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Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

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Abstract

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. We also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

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Notes

  1. While the Raman lidar can provide partial profiles of temperature (Newsom et al. 2013) those data are not used in the AERIoe retrieval described in Sect. 2.4, since the comparison was relative to wvDIAL.

  2. Cloudy scenes were not controlled for in this analysis as those comparisons are reserved for Sect. 3.3.

  3. In these applications, convolving the radiosonde data with the averaging kernel would act to minimize the vertical representativeness error in the comparison of the AERIoe retrievals and the radiosonde profiles. The authors purposefully chose not to take this step. In this sort of analysis we feel it is important to evaluate the data as most users would encounter it. This does mean that our results may make the retrieval appear to fare less well than it may if the reference data were convolved with the averaging kernel. See Turner and Löhnert (2014).

  4. This is one of two physical constraints added to the retrieval, and the level below which it is applied is configurable by the user. The other constraint requires relative humidity be less than 100% (Turner and Blumberg 2019). Metadata about these settings can always be found in retrieval output.

  5. It is of note that different methods of computing convection indices, in this case SBCAPE, can result in widely varied results, as is apparent from comparing values derived from radiosonde data by the University of Wyoming archive (orange dots on Fig. 13) and by SHARPpy (blue dots and error bars on Fig. 13).

References

  • Bell TM, Greene BR, Klein PM, Carney M, Chilson PB (2020) Confronting the boundary layer data gap: evaluating new and existing methodologies of probing the lower atmosphere. Atmos Meas Tech 13(7):3855–3872. https://doi.org/10.5194/amt-13-3855-2020

    Article  Google Scholar 

  • Benjamin SG, Weygandt SS, Brown JM, Hu M, Alexander CR, Smirnova TG, Olson JB, James EP, Dowell DC, Grell GA, Lin H, Peckham SE, Smith TL, Moninger WR, Kenyon JS, Manikin GS (2016) A North American hourly assimilation and model forecast cycle: the rapid refresh. Mon Wea Rev 144(4):1669–1694. https://doi.org/10.1175/MWR-D-15-0242.1

    Article  Google Scholar 

  • Blumberg W, Turner D, Löhnert U, Castleberry S (2015) Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions. J Appl Meteorol Clim 54(11):2305–2319

    Article  Google Scholar 

  • Blumberg W, Wagner T, Turner D, Correia J Jr (2017a) Quantifying the accuracy and uncertainty of diurnal thermodynamic profiles and convection indices derived from the atmospheric emitted radiance interferometer. J Appl Meteorol Clim 56(10):2747–2766

    Article  Google Scholar 

  • Blumberg WG, Halbert KT, Supinie TA, Marsh PT, Thompson RL, Hart JA (2017b) SHARPpy: an open-source sounding analysis toolkit for the atmospheric sciences. Bull Am Meteorol Soc 98(8):1625–1636

    Article  Google Scholar 

  • Bolton D (1980) The computation of equivalent potential temperature. Mon Wea Rev 108(7):1046–1053

    Article  Google Scholar 

  • Bothwell PD, Hart JA, L TR (2002) An integrated three-dimensional objective analysis scheme in use at the Storm Prediction Center. In: 21st conference on severe local storms, Am Meteorol Soc, vol JP3.1

  • Chilson PB, Bell TM, Brewster KA, Hupsel Britto, de Azevedo G, Carr FH, Carson K, Doyle W, Fiebrich CA, Greene BR, Grimsley JL, Kanneganti ST, Martin J, Moore A, Palmer RD, Pillar-Little EA, Salazar-Cerreno JL, Segales AR, Weber ME, Yeary M, Droegemeier KK (2019) Moving towards a network of autonomous UAS atmospheric profiling stations for observations in the Earth’s lower atmosphere: The 3D mesonet concept. Sensors 19(12):2720. https://doi.org/10.3390/s19122720

    Article  Google Scholar 

  • Chipilski HG, Wang X, Parsons DB (2020) Impact of assimilating PECAN profilers on the prediction of bore-driven nocturnal convection: A multiscale forecast evaluation for the 6 July 2015 case study. Mon Wea Rev 148(3):1147–1175

    Article  Google Scholar 

  • Coniglio MC, Romine GS, Turner DD, Torn RD (2019) Impacts of targeted AERI and Doppler lidar wind retrievals on short-term forecasts of the initiation and early evolution of thunderstorms. Mon Wea Rev 147(4):1149–1170

    Article  Google Scholar 

  • Degelia SK, Wang X, Stensrud DJ (2019) An evaluation of the impact of assimilating AERI retrievals, kinematic profilers, rawinsondes, and surface observations on a forecast of a nocturnal convection initiation event during the PECAN field campaign. Mon Wea Rev 147(8):2739–2764

    Article  Google Scholar 

  • de Boer G, Diehl C, Jacob J, Houston A, Smith SW, Chilson P, Schmale DG III, Intrieri J, Pinto J, Elston J et al (2020) Development of community, capabilities, and understanding through unmanned aircraft-based atmospheric research: the LAPSE-RATE campaign. Bull Am Meteorol Soc 101(5):E684–E699

    Article  Google Scholar 

  • Findell KL, Eltahir EA (2003a) Atmospheric controls on soil moisture-boundary layer interactions. Part I: Framework development. J Hydrometeorol 4(3):552–569

    Article  Google Scholar 

  • Findell KL, Eltahir EA (2003b) Atmospheric controls on soil moisture-boundary layer interactions. Part II: Feedbacks within the continental united states. J Hydrometeorol 4(3):570–583

    Article  Google Scholar 

  • Goldsmith J, Blair FH, Bisson SE, Turner DD (1998) Turn-key Raman lidar for profiling atmospheric water vapor, clouds, and aerosols. Appl Opt 37(21):4979–4990

    Article  Google Scholar 

  • Guo Z, Dirmeyer PA, Koster RD, Sud Y, Bonan G, Oleson KW, Chan E, Verseghy D, Cox P, Gordon C et al (2006) GLACE: the global land-atmosphere coupling experiment. part II: analysis. J Hydrometeorol 7(4):611–625

    Article  Google Scholar 

  • Hartung DC, Otkin JA, Petersen RA, Turner DD, Feltz WF (2011) Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. part II: Forecast assessment. Mon Wea Rev 139(8):2327–2346

    Article  Google Scholar 

  • Hu J, Yussouf N, Turner DD, Jones TA, Wang X (2019) Impact of ground-based remote sensing boundary layer observations on short-term probabilistic forecasts of a tornadic supercell event. Wea Forecast 34(5):1453–1476

    Article  Google Scholar 

  • Karan H, Knupp K (2006) Mobile Integrated Profiler System (MIPS) observations of low-level convergent boundaries during IHOP. Mon Wea Rev 134(1):92–112. https://doi.org/10.1175/MWR3058.1

    Article  Google Scholar 

  • Knupp KR, Coleman T, Phillips D, Ware R, Cimini D, Vandenberghe F, Vivekanandan J, Westwater E (2009) Ground-based passive microwave profiling during dynamic weather conditions. J Atmos Ocean Technol 26(6):1057–1073. https://doi.org/10.1175/2008JTECHA1150.1

    Article  Google Scholar 

  • Knuteson R, Revercomb H, Best F, Ciganovich N, Dedecker R, Dirkx T, Ellington S, Feltz W, Garcia R, Howell H et al (2004) Atmospheric emitted radiance interferometer. Part I: Instrument design. J Atmos Ocean Technol 21(12):1763–1776

    Article  Google Scholar 

  • Koch SE, Fengler M, Chilson PB, Elmore KL, Argrow B, Andra DL Jr, Lindley T (2018) On the use of unmanned aircraft for sampling mesoscale phenomena in the preconvective boundary layer. J Atmos and Ocean Technol 35(11):2265–2288

    Article  Google Scholar 

  • Koster R, Mahanama S, Yamada T, Balsamo G, Berg A, Boisserie M, Dirmeyer P, Doblas-Reyes F, Drewitt G, Gordon C et al (2011) The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J Hydrometeorol 12(5):805–822

    Article  Google Scholar 

  • Kral ST, Reuder J, Vihma T, Suomi I, Haualand KF, Urbancic GH, Greene BR, Steeneveld GJ, Lorenz T, Maronga B, Jonassen MO, Ajosenpää H, Båserud L, Chilson PB, Holtslag AAM, Jenkins AD, Kouznetsov R, Mayer S, Pillar-Little EA, Rautenberg A, Schwenkel J, Seidl AW, Wrenger B (2020) The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Project (ISOBAR) –Unique fine-scale observations under stable and very stable conditions. Bull Am Meteorol Soc 2020:1–64. https://doi.org/10.1175/BAMS-D-19-0212.1

    Article  Google Scholar 

  • Krishnamurthy R, Newsom RK, Berg LK, Xiao H, Ma PL, Turner DD (2020) On the estimation of boundary layer heights: a machine learning approach. Atmos Meas Tech Discuss (in review). https://doi.org/10.5194/amt-2020-439

  • Le Hoai PP, Abo M, Sakai T (2016) Development of field-deployable diode-laser-based water vapor DIAL. In: EPJ Web of Conferences, EDP Sciences, vol 119, p 05011

  • Löhnert U, Turner D, Crewell S (2009) Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part I: Simulated retrieval performance in clear-sky conditions. J Appl Meteorol Clim 5:1017–1032

    Article  Google Scholar 

  • Melnikov VM, Doviak RJ, Zrnić DS, Stensrud DJ (2011) Mapping Bragg scatter with a polarimetric WSR-88D. J Atmos Ocean Technol 28(10):1273–1285. https://doi.org/10.1175/JTECH-D-10-05048.1

    Article  Google Scholar 

  • Moninger WR, Benjamin SG, Jamison BD, Schlatter TW, Smith TL, Szoke EJ (2010) Evaluation of regional aircraft observations using TAMDAR. Wea Forecast 25(2):627–645. https://doi.org/10.1175/2009WAF2222321.1

    Article  Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine (2018) Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. The National Academies Press, Washington, DC

  • National Research Council (2009) Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. The National Academies Press, Washington, DC

  • National Research Council (2010) When weather matters: Science and services to meet critical societal needs. National Academies Press

  • NCEI (2020) Storm Data. Department of Commerce, National Centers for Environmental Information, NESDIS, NOAA, U.S

  • Newsom R, Turner DD, Lehtinen R, Münkel C, Kallio J, Roininen R (2020) Evaluation of a compact broadband differential absorption lidar for routine water vapor profiling in the atmospheric boundary layer. J Atmos Ocean Technol 37(1):47–65

    Article  Google Scholar 

  • Newsom RK, Turner DD, Mielke B, Clayton M, Ferrare R, Sivaraman C (2009) Simultaneous analog and photon counting detection for Raman lidar. Appl Opt 48(20):3903–3914

    Article  Google Scholar 

  • Newsom RK, Turner DD, Goldsmith JEM (2013) Long-term evaluation of temperature profiles measured by an operational Raman lidar. J Atmos Ocean Technol 30(8):1616–1634. https://doi.org/10.1175/JTECH-D-12-00138.1

    Article  Google Scholar 

  • Otkin JA, Hartung DC, Turner DD, Petersen RA, Feltz WF, Janzon E (2011) Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part I: Analysis impact. Mon Wea Rev 139(8):2309–2326

    Article  Google Scholar 

  • Parsons D, Dabberdt W, Cole H, Hock T, Martin C, Barrett AL, Miller E, Spowart M, Howard M, Ecklund W et al (1994) The integrated sounding system: Description and preliminary observations from TOGA COARE. Bull Am Meteorol Soc 75(4):553–568

    Article  Google Scholar 

  • Roininen R, Münkel C (2016) Results from continuous atmospheric boundary layer humidity profiling with a compact DIAL instrument. In: Proceedings of the WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation, Madrid, Spain, pp 27–30

  • Segales AR, Greene BR, Bell TM, Doyle W, Martin JJ, Pillar-Little EA, Chilson PB (2020) The coptersonde: an insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research. Atmos Meas Tech 13(5):2833–2848. https://doi.org/10.5194/amt-13-2833-2020

    Article  Google Scholar 

  • Sisterson D, Peppler R, Cress T, Lamb P, Turner D (2016) The ARM southern great plains (SGP) site. Meteorological Monographs 57:6–1

    Article  Google Scholar 

  • Spuler S, Weckwerth T, Repasky K, Hayman M, Nehrir A (2016) Testing and validation of a micro-pulse, differential absorption lidar (DIAL) for measuring the spatial and temporal distribution of water vapor in the lower atmosphere. In: Optics and Photonics for Energy and the Environment, Optical Society of America, pp EW3A–5

  • Stull RB (2012) An introduction to boundary layer meteorology, vol 13. Springer, Berlin

    Google Scholar 

  • Trenberth KE (1999) Atmospheric moisture recycling: role of advection and local evaporation. J Clim 12(5):1368–1381

    Article  Google Scholar 

  • Turner D, Löhnert U (2014) 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(3):752–771

    Article  Google Scholar 

  • Turner D, Goldsmith J, Ferrare R (2016) Development and applications of the ARM Raman lidar. Meteorological Monographs 57:18–1

    Google Scholar 

  • Turner DD, Blumberg WG (2019) Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE J Sel Top Appl Earth Obs Remote Sense 12(5):1339–1354

    Article  Google Scholar 

  • Turner DD, Goldsmith J (1999) Twenty-four-hour Raman lidar water vapor measurements during the Atmospheric Radiation Measurement Program’s 1996 and 1997 water vapor intensive observation periods. J Atmos Ocean Technol 16(8):1062–1076

    Article  Google Scholar 

  • Turner DD, Löhnert U (2020) Ground-based temperature and humidity profiling: combining active and passive remote sensors. Atmos Meas Tech (submitted)

  • Turner DD, Knuteson RO, Revercomb HE, Lo C, Dedecker RG (2006) Noise reduction of atmospheric emitted radiance interferometer (AERI) observations using principal component analysis. J Atmos Ocean Technol 23(9):1223–1238. https://doi.org/10.1175/JTECH1906.1

    Article  Google Scholar 

  • Wagner TJ, Klein PM, Turner DD (2019) A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer. Bull Am Meteorol Soc 100(1):137–153

    Article  Google Scholar 

  • Wakefield RA, Turner DD, Basara JB (2021) Evaluation of a land-atmosphere coupling metric computed from a ground-based infrared interferometer. J Hydrometeorol. https://doi.org/10.1175/JHM-D-20-0303.1

  • Weckwerth TM, Weber KJ, Turner DD, Spuler SM (2016) Validation of a water vapor micropulse differential absorption lidar (DIAL). J Atmos Ocean Technol 33(11):2353–2372

    Article  Google Scholar 

  • Wei J, Su H, Yang ZL (2016) Impact of moisture flux convergence and soil moisture on precipitation: a case study for the southern United States with implications for the globe. Clim Dyn 46(1–2):467–481

    Article  Google Scholar 

  • Wingo SM, Knupp KR (2015) Multi-platform observations characterizing the afternoon-to-evening transition of the planetary boundary layer in northern Alabama, USA. Boundary-Layer Meteorol 155(1):29–53

    Article  Google Scholar 

  • Wulfmeyer V (1999) Investigation of turbulent processes in the lower troposphere with water vapor DIAL and radar-RASS. J Atmos Sci 56(8):1055–1076

    Article  Google Scholar 

  • Wulfmeyer V, Hardesty RM, Turner DD, Behrendt A, Cadeddu MP, Di Girolamo P, Schlüssel P, Van Baelen J, Zus F (2015) A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles. Rev Geophys 53(3):819–895. https://doi.org/10.1002/2014RG000476

    Article  Google Scholar 

  • Zhang Y, Li D, Lin Z, Santanello JA Jr, Gao Z (2019) Development and evaluation of a long-term data record of planetary boundary layer profiles from aircraft meteorological reports. J Geophys Res Atmos 124(4):2008–2030

    Article  Google Scholar 

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Acknowledgements

E. N. Smith would like to acknowledge that most co-authors of this study are graduate students that chose to collaborate on this work as a voluntary side project related to their shared Boundary Layer, Urban Meteorology, and Land-Surface Processes Seminar course, bringing together varied expertise and offering new learning opportunities for all participants. Dr. Michael Coniglio provided a useful internal review to the paper. This work was partially supported by the DOE Atmospheric System Research (ASR) program via grants DE-SC0014375 and 89243019SSC000034, and by the NOAA Atmospheric Science for Renewable Energy (ASRE) program. This work was prepared by the authors with support from the NSSL Forecast Research and Development Division (ENS) and the NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreement NA11OAR4320072, U.S. Department of Commerce (TMB, QN). The contents of this paper do not necessarily reflect the views or official position of any organization of the U.S. Government.

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Authors and Affiliations

  1. NOAA/OAR/National Severe Storms Laboratory, Norman, OK, USA

    Elizabeth N. Smith & Tyler M. Bell

  2. School of Meteorology, University of Oklahoma, Norman, OK, USA

    Brian R. Greene, Tyler M. Bell, Ryann Wakefield, Dylan Reif, Qing Niu & Qingyu Wang

  3. Center for Autonomous Sensing and Sampling, University of Oklahoma, Norman, OK, USA

    Brian R. Greene & Tyler M. Bell

  4. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA

    Tyler M. Bell & Qing Niu

  5. NASA Postdoctoral Program/NASA Goddard Space Flight Center, Greenbelt, MD, USA

    William G. Blumberg

  6. NOAA/OAR/Global Systems Laboratory, Boulder, CO, USA

    David D. Turner

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  1. Elizabeth N. Smith
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Smith, E.N., Greene, B.R., Bell, T.M. et al. Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals. Boundary-Layer Meteorol 181, 95–123 (2021). https://doi.org/10.1007/s10546-021-00640-2

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