Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-4087-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-4087-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A multi-instrument fuzzy logic boundary-layer-top detection algorithm
Elizabeth N. Smith
CORRESPONDING AUTHOR
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
Jacob T. Carlin
Cooperative Institute for Severe and High-Impact Weather Research and Operations, The University of Oklahoma, Norman, Oklahoma, USA
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
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Cited articles
Ao, C. O., Chan, T. K., Iijima, B. A., Li, J.-L., Mannucci, A. J., Teixeira, J., Tian, B., and Waliser, D. E.: Planetary boundary layer information from GPS radio occultation measurements, in: GRAS SAF Workshop on Applications of GPSRO Measurements, 16–18 June 2008, ECMWF, Reading, United Kingdom, 123–131, https://www.ecmwf.int/sites/default/files/elibrary/2008/7459-planetary-boundary-layer-information-gps-radio-occultation-measurements.pdf (last access: 12 April 2023), 2008. a
Bell, T. M., Greene, B. R., Klein, P. M., Carney, M., and Chilson, P. B.: Confronting the boundary layer data gap: evaluating new and existing methodologies of probing the lower atmosphere, Atmos. Meas. Tech., 13, 3855–3872, https://doi.org/10.5194/amt-13-3855-2020, 2020. a
Bianco, L. and Wilczak, J. M.: Convective boundary layer depth: Improved measurement by Doppler radar wind profiler using fuzzy logic methods, J. Atmos. Ocean. Tech., 19, 1745–1758, https://doi.org/10.1175/1520-0426(2002)019<1745:CBLDIM>2.0.CO;2, 2002. a
Bianco, S. and Wilczak, J. M.: Convective boundary layer depth estimation from wind profilers: Statistical comparison between an automated algorithm and expert estimations, J. Atmos. Ocean. Techn., 19, 1745–1758, https://doi.org/10.1175/2008JTECHA981.1, 2008. a
Bonin, T. A., Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A. M., Pichugina, Y. L., Banta, R. M., Oncley, S. P., and Wolfe, D. E.: Evaluation of turbulence measurement techniques from a single Doppler lidar, Atmos. Meas. Tech., 10, 3021–3039, https://doi.org/10.5194/amt-10-3021-2017, 2017. a
Bonin, T. A., Caroll, B. J., Hardesty, R. M., Brewer, W. A., Hajny, K., Salmon, O. E., and Shepson, P. B.: Doppler lidar observations of the mixing height in Indianapolis using an automated composite fuzzy logic approach, J. Atmos. Ocean. Techn., 35, 473–490, https://doi.org/10.1175/JTECH-D-17-0159.1, 2018. a, b, c, d, e, f, g, h
Bradley, R. S., Keimig, F. T., and Diaz, H. F.: Recent changes in the North American Arctic boundary layer in winter, J. Geophys. Res.-Atmos., 98, 8851–8858, https://doi.org/10.1029/93JD00311, 1993. a
Brooks, I. M.: Finding boundary layer top: Application of a wavelet covariance transform to lidar backscatter profiles, J. Atmos. Ocean. Techn., 20, 1092–1105, 2003. a
Browning, K. A.: The convective storm initiation project, B. Am. Meteorol. Soc., 88, 1939–1955, https://doi.org/10.1175/BAMS-88-12-1939, 2007. a
Butterworth, B. J., Desai, A. R., Metzger, S., Townsend, P. A., Schwartz, M. D., Petty, G. W., Mauder, M., Vogelmann, H., Andresen, C. G., Augustine, T. J., Bertram, T. H., Brown, W. O., Buban, M., Cleary, P., Durden, D. J., Florian, C. R., Iglinski, T. J., Kruger, E. L., Lantz, K., Lee, T. R., Meyers, T. P., Mineau, J. K., Olson, E. R., Oncley, S. P., Paleri, S., Pertzborn, R. A., Pettersen, C., Plummer, D. M., Riihimaki, L. D., Guzman, E. R., Sedlar, J., Smith, E. N., Speidel, J., Stoy, P. C., Sühring, M., Thom, J. E., Turner, D. D., Vermeuel, M. P., Wagner, T. J., Wang, Z., Wanner, L., White, L. D., Wilczak, J. M., Wright, D. B., and Zheng, T.: Connecting land–atmosphere interactions to surface heterogeneity in CHEESEHEAD19, B. Am. Meteorol. Soc., 102, E421–E445, https://doi.org/10.1175/BAMS-D-19-0346.1, 2021. a
Caicedo, V., Rappenglück, B., Lefer, B., Morris, G., Toledo, D., and Delgado, R.: Comparison of aerosol lidar retrieval methods for boundary layer height detection using ceilometer aerosol backscatter data, Atmos. Meas. Tech., 10, 1609–1622, https://doi.org/10.5194/amt-10-1609-2017, 2017. a, b
Cimini, D., Haeffelin, M., Kotthaus, S., Löhnert, U., Martinet, P., O'Connor, E., Walden, C., Coen, M. C., and Preissler, J.: Towards the profiling of the atmospheric boundary layer at European scale—introducing the COST Action PROBE, Bulletin of Atmospheric Science and Technology, 1, 23–42, https://doi.org/10.1007/s42865-020-00003-8, 2020. a
Clements, C. B., Zhong, S., Goodrick, S., Li, J., Potter, B. E., Bian, X., Heilman, W. E., Charney, J. J., Perna, R., Jang, M., Lee, D., Patel, M., Street, S., and Aumann, G.: Observing the dynamics of wildland grass fires: FireFlux – a field validation experiment, B. Am. Meteorol. Soc., 88, 1369–1382, https://doi.org/10.1175/BAMS-88-9-1369, 2007. a
Cohen, A. E., Cavallo, S. M., Coniglio, M. C., and Brooks, H. E.: A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeastern U. S. Cold Season Severe Weather Environments, Weather Forecast., 30, 591–612, 2015. a
Cohen, A. E., Cavallo, S. M., Coniglio, M. C., Brooks, H. E., and Jirak, I. L.: Evaluation of multiple planetary boundary layer parameterization schemes in southeast U. S. cold season severe thunderstorm environments, Weather Forecast., 32, 1857–1884, https://doi.org/10.1175/WAF-D-16-0193.1, 2017. a
Cohn, S. A. and Angevine, W. M.: Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars, J. Appl. Meteorol., 39, 1233–1247, 2000. a
Comer, C. L., Stouffer, B., Stensrud, D. J., Kumjian, M., and Zhang, Y.: Automated detection of boundary layer depth using dual-polarization radar observations, in: 40th Conference on Radar Meteorology, Minneapolis, MN, 28 August–1 September 2023, American Meteorological Society, p. 13A.6, 2023. a
Coniglio, M. C., Correia J, J., Marsh, P. T., and Kong, F.: Verification of convection-allowing WRF model forcasts of the planetary boundary layer using sounding observations, Weather Forecast., 28, 842–862, https://doi.org/10.1175/WAF-D-12-00103.1, 2013. a, b
Crook, N. A.: Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields, Mon. Weather Rev., 124, 1767–1785, https://doi.org/10.1175/1520-0493(1996)124<1767:SOMCFB>2.0.CO;2, 1996. a
Dabberdt, W. F., Carroll, Mary A., Baumgardner, D., Carmichael, G., Cohen, R., Dye, T., Ellis, J., Grell, G., Grimmond, S., Hanna, S., Irwin, J., Lamb, B., Madronich, S., McQueen, J., Meagher, J., Odman, T., Pleim, J., Schmid, H. P., and Westphal, D. L.: Meteorological research needs for improved air quality forecasting: Report of the 11th prospectus development team of the U.S. Weather Research Program, B. Am. Meteorol. Soc., 85, 563–586, https://doi.org/10.1175/BAMS-85-4-563, 2004. a
Davis, K. J., Gamage, N., Hagelberg, C. R., Kiemle, C., Lenschow, D. H., and Sullivan, P. P.: An objective method for deriving atmospheric structure from airborne lidar observations, J. Atmos. Ocean. Techn., 17, 1455–1468, 2000. a
Davis, K. J., Deng, A., Lauvaux, T., Miles, N. L., Richardson, S. J., Sarmiento, D. P., Gurney, K. R., Hardesty, R. M., Bonin, T. A., Brewer, W. A., Lamb, B. K., Shepson, P. B., Harvey, R. M., Cambaliza, M. O., Sweeney, C., Turnbull, J. C., Whetstone, J., Karion, A., and Helmig, D.: The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements, Elem. Sci. Anth., 5, 21, https://doi.org/10.1525/elementa.188, 2017. a
Della Porta, D. T.: NEXRAD based convective boundary layer height compared to multiple instruments, in: 104th Annual Meeting, Baltimore, MD, 28 January–1 February 2024, American Meteorological Society, p. 641, 2024. a
Djalalova, I. V., Turner, D. D., Bianco, L., Wilczak, J. M., Duncan, J., Adler, B., and Gottas, D.: Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations, Atmos. Meas. Tech., 15, 521–537, https://doi.org/10.5194/amt-15-521-2022, 2022. a
Duncan Jr., J. B., Hirth, B. D., and Schroeder, J. L.: Doppler radar measurements of spatial turbulence intensity in the atmospheric boundary layer, J. Appl. Meteorol. Clim., 58, 1535–1555, https://doi.org/10.1175/JAMC-D-18-0151.1, 2019. a
Duncan Jr., J. B., Bianco, L., Adler, B., Bell, T., Djalalova, I. V., Riihimaki, L., Sedlar, J., Smith, E. N., Turner, D. D., Wagner, T. J., and Wilczak, J. M.: Evaluating convective planetary boundary layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign, Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, 2022. a
Ecklund, W. L., Carter, D. A., and Balsley, B. B.: A UHF wind profiler for the boundary layer: Brief description and initial results, J. Atmos. Ocean. Techn., 5, 432–441, https://doi.org/10.1175/1520-0426(1988)005<0432:AUWPFT>2.0.CO;2, 1988. a
Feltz, W. F., Smith, W. L., Knuteson, R. O., Revercomb, H. E., Woolf, H. M., and Howell, H. B.: Meteorological applications of temperature and water vapor retrievals from the ground-based Atmospheric Emitted Radiance Interferometer (AERI), J. Appl. Meteorol. Clim., 37, 857–875, https://doi.org/10.1175/1520-0450(1998)037<0857:MAOTAW>2.0.CO;2, 1998. a
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., et al.: The Perdigão: Peering into microscale details of mountain winds, B. Am. Meteorol. Soc., 100, 799–819, https://doi.org/10.1175/BAMS-D-17-0227.1, 2019. a
Froidevaux, M., Higgins, C. W., Simeonov, V., Ristori, P., Pardyjak, E., Serikov, I., Calhoun, R., van den Bergh, H., and Parlange, M. B.: A Raman lidar to measure water vapor in the atmospheric boundary layer, Adv. Wat. Resour., 51, 345–356, https://doi.org/10.1016/j.advwatres.2012.04.008, 2013. a
Gal-Chen, T. and Kropfli, R. A.: Buoyancy and pressure perturbations derived from dual-Doppler radar observations of the planetary boundary layer: Applications for matching models with observations, J. Atmos. Sci., 41, 3008–3020, https://doi.org/10.1175/1520-0469(1984)041<3007:BAPPDF>2.0.CO;2, 1984. a
Gourley, J. J., Tabary, P., and du Chatelet, J. P.: A fuzzy logic algorithm for the separation of precipitation from nonprecipitating echoes using polarimetric radar observations, J. Atmos. Ocean. Techn., 24, 1439–1451, https://doi.org/10.1175/JTECH2035.1, 2007. a
Grimsdell, A. W. and Angevine, W. M.: Convective boundary layer height measurement with wind profilers and comparison to cloud base, J. Atmos. Ocean. Techn., 15, 1331–1338, https://doi.org/10.1175/1520-0426(1998)015<1331:CBLHMW>2.0.CO;2, 1998. a
Grund, C. J., Banta, R. M., George, J. L., Howell, J. N., Post, M. J., Richter, R. A., and Weickmann, A. M.: High-resolution Doppler lidar for boundary layer and cloud research, J. Atmos. Ocean. Techn., 18, 376–393, https://doi.org/10.1175/1520-0426(2001)018<0376:HRDLFB>2.0.CO;2, 2001. a
Haar, A.: Zur Theorie der Orthogonalen Funktionensysteme, Math. Ann., 69, 331–371, 1910. a
Hane, C. E., Ziegler, C. L., and Bluestein, H. B.: Investigation of the dryline and convective storms initiated along the dryline: Field experiments during COPS-91, B. Am. Meteorol. Soc., 74, 2133–2145, https://doi.org/10.1175/1520-0477(1993)074<2133:IOTDAC>2.0.CO;2, 1993. a
Holzworth, G. C.: Estimates of mean maximum mixing depths in the contiguous United States, Mon. Weather Rev., 92, 235–242, https://doi.org/10.1175/1520-0493(1964)092<0235:EOMMMD>2.3.CO;2, 1964. a
Karan, H. and Knupp, K.: Mobile Integrated Profiler System (MIPS) Observations of Low-Level Convergent Boundaries during IHOP, Mon. Weather Rev., 134, 92–112, https://doi.org/10.1175/MWR3058.1, 2006. a
Klein, P., Smith, E., and Bell,T.: CLAMPS NWC/RIL Observations, Zenodo [data set], https://doi.org/10.5281/zenodo.12636990, 2020. a
Knupp, K. R., Coleman, T., Phillips, D., Ware, R., Cimini, D., Vandenberghe, F., Vivekanandan, J., and Westwater, E.: Ground-Based Passive Microwave Profiling during Dynamic Weather Conditions, J. Atmos. Ocean. Techn., 26, 1057–1073, https://doi.org/10.1175/2008JTECHA1150.1, 2009. a
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker, R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell, H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric Emitted Radiance Interferometer. Part I: Instrument design, J. Atmos. Ocean. Techn., 21, 1763–1776, https://doi.org/10.1175/JTECH-1662.1, 2004. a
Kotthaus, S., Bravo-Aranda, J. A., Collaud Coen, M., Guerrero-Rascado, J. L., Costa, M. J., Cimini, D., O'Connor, E. J., Hervo, M., Alados-Arboledas, L., Jiménez-Portaz, M., Mona, L., Ruffieux, D., Illingworth, A., and Haeffelin, M.: Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations, Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, 2023. a, b, c, d, e
Liu, H. and Chandrasekar, V.: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification, J. Atmos. Ocean. Techn., 17, 140–1643, https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2, 2000. a
Loeffler, S. and Davies, A. R.: Preliminary comparison of model and polarimetric radar derived estimates of boundary layer height to high temporal resolution radiosonde data, in: 104th Annual Meeting, Baltimore, MD, 28 January–1 February 2024, Ameri can Meteorological Society, p. 631, 2024. a
Mahale, V. N., Zhang, G., and Xue, M.: Fuzzy logic classification of S-band polarimetric radar echoes to identify three-body scattering and improve data quality, J. Atmos. Ocean. Techn., 53, 2017–2033, https://doi.org/10.1175/JAMC-D-13-0358.1, 2014. a
McGrath-Spangler, E. L. and Denning, A. S.: Estimates of North American summertime planetary boundary layer depths derived from space-borne lidar, J. Geophys. Res.-Atmos., 117, D15, https://doi.org/10.1029/2012JD017615, 2012. a
McGrath-Spangler, E. L. and Denning, A. S.: Global seasonal variations of midday planetary boundary layer depth fro CALIPSO space-borne LIDAR, J. Geophys. Res.-Atmos., 118, 1226–1233, https://doi.org/10.1002/jgrd.50198, 2013. a
Melnikov, V. M., Doviak, R. J., Zrnić, D. S., and Stensrud, D. J.: Mapping Bragg scatter with a polarimetric WSR-88D, J. Atmos. Ocean. Tech., 28, 1273–1285, https://doi.org/10.1175/JTECH-D-10-05048.1, 2011. a
Mendel, J.: Fuzzy logic systems for engineering: a tutorial, P. IEEE, 83, 345–377, https://doi.org/10.1109/5.364485, 1995. a
Minda, H., Furuzawa, F. A., Satoh, S., and Nakamura, K.: Convective boundary layer above a subtropical island observed by C-band radar and interpretation using a cloud resolving model, J. Meteorol. Soc. Jpn., 88, 285–312, https://doi.org/10.2151/jmsj.2010-303, 2010. a
National Academies of Sciences, Engineering, and Medicine: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, The National Academies Press, Washington, DC, https://doi.org/10.17226/24938, 2018a. a
National Academies of Sciences, Engineering, and Medicine: The Future of Atmospheric Boundary Layer Observing, Understanding, and Modeling: Proceedings of a Workshop, Warrenton, VA, 24–26 October 2017, The National Academies Press, Washington, DC, https://doi.org/10.17226/25138, 2018b. a
National Research Council: Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks, The National Academies Press, Washington, DC, https://doi.org/10.17226/12540, 2009. a
National Research Council: When weather matters: Science and services to meet critical societal needs, National Academies Press, https://doi.org/10.17226/12888, 2010. a
National Weather Service: NATIONAL WEATHER SERVICE MANUAL 10-1401, Department of Commerce/NOAA, https://www.weather.gov/media/directives/010_pdfs/pd01014002curr.pdf (last access: 5 July 2024), 2010. a
NCAR/EOL In-Situ Sensing Facility and University of Wisconsin-Madison Space Science and Engineering Center (SSEC): NCAR/EOL ISS and UWI SPARC Radiosonde Data, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/9WA4-KQKZ-9Q12, 2019. a
NOAA, DOD, FAA, and USNavy: Automated Surface Observing System: ASOS User's Guide, U.S. Dept. of Commerce/NOAA, http://www.weather.gov/media/asos/aum-toc.pdf (last access: 12 April 2023), 1998. a
Oke, T. R.: Boundary layer climates, 2nd edn., Halsted Press, New York, https://doi.org/10.1002/qj.49711448412, 1988. a
Park, H., Ryzhkov, A. V., Zrnić, D. S., and Kim, K.: The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS, Weather Forecast., 24, 730–748, https://doi.org/10.1175/2008WAF2222205.1, 2009. a
Rogers, R. R., Ecklund, W. L., Carter, D. A., Gage, K. S., and Ethier, S. A.: Research applications of a boundary-layer wind profiler, B. Am. Meteorol. Soc., 74, 567–580, https://doi.org/10.1175/1520-0477(1993)074<0567:RAOABL>2.0.CO;2, 1993. a
Rüfenacht, R., Haefele, A., Pospichal, B., Cimini, D., Bircher-Adrot, S., Turp, M., and Sugier, J.: EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe, Bulletin of Atmospheric Science and Technology, 2, 4, https://doi.org/10.1007/s42865-021-00033-w, 2021. a
Savitzky, A. and Golay, M. J. E.: Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627–1639, https://doi.org/10.1021/ac60214a047, 1964. a
Schwartz, B. and Govett, M.: A hydrostatically consistent North American Radiosonde Database at the Forecast Systems Laboratory, 1946–Present, NOAA Technical Memorandum ERL FSL-4, https://ruc.noaa.gov/raobs/radiosonde.pdf (last access: 12 April 2023), 1992. a
Schween, J. H., Hirsikko, A., Löhnert, U., and Crewell, S.: Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment, Atmos. Meas. Tech., 7, 3685–3704, https://doi.org/10.5194/amt-7-3685-2014, 2014. a
Segales, A. R., Greene, B. R., Bell, T. M., Doyle, W., Martin, J. J., Pillar-Little, E. A., and Chilson, P. B.: The CopterSonde: an insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research, Atmos. Meas. Tech., 13, 2833–2848, https://doi.org/10.5194/amt-13-2833-2020, 2020. a
Sisterson, D. L., Peppler, R. A., Cress, T. S., Lamb, P. J., and Turner, D. D.: The ARM southern great plains (SGP) site, Meteorol. Mon., 57, 1–6, 2016. a
Smith, E.: bliss-fl-v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.12641260, 2024. a
Smith, E., Bell, T. and Klein, P.: CHEESEHEAD CLAMPS observations, NOAA-OAR-NSSL/Zenodo [data set], https://doi.org/10.5281/zenodo.12636936, 2019. a
Smith, E., Carlin, J., Bell, T., and Bunting, L.: PBLTops CLAMPS observations, NOAA-OAR-NSSL/Zenodo [data set], https://doi.org/10.5281/zenodo.12636930, 2020. a
Spuler, S. M., Hayman, M., Stillwell, R. A., Carnes, J., Bernatsky, T., and Repasky, K. S.: MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling, Atmos. Meas. Tech., 14, 4593–4616, https://doi.org/10.5194/amt-14-4593-2021, 2021. a
Stensrud, D. J. and Weiss, S. J.: Mesoscale model ensemble forecasts of the 3 May 1999 tornado outbreak, Weather Forecast., 17, 526–543, https://doi.org/10.1175/1520-0434(2002)017<0526:MMEFOT>2.0.CO;2, 2002. a
Stensrud, D. J., Comer, C. L., STouffer, B., Zhang, Y., and Kumjian, M.: Evolution of monthly mean convective boundary layer depths detected from WSR–88D observations, in: 104th Annual Meeting, Baltimore, MD, 28 January–1 February 2024, American Meteorological Society, p. 12.3, 2024. a
Stouffer, B., Comer, C. L., Stensrud, D. J., Zhang, Y., and Kumjian, M.: Exploring convective boundary layer depth and entrainment zone properties with dual-polarization radar observations, in: 104th Annual Meeting, Baltimore, MD, 28 January–1 February 2024, American Meteorological Society, p. 12.4, 2024. a
Stull, R. B.: An introdution to boundary layer meteorology, Dordrecht, Kluwer, https://doi.org/10.1007/978-94-009-3027-8, 1988. a, b
Tangborn, A., Demoz, B., Carroll, B. J., Santanello, J., and Anderson, J. L.: Assimilation of lidar planetary boundary layer height observations, Atmos. Meas. Tech., 14, 1099–1110, https://doi.org/10.5194/amt-14-1099-2021, 2021. a
Troitsky, A. V., Gajkovich, K. P., Gromov, V. D., Kadygrov, E. N., and Kosov, A. S.: Thermal sounding of the atmospheric boundary layer in the oxygen band center at 60 GHz, IEEE T. Geosci. Remote, 31, 116–120, https://doi.org/10.1109/36.210451, 1993. a
Turner, D. D. and Löhnert, U.: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI), J. Appl. Meteorol. Clim., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1, 2014. a, b
Uzan, L., Egert, S., and Alpert, P.: Ceilometer evaluation of the eastern Mediterranean summer boundary layer height – first study of two Israeli sites, Atmos. Meas. Tech., 9, 4387–4398, https://doi.org/10.5194/amt-9-4387-2016, 2016. a
Vivekanandan, J., Zrnić, D. S., Ellis, S. M., Oye, R., Ryzhkov, A. V., and Straka, J.: Cloud microphysics retrieval using S-band dual-polarization radar measurements, B. Am. Meteorol. Soc., 80, 381–388, https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2, 1999. a
Wagner, T. J., Klein, P. M., and Turner, D. D.: A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer, B. Am. Meteorol. Soc., 100, 137–153, 2019. a
Wingo, S. M. and Knupp, K. R.: Multi-platform observations characterizing the afternoon-to-evening transition of the planetary boundary layer in northern Alabama, USA, Bound.-Lay. Meteorol., 155, 29–53, 2015. a
Yang, Y., Chen, X., and Qi, Y.: Classification of convective/stratiform echoes in radar reflectivity observations using a fuzzy logic algorithm, J. Geophys. Res.-Atmos., 118, 1896–1905, https://doi.org/10.1002/jgrd.50214, 2013. a
Short summary
Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.
Boundary-layer height observations remain sparse in time and space. In this study we create a...