Editorial Type:
Article
Article Type:
Research Article

Information Content of a Synergy of Ground-Based and Space-Based Infrared Sounders. Part I: Clear-Sky Environments

David M. Loveless aDepartment of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin
bCooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by David M. Loveless in
Current site
Google Scholar
PubMed Close
,
Timothy J. Wagner bCooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Timothy J. Wagner in
Current site
Google Scholar
PubMed Close
,
Robert O. Knuteson bCooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Robert O. Knuteson in
Current site
Google Scholar
PubMed Close
,
David D. Turner cNational Oceanic and Atmospheric Administration/Global Systems Laboratory, Boulder, Colorado

Search for other papers by David D. Turner in
Current site
Google Scholar
PubMed Close
, and
Steven A. Ackerman aDepartment of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin
bCooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Steven A. Ackerman in
Current site
Google Scholar
PubMed Close
Online Publication:
02 Jun 2022
Print Publication:
01 Jun 2022
DOI:
https://doi.org/10.1175/JTECH-D-21-0119.1
Page(s):
771–787
Restricted access

Abstract

Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David M. Loveless, dloveless@wisc.edu

Abstract

Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David M. Loveless, dloveless@wisc.edu
Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Aires, F., O. Aznay, C. Progent, M. Paul, and F. Bernardo, 2012: Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A. J. Geophys. Res., 117, D18304, https://doi.org/10.1029/2011JD017188.

    • Search Google Scholar
    • Export Citation

  • Bianchini, G., L. Palchetti, G. Muscari, I. Fiorucci, P. Di Girolamo, and T. Di Iorio, 2011: Water vapor sounding with the far infrared REFIR-PAD spectroradiometer from a high-altitude ground-based station during the ECOWAR campaign. J. Geophys. Res., 116, D02310, https://doi.org/10.1029/2010JD014530.

    • Search Google Scholar
    • Export Citation

  • Bloch, C., R. O. Knuteson, A. Gambacorta, N. R. Nalli, J. Gartzke, and L. Zhou, 2019: Near-real-time surface-based CAPE from merged hyperspectral IR satellite sounder and surface meteorological station data. J. Appl. Meteor. Climatol., 58, 1613–1632, https://doi.org/10.1175/JAMC-D-18-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Blumberg, W. G., D. D. Turner, U. Löhnert, and S. Castleberry, 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. Meteor. Climatol., 54, 2305–2319, https://doi.org/10.1175/JAMC-D-15-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Blumberg, W. G., K. T. Halbert, T. A. Supinie, P. T. Marsh, R. L. Thompson, and J. A. Hart, 2017: SHARPpy: An open-source sounding analysis toolkit for the atmospheric sciences. Bull. Amer. Meteor. Soc., 98, 1625–1636, https://doi.org/10.1175/BAMS-D-15-00309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Borbas, E. E., S. W. Seemann, H. Huang, J. Li, and W. P. Menzel, 2005: Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity. Proc. 14th Int. ATOVS Study Conf., Beijing, China, ITWG, 763–770.

    • Search Google Scholar
    • Export Citation

  • Borbas, E. E., G. Hulley, M. Feltz, R. Knuteson, and S. Hook, 2018: The Combined ASTER MODIS Emissivity over Land (CAMEL) part 1: Methodology and high spectral resolution application. Remote Sens., 10, 643, https://doi.org/10.3390/rs10040643.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chahine, M. T., and Coauthors, 2006: AIRS: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc., 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cimini, D., P. W. Rosenkranz, M. Y. Tretyakov, M. A. Koshelev, and F. Romano, 2018: Uncertainty of atmospheric microwave absorption model: Impact on ground-based radiometer simulations and retrievals. Atmos. Chem. Phys., 18, 15 231–15 259, https://doi.org/10.5194/acp-18-15231-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Coniglio, M. C., G. S. Romine, D. D. Turner, and R. D. Torn, 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, 1149–1170, https://doi.org/10.1175/MWR-D-18-0351.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Degelia, S. K., X. Wang, D. J. Stensrud, and D. D. Turner, 2020: Systematic evaluation of the impact of assimilating a network of ground-based remote sensing profilers for forecasts of nocturnal convection initiation during PECAN. Mon. Wea. Rev., 148, 4703–4728, https://doi.org/10.1175/MWR-D-20-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dong, X., B. Xi, A. Kennedy, P. Minnis, and R. Wood, 2014: A 19-month record of marine aerosol–cloud–radiation properties derived from DOE ARM mobile facility deployment at the Azores. Part I: Cloud fraction and single-layered MBL cloud properties. J. Climate, 27, 3665–3682, https://doi.org/10.1175/JCLI-D-13-00553.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ebell, K., E. Orlandi, A. Hünerbein, U. Löhnert, and S. Crewell, 2013: Combining ground-based with satellite-based measurements in the atmospheric state retrieval: Assessment of the information content. J. Geophys. Res. Atmos., 118, 6940–6956, https://doi.org/10.1002/jgrd.50548.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Elwell, J. D., G. W. Cantwell, D. K. Schott, R. W. Esplin, G. B. Hansen, and S. M. Jensen, 2006: A Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) for hyperspectral atmospheric remote sensing: Instrument overview and preliminary performance results. Space Dynamics Lab Publ., 36 pp., https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1035&context=sdl_pubs.

    • Search Google Scholar
    • Export Citation

  • Feltz, W. F., W. L. Smith, H. B. Howell, R. O. Knuteson, H. Woolf, and H. E. Revercomb, 2003: Near-continuous profiling of temperature, moisture, and atmospheric stability using the Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor., 42, 584–597, https://doi.org/10.1175/1520-0450(2003)042<0584:NPOTMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gambacorta, A., 2013: The NOAA unique CrIS/ATMS processing system (NUCAPS). Algorithm theoretical basis documentation. NOAA ATBD, 73 pp., http://www.ospo.noaa.gov/Products/atmosphere/soundings/nucaps/docs/NUCAPS_ATBD_20130821.pdf.

  • Gartzke, J., R. Knuteson, G. Przybyl, S. Ackerman, and H. Revercomb, 2017: Comparison of satellite-, model-, and radiosonde-derived convective available potential energy in the Southern Great Plains region. J. Appl. Meteor. Climatol., 56, 1499–1513, https://doi.org/10.1175/JAMC-D-16-0267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Han, Y., and Coauthors, 2013: Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities and record data quality. J. Geophys. Res. Atmos., 118, 12 734–12 748, https://doi.org/10.1002/2013JD020344.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hewison, T. J., 2007: 1D-VAR retrieval of temperature and humidity profiles from a ground-based microwave radiometer. IEEE Trans. Geosci. Remote Sens., 45, 2163–2168, https://doi.org/10.1109/TGRS.2007.898091.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hilton, F., and Coauthors, 2012: Hyperspectral Earth observation from IASI: Five years of accomplishments. Bull. Amer. Meteor. Soc., 93, 347–370, https://doi.org/10.1175/BAMS-D-11-00027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ho, S., W. L. Smith, and H. Huang, 2002: Retrieval of atmospheric-temperature and water-vapor profiles by use of combined satellite and ground-based infrared spectral-radiance measurements. J. Appl. Opt., 41, 4057–4069, https://doi.org/10.1364/AO.41.004057.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hu, J., N. Yussouf, D. D. Turner, T. A. Jones, and X. Wang, 2019: Impact of ground-based remote sensing boundary layer observation on short-term probabilistic forecasts of a tornadic supercell event. Wea. Forecasting, 34, 1453–1476, https://doi.org/10.1175/WAF-D-18-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Klein, P., and Coauthors, 2015: LABLE: A multi-institutional, student-led, atmospheric boundary layer experiment. Bull. Amer. Meteor. Soc., 96, 1743–1764, https://doi.org/10.1175/BAMS-D-13-00267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Knuteson, R. O., and Coauthors, 2004a: The Atmospheric Emitted Radiance Interferometer. Part I: Instrument design. J. Atmos. Oceanic Technol., 21, 1763–1776, https://doi.org/10.1175/JTECH-1662.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Knuteson, R. O., and Coauthors, 2004b: The Atmospheric Emitted Radiance Interferometer. Part II: Instrument performance. J. Atmos. Oceanic Technol., 21, 1777–1789, https://doi.org/10.1175/JTECH-1663.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56, 189–201, https://doi.org/10.3402/tellusa.v56i3.14413.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • LaRoche, S., and R. Sarrazin, 2010: Impact study with observations assimilated over North America and the North Pacific Ocean on the MSC global forecast system. Part I: Contribution of radiosonde, aircraft, and satellite data. Atmos.–Ocean, 48, 10–25, https://doi.org/10.3137/AO1006.2010.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Löhnert, U., D. D. Turner, and S. Crewell, 2009: Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part I: Simulated retrieval performance in clear-sky conditions. J. Appl. Meteor. Climatol., 48, 1017–1032, https://doi.org/10.1175/2008JAMC2060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Loveless, D. M., T. J. Wagner, D. D. Turner, S. A. Ackerman, and W. F. Feltz, 2019: A composite perspective on bore passages during the PECAN campaign. Mon. Wea. Rev., 147, 1395–1413, https://doi.org/10.1175/MWR-D-18-0291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Loveless, M., E. E. Borbas, R. Knuteson, K. Cawse-Nicholson, G. Hulley, and S. Hook, 2021: Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) version 2. Remote Sens., 13, 111, https://doi.org/10.3390/rs13010111.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Masiello, G., C. Serio, and P. Antonelli, 2012: Inversion for atmospheric thermodynamic parameters of IASA data in the principal components space. Quart. J. Roy. Meteor. Soc., 138, 103–117, https://doi.org/10.1002/qj.909.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McClatchey, R. A., 1972: Optical properties of the atmosphere. Air Force Cambridge Research Laboratories Rep., 3rd ed., 108 pp.

  • Mlawer, E. J., and D. D. Turner, 2016: Spectral radiation measurements and analysis in the ARM program. The Atmospheric Radiation Measurements Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0027.1.

    • Crossref
    • Export Citation

  • Moncet, J., G. Uymin, A. E. Lipton, and H. E. Snell, 2008: Infrared radiance modeling by optimal spectral sampling. J. Atmos. Sci., 65, 3917–3934, https://doi.org/10.1175/2008JAS2711.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moncet, J., G. Uymin, P. Liang, and A. E. Lipton, 2015: Fast and accurate radiative transfer in the thermal regime by simultaneous optimal spectral sampling over all channels. J. Atmos. Sci., 72, 2622–2641, https://doi.org/10.1175/JAS-D-14-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nalli, N. R., and Coauthors, 2018: Validation of atmospheric profile retrievals from the SNPP NOAA-unique combined atmospheric processing system. Part I: Temperature and moisture. IEEE Trans. Geosci. Remote Sens., 56, 180–190, https://doi.org/10.1109/TGRS.2017.2744558.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • National Academies of Sciences, Engineering, and Medicine, 2018: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. National Academies Press, 716 pp.

  • National Research Council, 2009: Committee on Developing Mesoscale Meteorological Observational Capabilities to Meet Multiple National Needs. National Academies Press, 234 pp.

  • Pagano, T. S., S. Broberg, H. H. Aumann, D. Elliott, E. Manning, and L. Strow, 2014: Performance status of the atmospheric infrared sounder ten years after launch. NASA JPL Rep., 9 pp., https://trs.jpl.nasa.gov/bitstream/handle/2014/42928/12-5086_A1b.pdf.

  • Peppler, R. A., K. E. Kehoe, J. W. Monroe, A. K. Theisen, and S. T. Moore, 2016: The ARM data quality program. The Atmospheric Radiation Measurements Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0039.1.

    • Crossref
    • Export Citation

  • Rodgers, C. D., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. Series on Atmospheric, Oceanic, and Planetary Physics, Vol. 2, World Scientific, 238 pp.

    • Crossref
    • Export Citation

  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681–698, https://doi.org/10.1175/BAMS-D-15-00230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sisterson, D. L., R. A. Peppler, T. S. Cressm, P. J. Lamb, and D. D. Turner, 2016: The ARM Southern Great Plains (SGP) site. The Atmospheric Radiation Measurements Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0004.1.

    • Crossref
    • Export Citation

  • Smith, N., and C. D. Barnet, 2020: CLIMCAPS observing capability for temperature, moisture and trace gases from AIRS/AMSU and CrIS/ATMS. Atmos. Meas. Tech., 13, 4437–4459, https://doi.org/10.5194/amt-13-4437-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, W. L., H. Revercomb, E. Weisz, D. Tobin, R. Knuteson, J. Taylor, and W. P. Menzel, 2021: Hyperspectral satellite radiance atmospheric profile information content and its dependence on spectrometer technology. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 4720–4736, https://doi.org/10.1109/JSTARS.2021.3073482.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stephens, G., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2018: CloudSat and CALIPSO within the A-Train: Ten years of actively observing the Earth system. Bull. Amer. Meteor. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, B., A. Reale, F. H. Tilley, M. E. Pettey, N. R. Nalli, and C. D. Barnet, 2017: Assessment of NUCAPS S-NPP CrIS/ATMS sounding products using reference and conventional radiosonde observations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 2499–2509, https://doi.org/10.1109/JSTARS.2017.2670504.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tanamachi, R. L., W. F. Feltz, and M. Xue, 2008: Observations and numerical simulation of upper boundary layer rapid drying and moistening events during the International H2O Project (IHOP_2002). Mon. Wea. Rev., 136, 3106–3120, https://doi.org/10.1175/2008MWR2204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Toporov, M., and U. Löhnert, 2020: Synergy of satellite- and ground-based observations for continuous monitoring of atmospheric stability, liquid water path, and integrated water vapor: Theoretical evaluations using reanalysis and neural networks. J. Appl. Meteor. Climatol., 59, 1153–1170, https://doi.org/10.1175/JAMC-D-19-0169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Verlinde, J., B. D. Zak, M. D. Shupe, M. D. Ivey, and K. Stamnes, 2016: The ARM North Slope of Alaska sites. The Atmospheric Radiation Measurements Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0023.1.

    • Crossref
    • Export Citation

  • Wagner, T. J., W. F. Feltz, and S. A. Ackerman, 2008: The temporal evolution of convective indices in storm-producing environments. Wea. Forecasting, 23, 786–794, https://doi.org/10.1175/2008WAF2007046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wagner, T. J., A. C. Czarnetzki, M. Christiansen, R. B. Pierce, C. O. Stainer, A. F. Dickens, and E. W. Eloranta, 2022: Observations of the development and vertical structure of the lake breeze circulation during the 2017 Lake Michigan ozone study. J. Atmos. Sci., 79, 1005–1020, https://doi.org/10.1175/JAS-D-20-0297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yang, J., Z. Zhang, C. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteor. Soc., 98, 1637–1658, https://doi.org/10.1175/BAMS-D-16-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zavyalov, V., and Coauthors, 2013: Noise performance of the CrIS instrument. J. Geophys. Res. Atmos., 118, 13 108–13 120, https://doi.org/10.1002/2013JD020457.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 370 0 0
Full Text Views 372 192 23
PDF Downloads 348 171 16