Articles | Volume 16, issue 20
https://doi.org/10.5194/amt-16-4807-2023
© Author(s) 2023. 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-16-4807-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2023
Research article |
| 24 Oct 2023
| 24 Oct 2023
Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals
NASA Langley Research Center, Hampton, VA 23682, USA
NASA Langley Research Center, Hampton, VA 23682, USA
Liqiao Lei
Science Systems and Applications, Inc., Hampton, VA 23666, USA
Xiaozhen Xiong
NASA Langley Research Center, Hampton, VA 23682, USA
Qiguang Yang
Science Systems and Applications, Inc., Hampton, VA 23666, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Daniel K. Zhou
NASA Langley Research Center, Hampton, VA 23682, USA
Allen M. Larar
NASA Langley Research Center, Hampton, VA 23682, USA
Related authors
Xiaozhen Xiong, Xu Liu, Robert Spurr, Ming Zhao, Qiguang Yang, Wan Wu, and Liqiao Lei
Atmos. Meas. Tech., 17, 1965–1978, https://doi.org/10.5194/amt-17-1965-2024, https://doi.org/10.5194/amt-17-1965-2024, 2024
Short summary
Short summary
The term “hotspot” refers to the sharp increase in reflectance occurring when incident (solar) and reflected (viewing) directions coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications, but current models typically require large values of computations to represent the hotspot accurately. This paper provides a numerically improved hotspot BRDF model that converges much faster and is used in VLIDORT.
Xiaozhen Xiong, Xu Liu, Robert Spurr, Ming Zhao, Qiguang Yang, Wan Wu, and Liqiao Lei
Atmos. Meas. Tech., 17, 1965–1978, https://doi.org/10.5194/amt-17-1965-2024, https://doi.org/10.5194/amt-17-1965-2024, 2024
Short summary
Short summary
The term “hotspot” refers to the sharp increase in reflectance occurring when incident (solar) and reflected (viewing) directions coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications, but current models typically require large values of computations to represent the hotspot accurately. This paper provides a numerically improved hotspot BRDF model that converges much faster and is used in VLIDORT.
Nenghan Wan, Xiaozhen Xiong, Gerard J. Kluitenberg, J. M. Shawn Hutchinson, Robert Aiken, Haidong Zhao, and Xiaomao Lin
Atmos. Chem. Phys., 23, 711–724, https://doi.org/10.5194/acp-23-711-2023, https://doi.org/10.5194/acp-23-711-2023, 2023
Short summary
Short summary
This study used new TROPOMI measurements of NO2 and CO to characterize regional biomass burning characteristics and efficiency. We found that the NO2 / CO emission ratio was consistent with recent studies over temperate forest fires but slightly lower in savanna vegetation fires. Our results can help identify the relative contribution of smoldering and flaming activities as well as their impacts on the regional atmospheric composition and air quality.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
Short summary
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Cited articles
Ackerman, S., Frey, R., Strabala, K., Liu, Y., Gumley, L., Baum, B., and Menzel, P.: VIIRS/SNPP Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m, Version-1, NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [data set], Goddard Space Flight Center, USA, https://doi.org/10.5067/VIIRS/CLDMSK_L2_VIIRS_SNPP.001, 2019.
Aeris: The IASI O3 products processed with FORLI-O3 v20151001, Aeris [data set], https://iasi.aeris-data.fr/o3/, last access: 22 August 2022a.
Aeris: The IASI CO products processed with FORLI-CO v20151001, Aeris [data set], https://iasi.aeris-data.fr/co/, last access: 2 August 2022b.
ASDC: MOPITT version 8 CO product, ASDC [data set], https://asdc.larc.nasa.gov/data/MOPITT/MOP02J.008/2016.01.14/.MOPITT/ (last access: 14 August 2021), 2022.
August, T., Klaes, D., Schlüssel, P., Hultberg, T., Crapeau, M., Arriaga, A., O'Carroll, A., Coppens, D., Munro R., and Calbet, X.: IASI on Metop-A: Operational Level 2 retrievals after five years in orbit, J. Quant. Spectrosc. Ra., 113, 1340–1371, 2012.
Aumann, H. H., Chen, X., Fishbein, E., Geer, A., Havemann, S., Huang, X., Liu, X., Liuzzi, G., DeSouza-Machado, S., Manning, E. M., Masiello, G., Matricardi, M., Moradi, I., Natraj, V., Serio, C., Strow, L., Vidot, J., Chris Wilson, R., Wu, W., Yang, Q., and Yung, Y. L.: Evaluation of radiative transfer models with clouds, J. Geophys. Res.-Atmos., 123, 6142–6157, https://doi.org/10.1029/2017JD028063, 2018.
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER Spectral Library Version 2.0, Remote Sens. Environ., 113, 711–715, 2009.
Barnet, C.: Sounder SIPS: Suomi NPP CrIMSS Level 2 CLIMCAPS Full Spectral Resolution: Atmosphere cloud and surface geophysical state V2, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/62SPJFQW5Q9B, 2019.
Barnet, C. D., Divakarla, M., Gambacorta, A., Iturbide-Sanchez, F., Nalli, N. R., Pryor, K., Tan, C., Wang, T., Warner, J., Zhang, K., and Zhu, T.: NOAA Unique CrIS/ATMS Processing System (NUCAPS): Algorithm Theoretical Basis Documentation, NOAA NESDIS STAR, Version 3.1, https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_NUCAPS_v3.1.pdf (last access: 21 April 2021), 2021.
Buchholz, R. R., Emmons, L. K., Tilmes, S., and the CESM2 Development Team: CESM2.1/CAM-chem Instantaneous Output for Boundary Conditions, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory [data set], https://doi.org/10.5065/NMP7-EP60, 2019.
Boynard, A., Hurtmans, D., Garane, K., Goutail, F., Hadji-Lazaro, J., Koukouli, M. E., Wespes, C., Vigouroux, C., Keppens, A., Pommereau, J.-P., Pazmino, A., Balis, D., Loyola, D., Valks, P., Sussmann, R., Smale, D., Coheur, P.-F., and Clerbaux, C.: Validation of the IASI FORLI/EUMETSAT ozone products using satellite (GOME-2), ground-based (Brewer–Dobson, SAOZ, FTIR) and ozonesonde measurements, Atmos. Meas. Tech., 11, 5125–5152, https://doi.org/10.5194/amt-11-5125-2018, 2018.
Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C., Blaisdell, J., Chen, L., Divakarla, M., Fetzer, E. J., Goldberg, M., Gautier, C., Granger, S., Hannon, S., Irion, F. W., Kakar R., Kalnay, E., Lambrigsten, B. H., Lee, S. Marshall, J. L., Mcmillan, W. W., Mcmillin, L., Olsen, E. T., Revercomb, H, Rosenkranz, P., Smith, W. L., Staelin, D., Strow, L. L., Susskind, J., Tobin, D., Wolf, W., and Zhou, L.: AIRS: Improving weather forecasting and providing new data on greenhouse gases, B. Am. Meteorol. Soc., 87, 911–926, 2006.
Chahine, M. T., Chen, L., Dimotakis, P., Jiang, X., Li, Q., Olsen, E. T., Pagano, T., Randerson, J., and Yung, Y. L.: Satellite remote sounding of mid-tropospheric CO2, Geophys. Res. Lett., 35, L17807, https://doi.org/10.1029/2008GL035022, 2008.
Cousins, D. and Smith, W. L.: National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I), in: Proceedings from the SPIE, Applicatoin of Lidar to Current Atmospheric Topics II Conference, San Diego, CA, 31 October 1997, 3127, 323–331, 1997.
Dee, D. P. and Uppala, S.: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis, Q. J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.21957/8si33ip1, 2009.
DeSouza-Machado, S., Strow, L. L., Tangborn, A., Huang, X., Chen, X., Liu, X., Wu, W., and Yang, Q.: Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm, Atmos. Meas. Tech., 11, 529–550, https://doi.org/10.5194/amt-11-529-2018, 2018.
Didan, K.: MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes Distributed Active Archive Center [datsa set], https://doi.org/10.5067/MODIS/MYD13C2.061, 2021.
Didan, K. and Huete, A.: MOD13C2 MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG., NASA LP DAAC [data set], https://doi.org/10.5067/MODIS/MOD13C2.006, 2015.
Dlugokencky, E. J., Steele, L. P., Lang, P. M., and Masarie, K. A.: The growth rate and distribution of atmospheric methane, J. Geophys. Res., 99, 17021–17043, 1994.
Elsaesser, G., Jiang, J., Su, H., and Schiro, K.: AIRS vs MERRA-2: when and where do they both agree on the impact of convection on PBL thermodynamics?, NASA Sounder Science Team Meeting, 25–27 September 2020, College Park, MD, 2019.
Ern, M., Hoffmann, L., and Preusse, P.: Directional gravity wave momentum fluxes in the stratosphere derived from highresolution AIRS temperature data, Geophys. Res. Lett., 44, 475–485, https://doi.org/10.1002/2016GL072007, 2017.
Fetzer, E., Qing, Y., Manipon, G., and Wang, L.: SNPP CrIS-VIIRS 750-m Matchup Indexes V1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/MEASURES/WVCC/DATA211, 2022.
Fu, D., Kulawik, S. S., Miyazaki, K., Bowman, K. W., Worden, J. R., Eldering, A., Livesey, N. J., Teixeira, J., Irion, F. W., Herman, R. L., Osterman, G. B., Liu, X., Levelt, P. F., Thompson, A. M., and Luo, M.: Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation, Atmos. Meas. Tech., 11, 5587–5605, https://doi.org/10.5194/amt-11-5587-2018, 2018.
Gettelman, A. and Fu, Q.: Observed and Simulated upper-tropospheric water vapor feedback, J. Climate, 21, 3282–3289, https://doi.org/10.1175/2007JCLI2142.1, 2008.
George, M., Clerbaux, C., Hurtmans, D., Turquety, S., Coheur, P.-F., Pommier, M., Hadji-Lazaro, J., Edwards, D. P., Worden, H., Luo, M., Rinsland, C., and McMillan, W.: Carbon monoxide distributions from the IASI/METOP mission: evaluation with other space-borne remote sensors, Atmos. Chem. Phys., 9, 8317–8330, https://doi.org/10.5194/acp-9-8317-2009, 2009.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG, Version 61, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10C1.061, 2021.
Han, Y., Delst, P., Liu, Q., Weng, F., Yan, B., and Derber, J.: CRTM: v2.4.0 User Guide, NOAA/JCSDA, https://github.com/JCSDA/crtm/wiki/files/CRTM_User_Guide.pdf (last access: 28 October 2023), 2020.
Heidinger, A. K. and Li, Y.: AWG Cloud Height Algorithm Theoretical Basis Document, NOAA NESDIS CENTER for SATELLITE APPLICATIONS and RESEARCH, https://www.star.nesdis.noaa.gov/goesr/documents/ATBDs/Enterprise/ATBD_Enterprise_Cloud_Height_v3.1_Mar2017.pdf (last access: 11 June 2013), 2017.
Heidinger, A. K., Bearson, N., Foster, M. J., Li, Y., Wanzong, S., Ackerman, S., Holz, R. E., Platnick, S., and Meyer, K.: Using Sounder Data to Improve Cirrus Cloud Height Estimation from Satellite Imagers, J. Atmos. Ocean. Tech., 1331–1342, https://doi.org/10.1175/JTECH-D-18-0079.1, 2019.
Hewison, J. and English, S. J.: Airborne Retrievals of Snow and Ice Surface Emissivity at Millimetre Wavelengths, IEEE T. Geosci. Remote, 37, 1871–1879, https://doi.org/10.1109/36.774700, 1999.
Hulley, G., Malakar, N., Hughes, T., Islam, T., Hook, S.: Moderate Resolution Imaging Spectroradiometer (MODIS) MOD21 Land Surface Temperature and Emissivity Algorithm Theoretical Basis Document, USGS, https://modis-land.gsfc.nasa.gov/pdf/MOD21_ATBD_Hulley_v2.4.pdf (last access: 8 May 2017), 2016.
Hurtmans, D., Coheur, P.-F, Wespes, C., Clarisse, L., Scharf, O., Clerbaux, C., Hadji-Lazaro, J., George, M., and Turquety, S.: FORLI radiative transfer and retrieval code for IASI, J. Quant. Spectrosc. Ra., 113, 1391–1408, https://doi.org/10.1016/j.jqsrt.2012.02.036, 2012.
Irion, F. W., Kahn, B. H., Schreier, M. M., Fetzer, E. J., Fishbein, E., Fu, D., Kalmus, P., Wilson, R. C., Wong, S., and Yue, Q.: Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS, Atmos. Meas. Tech., 11, 971–995, https://doi.org/10.5194/amt-11-971-2018, 2018.
Jaross, G.: OMPS-NPP L2 NM Ozone (O3) Total Column swath orbital V2, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/0WF4HAAZ0VHK, 2017.
Johnson, M. S., Liu, X., Zoogman, P., Sullivan, J., Newchurch, M. J., Kuang, S., Leblanc, T., and McGee, T.: Evaluation of potential sources of a priori ozone profiles for TEMPO tropospheric ozone retrievals, Atmos. Meas. Tech., 11, 3457–3477, https://doi.org/10.5194/amt-11-3457-2018, 2018.
Jones, T. A. and Stensrud, D. J.: Assimilating AIRS temperature and mixing ratio profiles using an ensemble kalman filter approach for convective-scale forecasts, Weather Forecast., 27, 541–564, 2012.
Kahn, B. H., Fishbein, E., Nasiri, S. L., Eldering, A., Fetzer, E. J., Garay, M. J., and Lee, S.-Y.: The radiative consistency of Atmospheric Infrared Sounder and Moderate Resolution Imaging Spectroradiometer cloud retrievals, J. Geophys. Res., 112, D09201, https://doi.org/10.1029/2006JD007486, 2007.
LeMarshall, J., Jung, J., Derber, J., Chahine, M., Treadon, R., Lord, S. J., Goldberg, M., Wolf, W., Liu, H. C., Joiner, J., Woollen, J., Todling, R., vab Delst, P., and Tahara, Y.: Improving global analysis and forecasting with AIRS, Bull. Am. Meteorol. Soc., 87, 891–895, 2006.
Leroy, S. S., Ao, C. O., and Verkhoglyadova, O. P.: Temperature trends and anomalies in modern satellite data: Infrared sounding and GPS radio occultation, J. Geophys. Res.-Atmos., 123, 11431–11444, https://doi.org/10.1029/2018JD028990, 2018.
Li, Y., Baum, B. A., Heidinger, A. K., Menzel, W. P., and Weisz, E.: Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion, Atmos. Meas. Tech., 13, 4035–4049, https://doi.org/10.5194/amt-13-4035-2020, 2020.
Liu, Q., Weng, F., and English, S. J.: An Improved Fast Microwave Water Emissivity Model, IEEE T. Geosci. Remote, 49, 1238–1250, https://doi.org/10.1109/TGRS.2010.2064779, 2011.
Liu, Q., van Delst, P., Chen, Y., Groff, D., Han, Y., Collard, A., Weng, F., Boukabara, S.-A., and Derber, J.: Community radiative transfer model for radiance assimilation and applications, in: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012, IEEE, 3700–3703, https://doi.org/10.1109/IGARSS.2012.6350612, 2012.
Liu, R., Su, H., Liou, K.-N., Jiang, J. H., Gu, Y., Liu, S. C., and Shiu, C.-J.: An assessment of tropospheric water vapor feedback using radiative kernels, J. Geophys. Res.-Atmos., 123, 1499–1509, https://doi.org/10.1002/2017JD027512, 2018.
Liu, X., Smith, W. L., Zhou, D. K., and Larar, A.: Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept, Appl. Optics, 45, 201–209, https://doi.org/10.1364/AO.45.000201, 2006.
Liu, X., Zhou, D. K., Larar, A., Smith, W. L., and Mango, S. A.: Case-study of a principal-component-based radiative transfer forward model and retrieval algorithm using EAQUATE data, Q. J. Roy. Meteor. Soc., 133, 243–256, https://doi.org/10.1002/qj.156, 2007.
Liu, X., Zhou, D. K., Larar, A. M., Smith, W. L., Schluessel, P., Newman, S. M., Taylor, J. P., and Wu, W.: Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels, Atmos. Chem. Phys., 9, 9121–9142, https://doi.org/10.5194/acp-9-9121-2009, 2009.
Liu, X., Wu, W., Wielicki, B. A., Yang, Q., Kizer, S. H., Huang, X., Chen, X., Kato, S., Shea, Y. L., and Mlynczak, M. G.: Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection, J. Climate, 30, 3979–3998, 2017.
Lynch, R., Moncet, J.-L., and Liu, X.: Efficient nonlinear inversion for atmospheric sounding and other applications, Appl. Optics, 48, 1790–1796, 2009.
Masuda, K., Takashima, T., and Takayama, Y.: Emissivity of pure water and sea waters for the sea surface in the infrared window regions, Remote Sens. Environ., 24, 313–329, 1988.
McCoy, D. T., Field, P. R., Elsaesser, G. S., Bodas-Salcedo, A., Kahn, B. H., Zelinka, M. D., Kodama, C., Mauritsen, T., Vanniere, B., Roberts, M., Vidale, P. L., Saint-Martin, D., Voldoire, A., Haarsma, R., Hill, A., Shipway, B., and Wilkinson, J.: Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations, Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, 2019.
Meerdink, S. K., Hook, S. J., Roberts, D. A., and Abbott, E. A.: The ECOSTRESS spectral library version 1.0, Remote Sens. Environ., 230, 1–8, 2019.
Nalli, N. R., Minnett, P. J., and Van Delst, P.: Emissivity and reflection model for calculating unpolarized isotropic water surface-leaving radiance in the infrared. I: Theoretical development and calculations, Appl. Optics, 47, 3701–3721, https://doi.org/10.1364/AO.47.003701, 2008a.
Nalli, N. R, Minnett, P. J., Maddy, E., McMillan, W. W., and Goldberg, M. D.: Emissivity and reflection model for calculating unpolarized isotropic water surface-leaving radiance in the infrared. 2: Validation using Fourier transform spectrometers, Appl. Optics, 47, 4649–4671, https://doi.org/10.1364/AO.47.004649, 2008b.
Nalli, N. R., Tan, C., Warner, J., Divakarla, M., Gambacorta, A., Wilson, M., Zhu, T., Wang, T., Wei, Z., Pryor, K., Kalluri, S., Zhou, L., Sweeney, C., Baier, B. C., McKain, K., Wunch, D., Deutscher, N. M., Hase, F., Iraci, L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Pollard, D. F., Té, Y., Velazco, V. A., Warneke, T., Sussmann, R., and Rettinger, M.: Validation of Carbon Trace Gas profile retrievals from the NOAA-Unique Combined Atmospheric Processing System for the cross-track infrared sounder, Remote Sens., 12, 3245, https://doi.org/10.3390/rs12193245, 2020.
Nasiri, S. L., Dang, H. V. T., Kahn, B. H., Fetzer, E. J., Manning, E. M., Schreier, M. M., and Frey, R. A.: Comparing MODIS and AIRS Infrared-Based Cloud Retrievals, J. Appl. Meteorol. Clim., 50, 1057–1072, https://doi.org/10.1175/2010JAMC2603.1, 2011.
Perrett, J. A., Wright, C. J., Hindley, N. P., Hoffmann, L., Mitchell, N. J., Preusse, P., Strube, C., and Eckermann, S. D.: Determining gravity wave sources and propagation in the Southern Hemisphere by ray-tracing AIRS measurements, Geophys. Res. Lett., 48, e2020GL088621, https://doi.org/10.1029/2020GL088621, 2021.
Platnick, S., Meyer, K. G., Heidinger, A. K., and Holz, R.: VIIRS Atmosphere L2 Cloud Properties Product, Version-1, NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [data set], Goddard Space Flight Center, USA, https://doi.org/10.5067/VIIRS/CLDPROP_L2_VIIRS_SNPP.001, 2017.
Rodgers, C. D.: Characterization and error analysis of profiles retrieved from remote sounding measurements, J. Geophys. Res., 95, 5587–5595, 1990.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Pub Co Inc, Singapore, ISBN: 978-981-281-371-8, 2000.
Ribeiro, I. O., Andreoli, R. V., Kayano, M. T., De Sousa, T. R., Medeiros, A. S., Guimaraes, P. C., Barbosa, C. G. G., Godoi, R. H. M., Martin, S. T., and De Souza, R. A. F.: Impact of the biomass burning on methane variability during dry years in the Amazon measured from an aircraft and the AIRS sensor, Sci. Total Environ., 624, 509–516, https://doi.org/10.1016/j.scitotenv.2017.12.147, 2018.
Sato, K., Tsuchiya, C., Alexander, M. J., and Hoffmann, L.: Climatology and ENSO-related interannual variability of gravity waves in the southern hemisphere subtropical stratosphere revealed by high-resolution AIRS observations, J. Geophys. Res.-Atmos., 121, 7622–7640, https://doi.org/10.1002/2015JD024462, 2016.
Schulte, R. M. and Kummerow, C. D.: An Optimal Estimation Retrieval Algorithm for Microwave Humidity Sounding Channels with Minimal Scan Position Bias, J. Atmos. Ocean. Tech., 36, 409–425, https://doi.org/10.1175/JTECH-D-18-0133.1, 2019.
Smith, N. and Barnet, C. D.: Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS), Remote Sens.-Basel, 11, 1227, https://doi.org/10.3390/rs11101227, 2019.
Smith, N. and Barnet, C. D.: 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, 2020.
Smith Sr., W. L. and Weisz, E.: 7.09 - Dual-Regression Approach for High-Spatial-Resolution Infrared Soundings, Comprehensive Remote Sensing, 7, 297–311, https://doi.org/10.1016/B978-0-12-409548-9.10394-X, 2018.
Smith Sr., W. L., Weisz, E., Kireev, S. V., Zhou, D. K., Li, Z., and Borbas, E. E.: Dual-Regression Retrieval Algorithm for Real-Time Processing of Satellite Ultraspectral Radiances, J. Appl. Meteorol. Clim., 51, 1455–1476, 2012.
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting media, Appl. Optics, 27, 2502–2509, https://doi.org/10.1364/AO.27.002502, 1988.
Strow, L. L., Hepplewhite, C., Motteler, H., Buczkowski, S., and DeSouza-Machado, S.: A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record, Remote Sens.-Basel, 13, 418, https://doi.org/10.3390/rs13030418, 2021.
Susskind, J. and Blaisdell, J.: CrIS CHART Retrieval Algorithm ATBD, NASA GFSC, https://docserver.gesdisc.eosdis.nasa.gov/public/project/SNPP/SNPP_limited_edition/SNPP.CrIMSS.CHART_V1.ATBD.pdf (last access: 30 December 2017), 2017.
Walther, A. and Heidinger, A. K.: Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x, J. Appl. Meteorol. Clim., 51, 1371–1390, https://doi.org/10.1175/JAMC-D-11-0108.1, 2012.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface Temperature/Emissivity Monthly L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD11C3.061, 2021.
Wang, T., Roman, J., Yue, Q., and Sun, W.: Test Report of Performance of CLIMCAPS-SNPP and CLIMCAPS-JPSS1 Retrievals, Jet Propulsion Lab, https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/CLIMCAPS.V2.Test.Report.pdf (last access: 26 August 2020), 2020.
Warner, J. X., Dickerson, R. R., Wei, Z., Strow, L. L., Wang, Y., and Liang, Q.: Increased atmospheric ammonia over the world's major agricultural areas detected from space, Geophys. Res. Lett., 44, 2875–2884, https://doi.org/10.1002/2016GL072305, 2017.
Wei, J. C., Pan, L. L., Maddy, E., Pittman, J. V., Divarkarla, M., Xiong, X., and Barnet, C.: Ozone Profile Retrieval from an Advanced Infrared Sounder: Experiments with Tropopause-Based Climatology and Optimal Estimation Approach, J. Atmos. Ocean. Tech., 27, 1123–1139, 2010.
Wilheit, T. T.: A Model for the Microwave Emissivity of the Ocean's Surface as a Function of Wind Speed, IEEE T. Geosci. Elect., 17, 244–249, https://doi.org/10.1109/TGE.1979.294653, 1979.
Wu, W., Liu, X., Zhou, D. K., Larar, A. M., Yang, Q., Kizer, S. H., and Liu, Q.: The Application of PCRTM Physical Retrieval Methodology for IASI Cloudy Scene Analysis, IEEE T. Geosci. Remote, 55, 5042–5056, https://doi.org/10.1109/TGRS.2017.2702006, 2017.
Wu, W., Liu, X., Yang, Q., Zhou, D. K., Larar, A., Zhao, M., and Zhou, L., All Sky Single Field of View Retrieval System for Hyperspectral Sounding, in: Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium, 28 July–2 August 2019, Yokohama, Japan, https://doi.org/10.1109/IGARSS.2019.8898307, 7560–7563, 2019.
Wu, W., Liu, X., Yang, Q., Zhou, D. K., and Larar, A. M.: Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations, Remote Sens.-Basel, 12, 1291, https://doi.org/10.3390/rs12081291, 2020.
Xiong, X., Liu, X., Wu, W., Knowland, K. E., Yang, Q., Welsh, J., and Zhou, D. K.: Satellite observation of stratospheric intrusions and ozone transport using CrIS on SNPP, Atmos. Environ., 273, 118956, https://doi.org/10.1016/j.atmosenv.2022.118956, 2022a.
Xiong, X., Liu, X., Wu, W., Knowland, K. E., Yang, F., Yang, Q., and Zhou, D. K.: Impact of Stratosphere on Cold Air Outbreak: Observed Evidence by CrIS on SNPP and Its Comparison with Models, Atmosphere, 13, 876, https://doi.org/10.3390/atmos13060876, 2022b.
Xiong, X., Liu, X., Wu, W., Yang, Q., and Zhou, D. K.: Observtion of Carbon Monoxide and Ozone From 2019–2020 Australia Fires Using Thermal Infrared and Near-Infrared Satellite Sensors, in: Proceedings of 2022 IEEE International Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia, IEEE, 6502–6505, https://doi.org/10.1109/IGARSS46834.2022.9884471, 2022c.
Yang, K. and Liu, X.: Ozone profile climatology for remote sensing retrieval algorithms, Atmos. Meas. Tech., 12, 4745–4778, https://doi.org/10.5194/amt-12-4745-2019, 2019.
Yue, Q., Lambrigtsen, B., Farahmand, A., Fetzer, E. J., Fishben, E. Griffin, E., Iredell, L., Irion, F. W., Kahn, B. H., Kalmus, P., Manning, E., Marchetti, Y., Pagano, T., Smith, N., Susskind, J., Teixeira, J., Thrastarson, H. T., Wang, T., Wen, Y., Wilson, R. Ch., and Wong, S.: AIRS Version 7 Level 2 Performance Test and Validation Report, Jet Propulsion Lab, https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/V7_L2_Performance_Test_and_Validation_report.pdf (last access: 1 October 2021), 2020.
Yue, Q., Fetzer, E. J., Wang, L., Kahn, B. H., Smith, N., Blaisdell, J. M., Meyer, K. G., Schreier, M., Lambrigtsen, B., and Tkatcheva, I.: Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership), Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, 2022.
Zhou, D. K., Smith Sr., W. L., Liu, X., Larar, A. M., and Mango, S. A.: Tropospheric CO observed with the NAST-I retrieval methodology, analyses, and first results, Appl. Optics, 44, 3032–3044, https://doi.org/10.1364/AO.44.003032, 2005.
Zhou, D. K., Smith Sr., W. L., Liu, X., Larar, A. M., Mango, S. A., and Huang, H.: Physically Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements, J. Atmos. Sci., 64, 969–982, https://doi.org/10.1175/JAS3877.1, 2007.
Zhou, D. K., Smith, W. L., Larar, A. M., Liu, X., Taylor, J. P., Schlüssel, P., Strow, L. L., and Mango, S. A.: All weather IASI single field-of-view retrievals: case study – validation with JAIVEx data, Atmos. Chem. Phys., 9, 2241–2255, https://doi.org/10.5194/acp-9-2241-2009, 2009.
Zhou, D. K., Larar, A. M., Liu, X., Smith, W. L., Strow, L. L., Yang, P., Schlüssel, P., and Calbet, X.: Global land surface emissivity retrieved from satellite ultraspectral IR measurements, IEEE T. Geosci. Remote, 49, 1277–1290, https://doi.org/10.1109/TGRS.2010.2051036, 2010.
Zhou, Y. and Grassotti, C.: Development of a Machine Learning-Based Radiometric Bias Correction for NOAA's Microwave Integrated Retrieval System (MiRS), Remote Sens.-Basel, 12, 3160, https://doi.org/10.3390/rs12193160, 2020.
Zhu, Y., Derber, J., Collard, A., Dee, D. P., Treadon, R., Gayno, G., and Jung, J. A.: Enhanced radiance bias correction in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation data assimilation system, Q. J. Roy. Meteor. Soc., 140, 1479–1492, https://doi.org/10.1002/qj.2233, 2014.
Short summary
We present a new operational physical retrieval algorithm that is used to retrieve atmospheric properties for each single field-of-view measurement of hyper-spectral IR sounders. The physical scheme includes a cloud-scattering calculation in its forward-simulation part. The data product generated using this algorithm has an advantage over traditional IR sounder data production algorithms in terms of improved spatial resolution and minimized error due to cloud contamination.
We present a new operational physical retrieval algorithm that is used to retrieve atmospheric...
