Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5555-2021
© Author(s) 2021. 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-14-5555-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2021
Research article |
| 13 Aug 2021
| 13 Aug 2021
Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 90095, USA
David R. Thompson
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Marcin J. Kurowski
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Matthew D. Lebsock
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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Convection over land often triggers hours after a satellite last passed overhead and measured the state of the atmosphere, and during those hours the atmosphere can change greatly. Here we show that it is possible to reconstruct most of those changes by using weather forecast winds to predict where warm and moist air parcels will travel. The results can be used to better predict where precipitation is likely to happen in the hours after satellite measurements.
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Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field in a way that
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towardsor
away fromthe Sun, so calculating
across the solar direction allows sub-kilometre information about water vapour's spatial scaling to be calculated. This could be tested by airborne campaigns and used to obtain new information from upcoming spaceborne data products.
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The AVIRIS-NG hyperspectral imager has been used successfully to identify and quantify anthropogenic methane sources utilizing different retrieval and inversion methods. Here, we examine the adaption and application of the WFM-DOAS algorithm to AVIRIS-NG measurements to retrieve local methane column enhancements, compare the results with other retrievals, and quantify the uncertainties resulting from the retrieval method. Additionally, we estimate emissions from five detected methane plumes.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
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Luis Millán, Richard Roy, and Matthew Lebsock
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Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
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Cited articles
Bedka, K. M., Nehrir, A. R., Kavaya, M., Barton-Grimley, R., Beaubien, M., Carroll, B., Collins, J., Cooney, J., Emmitt, G. D., Greco, S., Kooi, S., Lee, T., Liu, Z., Rodier, S., and Skofronick-Jackson, G.: Airborne lidar observations of wind, water vapor, and aerosol profiles during the NASA Aeolus calibration and validation (Cal/Val) test flight campaign, Atmos. Meas. Tech., 14, 4305–4334, https://doi.org/10.5194/amt-14-4305-2021, 2021.
Bennartz, R. and Fischer, J.: Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer, Remote Sens. Environ., 78, 274–283, https://doi.org/10.1016/S0034-4257(01)00218-8, 2001.
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes,
F., and van den Bosch, J.: MODTRAN6: a major upgrade of the MODTRAN
radiative transfer code, Velez-Reyes, M. and Kruse, F. A. (Eds.), Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, SPIE Defense + Security, 2014, Baltimore, Maryland, United States, p. 90880H, https://doi.org/10.1117/12.2050433,
2014 (data available at: http://modtran.spectral.com, last
access: 19 March 2020).
Berk, A., Conforti, P., and Hawes, F.: An accelerated
line-by-line option for MODTRAN combining on-the-fly generation of
line center absorption within 0.1 cm−1 bins and pre-computed line
tails, edited by: Velez-Reyes, M. and Kruse, F. A., Proceedings Volume 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, SPIE Defense + Security, 2015, Baltimore, Maryland, United States, p. 947217, https://doi.org/10.1117/12.2177444, 2015 (data available at: http://modtran.spectral.com, last
access: 19 March 2020).
Borger, C., Beirle, S., Dörner, S., Sihler, H., and Wagner, T.: Total column water vapour retrieval from S-5P/TROPOMI in the visible blue spectral range, Atmos. Meas. Tech., 13, 2751–2783, https://doi.org/10.5194/amt-13-2751-2020, 2020.
Brodrick, P., Erickson, A., Fahlen, J., Olson, W., Thompson, D. R., Shiklomanov, A., Serbin, S. P., Carmon, N., and McGibbney, L. J.: Isofit 2.8.0, Zenodo [data set], https://doi.org/10.5281/zenodo.4614338, 2021.
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J.-C., Khairoutdinov, M., Lewellen, D. C., Lock, A. P., MacVean, M. K., Moeng, C.-H., Neggers, R. A. J., Siebesma, A. P., and Stevens, B.: Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land, Q. J. Roy. Meteor. Soc., 128, 1075–1093, https://doi.org/10.1256/003590002320373210, 2002.
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M.: Resolution Requirements for the Simulation of Deep Moist Convection, Mon. Weather Rev., 131, 2394–2416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2, 2003.
Candela, L., Formaro, R., Guarini, R., Loizzo, R., Longo, F., and Varacalli, G.: The PRISMA mission, in: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Beijing, China, 10–15 July 2016, pp. 253–256, 2016.
Carbajal Henken, C. K., Diedrich, H., Preusker, R., and Fischer, J.: MERIS full-resolution total column water vapor: Observing horizontal convective rolls, Geophys. Res. Lett., 42, 10074–10081, https://doi.org/10.1002/2015GL066650, 2015.
Couvreux, F., Guichard, F., Austin, P. H., and Chen, F.: Nature of the Mesoscale Boundary Layer Height and Water Vapor Variability Observed 14 June 2002 during the IHOP_2002 Campaign, Mon. Weather Rev., 137, 414–432, https://doi.org/10.1175/2008MWR2367.1, 2009.
Diedrich, H., Preusker, R., Lindstrot, R., and Fischer, J.: Retrieval of daytime total columnar water vapour from MODIS measurements over land surfaces, Atmos. Meas. Tech., 8, 823–836, https://doi.org/10.5194/amt-8-823-2015, 2015.
Drouin, B. J., Benner, D. C., Brown, L. R., Cich, M. J., Crawford, T. J., Devi, V. M., Guillaume, A., Hodges, J. T., Mlawer, E. J., Robichaud, D. J., Oyafuso, F., Payne, V. H., Sung, K., Wishnow, E. H., and Yu, S.: Multispectrum analysis of the oxygen A-band, J. Quant. Spectrosc. Ra., 186, 118–138, https://doi.org/10.1016/j.jqsrt.2016.03.037, 2016.
Drusch, M., Del Bello, U., Carlier, S., Colin, O.,
Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P.,
Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and
Bargellini, P.: Sentinel-2: ESA's Optical High-Resolution Mission
for GMES Operational Services, Remote Sens. Environ., 120, 25–36,
https://doi.org/10.1016/j.rse.2011.11.026, 2012.
Elsey, J., Coleman, M. D., Gardiner, T. D., Menang,
K. P., and Shine, K. P.: Atmospheric observations of the water
vapour continuum in the near-infrared windows between 2500 and
6600 cm−1, Atmos. Meas. Tech., 13, 2335–2361,
https://doi.org/10.5194/amt-13-2335-2020, 2020.
Gao, B.-C. and Kaufman, Y. J.: Water vapor retrievals
using Moderate Resolution Imaging Spectroradiometer (MODIS)
near-infrared channels, J. Geophys. Res.-Atmos., 108,
4389, https://doi.org/10.1029/2002JD003023, 2003.
Golaz, J.-C., Larson, V. E., and Cotton, W. R.: A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description, J. Atmos. Sci., 59, 3540–3551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2, 2002.
Green, R. O. and Thompson, D. R.: An Earth Science
Imaging Spectroscopy Mission: The Earth Surface Mineral Dust Source
Investigation (EMIT), in: IGARSS 2020–2020 IEEE International
Geoscience and Remote Sensing Symposium, IEEE, Waikoloa, Hawaii, USA, 26 September–2 October 2020, pp. 6262–6265,
https://doi.org/10.1109/IGARSS39084.2020.9323741,
2020.
Grossi, M., Valks, P., Loyola, D., Aberle, B., Slijkhuis, S., Wagner, T., Beirle, S., and Lang, R.: Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B, Atmos. Meas. Tech., 8, 1111–1133, https://doi.org/10.5194/amt-8-1111-2015, 2015.
Guanter, L., Gómez-Chova, L., and Moreno, J.: Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land, Remote Sens. Environ., 112, 2898–2913, https://doi.org/10.1016/j.rse.2008.02.001, 2008.
Kobayashi, S. and Sanga-Ngoie, K.: The integrated radiometric correction of optical remote sensing imageries, Int. J. Remote Sens., 29, 5957–5985, https://doi.org/10.1080/01431160701881889, 2008.
Krutz, D., Müller, R., Knodt, U., Günther, B.,
Walter, I., Sebastian, I., Säuberlich, T., Reulke, R., Carmona,
E., Eckardt, A., Venus, H., Fischer, C., Zender, B., Arloth, S.,
Lieder, M., Neidhardt, M., Grote, U., Schrandt, F., Gelmi, S., and
Wojtkowiak, A.: The Instrument Design of the DLR Earth
Sensing Imaging Spectrometer (DESIS), Sensors, 19, 1622, https://doi.org/10.3390/s19071622, 2019.
Kurowski, M. J., Grabowski, W. W., Suselj, K., and Teixeira, J.: The Strong Impact of Weak Horizontal Convergence on Continental Shallow Convection, J. Atmos. Sci., 77, 3119–3137, https://doi.org/10.1175/JAS-D-19-0351.1, 2020.
Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.: Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl. Opt., 48, 6716, https://doi.org/10.1364/AO.48.006716, 2009.
Larson, V. E., Golaz, J.-C., and Cotton, W. R.: Small-Scale and Mesoscale Variability in Cloudy Boundary Layers: Joint Probability Density Functions, J. Atmos. Sci., 59, 3519–3539, https://doi.org/10.1175/1520-0469(2002)059<3519:SSAMVI>2.0.CO;2, 2002.
Laszlo, I., Stamnes, K., Wiscombe, W. J., and Tsay, S.-C.: The Discrete Ordinate Algorithm, DISORT for Radiative Transfer, in Light Scattering Reviews, vol. 11, pp. 3–65, Springer, Berlin, Heidelberg, 2016.
Lechevallier, L., Vasilchenko, S., Grilli, R., Mondelain,
D., Romanini, D., and Campargue, A.: The water vapour self-continuum
absorption in the infrared atmospheric windows: new laser
measurements near 3.3 and 2.0 µm,
Atmos. Meas. Tech., 11, 2159–2171, https://doi.org/10.5194/amt-11-2159-2018, 2018.
Lee, C. M., Cable, M. L., Hook, S. J., Green, R. O., Ustin, S. L., Mandl, D. J., and Middleton, E. M.: An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities, Remote Sens. Environ., 167, 6–19, https://doi.org/10.1016/j.rse.2015.06.012, 2015.
Lindstrot, R., Preusker, R., Diedrich, H., Doppler, L., Bennartz, R., and Fischer, J.: 1D-Var retrieval of daytime total columnar water vapour from MERIS measurements, Atmos. Meas. Tech., 5, 631–646, https://doi.org/10.5194/amt-5-631-2012, 2012.
Massie, S. T., Cronk, H., Merrelli, A., O'Dell, C., Schmidt, K. S., Chen, H., and Baker, D.: Analysis of 3D cloud effects in OCO-2 XCO2 retrievals, Atmos. Meas. Tech., 14, 1475–1499, https://doi.org/10.5194/amt-14-1475-2021, 2021.
Matheou, G. and Chung, D.: Large-Eddy Simulation of Stratified Turbulence. Part II: Application of the Stretched-Vortex Model to the Atmospheric Boundary Layer, J. Atmos. Sci., 71, 4439–4460, https://doi.org/10.1175/JAS-D-13-0306.1, 2014.
Menang, K. P., Gbode, I. E., and Adeyeri, O. E.: The effect of the differences in near-infrared water vapour continuum models on the absorption of solar radiation, Meteorol. Atmos. Phys., https://doi.org/10.1007/s00703-021-00781-6, 2021.
Millán, L., Lebsock, M., Fishbein, E., Kalmus, P., and Teixeira, J.: Quantifying Marine Boundary Layer Water Vapor beneath Low Clouds with Near-Infrared and Microwave Imagery, J. Appl. Meteorol. Clim., 55, 213–225, https://doi.org/10.1175/JAMC-D-15-0143.1, 2016.
National Academies of Science, Engineering, and Medicine: Thriving on Our Changing Planet, National Academies Press, Washington, DC, 2018.
Nelson, R. R., Crisp, D., Ott, L. E., and O'Dell, C. W.: High-accuracy measurements of total column water vapor from the Orbiting Carbon Observatory-2, Geophys. Res. Lett., 43, 12261–12269, https://doi.org/10.1002/2016GL071200, 2016.
Noël, S., Buchwitz, M., Bovensmann, H., Hoogen, R., and Burrows, J. P.: Atmospheric water vapor amounts retrieved from GOME satellite data, Geophys. Res. Lett., 26, 1841–1844, https://doi.org/10.1029/1999GL900437, 1999.
Noël, S., Buchwitz, M., and Burrows, J. P.: First retrieval of global water vapour column amounts from SCIAMACHY measurements, Atmos. Chem. Phys., 4, 111–125, https://doi.org/10.5194/acp-4-111-2004, 2004.
Obregón, M. Á., Rodrigues, G., Costa, M. J., Potes, M., and Silva, A. M.: Validation of ESA Sentinel-2 L2 A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018, Remote Sens., 11, 1649, https://doi.org/10.3390/rs11141649, 2019.
O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher, B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A., Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E., Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M., Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer, P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl, C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco, V. A.: Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas. Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, 2018.
Painter, T. H., Molotch, N. P., Cassidy, M., Flanner, M., and Steffen, K.: Contact spectroscopy for determination of stratigraphy of snow optical grain size, J. Glaciol., 53, 121–127, https://doi.org/10.3189/172756507781833947, 2007.
Payne, V. H., Drouin, B. J., Oyafuso, F., Kuai, L., Fisher, B. M., Sung, K., Nemchick, D., Crawford, T. J., Smyth, M., Crisp, D., Adkins, E., Hodges, J. T., Long, D. A., Mlawer, E. J., Merrelli, A., Lunny, E., and O'Dell, C. W.: Absorption coefficient (ABSCO) tables for the Orbiting Carbon Observatories: Version 5.1, J. Quant. Spectrosc. Ra., 255, 107217, https://doi.org/10.1016/j.jqsrt.2020.107217, 2020.
Pérez-Ramírez, D., Whiteman, D. N., Smirnov, A., Lyamani, H., Holben, B. N., Pinker, R., Andrade, M., and Alados-Arboledas, L.: Evaluation of AERONET precipitable water vapor versus microwave radiometry, GPS, and radiosondes at ARM sites, J. Geophys. Res.-Atmos., 119, 9596–9613, https://doi.org/10.1002/2014JD021730, 2014.
Preusker, R., Carbajal Henken, C., and Fischer, J.: Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces, Remote Sens., 13, 932, https://doi.org/10.3390/rs13050932, 2021.
Prusa, J. M., Smolarkiewicz, P. K., and Wyszogrodzki, A. A.: EULAG, a computational model for multiscale flows, Comput. Fluids, 37, 1193–1207, https://doi.org/10.1016/j.compfluid.2007.12.001, 2008.
Rast, M., Ananasso, C., Bach, H., Ben-Dor, E.,
Chabrillat, S., Colombo, R., Del Bello, U., Feret, J., Giardino, C.,
Green, R., Guanter, L., Marsh, S., Nieke, J., Ong, C. C. H., Rum, G., Schaepman, M., Schlerf, M., Skidmore, A., and Strobl, P.: Copernicus hyperspectral imaging mission for the environment: Mission requirements document, v. 2.1, ESA/ESTEC, Noordwijk, the Netherlands, 2019.
Rothman, L. S., Gordon, I. E., Barbe, A., Benner, D. C., Bernath, P. F., Birk, M., Boudon, V., Brown, L. R., Campargue, A., Champion, J.-P., Chance, K., Coudert, L. H., Dana, V., Devi, V. M., Fally, S., Flaud, J.-M., Gamache, R. R., Goldman, A., Jacquemart, D., Kleiner, I., Lacome, N., Lafferty, W. J., Mandin, J.-Y., Massie, S. T., Mikhailenko, S. N., Miller, C. E., Moazzen-Ahmadi, N., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V. I., Perrin, A., Predoi-Cross, A., Rinsland, C. P., Rotger, M., Šimečková, M., Smith, M. A. H., Sung, K., Tashkun, S. A., Tennyson, J., Toth, R. A., Vandaele, A. C., and Vander Auwera, J.: The HITRAN 2008 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 110, 533–572, https://doi.org/10.1016/j.jqsrt.2009.02.013, 2009.
Roy, R. J., Lebsock, M., Millán, L., Dengler, R., Rodriguez Monje, R., Siles, J. V., and Cooper, K. B.: Boundary-layer water vapor profiling using differential absorption radar, Atmos. Meas. Tech., 11, 6511–6523, https://doi.org/10.5194/amt-11-6511-2018, 2018.
Roy, R. J., Lebsock, M., Millán, L., and Cooper, K. B.: Validation of a G-Band Differential Absorption Cloud Radar for Humidity Remote Sensing, J. Atmos. Ocean. Techn., 37, 1085–1102, https://doi.org/10.1175/JTECH-D-19-0122.1, 2020.
Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., and Martonchik, J. V.: Reflectance quantities in optical remote sensing–definitions and case studies, Remote Sens. Environ., 103, 27–42, https://doi.org/10.1016/j.rse.2006.03.002, 2006.
Schneider, A., Borsdorff, T., aan de Brugh, J., Aemisegger, F., Feist, D. G., Kivi, R., Hase, F., Schneider, M., and Landgraf, J.: First data set of H2O/HDO columns from the Tropospheric Monitoring Instrument (TROPOMI), Atmos. Meas. Tech., 13, 85–100, https://doi.org/10.5194/amt-13-85-2020, 2020.
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J., Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H., Sanchez, E., Stevens, B., and Stevens, D. E.: A Large Eddy Simulation Intercomparison Study of Shallow Cumulus Convection, J. Atmos. Sci., 60, 1201–1219, https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
Sissenwine, N., Dubin, M., and Teweles, S.: US Standard
Atmosphere, National Oceanographic and Atmospheric Administration, Washington, DC, 1976.
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Opt., 27, 2502, https://doi.org/10.1364/AO.27.002502, 1988.
Stirling, A. J. and Petch, J. C.: The impacts of spatial variability on the development of convection, Q. J. Roy. Meteor. Soc., 130, 3189–3206, https://doi.org/10.1256/qj.03.137, 2004.
Suselj, K., Kurowski, M. J., and Teixeira, J.: A Unified Eddy-Diffusivity/Mass-Flux Approach for Modeling Atmospheric Convection, J. Atmos. Sci., 76, 2505–2537, https://doi.org/10.1175/JAS-D-18-0239.1, 2019.
Szczodrak, M., Austin, P. H., and Krummel, P. B.: Variability of Optical Depth and Effective Radius in Marine Stratocumulus Clouds, J. Atmos. Sci., 58, 2912–2926, https://doi.org/10.1175/1520-0469(2001)058<2912:VOODAE>2.0.CO;2, 2001.
Teillet, P. M., Guindon, B., and Goodenough, D. G.: On the Slope-Aspect Correction of Multispectral Scanner Data, Can. J. Remote Sens., 8, 84–106, https://doi.org/10.1080/07038992.1982.10855028, 1982.
Thompson, D. R., Natraj, V., Green, R. O., Helmlinger, M. C., Gao, B.-C., and Eastwood, M. L.: Optimal estimation for imaging spectrometer atmospheric correction, Remote Sens. Environ., 216, 355–373, https://doi.org/10.1016/j.rse.2018.07.003, 2018.
Thompson, D. R., Cawse-Nicholson, K., Erickson, Z., Fichot, C. G., Frankenberg, C., Gao, B.-C., Gierach, M. M., Green, R. O., Jensen, D., Natraj, V., and Thompson, A.: A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy, Remote Sens. Environ., 231, 111198, https://doi.org/10.1016/j.rse.2019.05.017, 2019.
Thompson, D. R., Braverman, A., Brodrick, P. G., Candela, A., Carmon, N., Clark, R. N., Connelly, D., Green, R. O., Kokaly, R. F., Li, L., Mahowald, N., Miller, R. L., Okin, G. S., Painter, T. H., Swayze, G. A., Turmon, M., Susilouto, J., and Wettergreen, D. S.: Quantifying uncertainty for remote spectroscopy of surface composition, Remote Sens. Environ., 247, 111898, https://doi.org/10.1016/j.rse.2020.111898, 2020.
Thompson, D. R., Kahn, B. H., Brodrick, P. G., Lebsock, M. D., Richardson, M., and Green, R. O.: Spectroscopic imaging of sub-kilometer spatial structure in lower-tropospheric water vapor, Atmos. Meas. Tech., 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021, 2021.
Trent, T., Boesch, H., Somkuti, P., and Scott, N.: Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared, Remote Sens., 10, 1469, https://doi.org/10.3390/rs10091469, 2018.
vanZanten, M. C., Stevens, B., Nuijens, L., Siebesma,
A. P., Ackerman, A. S., Burnet, F., Cheng, A., Couvreux, F., Jiang,
H., Khairoutdinov, M., Kogan, Y., Lewellen, D. C., Mechem, D.,
Nakamura, K., Noda, A., Shipway, B. J., Slawinska, J., Wang, S., and
Wyszogrodzki, A.: Controls on precipitation and cloudiness in
simulations of trade-wind cumulus as observed during RICO,
J. Adv. Model. Earth Sy., 3, M06001, https://doi.org/10.1029/2011MS000056, 2011.
Vermote, E. F., El Saleous, N., Justice, C. O., Kaufman, Y. J., Privette, J. L., Remer, L., Roger, J. C., and Tanré, D.: Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation, J. Geophys. Res.-Atmos., 102, 17131–17141, https://doi.org/10.1029/97JD00201, 1997.
von Engeln, A. and Teixeira, J.: A Planetary Boundary Layer Height Climatology Derived from ECMWF Reanalysis Data, J. Climate, 26, 6575–6590, https://doi.org/10.1175/JCLI-D-12-00385.1, 2013.
Wulfmeyer, V., Bauer, H.-S., Grzeschik, M., Behrendt, A., Vandenberghe, F., Browell, E. V., Ismail, S., and Ferrare, R. A.: Four-Dimensional Variational Assimilation of Water Vapor Differential Absorption Lidar Data: The First Case Study within IHOP_2002, Mon. Weather Rev., 134, 209–230, https://doi.org/10.1175/MWR3070.1, 2006.
Short summary
Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as 20 m. They can retrieve column water vapour, and we show evidence that from these column measurements you can get statistics of planetary boundary layer (PBL) water vapour. This is important information for climate models that need to account for sub-grid mixing of water vapour near the surface in their PBL schemes.
Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as...
