Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data
Abstract
:1. Introduction
2. Data and Geographical Information
2.1. The Cloud-Aerosol Transport System (CATS) Data
2.2. The Atmospheric Radiation Measurement (ARM) Data
3. Methodology
3.1. Denoising Raw Photon Counts
3.2. Retrieval of the PBLH from Satellite Information Using Traditional Lidar-Based Algorithms
3.3. Retrieval of the PBLH from Lidar Data Using DTDS
- The ground-based data allows for continuous tracking of the diurnal variation of the PBLH, enabling the original DTDS algorithm to account for both growing and decaying periods within a single day. In contrast, CATS statistically resolves the diurnal cycle but does not capture those growing or decaying periods in a single pass. Therefore, the adjusted version of DTDS only incorporates “other periods” from the DTDS selection scheme.
- DTDS algorithm tackles cloudy situations by examining the coupling between the cloud and its surface. Obtaining the PBLH in cloudy conditions is crucial for enhancing the reliability of lidar retrieval systems, as emphasized by Li et al. [29] in their work with CATS. This process necessitates knowledge of the CBH, which can be readily obtained through ground-based lidar. However, the viewing geometry of satellite lidars (such as CATS) renders their signal highly attenuated beneath the cloud top. As a result, the CBH is often impossible to discern for optically thick clouds. To address this challenge, the modified version of the DTDS focuses exclusively on single-layer clouds that are less than 1 km thick and with a CTH lower than the maximum integration limit ( of the Haar function (see Section 3.2). It is important to note that accurate determination of the cloud thickness depends on knowledge of its CBH. Consequently, we only used CATS data from cloudy conditions in which the mean Feature Type CATS’ classification between the cloud base and the earth’s surface is lower than 3, indicating that the CATS signal is not entirely attenuated beneath the cloud base, and hence, an aerosol layer or ground surface is observable below the cloud base. This serves as confirmation that the estimated CBH is an authentic value and not a byproduct of cloud attenuation.
- DTDS evaluates the cloud-surface coupling using the LCL. This value is computed based on an exact expression [51] and using ARM’s surface meteorological data (relative humidity, temperature, pressure). Since CATS does not have collocated meteorological measurements, the representation of the LCL is not as robust as ground-based meteorological measurements. CATS meteorological data originates from MERRA-2 reanalysis data at coarse horizontal resolutions (0.5° lat × 0.625° lon), and this data is interpolated down to the CATS horizontal resolution of 5 km; natural variability and interpolation errors can lead to biases in the computed LCL. Therefore, instead of using individual LCL values, our modified version uses the mean LCL value computed from the mean temperature, relative humidity, and atmospheric pressure profiles provided in the CATS data files for the defined ARM-site grid box.
4. Results
4.1. The Southern Great Plains (SGP)
4.2. The Eastern North Atlantic (ENA)
5. Summary
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Stull, R.B. An Introduction to Boundary Layer Meteorology; Springer Science & Business Media: Berlin, Germany, 1988; Volume 13. [Google Scholar]
- Kaimal, J.C.; Finnigan, J.J. Atmospheric Boundary Layer Flows: Their Structure and Measurement; Oxford University Press: Oxford, UK, 1994. [Google Scholar]
- Garratt, J.R. The Internal Boundary Layer—A Review. Bound. Layer Meteorol. 1990, 50, 171–203. [Google Scholar] [CrossRef]
- Caughey, S.J.; Palmer, S.G. Some Aspects of Turbulence Structure through the Depth of the Convective Boundary Layer. Q. J. R. Meteorol. Soc. 1979, 105, 811–827. [Google Scholar] [CrossRef]
- Holtslag, A.A.M.; Nieuwstadt, F.T.M. Scaling the Atmospheric Boundary Layer. Bound. Layer Meteorol. 1986, 36, 201–209. [Google Scholar] [CrossRef]
- Mahrt, L. Stratified Atmospheric Boundary Layers. Bound. Layer Meteorol. 1999, 90, 375–396. [Google Scholar] [CrossRef]
- Teixeira, J.; Piepmeier, J.R.; Nehrir, A.R.; Ao, C.O.; Chen, S.S.; Clayson, C.A.; Fridlind, A.M.; Lebsock, M.; McCarty, W.; Salmun, H.; et al. Toward a Global Planetary Boundary Layer Observing System: The NASA PBL Incubation Study Team Report. In Toward a Global Planetary Boundary Layer Observing System: The NASA PBL Incubation Study Team Report; NASA: Washington, DC, USA, 2021. [Google Scholar]
- Moeng, C.H.; Cotton, W.R.; Bretherton, C.; Chlond, A.; Khairoutdinov, M.; Krueger, S.; Lewellen, W.S.; MacVean, M.K.; Pasquier, J.R.M.; Rand, H.A.; et al. Simulation of a Stratocumulus-Topped Planetary Boundary Layer: Intercomparison among Different Numerical Codes. Bull. Am. Meteorol. Soc. 1996, 77, 261–278. [Google Scholar] [CrossRef]
- Santanello, J.A.; Dirmeyer, P.A.; Ferguson, C.R.; Findell, K.L.; Tawfik, A.B.; Berg, A.; Ek, M.; Gentine, P.; Guillod, B.P.; Van Heerwaarden, C.; et al. Land-Atmosphere Interactions the LoCo Perspective. Bull. Am. Meteorol. Soc. 2018, 99, 1253–1272. [Google Scholar] [CrossRef]
- Knote, C.; Tuccella, P.; Curci, G.; Emmons, L.; Orlando, J.J.; Madronich, S.; Baró, R.; Jiménez-Guerrero, P.; Luecken, D.; Hogrefe, C.; et al. Influence of the Choice of Gas-Phase Mechanism on Predictions of Key Gaseous Pollutants during the AQMEII Phase-2 Intercomparison. Atmos. Environ. 2015, 115, 553–568. [Google Scholar] [CrossRef]
- Li, Z.; Guo, J.; Ding, A.; Liao, H.; Liu, J.; Sun, Y.; Wang, T.; Xue, H.; Zhang, H.; Zhu, B. Aerosol and Boundary-Layer Interactions and Impact on Air Quality. Natl. Sci. Rev. 2017, 4, 810–833. [Google Scholar] [CrossRef]
- Hu, X.M.; Nielsen-Gammon, J.W.; Zhang, F. Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model. J. Appl. Meteorol. Clim. 2010, 49, 1831–1844. [Google Scholar] [CrossRef]
- Lemone, M.A.; Angevine, W.M.; Bretherton, C.S.; Chen, F.; Dudhia, J.; Fedorovich, E.; Katsaros, K.B.; Lenschow, D.H.; Mahrt, L.; Patton, E.G.; et al. 100 Years of Progress in Boundary Layer Meteorology. Meteorol. Monogr. 2018, 59, 9.1–9.85. [Google Scholar] [CrossRef]
- Seidel, D.J.; Ao, C.O.; Li, K. Estimating Climatological Planetary Boundary Layer Heights from Radiosonde Observations: Comparison of Methods and Uncertainty Analysis. J. Geophys. Res. Atmos. 2010, 115, 16113. [Google Scholar] [CrossRef]
- Liu, S.; Liang, X.Z. Observed Diurnal Cycle Climatology of Planetary Boundary Layer Height. J. Clim. 2010, 23, 5790–5809. [Google Scholar] [CrossRef]
- Molod, A.; Salmun, H.; Dempsey, M. Estimating Planetary Boundary Layer Heights from NOAA Profiler Network Wind Profiler Data. J. Atmos. Ocean. Technol. 2015, 32, 1545–1561. [Google Scholar] [CrossRef]
- McGrath-Spangler, E.L.; Denning, A.S. Estimates of North American Summertime Planetary Boundary Layer Depths Derived from Space-Borne Lidar. J. Geophys. Res. Atmos. 2012, 117, 15101. [Google Scholar] [CrossRef]
- Herrera-Mejía, L.; Hoyos, C.D. Characterization of the Atmospheric Boundary Layer in a Narrow Tropical Valley Using Remote-Sensing and Radiosonde Observations and the WRF Model: The Aburrá Valley Case-Study. Q. J. R. Meteorol. Soc. 2019, 145, 2641–2665. [Google Scholar] [CrossRef]
- Dai, C.; Wang, Q.; Kalogiros, J.A.; Lenschow, D.H.; Gao, Z.; Zhou, M. Determining Boundary-Layer Height from Aircraft Measurements. Bound. Layer Meteorol. 2014, 152, 277–302. [Google Scholar] [CrossRef]
- Kalmus, P.; Ao, C.O.; Wang, K.N.; Manzi, M.P.; Teixeira, J. A High-Resolution Planetary Boundary Layer Height Seasonal Climatology from GNSS Radio Occultations. Remote Sens. Environ. 2022, 276, 113037. [Google Scholar] [CrossRef]
- Winker, D.M.; Hunt, W.H.; McGill, M.J. Initial Performance Assessment of CALIOP. Geophys. Res. Lett. 2007, 34, L19803. [Google Scholar] [CrossRef]
- Jordan, N.S.; Hoff, R.M.; Bacmeister, J.T. Validation of Goddard Earth Observing System-Version 5 MERRA Planetary Boundary Layer Heights Using CALIPSO. J. Geophys. Res. Atmos. 2010, 115, D24218. [Google Scholar] [CrossRef]
- Leventidou, E.; Zanis, P.; Balis, D.; Giannakaki, E.; Pytharoulis, I.; Amiridis, V. Factors Affecting the Comparisons of Planetary Boundary Layer Height Retrievals from CALIPSO, ECMWF and Radiosondes over Thessaloniki, Greece. Atmos. Environ. 2013, 74, 360–366. [Google Scholar] [CrossRef]
- Zhang, W.; Guo, J.; Miao, Y.; Liu, H.; Zhang, Y.; Li, Z.; Zhai, P. Planetary Boundary Layer Height from CALIOP Compared to Radiosonde over China. Atmos. Chem. Phys. 2016, 16, 9951–9963. [Google Scholar] [CrossRef]
- Su, T.; Li, J.; Li, C.; Xiang, P.; Lau, A.K.H.; Guo, J.; Yang, D.; Miao, Y. An Intercomparison of Long-Term Planetary Boundary Layer Heights Retrieved from CALIPSO, Ground-Based Lidar, and Radiosonde Measurements over Hong Kong. J. Geophys. Res. 2017, 122, 3929–3943. [Google Scholar] [CrossRef]
- Melfi, S.H.; Spinhirne, J.D.; Chou, S.-H.; Palm, S.P. Lidar Observations of Vertically Organized Convection in the Planetary Boundary Layer over the Ocean. J. Clim. Appl. Meteorol. 1985, 24, 806–821. [Google Scholar] [CrossRef]
- Yorks, J.E.; McGill, M.J.; Palm, S.P.; Hlavka, D.L.; Selmer, P.A.; Nowottnick, E.P.; Vaughan, M.A.; Rodier, S.D.; Hart, W.D. An Overview of the CATS Level 1 Processing Algorithms and Data Products. Geophys. Res. Lett. 2016, 43, 4632–4639. [Google Scholar] [CrossRef]
- Palm, S.P.; Selmer, P.; Yorks, J.; Nicholls, S.; Nowottnick, E. Planetary Boundary Layer Height Estimates From ICESat-2 and CATS Backscatter Measurements. Front. Remote Sens. 2021, 2, 716951. [Google Scholar] [CrossRef]
- Li, Y.; Li, J.; Xu, S.; Li, J.; He, J.; Huang, J. Diurnal Variation in the Near-Global Planetary Boundary Layer Height from Satellite-Based CATS Lidar: Retrieval, Evaluation, and Influencing Factors. Remote Sens. Environ. 2023, 299, 113847. [Google Scholar] [CrossRef]
- Su, T.; Li, Z.; Kahn, R. A New Method to Retrieve the Diurnal Variability of Planetary Boundary Layer Height from Lidar under Different Thermodynamic Stability Conditions. Remote Sens. Environ. 2019, 237, 111519. [Google Scholar] [CrossRef]
- Roldán-Henao, N.; Su, T.; Li, Z. Refining Planetary Boundary Layer Height Retrievals From Micropulse-Lidar at Multiple ARM Sites Around the World. J. Geophys. Res. Atmos. 2024, 129, e2023JD040207. [Google Scholar] [CrossRef]
- Noel, V.; Chepfer, H.; Chiriaco, M.; Yorks, J. The Diurnal Cycle of Cloud Profiles over Land and Ocean between 51°S and 51°N, Seen by the CATS Spaceborne Lidar from the International Space Station. Atmos. Chem. Phys. 2018, 18, 9457–9473. [Google Scholar] [CrossRef]
- Lee, L.; Zhang, J.; Reid, J.S.; Yorks, J.E. Investigation of CATS Aerosol Products and Application toward Global Diurnal Variation of Aerosols. Atmos. Chem. Phys. 2019, 19, 12687–12707. [Google Scholar] [CrossRef]
- Nowottnick, E.P.; Christian, K.E.; Yorks, J.E.; McGill, M.J.; Midzak, N.; Selmer, P.A.; Lu, Z.; Wang, J.; Salinas, S.V. Aerosol Detection from the Cloud–Aerosol Transport System on the International Space Station: Algorithm Overview and Implications for Diurnal Sampling. Atmosphere 2022, 13, 1439. [Google Scholar] [CrossRef]
- Pauly, R.M.; Yorks, J.E.; Hlavka, D.L.; McGill, M.J.; Amiridis, V.; Palm, S.P.; Rodier, S.D.; Vaughan, M.A.; Selmer, P.A.; Kupchock, A.W.; et al. Cloud-Aerosol Transport System (CATS) 1064 Nm Calibration and Validation. Atmos. Meas. Tech. 2019, 12, 6241–6258. [Google Scholar] [CrossRef] [PubMed]
- Yorks, J.E.; Selmer, P.A.; Kupchock, A.; Nowottnick, E.P.; Christian, K.E.; Rusinek, D.; Dacic, N.; McGill, M.J. Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data. Atmosphere 2021, 12, 606. [Google Scholar] [CrossRef]
- Su, T.; Zheng, Y.; Li, Z. Methodology to Determine the Coupling of Continental Clouds with Surface and Boundary Layer Height under Cloudy Conditions from Lidar and Meteorological Data. Atmos. Chem. Phys. 2022, 22, 1453–1466. [Google Scholar] [CrossRef]
- Flynn, D.; Shi, Y.; Lim, K.; Riihimaki, L. Cloud Type Classification (CLDTYPE) Value-Added Product. 2017. Available online: https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-200.pdf (accessed on 22 July 2024).
- Ritsche, M. ARM Surface Meteorology Systems Instrument Handbook; PNNL: Richland, WA, USA, 2011. [Google Scholar]
- Holdridge, D.; Ritsche, M.; Prell, J.; Coulter, R. Balloon-Borne Sounding System (SONDE) Handbook. 2011. Available online: https://www.arm.gov/capabilities/instruments/sonde (accessed on 26 August 2024).
- Sisterson, D.L.; Peppler, R.A.; Cress, T.S.; Lamb, P.J.; Turner, D.D. The ARM Southern Great Plains (SGP) Site. Meteorol. Monogr. 2016, 57, 6.1–6.14. [Google Scholar] [CrossRef]
- Islam, M.M.; Meskhidze, N.; Rasheeda Satheesh, A.; Petters, M.D. Turbulent Flux Measurements of the Near-Surface and Residual-Layer Small Particle Events. J. Geophys. Res. Atmos. 2022, 127, e2021JD036289. [Google Scholar] [CrossRef]
- Sengupta, M.; Clothiaux, E.E.; Ackerman, T.P. Climatology of Warm Boundary Layer Clouds at the ARM SGP Site and Their Comparison to Models. J. Clim. 2004, 17, 4760–4782. [Google Scholar] [CrossRef]
- Dong, X.; Xi, B.; Minnis, P. A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility. Part II: Cloud Fraction and Surface Radiative Forcing. J. Clim. 2006, 19, 1765–1783. [Google Scholar] [CrossRef]
- Albrecht, B.A.; Bretherton, C.S.; Johnson, D.; Schubert, W.H.; Frisch, A.S. The Atlantic Stratocumulus Transition Experiment—ASTEX. Bull. Am. Meteorol. Soc. 1995, 76, 889–904. [Google Scholar] [CrossRef]
- Jia, F.; Wong, W.H.; Zeng, T. DDUNet: Dense Dense U-Net With Applications in Image Denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Virtual, 11–17 October 2021; pp. 354–364. [Google Scholar]
- Selmer, P.; Yorks, J.; Nowottnick, E.; Christian, K.; Cresanti, A. A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection. Remote Sens. 2024, 16, 2735. [Google Scholar] [CrossRef]
- Davis, K.J.; Gamage, N.; Hagelberg, C.R.; Kiemle, C.; Lenschow, D.H.; Sullivan, P.P. An Objective Method for Deriving Atmospheric Structure from Airborne Lidar Observations. J. Atmos. Ocean. Technol. 2000, 17, 1455–1468. [Google Scholar] [CrossRef]
- Brooks, I.M. Finding Boundary Layer Top: Application of a Wavelet Covariance Transform to Lidar Backscatter Profiles. J. Atmos. Ocean. Technol. 2003, 20, 1092–1105. [Google Scholar] [CrossRef]
- Zhang, H.; Zhang, X.; Li, Q.; Cai, X.; Fan, S.; Song, Y.; Hu, F.; Che, H.; Quan, J.; Kang, L.; et al. Research Progress on Estimation of the Atmospheric Boundary Layer Height. J. Meteorol. Res. 2020, 34, 482–498. [Google Scholar] [CrossRef]
- Romps, D.M. Exact Expression for the Lifting Condensation Level. J. Atmos. Sci. 2017, 74, 3891–3900. [Google Scholar] [CrossRef]
- Zheng, Q.; Miller, M.A. Summertime Marine Boundary Layer Cloud, Thermodynamic, and Drizzle Morphology over the Eastern North Atlantic: A Four-Year Study. J. Clim. 2022, 35, 4805–4825. [Google Scholar] [CrossRef]
- Rémillard, J.; Tselioudis, G. Cloud Regime Variability over the Azores and Its Application to Climate Model Evaluation. J. Clim. 2015, 28, 9707–9720. [Google Scholar] [CrossRef]
- Jensen, M.P.; Ghate, V.P.; Wang, D.; Apoznanski, D.K.; Bartholomew, M.J.; Giangrande, S.E.; Johnson, K.L.; Thieman, M.M. Contrasting Characteristics of Open-and Closed-Cellular Stratocumulus Cloud in the Eastern North Atlantic. Atmos. Chem. Phys. 2021, 21, 14557–14571. [Google Scholar] [CrossRef]
- Zhang, Y.; Sun, K.; Gao, Z.; Pan, Z.; Shook, M.A.; Li, D. Diurnal Climatology of Planetary Boundary Layer Height Over the Contiguous United States Derived From AMDAR and Reanalysis Data. J. Geophys. Res. Atmos. 2020, 125, e2020JD032803. [Google Scholar] [CrossRef]
- Seidel, D.J.; Zhang, Y.; Beljaars, A.; Golaz, J.C.; Jacobson, A.R.; Medeiros, B. Climatology of the Planetary Boundary Layer over the Continental United States and Europe. J. Geophys. Res. Atmos. 2012, 117, D17106. [Google Scholar] [CrossRef]
- Molod, A.; Salmun, H.; Marquardt Collow, A.B. Annual Cycle of Planetary Boundary Layer Heights Estimated From Wind Profiler Network Data. J. Geophys. Res. Atmos. 2019, 124, 6207–6221. [Google Scholar] [CrossRef]
- De Bruine, M.; Apituley, A.; Donovan, D.P.; Baltink, H.K.; De Haij, M.J. Pathfinder: Applying Graph Theory to Consistent Tracking of Daytime Mixed Layer Height with Backscatter Lidar. Atmos. Meas. Tech. 2017, 10, 1893–1909. [Google Scholar] [CrossRef]
- Kambezidis, H.D.; Psiloglou, B.E.; Gavriil, A.; Petrinoli, K. Detection of Upper and Lower Planetary-boundary Layer Curves and Estimation of Their Heights from Ceilometer Observations under All-weather Conditions: Case of Athens, Greece. Remote Sens. 2021, 13, 2175. [Google Scholar] [CrossRef]
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Roldán-Henao, N.; Yorks, J.E.; Su, T.; Selmer, P.A.; Li, Z. Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data. Remote Sens. 2024, 16, 3252. https://doi.org/10.3390/rs16173252
Roldán-Henao N, Yorks JE, Su T, Selmer PA, Li Z. Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data. Remote Sensing. 2024; 16(17):3252. https://doi.org/10.3390/rs16173252
Chicago/Turabian StyleRoldán-Henao, Natalia, John E. Yorks, Tianning Su, Patrick A. Selmer, and Zhanqing Li. 2024. "Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data" Remote Sensing 16, no. 17: 3252. https://doi.org/10.3390/rs16173252