Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6443-2021
© 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-14-6443-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2021
Research article |
| 07 Oct 2021
| 07 Oct 2021
Spaceborne differential absorption radar water vapor retrieval capabilities in tropical and subtropical boundary layer cloud regimes
Richard J. Roy
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Matthew Lebsock
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Marcin J. Kurowski
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
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Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
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Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
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This paper describes the feasibility of using a differential absorption radar technique for the remote sensing of total column water vapor from a spaceborne platform.
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This study explores the potential of a hypothetical spaceborne radar to observe water vapor within clouds.
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
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Kuo-Nung Wang, Chi O. Ao, Mary G. Morris, George A. Hajj, Marcin J. Kurowski, Francis J. Turk, and Angelyn W. Moore
Atmos. Meas. Tech., 17, 583–599, https://doi.org/10.5194/amt-17-583-2024, https://doi.org/10.5194/amt-17-583-2024, 2024
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Luis F. Millán, Matthew D. Lebsock, Ken B. Cooper, Jose V. Siles, Robert Dengler, Raquel Rodriguez Monje, Amin Nehrir, Rory A. Barton-Grimley, James E. Collins, Claire E. Robinson, Kenneth L. Thornhill, and Holger Vömel
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Matthew D. Lebsock and Mikael Witte
Atmos. Chem. Phys., 23, 14293–14305, https://doi.org/10.5194/acp-23-14293-2023, https://doi.org/10.5194/acp-23-14293-2023, 2023
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This paper evaluates measurements of cloud drop size distributions made from airplanes. We find that as the number of cloud drops increases the distribution of the cloud drop sizes narrows. The data are used to develop a simple equation that relates the drop number to the width of the drop sizes. We then use this equation to demonstrate that existing approaches to observe the drop number from satellites contain errors that can be corrected by including the new relationship.
Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
Atmos. Meas. Tech., 16, 3531–3546, https://doi.org/10.5194/amt-16-3531-2023, https://doi.org/10.5194/amt-16-3531-2023, 2023
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In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
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Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 15, 117–129, https://doi.org/10.5194/amt-15-117-2022, https://doi.org/10.5194/amt-15-117-2022, 2022
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Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field in a way that
smears2-D maps of imaging spectroscopy vapour retrievals. In simulations we show how this smearing is
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.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 14, 5555–5576, https://doi.org/10.5194/amt-14-5555-2021, https://doi.org/10.5194/amt-14-5555-2021, 2021
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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.
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
Short summary
Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
David R. Thompson, Brian H. Kahn, Philip G. Brodrick, Matthew D. Lebsock, Mark Richardson, and Robert O. Green
Atmos. Meas. Tech., 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021, https://doi.org/10.5194/amt-14-2827-2021, 2021
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Concentrations of water vapor in the atmosphere vary dramatically over space and time. Mapping this variability can provide insights into atmospheric processes that help us understand atmospheric processes in the Earth system. Here we use a new measurement strategy based on imaging spectroscopy to map atmospheric water vapor concentrations at very small spatial scales. Experiments demonstrate the accuracy of this technique and some initial results from an airborne remote sensing experiment.
Luis Millán, Richard Roy, and Matthew Lebsock
Atmos. Meas. Tech., 13, 5193–5205, https://doi.org/10.5194/amt-13-5193-2020, https://doi.org/10.5194/amt-13-5193-2020, 2020
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This paper describes the feasibility of using a differential absorption radar technique for the remote sensing of total column water vapor from a spaceborne platform.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961, https://doi.org/10.5194/amt-13-4947-2020, https://doi.org/10.5194/amt-13-4947-2020, 2020
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We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
Cited articles
Abel, S. J. and Boutle, I. A.: An improved representation of the raindrop size
distribution for single-moment microphysics schemes, Q. J.
Roy. Meteor. Soc., 138, 2151–2162, https://doi.org/10.1002/qj.1949, 2012. a
Barrick, D.: Rough Surface Scattering Based on the Specular Point Theory,
IEEE T. Antenn. Propag., 16, 449–454,
https://doi.org/10.1109/TAP.1968.1139220, 1968. a
Basten, M. A., Tucek, J. C., Gallagher, D. A., and Kreischer, K. E.:
233 GHz high Power amplifier development at Northrop Grumman, in: 2016 IEEE
International Vacuum Electronics Conference (IVEC), 19–21 April 2016, Monterey CA, USA, pp. 1–2,
https://doi.org/10.1109/IVEC.2016.7561775, 2016. a
Battaglia, A. and Kollias, P.: Evaluation of differential absorption radars in the 183 GHz band for profiling water vapour in ice clouds, Atmos. Meas. Tech., 12, 3335–3349, https://doi.org/10.5194/amt-12-3335-2019, 2019. a, b, c, d
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and
Furukawa, K.: Spaceborne Cloud and Precipitation Radars: Status, Challenges,
and Ways Forward, Rev. Geophys., 58, e2019RG000686,
https://doi.org/10.1029/2019RG000686, 2020. a
Berner, A. H., Bretherton, C. S., and Wood, R.: Large-eddy simulation of mesoscale dynamics and entrainment around a pocket of open cells observed in VOCALS-REx RF06, Atmos. Chem. Phys., 11, 10525–10540, https://doi.org/10.5194/acp-11-10525-2011, 2011. a
Bohren, C. and Huffman, D.: Absorption and Scattering of Light by Small
Particles, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany,
https://doi.org/10.1002/9783527618156, 2004. a
Cooper, K. B., Roy, R. J., Dengler, R., Monje, R. R., Alonso-delPino,
M., Siles, J. V., Yurduseven, O., Parashare, C., Millán, L., and
Lebsock, M.: G-Band Radar for Humidity and Cloud Remote Sensing, IEEE
T. Geosci. Remote, 59, 1106–1117,
https://doi.org/10.1109/TGRS.2020.2995325, 2020. a, b
Cox, C. and Munk, W.: Measurement of the Roughness of the Sea Surface from
Photographs of the Sun's Glitter, J. Opt. Soc. Am., 44, 838–850,
https://doi.org/10.1364/JOSA.44.000838, 1954. a
Cox, G. P.: Modelling precipitation in frontal rainbands, Q. J.
Roy. Meteor. Soc., 114, 115–127,
https://doi.org/10.1002/qj.49711447906, 1988. a
Field, P. R., Heymsfield, A. J., Detwiler, A. G., and Wilkinson, J. M.:
Normalized Hail Particle Size Distributions from the T-28 Storm-Penetrating
Aircraft, J. Appl. Meteorol. Clim., 58, 231–245,
https://doi.org/10.1175/JAMC-D-18-0118.1, 2019. a
Gelaro, R., McCarty, W., Su?rez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gentine, P., Garelli, A., Park, S.-B., Nie, J., Torri, G., and Kuang, Z.: Role
of surface heat fluxes underneath cold pools, Geophys. Res. Lett.,
43, 874–883, https://doi.org/10.1002/2015GL067262, 2016. a
Hogan, R. J. and Battaglia, A.: Fast Lidar and Radar Multiple-Scattering
Models. Part II: Wide-Angle Scattering Using the Time-Dependent Two-Stream
Approximation, J. Atmos. Sci., 65, 3636–3651,
https://doi.org/10.1175/2008JAS2643.1, 2008. a
Hogan, R. J., Gaussiat, N., and Illingworth, A. J.: Stratocumulus Liquid Water
Content from Dual-Wavelength Radar, J. Atmos. Ocean.
Tech., 22, 1207–1218, https://doi.org/10.1175/JTECH1768.1, 2005. a
Khairoutdinov, M. F. and Randall, D. A.: Cloud Resolving Modeling of the ARM
Summer 1997 IOP: Model Formulation, Results, Uncertainties, and
Sensitivities, J. Atmos. Sci., 60, 607–625,
https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2, 2003. a, b
Kodis, R.: A note on the theory of scattering from an irregular surface, IEEE
T. Antenn. Propag., 14, 77–82,
https://doi.org/10.1109/TAP.1966.1138626, 1966. a
Lamer, K., Oue, M., Battaglia, A., Roy, R. J., Cooper, K. B., Dhillon, R., and Kollias, P.: Multifrequency radar observations of clouds and precipitation including the G-band, Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, 2021. a
Lebsock, M.: Various LES outputs, Zenodo [data set], https://doi.org/10.5281/zenodo.5544938, 2021. a
Lebsock, M. D., Suzuki, K., Millán, L. F., and Kalmus, P. M.: The feasibility of water vapor sounding of the cloudy boundary layer using a differential absorption radar technique, Atmos. Meas. Tech., 8, 3631–3645, https://doi.org/10.5194/amt-8-3631-2015, 2015. a, b
Leinonen, J.: High-level interface to T-matrix scattering calculations:
architecture, capabilities and limitations, Opt. Express, 22, 1655–1660,
https://doi.org/10.1364/OE.22.001655, 2014. a
Leinonen, J. and Szyrmer, W.: Radar signatures of snowflake riming: A modeling
study, Earth and Space Science, 2, 346–358,
https://doi.org/10.1002/2015EA000102, 2015. a, b
Liebe, H. J., Hufford, G. A., and Manabe, T.: A model for the complex
permittivity of water at frequencies below 1 THz, Int. J.
Infrared Milli., 12, 659–675, https://doi.org/10.1007/BF01008897, 1991. a
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk Parameterization of the
Snow Field in a Cloud Model, J. Appl. Meteorol. Clim.,
22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2,
1983. a
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. a, b
Meissner, T. and Wentz, F. J.: The complex dielectric constant of pure and
sea water from microwave satellite observations, IEEE T.
Geosci. Remote, 42, 1836–1849,
https://doi.org/10.1109/TGRS.2004.831888, 2004. a
Meyers, M. P., Walko, R. L., Harrington, J. Y., and Cotton, W. R.: New RAMS
cloud microphysics parameterization. Part II: The two-moment scheme,
Atmos. Res., 45, 3–39,
https://doi.org/10.1016/S0169-8095(97)00018-5, 1997. a
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud Droplet Size
Distributions in Low-Level Stratiform Clouds, J. Atmos.
Sci., 57, 295–311,
https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000. a, b
Millán, L., Lebsock, M., Livesey, N., and Tanelli, S.: Differential absorption radar techniques: water vapor retrievals, Atmos. Meas. Tech., 9, 2633–2646, https://doi.org/10.5194/amt-9-2633-2016, 2016. a, b
Millán, L., Roy, R., and Lebsock, M.: Assessment of global total column water vapor sounding using a spaceborne differential absorption radar, Atmos. Meas. Tech., 13, 5193–5205, https://doi.org/10.5194/amt-13-5193-2020, 2020. a
Mishchenko, M. I. and Travis, L. D.: Capabilities and limitations of a current
FORTRAN implementation of the T-matrix method for randomly oriented,
rotationally symmetric scatterers, J. Quant. Spectrosc.
Ra., 60, 309–324,
https://doi.org/10.1016/S0022-4073(98)00008-9, 1998. a
Mishchenko, M. I., Travis, L. D., and Mackowski, D. W.: T-matrix computations
of light scattering by nonspherical particles: A review, J.
Quant. Spectrosc. Ra., 55, 535–575,
https://doi.org/10.1016/0022-4073(96)00002-7, 1996. a
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A New Double-Moment
Microphysics Parameterization for Application in Cloud and Climate Models.
Part I: Description, J. Atmos. Sci., 62, 1665–1677,
https://doi.org/10.1175/JAS3446.1, 2005. a, b
National Academies of Sciences, Engineering, and Medicine (NASEM): 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, 2018. a, b
National Telecommunications & Information Administration (NTIA): Manual of Regulations and Procedures for Federal Radio Frequency Management, September 2017 Revision of the September 2015 Edition, available at: https://www.ntia.doc.gov/page/2011/manual-regulations-and-procedures-federal-radio-frequency-management-redbook (last access: 28 September 2021), 2015. a
Nehrir, A. R., Kiemle, C., Lebsock, M. D., Kirchengast, G., Buehler, S. A.,
Löhnert, U., Liu, C.-L., Hargrave, P. C., Barrera-Verdejo, M., and
Winker, D. M.: Emerging Technologies and Synergies for Airborne and
Space-Based Measurements of Water Vapor Profiles, Surv. Geophys., 38,
1445–1482, https://doi.org/10.1007/s10712-017-9448-9, 2017. a
Oue, M., Tatarevic, A., Kollias, P., Wang, D., Yu, K., and Vogelmann, A. M.: The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory, Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, 2020. a
Peral, E., Tanelli, S., Statham, S., Joshi, S., Imken, T., Price, D., Sauder,
J., Chahat, N., and Williams, A.: RainCube: the first ever radar
measurements from a CubeSat in space, J. Appl. Remote Sens., 13,
1–13, https://doi.org/10.1117/1.JRS.13.032504, 2019. a
Ray, P. S.: Broadband Complex Refractive Indices of Ice and Water, Appl. Opt.,
11, 1836–1844, https://doi.org/10.1364/AO.11.001836, 1972. a
Read, W. G., Shippony, Z., and Snyder, W.: EOS MLS forward model algorithm
theoretical basis document, Jet Propulsion Laboratory, JPL
D-18130/CL#04-2238, Pasadena, CA, USA, 2004. a
Read, W. G., Shippony, Z., Schwartz, M. J., Livesey, N. J., and Van
Snyder, W.: The clear-sky unpolarized forward model for the EOS aura
microwave limb sounder (MLS), IEEE T. Geosci. Remote, 44, 1367–1379, https://doi.org/10.1109/TGRS.2006.873233, 2006. a
Reiter, C. A.: A local cellular model for snow crystal growth, Chaos Soliton.
Fract., 23, 1111–1119,
https://doi.org/10.1016/j.chaos.2004.06.071, 2005. a
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. a, b
Sahoo, S., Bosch-Lluis, X., Reising, S. C., and Vivekanandan, J.:
Radiometric Information Content for Water Vapor and Temperature Profiling in
Clear Skies Between 10 and 200 GHz, IEEE J. Sel. Top.
Appl., 8, 859–871,
https://doi.org/10.1109/JSTARS.2014.2364394, 2015. a
Schneider, E. K.: Axially Symmetric Steady-State Models of the Basic State for
Instability and Climate Studies. Part II. Nonlinear Calculations, J.
Atmos. Sci., 34, 280–296,
https://doi.org/10.1175/1520-0469(1977)034<0280:ASSSMO>2.0.CO;2, 1977. a
Schnitt, S., Löhnert, U., and Preusker, R.: Potential of Dual-Frequency Radar and Microwave Radiometer Synergy for Water Vapor Profiling in the Cloudy Trade Wind Environment, J. Atmos. Ocean. Tech., 37,
1973–1986, https://doi.org/10.1175/JTECH-D-19-0110.1, 2020. a
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. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda,
M. G., Huang, X. Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR, Tech. Rep.,
https://doi.org/10.5065/D68S4MVH, 2008. a
Stevens, B. and Lenschow, D. H.: Observations, Experiments, and Large Eddy
Simulation, B. Am. Meteorol. Soc., 82, 283–294,
https://doi.org/10.1175/1520-0477(2001)082<0283:OEALES>2.3.CO;2, 2001. a
Stevens, B., Moeng, C.-H., Ackerman, A. S., Bretherton, C. S., Chlond, A.,
de Roode, S., Edwards, J., Golaz, J.-C., Jiang, H., Khairoutdinov, M.,
Kirkpatrick, M. P., Lewellen, D. C., Lock, A., Müller, F., Stevens, D. E.,
Whelan, E., and Zhu, P.: Evaluation of Large-Eddy Simulations via
Observations of Nocturnal Marine Stratocumulus, Mon. Weather Rev., 133,
1443–1462, https://doi.org/10.1175/MWR2930.1, 2005. a
Stevens, B., Brogniez, H., Kiemle, C., Lacour, J., Crevoisier, C., and Kiliani,
J.: Structure and Dynamical Influence of Water Vapor in the Lower Tropical
Troposphere, Surv. Geophys., 38, 1371–1397,
https://doi.org/10.1007/s10712-017-9420-8, 2017.
a
Tanelli, S., Im, E., Durden, S. L., Facheris, L., and Giuli, D.: The Effects of
Nonuniform Beam Filling on Vertical Rainfall Velocity Measurements with a
Spaceborne Doppler Radar, J. Atmos. Ocean. Tech., 19,
1019–1034, https://doi.org/10.1175/1520-0426(2002)019<1019:TEONBF>2.0.CO;2, 2002. a, b
Torres, S. M.: Estimation of Doppler and polarimetric variables for weather
radars, PhD thesis, University of Oklahoma, Norman, OK, USA, available at:
https://cimms.ou.edu/%7Etorres/Documents/Sebastian%20Torres%20-%20Dissertation.pdf (last access: 15 January 2021),
2001. a
Valenzuela, G. R.: Theories for the Interaction of Electromagnetic and Oceanic
Waves – A Review, Bound.-Lay. Meteorol., 13, 61–85,
https://doi.org/10.1007/BF00913863, 1978. a
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. a
Walko, R., Cotton, W., Meyers, M., and Harrington, J.: New RAMS cloud
microphysics parameterization part I: the single-moment scheme, Atmos.
Res., 38, 29–62, https://doi.org/10.1016/0169-8095(94)00087-T,
1995. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008. a
Wood, N. B.: Estimation of snow microphysical properties with application to
millimeter-wavelength radar retrievals for snowfall rate, PhD thesis,
Colorado State University, Fort Collins, CO, USA, available at: http://hdl.handle.net/10217/48170 (last access: 3 February 2021),
2011. a
Wulfmeyer, V., Hardesty, R. M., Turner, D. D., Behrendt, A., Cadeddu, M. P.,
Di Girolamo, P., Schlüssel, P., Van Baelen, J., and Zus, F.: 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, 819–895,
https://doi.org/10.1002/2014RG000476, 2015. a
Yurkin, M. A. and Hoekstra, A. G.: The discrete-dipole-approximation code ADDA:
Capabilities and known limitations, J. Quant. Spectrosc.
Ra., 112, 2234–2247,
https://doi.org/10.1016/j.jqsrt.2011.01.031, 2011. a
Short summary
This study describes the potential capabilities of a hypothetical spaceborne radar to observe water vapor within clouds.
This study describes the potential capabilities of a hypothetical spaceborne radar to observe...
