Articles | Volume 15, issue 3
https://doi.org/10.5194/gmd-15-1317-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1317-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator
Cheng-Hsuan Lu
CORRESPONDING AUTHOR
Joint Center for Satellite Data Assimilation, Boulder, CO, USA
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Quanhua Liu
Center for Satellite Applications and Research, NOAA/NESDIS, College Park, MD, USA
Shih-Wei Wei
Joint Center for Satellite Data Assimilation, Boulder, CO, USA
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Benjamin T. Johnson
Joint Center for Satellite Data Assimilation, College Park, MD, USA
Cheng Dang
Joint Center for Satellite Data Assimilation, Boulder, CO, USA
Patrick G. Stegmann
Joint Center for Satellite Data Assimilation, College Park, MD, USA
Dustin Grogan
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Guoqing Ge
Cooperative Institute for Research in Environmental Sciences, CU
Boulder, CO, USA
Global System Laboratory, NOAA, Boulder, CO, USA
Ming Hu
Global System Laboratory, NOAA, Boulder, CO, USA
Michael Lueken
I.M. Systems Group, Inc., Rockville, MD, USA
Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, USA
Related authors
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Dustin Francis Phillip Grogan, Cheng-Hsuan Lu, Shih-Wei Wei, and Sheng-Po Chen
Atmos. Chem. Phys., 22, 2385–2398, https://doi.org/10.5194/acp-22-2385-2022, https://doi.org/10.5194/acp-22-2385-2022, 2022
Short summary
Short summary
This study shows that incorporating aerosols into satellite radiance calculations affects the representation of African easterly waves (AEWs), and their environment, over North Africa and the eastern Atlantic in a numerical weather model. These changes are driven by radiative effects of Saharan dust captured by the aerosol-affected radiances, which modify the initial fields and can improve the forecasting of AEWs.
Ying-Chieh Chen, Sheng-Hsiang Wang, Qilong Min, Sarah Lu, Pay-Liam Lin, Neng-Huei Lin, Kao-Shan Chung, and Everette Joseph
Atmos. Chem. Phys., 21, 4487–4502, https://doi.org/10.5194/acp-21-4487-2021, https://doi.org/10.5194/acp-21-4487-2021, 2021
Short summary
Short summary
In this study, we integrate satellite and surface observations to statistically quantify aerosol impacts on low-level warm-cloud microphysics and drizzle over northern Taiwan. Our result provides observational evidence for aerosol indirect effects. The frequency of drizzle is reduced under polluted conditions. For light-precipitation events (≤ 1 mm h-1), however, higher aerosol concentrations drive raindrops toward smaller sizes and thus increase the appearance of the drizzle drops.
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
Short summary
Short summary
Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022, https://doi.org/10.5194/gmd-15-6891-2022, 2022
Short summary
Short summary
A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
Short summary
Short summary
Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Dustin Francis Phillip Grogan, Cheng-Hsuan Lu, Shih-Wei Wei, and Sheng-Po Chen
Atmos. Chem. Phys., 22, 2385–2398, https://doi.org/10.5194/acp-22-2385-2022, https://doi.org/10.5194/acp-22-2385-2022, 2022
Short summary
Short summary
This study shows that incorporating aerosols into satellite radiance calculations affects the representation of African easterly waves (AEWs), and their environment, over North Africa and the eastern Atlantic in a numerical weather model. These changes are driven by radiative effects of Saharan dust captured by the aerosol-affected radiances, which modify the initial fields and can improve the forecasting of AEWs.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Ying-Chieh Chen, Sheng-Hsiang Wang, Qilong Min, Sarah Lu, Pay-Liam Lin, Neng-Huei Lin, Kao-Shan Chung, and Everette Joseph
Atmos. Chem. Phys., 21, 4487–4502, https://doi.org/10.5194/acp-21-4487-2021, https://doi.org/10.5194/acp-21-4487-2021, 2021
Short summary
Short summary
In this study, we integrate satellite and surface observations to statistically quantify aerosol impacts on low-level warm-cloud microphysics and drizzle over northern Taiwan. Our result provides observational evidence for aerosol indirect effects. The frequency of drizzle is reduced under polluted conditions. For light-precipitation events (≤ 1 mm h-1), however, higher aerosol concentrations drive raindrops toward smaller sizes and thus increase the appearance of the drizzle drops.
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
Short summary
Short summary
Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Cited articles
American Meteorological Society: Brightness Temperature, Glossary of
Meteorology, available at: https://glossary.ametsoc.org/wiki/Brightness_temperature (last access: 8 February 2022), 2012.
Binkowski, F. S. and Roselle, S. J.: Models-3 Community multiscale air quality
(CMAQ) model aerosol component, 1 Model description, J. Geophys. Res., 108,
4183, https://doi.org/10.1029/2001JD001409, 2003.
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, 2015.
Bullard, J. E., Baddock, M., Bradwell, T., Crusius, J., Darlington, E., Gaiero, D., Gasso, S., Gisladottir, G., Hodgkins, R., McCulloch, R., McKenna-Neuman, C., Mockford, T., Stewart, H., and Thorsteinsson, T.: High-latitude dust in the Earth system, Rev.
Geophys., 54, 447–485, https://doi.org/10.1002/2016RG000518, 2016.
Chen, Y., Weng, F., Han, Y., and Liu, Q.: Planck-Weighted Transmittance and
Correction of Solar Reflection for Broadband Infrared Satellite Channels, J.
Atmos. Sci., 29, 382–396, 2012.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., and Higurashi, A.: Tropospheric aerosol optical
thickness from the GOCART model and comparisons with satellite and Sun
photometer measurements, J. Atmos. Sci., 59, 461–483,
https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2,
2002.
Chin, M., Diehl, T., Tan, Q., Prospero, J. M., Kahn, R. A., Remer, L. A., Yu, H., Sayer, A. M., Bian, H., Geogdzhayev, I. V., Holben, B. N., Howell, S. G., Huebert, B. J., Hsu, N. C., Kim, D., Kucsera, T. L., Levy, R. C., Mishchenko, M. I., Pan, X., Quinn, P. K., Schuster, G. L., Streets, D. G., Strode, S. A., Torres, O., and Zhao, X.-P.: Multi-decadal aerosol variations from 1980 to 2009: a perspective from observations and a global model, Atmos. Chem. Phys., 14, 3657–3690, https://doi.org/10.5194/acp-14-3657-2014, 2014.
Clough, S., Iacano, M. J., and Moncet, J.-L.: Line-by-line Calculations of
Atmospheric Fluxes and Cooling Rates: Application to Water Vapor, J.
Geophys. Res., 97, 15761–15785, 1992.
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res., 115,
D14207, https://doi.org/10.1029/2009JD012820, 2010.
d'Almeida, G. A., Koepke, P., and Shettle, E.P.: Atmospheric Aerosols:
global climatology and radiative characteristics, A. Deepak Publishing,
Hampton, VA, ISBN 978-0-937-19422-5, 1991.
Diaz, H. F., Carlson, T. N., and Prospero, J. M.: A study of the structure
and dynamics of the Saharan air layer over the northern equatorial Atlantic
during BOMEX, National Hurricane and Experimental Meteorology Laboratory
NOAA, Tech. Memo., ERL WMPO-32, 61 pp., available at: https://repository.library.noaa.gov/view/noaa/32843 (last access: 8 February 2022), 1976.
Diaz, J. P., Arbelo, M., Expósito, F. J.,
Podestá, G., Prospero, J. M., and Evans, R.: Relationship
between errors in AVHRR-derived sea surface temperature and the TOMS Aerosol
Index, Geophys. Res. Lett., 28, 1989–1992, 2001.
Divakarla, M., Barnet, C., Goldberg, M., Gu, D., Liu, X., Xiong, X., Kizer, S., Guo, G., Wilson, M., Maddy, E., Nalli, N., Gambacorta, A., King, T., Ma, X., and Blackwell, W.: Evaluation of CrIMSS operational products
using in-situ measurements, model analysis fields, and retrieval products
from heritage algorithms, IEEE International Geoscience and Remote Sensing
Symposium, Munich, Germany, 22–27 July 2012, 1046–1049, https://doi.org/10.1109/IGARSS.2012.6350818, 2012.
Gelaro, R., McCarty, W., Suarez, 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.
Grogan, D. F. P., Lu, C.-H., Wei, S.-W., and Chen, S.-P.: Effects of Saharan Dust on African Easterly Waves: The Impact of Aerosol-Affected Satellite Radiances on Data Assimilation, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-129, in review, 2021.
Hale, G. M. and Querry, M. R.: Optical constants of water in the 200-nm to
200-mm wavelength region, Appl. Opt., 12, 555–563, 1973.
Han, Y., van Delst, P., Liu, Q., Weng, F., Yan, B., Treadon, R., and Derber,
J.: JCSDA Community Radiative Transfer Model (CRTM) – Version 1, NOAA
NESDIS, Tech. Rep., 122, NOAA, Silver Spring, Md, 33 pp., available at: https://repository.library.noaa.gov/view/noaa/1157 (last access: 8 February 2022), 2006.
Han, Y., Weng, F., Liu, Q., and van Delst, P.: A fast radiative transfer
model for SSMIS upper atmosphere sounding channels, J. Geophys. Res., 112,
D11121, https://doi.org/10.1029/2006JD008208, 2007.
Hess, M., Koepke, P., and Schult I.: Optical properties of aerosols and clouds:
the software package OPAC, B. Am. Meteorol. Soc., 79, 831–844, 1998.
Highwood, E. J., Haywood, J. M., Silverstone, M. D., Newman, S. M., and
Taylor, J. P.: Radiative properties and direct effect of Saharan dust
measured by the C-130 aircraft during Saharan Dust Experiment (SHADE): 2.
Terrestrial spectrum, J. Geophys. Res., 108, 8578,
https://doi.org/10.1029/2002JD002552, 2003.
Johnson, B., Dang, C., Rosinski, J., Ma, Y., and Stegmann, P. G.: JCSDA/crtm: Tagged release for CRTM v2.3 for Zenodo archival and DOI (v2.3.0-Public-Zenodo), Zenodo [code], https://doi.org/10.5281/zenodo.5695707, 2021.
Karyampudi, V. M., Palm, S. P., Reagen, J. A., Fang, H., Grant, W.
B., Hoff, R. M., Moulin, C., Pierce, H. F., Torres, O., Browell, E. V.,
and Melfi, S. H.: Validation of the Saharan dust plume conceptual model
using lidar, Meteosat, and ECMWF data, B. Am. Meteorol. Soc., 80,
1045–1075, https://doi.org/10.1175/1520-0477(1999)080<1045:VOTSDP>2.0.CO;2, 1999.
Kim, J., Akella, S., da Silva, A. M., Todling, R., and McCarty, W.: Preliminary
evaluation of influence of aerosols on the simulation of brightness
temperature in the NASA's Goddard Earth Observing System Atmospheric Data
Assimilation System, Tech. Rep. Ser. Glob. Model. Data Assim., vol. 49,
TM–2018-104606, Goddard Space Flight Center, National Aeronautics and Space
Administration, Greenbelt, Maryland, US, available at: https://ntrs.nasa.gov/citations/20180001946 (last access: 8 February 2022), 2018.
Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W. S., and
Lord, S.: Introduction of the GSI into the NCEP Global Data Assimilation
System, Weather Forecast., 24, 1691–1705, https://doi.org/10.1175/2009WAF2222201.1, 2009.
Liu, Q. and Lu, C.-H.: Community Radiative Transfer Model for Air Quality
Studies, in: Light Scattering Reviews, volume 11, edited by: Kokhanovsky, A., Springer
Praxis Books, Springer, Berlin, Heidelberg, 67–115, ISBN 978-3-662-49536-0, https://doi.org/10.1007/978-3-662-49538-4_2,
2016.
Liu, Q. and Weng, F.: Advanced doubling-adding method for radiative transfer
in planetary atmosphere, J. Atmos. Sci., 63, 3459–3465,
https://doi.org/10.1175/JAS3808.1, 2006.
Liu, Q., Han, Y., van Delst, P., and Weng, F.: Modeling aerosol radiance for
NCEP data assimilation, in Fourier Transform Spectroscopy/Hyperspectral
Imaging and Sounding of the Environment, paper HThA5, OSA Technical Digest
Series, Optical Society of America, 11–15 February 2007, ISBN 1-55752-828-4, https://doi.org/10.1364/HISE.2007.HThA5, 2007.
Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T.:
Three-dimensional variational assimilation of MODIS aerosol optical depth:
Implementation and application to a dust storm over East Asia, J. Geophys.
Res., 116, D23206, https://doi.org/10.1029/2011JD016159, 2011.
Lu, C.-H., da Silva, A., Wang, J., Moorthi, S., Chin, M., Colarco, P., Tang, Y., Bhattacharjee, P. S., Chen, S.-P., Chuang, H.-Y., Juang, H.-M. H., McQueen, J., and Iredell, M.: The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP, Geosci. Model Dev., 9, 1905–1919, https://doi.org/10.5194/gmd-9-1905-2016, 2016.
Lu, C.-H., Liu, Q., Wei, S.-W., Johnson, B. T., Dang, C., Stegmann, P. G., Grogan, D., Ge, G., Hu, M., and Lueken, M.: Sample data and fixed files for running fv3aerorad in GSI, Zenodo [data set], https://doi.org/10.5281/zenodo.5736503, 2021.
Lueken, M., Safford, E., Treadon, R., Mahajan, R., Whitaker, J., Derber, J., Kumar, K., Wu, W., Bathmann, K., Tong, M., Li, X., Potts, M., Liu, E., Pondeca, M., Zhu, Y., Collard, A., Jones, E., Hu, M., Carley, J., Kleist, D., Jung, J., Su, X., Thomas, C., Yang, R.., Genkova, I., Ma, Z., Ge., G., Liu, H., Gayno, G., and Nebuda, S.: comgsi/GSI: comgsi.2021-11-29 (comgsi.2021-11-29), Zenodo [code], https://doi.org/10.5281/zenodo.5735601, 2021.
Matricardi, M.: The inclusion of aerosols and clouds in RTIASI, the ECMWF
fast radiative transfer model for the infrared atmospheric sounding
interferometer, ECMWF Tech. Memo., 474, https://doi.org/10.21957/1krvb28ql, 2005.
Merchant, C. J., Embury, O., Le Borgne, P., and Bellecm, B.: Saharan dust in
nighttime thermal imagery: Detection and reduction of related biases in
retrieved sea surface temperature, Remote Sens. Environ., 104, 15–30,
https://doi.org/10.1016/j.rse.2006.03.007, 2006.
Nalli, N. R. and Stowe, L. L.: Aerosol correction for remotely sensed sea
surface temperatures from the National Oceanic and Atmospheric
Administration advanced very high resolution radiometer, J. Geophys. Res.,
107, 3172, https://doi.org/10.1029/2001JC001162, 2002.
Pagowski, M., Liu, Z., Grell, G. A., Hu, M., Lin, H.-C., and Schwartz, C. S.: Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v. 3.4.1), Geosci. Model Dev., 7, 1621–1627, https://doi.org/10.5194/gmd-7-1621-2014, 2014.
Petty, G.: A First Course in Atmospheric Radiation, 2nd edn., Sundog
Publishing, Madison, WI, ISBN 978-0-972-90331-8, 2006.
Peyridieu, S., Chédin, A., Tanré, D., Capelle, V., Pierangelo, C., Lamquin, N., and Armante, R.: Saharan dust infrared optical depth and altitude retrieved from AIRS: a focus over North Atlantic – comparison to MODIS and CALIPSO, Atmos. Chem. Phys., 10, 1953–1967, https://doi.org/10.5194/acp-10-1953-2010, 2010.
Pierangelo, C., Chédin, A., Heilliette, S., Jacquinet-Husson, N., and Armante, R.: Dust altitude and infrared optical depth from AIRS, Atmos. Chem. Phys., 4, 1813–1822, https://doi.org/10.5194/acp-4-1813-2004, 2004.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka,
Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I:
System Description and Data Assimilation Evaluation, J. Climate,
30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Schwartz, C. S., Liu, Z., Lin, H.-C., and Cetola, J. D.: Assimilating
aerosol observations with a “hybrid” variational-ensemble data
assimilation system, J. Geophys. Res.-Atmos., 119, 4043–4069,
https://doi.org/10.1002/2013JD020937, 2014.
Sokolik, I. N.: The spectral radiative signature of wind-blown mineral dust:
Implications for remote sensing in the thermal IR region: The spectral
radiative signature of wind-blown mineral dust, Geophys. Res. Lett., 29,
2154, https://doi.org/10.1029/2002GL015910, 2002.
Stegmann, P. G., Tang, G., Yang, P., and Johnson, B. T.: A stochastic model
for density-dependent microwave Snow- and Graupel scattering coefficients of
the NOAA JCSDA community radiative transfer model, J. Quant. Spectrosc. Ra., 211, 9–24, https://doi.org/10.1016/j.jqsrt.2018.02.026, 2018.
Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.
Wang, J., Bhattacharjee, P. S., Tallapragada, V., Lu, C.-H., Kondragunta, S., da Silva, A., Zhang, X., Chen, S.-P., Wei, S.-W., Darmenov, A. S., McQueen, J., Lee, P., Koner, P., and Harris, A.: The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions, Geosci. Model Dev., 11, 2315–2332, https://doi.org/10.5194/gmd-11-2315-2018, 2018.
Weaver, C. J., Joiner, J., and Ginoux, P.: Mineral aerosol contamination of
TIROS Operational Vertical Sounder (TOVS) temperature and moisture
retrievals, J. Geophys. Res., 108, 4246, https://doi.org/10.1029/2002JD002571, 2003.
Wei, S.-W., Lu, C.-H., Liu, Q., Collard, A., Zhu, T., Grogan, D., Li, X.,
Wang, J., Grimbine, R., and Bhattacharjee, P.: The impact of aerosols on
satellite radiance data assimilation using NCEP global data assimilation
system, Atmosphere, 12, 432–451, https://doi.org/10.3390/atmos12040432, 2021.
Wei, S.-W., Lu, C.-H., Johnson, B. T., Dang, C., Stegmann, P., Grogan, D.,
Ge, G., and Hu, M.: The influence of aerosols on satellite infrared radiance
simulations and Jacobians: Numerical experiments of CRTM and GSI, Remote.
Sens., 14, 683–702, https://doi.org/10.3390/rs14030683, 2022.
Weng, F.: Advances in radiative transfer modeling in support of satellite
data assimilation, J. Atmos. Sci., 64, 3799–3807,
https://doi.org/10.1175/2007JAS2112.1, 2007.
Wu, M., Liu, X., Yu, H., Wang, H., Shi, Y., Yang, K., Darmenov, A., Wu, C., Wang, Z., Luo, T., Feng, Y., and Ke, Z.: Understanding processes that control dust spatial distributions with global climate models and satellite observations, Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020, 2020.
Wu, W.-S., Purser, R. J., and Parrish, D. F.: Three-dimensional variational
analysis with spatially inhomogeneous covariances, Mon. Weather Rev., 130,
2905–2916, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2, 2002.
Zhang, L., Montuoro, R., McKeen, S. A., Baker, B., Bhattacharjee, P. S., Grell, G. A., Henderson, J., Pan, L., Frost, G. J., McQueen, J., Saylor, R., Li, H., Ahmadov, R., Wang, J., Stajner, I., Kondragunta, S., Zhang, X., and Li, F.: Development and Evaluation of the Aerosol Forecast Member in NCEP’s Global Ensemble Forecast System (GEFS-Aerosols v1), Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-378, in review, 2021.
Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
This article is a technical note on the aerosol absorption and scattering calculations of the...