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Filtered by keyword:app: all-sky remote sensing

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  1. Adams, Ian Stuart and Justin Bobak (2018), The Feasibility of Detecting Supercooled Liquid With a Forward-Looking Radiometer, IEEE J. Sel. Top. Appl. Rem. Sens., 11(6, SI), 1932–1938, Publisher: Institute of Elect & Electron Engineers Geoscience & Remote Sensing Soc; IEEE; IEEE GRSS, doi:10.1109/JSTARS.2018.2844684.
  2. Adams, Ian S., S. Joseph Munchak, Kwo-Sen Kuo, Craig Pelissier, Thomas Clune, Rachael Kroodsma, Adrian Loftus, and Xioawen Li (2019), Active and passive radiative transfer simulations for GMP-related field campaigns, In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), pp. 4553–4556, Inst Elect & Elect Engineers; Inst Elect & Elect Engineers, Geoscience & Remote Sensing Soc, ISSN: 2153-6996.
  3. Barlakas, Vasileios and Patrick Eriksson (2020), Three Dimensional Radiative Effects in Passive Millimeter/Sub-Millimeter All-sky Observations, Rem. Sens., 12(3), doi:10.3390/rs12030531.
  4. Casella, Daniele, Giulia Panegrossi, Paolo Sano, Bengt Rydberg, Vinia Mattioli, Christophe Accadia, Mario Papa, Frank S. Marzano, and Mario Montopoli (2022), Can We Use Atmospheric Targets for Geolocating Spaceborne Millimeter-Wave Ice Cloud Imager (ICI) Acquisitions?, IEEE T. Geosci. Remote, 60, doi:10.1109/TGRS.2022.3145638.
  5. Coy, James J., Adam Bell, Ping Yang, and Dong L. Wu (2020), Sensitivity Analyses for the Retrievals of Ice Cloud Properties From Radiometric and Polarimetric Measurements in Sub-mm/mm and Infrared Bands, J. Geophys. Res.: Atm., 125(13), doi:10.1029/2019JD031422.
  6. Cutraro, Federico, Victoria Sol Galligani, and Yanina Garcia Skabar (2021), Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES-13/16 observations, Q. J. R. Meteorol. Soc., 147(738), 2988–3003, doi:10.1002/qj.4111.
  7. Dong, Pingyi, Lei Liu, Shulei Li, Shuai Hu, and Lingbing Bu (2021), Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region, Rem. Sens., 13(13), doi:10.3390/rs13132569.
  8. Duncan, David Ian, Patrick Eriksson, Simon Pfreundschuh, Christian Klepp, and Daniel C. Jones (2019), On the distinctiveness of observed oceanic raindrop distributions, Atmos. Chem. Phys., 19(10), 6969–6984, doi:10.5194/acp-19-6969-2019.
  9. Ekelund, Robin, Patrick Eriksson, and Michael Kahnert (2020), Microwave single-scattering properties of non-spheroidal raindrops, Atmos. Meas. Tech., 13(12), 6933–6944, doi:10.5194/amt-13-6933-2020.
  10. Ekelund, Robin, Patrick Eriksson, and Simon Pfreundschuh (2020), Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models, Atmos. Meas. Tech., 13(2), 501–520, doi:10.5194/amt-13-501-2020.
  11. Fox, S. (2020), An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations, Rem. Sens., 12(17), doi:10.3390/rs12172758.
  12. Galligani, Victoria Sol, Die Wang, Paola Belen Corrales, and Catherine Prigent (2021), A Parameterization of the Cloud Scattering Polarization Signal Derived From GPM Observations for Microwave Fast Radative Transfer Models, IEEE T. Geosci. Remote, 59(11), 8968–8977, doi:10.1109/TGRS.2021.3049921.
  13. Geer, Alan J., Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon (2021), Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev., 14(12), 7497–7526, doi:10.5194/gmd-14-7497-2021.
  14. Gong, Jie, Dong L. Wu, and Patrick Eriksson (2021), The first global 883 GHz cloud ice survey: IceCube Level 1 data calibration, processing and analysis, Earth Syst. Sci. Data, 13(11), 5369–5387, doi:10.5194/essd-13-5369-2021.
  15. He, Qiu-rui, Rui-ling Zhang, Jiao-yang Li, and Zhen-zhan Wang (2022), Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm, J. Trop. Meteorol., 28(3), 326–342, doi:10.46267/j.1006-8775.2022.025.
  16. Jones, Alexandra L. and Larry Di Girolamo (2018), Design and Verification of a New Monochromatic Thermal Emission Component for the I3RC Community Monte Carlo Model, J. Atmos. Sci., 75(3), 885–906, doi:10.1175/JAS-D-17-0251.1.
  17. Kaur, Inderpreet, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan (2021), Can machine learning correct microwave humidity radiances for the influence of clouds?, Atmos. Meas. Tech., 14(4), 2957–2979, doi:10.5194/amt-14-2957-2021.
  18. Kaur, Inderpreet, Patrick Eriksson, Vasileios Barlakas, Simon Pfreundschuh, and Stuart Fox (2022), Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals, Rem. Sens., 14(7), doi:10.3390/rs14071594.
  19. Leppert, II, Kenneth D. and Daniel J. Cecil (2019), Sensitivity of Simulated GMI Brightness Temperatures to Variations in Particle Size Distributions in a Severe Hailstorm, J. Appl. Meteorol. Clim., 58(9), 1905–1930, doi:10.1175/JAMC-D-19-0031.1.
  20. Li, Hai-Ying, Zhen-Sen Wu, Jia-Ji Wu, Le-Ke Lin, Chang-Sheng Lu, Zhen-Wei Zhao, and Tan Qu (2020), THz wave background radiation at upper troposphere, Multimed. Tools and Appl., 79(13-14), 8767–8780, doi:10.1007/s11042-018-6803-x.
  21. Li, S., L. Liu, H. Letu, S. Hu, P. Dong, H. Ren, and J. Ye (2023), Evaluation of the impacts of ice cloud vertical inhomogeneity on spaceborne passive submillimeter-wave simulations, Q. J. R. Meteorol. Soc., 149(752), 1073–1089, doi:10.1002/qj.4457.
  22. Liu, Yuli and Gerald G. Mace (2020), Synthesizing the Vertical Structure of Tropical Cirrus by Combining CloudSat Radar Reflectivity With In Situ Microphysical Measurements Using Bayesian Monte Carlo Integration, J. Geophys. Res.: Atm., 125(18), doi:10.1029/2019JD031882.
  23. Liu, L., C. Weng, S. Li, L. Husi, S. Hu, and P. Dong (2021), Technical Note: Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm, Rem. Sens., 13(4), 1–13, doi:10.3390/rs13040735.
  24. Liu, Yuli and Gerald G. Mace (2022), Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms, Atmos. Meas. Tech., 15(4), 927–944, doi:10.5194/amt-15-927-2022.
  25. Liu, Yuli, Gerald G. Mace, and Derek J. Posselt (2022), Assessing Synergistic Radar and Radiometer Retrievals of Ice Cloud Microphysics for the Atmosphere Observing System (AOS) Architecture, IEEE T. Geosci. Remote, 60, doi:10.1109/TGRS.2022.3165578.
  26. McCusker, K., C. D. Westbrook, and A. Moiola (2019), Analysis of the internal electric fields of pristine ice crystals and aggregate snowflakes, and their effect on scattering, J. Quant. Spectrosc. Radiat. Transfer, 230, 155–171, doi:10.1016/j.jqsrt.2019.04.019.
  27. Mech, Mario, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell (2020), PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13(9), 4229–4251, doi:10.5194/gmd-13-4229-2020.
  28. Peers, Fanny, Peter Francis, Steven J. Abel, Paul A. Barrett, Keith N. Bower, Michael Cotterell, I, Ian Crawford, Nicholas W. Davies, Cathryn Fox, Stuart Fox, Justin M. Langridge, Kerry G. Meyer, Steven E. Platnick, Kate Szpek, and Jim M. Haywood (2021), Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI - Part 2: Comparison with MODIS and aircraft measurements from the CLARIFY-2017 field campaign, Atmos. Chem. Phys., 21(4), 3235–3254, doi:10.5194/acp-21-3235-2021.
  29. Pfreundschuh, S., P. Eriksson, D. Duncan, B. Rydberg, N. Hakansson, and A. Thoss (2018), A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems, Atmos. Meas. Tech., 11(8), 4627–4643, doi:10.5194/amt-11-4627-2018.
  30. Ren, T., P. Yang, K. Garrett, Y. Ma, J. Ding, and J. Coy (2023), A Microphysics-Scheme-Consistent Snow Optical Parameterization for the Community Radiative Transfer Model, Mon. Weather Rev., 151(2), 383–402, doi:arts_2018_2023.
  31. Shu-Lei, Li, Liu Lei, Gao Tai-Chang, Shi Li-Huai, Qiu Shi, and Hu Shuai (2018), Radiation characteristics of the selected channels for cirrus remote sensing in terahertz waveband and the influence factors for the retrieval method, JIMW, 37(1), 60–71, doi:10.11972/j.issn.1001-9014.2018.01.012.
  32. Stegmann, Patrick G., Guanglin Tang, Ping Yang, and Benjamin T. Johnson (2018), A stochastic model for density-dependent microwave Snow- and Graupel scattering coefficients of the NOAA JCSDA community radiative transfer model, J. Quant. Spectrosc. Radiat. Transfer, 211, 9–24, doi:10.1016/j.jqsrt.2018.02.026.
  33. Weng, Chensi, Lei Liu, Taichang Gao, Shuai Hu, Shulei Li, Fangli Dou, and Jian Shang (2019), Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band, Atmos., 10(8), doi:10.3390/atmos10080437.
  34. Yang, Jun, Shouguo Ding, Peiming Dong, Lei Bi, and Bingqi Yi (2020), Advanced radiative transfer modeling system developed for satellite data assimilation and remote sensing applications, J. Quant. Spectrosc. Radiat. Transfer, 251, doi:10.1016/j.jqsrt.2020.107043.