optical turbulence, and boundary layer processes. Notably, the model has been successfully applied in the following studies: Bran et al. (2022), Macatangay et al. (2024), and Bran et al. (2024). These validations support the robustness of WRF-Chem for use as a benchmark in our comparison with LiDAR-derived PBL heights.
Figure 3 caption:
Figure 3. Comparison of planetary boundary layer (PBL) height estimates from LiDAR retrievals (red line) and WRF-Chem forecasts (black line) at NARIT AstroPark, Chiang Mai, Thailand, for (a) December 2023, (b) January 2024, and (c) February 2024 (left column). The middle column shows time series of differences between WRF-Chem and LiDAR estimates (WRF – LiDAR), while the right column presents scatter plots with correlation coefficients (r), root mean square error (RMSE), percentage RMSE (%RMSE), and number of matched data points (n). The WRF-Chem model configuration used in this comparison has been previously validated under similar regional conditions in northern Thailand, supporting its use as a reference for PBL height estimation.
Conclusion (2nd paragraph):
This study highlights the complexities of aerosol layering and PBL identification in regions with high aerosol loading, such as Chiang Mai, where seasonal forest fires and agricultural burning contribute to significant atmospheric pollution. By enhancing the accuracy of LiDAR-based PBL height estimations, the research provides critical insights for improving air quality modeling and understanding pollutant transport under complex meteorological conditions. The results demonstrate a generally consistent relationship between LiDAR-derived and model-based PBL height estimates, with seasonal variations in agreement metrics. The WRF-Chem model configuration, previously validated over northern Thailand for air quality, turbulence, and PBL structure, continues to perform reliably as a comparison benchmark. These findings support the value of integrating high-resolution LiDAR retrievals with regional models to evaluate boundary layer processes and diagnose modeling uncertainties across different seasons.
References:
Bran, S. H., Macatangay, R., Surapipith, V., Chotamonsak, C., Chantara, S., Han, Z., & Li, J. (2022). Chemical transport modeling of transboundary haze events in northern Thailand. Atmospheric Research, 277, 106303. https://doi.org/10.1016/j.atmosres.2022.106303Macatangay, R., Rattanasoon, S., Butterley, T., Bran, S. H., Sonkaew, T., Sukaum, B., Sookjai, D., Panya, M., & Supasri, T. (2024). Seeing and turbulence profile simulations over complex terrain at the Thai National Observatory using a chemistry-coupled regional forecasting model. Monthly Notices of the Royal Astronomical Society, 530(2), 1414–1423. https://doi.org/10.1093/mnras/stae727
Bran, S. H., Macatangay, R., Chotamonsak, C., Chantara, S., & Surapipith, V. (2024). Understanding the seasonal dynamics of surface PM₂.₅ mass distribution and source contributions over Thailand. Atmospheric Environment, 331, 120613. https://doi.org/10.1016/j.atmosenv.2024.120613
- Stull, R. B. (1988). An Introduction to Boundary Layer Meteorology. Springer. https://doi.org/10.1007/978-94-009-3027-8
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