Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-255-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-42-255-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the importance of middle-atmosphere observations on ionospheric dynamics using WACCM-X and SAMI3
Fabrizio Sassi
Naval Research Laboratory – Space Science Division, 4555 Overlook Ave SW, Washington, DC 20375, USA
Heliophysics Division, Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771, USA
Angeline G. Burrell
CORRESPONDING AUTHOR
Naval Research Laboratory – Space Science Division, 4555 Overlook Ave SW, Washington, DC 20375, USA
Sarah E. McDonald
Naval Research Laboratory – Space Science Division, 4555 Overlook Ave SW, Washington, DC 20375, USA
Jennifer L. Tate
Computational Physics Inc., 8001 Braddock Rd, Suite 210, Springfield, VA 22151, USA
John P. McCormack
Naval Research Laboratory – Space Science Division, 4555 Overlook Ave SW, Washington, DC 20375, USA
Heliophysics Division, Science Mission Directorate, 300 Hidden Figures Way SW, Washington, DC 20546, USA
Related authors
John P. McCormack, V. Lynn Harvey, Cora E. Randall, Nicholas Pedatella, Dai Koshin, Kaoru Sato, Lawrence Coy, Shingo Watanabe, Fabrizio Sassi, and Laura A. Holt
Atmos. Chem. Phys., 21, 17577–17605, https://doi.org/10.5194/acp-21-17577-2021, https://doi.org/10.5194/acp-21-17577-2021, 2021
Short summary
Short summary
In order to have confidence in atmospheric predictions, it is important to know how well different numerical model simulations of the Earth’s atmosphere agree with one another. This work compares four different data assimilation models that extend to or beyond the mesosphere. Results shown here demonstrate that while the models are in close agreement below ~50 km, large differences arise at higher altitudes in the mesosphere and lower thermosphere that will need to be reconciled in the future.
David E. Siskind, V. Lynn Harvey, Fabrizio Sassi, John P. McCormack, Cora E. Randall, Mark E. Hervig, and Scott M. Bailey
Atmos. Chem. Phys., 21, 14059–14077, https://doi.org/10.5194/acp-21-14059-2021, https://doi.org/10.5194/acp-21-14059-2021, 2021
Short summary
Short summary
General circulation models have had a very difficult time simulating the descent of nitric oxide through the polar mesosphere to the stratosphere. Here, we present results suggesting that, with the proper specification of middle atmospheric meteorology, the simulation of this process can be greatly improved. Despite differences in the detailed geographic morphology of the model NO as compared with satellite data, we show that the overall abundance is likely in good agreement with the data.
Angeline G. Burrell, Sarah E. McDonald, Dustin A. Hickey, Meghan R. Burleigh, Eliana Nossa, Christopher A. Metzler, Manbharat Dhadly, Jennifer Tate, and Ellen J. Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2025-4967, https://doi.org/10.5194/egusphere-2025-4967, 2025
This preprint is open for discussion and under review for Annales Geophysicae (ANGEO).
Short summary
Short summary
The Next-generation Ionospheric Model for Operations (NIMO) is a space weather model developed to provide historic, current, and forecasted information about the density of the ionosphere. This article discusses how NIMO is configured, demonstrates potential use cases for the research community, and validates historic runs using a new suite of metrics designed to allow repeatable, quantitative, model-independent evaluations against observations that may be adopted by other ionospheric models.
John P. McCormack, V. Lynn Harvey, Cora E. Randall, Nicholas Pedatella, Dai Koshin, Kaoru Sato, Lawrence Coy, Shingo Watanabe, Fabrizio Sassi, and Laura A. Holt
Atmos. Chem. Phys., 21, 17577–17605, https://doi.org/10.5194/acp-21-17577-2021, https://doi.org/10.5194/acp-21-17577-2021, 2021
Short summary
Short summary
In order to have confidence in atmospheric predictions, it is important to know how well different numerical model simulations of the Earth’s atmosphere agree with one another. This work compares four different data assimilation models that extend to or beyond the mesosphere. Results shown here demonstrate that while the models are in close agreement below ~50 km, large differences arise at higher altitudes in the mesosphere and lower thermosphere that will need to be reconciled in the future.
David E. Siskind, V. Lynn Harvey, Fabrizio Sassi, John P. McCormack, Cora E. Randall, Mark E. Hervig, and Scott M. Bailey
Atmos. Chem. Phys., 21, 14059–14077, https://doi.org/10.5194/acp-21-14059-2021, https://doi.org/10.5194/acp-21-14059-2021, 2021
Short summary
Short summary
General circulation models have had a very difficult time simulating the descent of nitric oxide through the polar mesosphere to the stratosphere. Here, we present results suggesting that, with the proper specification of middle atmospheric meteorology, the simulation of this process can be greatly improved. Despite differences in the detailed geographic morphology of the model NO as compared with satellite data, we show that the overall abundance is likely in good agreement with the data.
Cited articles
Alken, P., Thébault, E., Beggan, C. D., Amit, H., Aubert, J., Baerenzung, J., Bondar, T. N., Brown, W. J., Califf, S., Chambodut, A., Chulliat, A., Cox, G. A., Finlay, C. C., Fournier, A., Gillet, N., Grayver, A., Hammer, M. D., Holschneider, M., Huder, L., Hulot, G., Jager, T., Kloss, C., Korte, M., Kuang, W., Kuvshinov, A., Langlais, B., Léger, J. M., Lesur, V., Livermore, P. W., Lowes, F. J., Macmillan, S., Magnes, W., Mandea, M., Marsal, S., Matzka, J., Metman, M. C., Minami, T., Morschhauser, A., Mound, J. E., Nair, M., Nakano, S., Olsen, N., Pavón-Carrasco, F. J., Petrov, V. G., Ropp, G., Rother, M., Sabaka, T. J., Sanchez, S., Saturnino, D., Schnepf, N. R., Shen, X., Stolle, C., Tangborn, A., Tøffner-Clausen, L., Toh, H., Torta, J. M., Varner, J., Vervelidou, F., Vigneron, P., Wardinski, I., Wicht, J., Woods, A., Yang, Y., Zeren, Z., and Zhou, B.: International Geomagnetic Reference Field: the thirteenth generation, Earth Planets Space, 73, 49, https://doi.org/10.1186/s40623-020-01288-x, 2021. a
Burrell, A. and Heelis, R.: The influence of hemispheric asymmetries on field-aligned ion drifts at the geomagnetic equator, Geophys. Res. Lett., 39, L19101, https://doi.org/10.1029/2012GL053637, 2012. a
Burrell, A., Heelis, R., and Stoneback, R.: Equatorial longitude and local time variations of topside magnetic field-aligned ion drifts at solar minimum, J. Geophys. Res., 117, A04304, https://doi.org/10.1029/2011JA017264, 2012. a
Coster, A.: MIT/Haystack Observatory, Data from the CEDAR Madrigal database [data set], XX in the URL is the day of month, https://w3id.org/cedar?experiment_list=experiments3/2013/gps/XXjan13&file_list=los_201301XX.001.h5 (last access: 3 October 2023), 2013. a
Eckermann, S. D., Hoppel, K. W., Coy, L., McCormack, J. P., Siskind, D., Nielsen, K., Kochenash, A., Stevens, M. H., Englert, C. R., Singer, W., and Hervig, M.: High-Altitude data assimilation system experiments for the northern summer mesosphere season of 2007, J. Atmos. Sol.-Terr. Phy., 71, 531–551, https://doi.org/10.1016/j.jastp.2008.09.036, 2009. a
Eckermann, S. D., Ma, J., Hoppel, K. W., Kuhl, D., Allen, D. R., Doyle, J., Viner, K. C., Ruston, B. C., Baker, N. L., Swadley, S. D., Whitcomb, T. R., Reynolds, C. A., Xu, L., Kaifler, N., Keifler, B., Reid, I. M., Murphy, D. J., and Love, P. T.: High-Altitude (0–100 km) global atmospheric reanalysis system: Description and application to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), Mon. Weather Rev., 146, 2639–2666, https://doi.org/10.1175/MWR-D-17-0386.1, 2018 (data available at: https://map.nrl.navy.mil (cd to map/pub/nrl/navgem-ha/2013) in netCDF format, last access: 1 April 2022). a, b, c
Emmert, J. T., Drob, D. P., Picone, J. M., Siskind, D. E., Jones Jr., M., Mlynczak, M. G., Bernath, P. F., Chu, X., Doornbos, E., Funke, B., Goncharenko, L. P., Hervig, M. E., Schwartz, M. J., Sheese, P. E., Vargas, F., Williams, B. P., and Yuan, T.: NRLMSIS 2.0: A Whole-Atmosphere Empirical Model of Temperature and Neutral Species Densities, Earth Space Sci., 8, e2020EA001321, https://doi.org/10.1029/2020EA001321, 2021. a
Erwin, S. and Berger, B.: A race against time to replace aging military weather satellites, https://spacenews.com/a-race-against-time-to-replace-aging-military-weather-satellites/ (last access: 17 December 2023), 2021. a
Fang, T., Akmaev, R., Fuller-Rowell, T., Wu, F. Maruyama, N., and Millward, G.: Longitudinal and day-to-day variability in the ionosphere from lower atmosphere tidal forcing, Geophys. Res. Lett., 40, 2523–2528, https://doi.org/10.1002/grl.50550, 2013. a
Forbes, J. and Zhang, X.: Quasi 2-day oscillation of the ionosphere: A statistical study, J. Atmos. Sol.-Terr. Phy., 59, 1025–1034, https://doi.org/10.1016/S1364-6826(96)00175-7, 1997. a
Forbes, J., Palo, S., and Zhang, X.: Variability of the ionosphere, J. Atmos. Sol.-Terr. Phy., 62, 685–693, https://doi.org/10.1016/S1364-6826(00)00029-8, 2000. a
Forbes, J., Russell, J., Miyahara, S., Zhang, X., Palo, S., Mlynczak, M., Mertens, C., and Hagan, M.: Troposphere-thermosphere tidal coupling as measured by the SABER instrument on TIMED during July–September 2002, J. Geophys. Res., 111, A10S06, https://doi.org/10.1029/2005JA011492, 2006. a, b
Forbes, J., Zhang, X., Palo, S., Russell, J., Mertens, C., and Mlynczak, M.: Tidal variability in the ionospheric dynamo region, J. Geophys. Res., 113, A02310, https://doi.org/10.1029/2007JA012737, 2008. a
Fuller‐Rowell, T., Fang, T., Wang, H., Matthias, V., Hoffmann, P., Hocke, K., and Studer, S.: Impact of Migrating Tides on Electrodynamics During the January 2009 Sudden Stratospheric Warming, in: Ionospheric space weather: longitude and hemispheric dependencies and lower atmosphere forcing, edited by: Fuller-Rowell, T., Yizengaw, E., Doherty, P., and Basu, S., Vol. 220 of Geophysical Monograph Series, 165–174, American Geophysical Union, https://doi.org/10.1002/9781118929216.ch14, 2017. a
Goncharenko, L., Coster, A., Chau, L., and Valladares, C.: Impact of sudden stratospheric warmings on equatorial ionization anomaly, J. Geophys. Res., 115, A00G07, https://doi.org/10.1029/2010JA015400, 2010. a
Hagan, M., Maute, A., Roble, R., Richmond, A., Immel, T., and England, S.: Connections between deep tropical clouds and the Earth's ionosphere, Geophys. Res. Lett., 34, L20109, https://doi.org/10.1029/2007GL030142, 2007. a
Hanson, W. and Moffett, R.: Ionization transport effects in the equatorial F region, J. Geophys. Res., 71, 5559–5572, https://doi.org/10.1029/JZ071i023p05559, 1966. a
Heelis, R.: Electrodynamics in the low and middle latitude ionosphere: a tutorial, J. Atmos. Sol.-Terr. Phy., 66, 825–838, https://doi.org/10.1016/j.jastp.2004.01.034, 2004. a
Hines, C.: Internal atmospheric gravity waves at ionospheric heights, Can. J. Phys., 38, 1441–1481, https://doi.org/10.1139/p60-150, 1960. a
Hoppel, K. W., Eckermann, S. D., Coy, L., Nedoluha, G., and Allen, D. R.: Evaluation of SSMIS upper atmosphere sounding channels for high-altitude data assimilation, Mon. Weather Rev., 141, 3314–3330, https://doi.org/10.1175/MWR-D-13-00003.1, 2013. a
Huba, J., Joyce, G., and Fedder, J.: Sami2 is Another Model of the Ionosphere (SAMI2): A new low-latitude ionosphere model, J. Geophys. Res., 105, 23035–23053, https://doi.org/10.1029/2000JA000035, 2000. a
Huba, J., Joyce, G., Krall, J., Siefring, C., and Bernhardt, P.: Self-consistent modeling of equatorial dawn density depletions, Geophys. Res. Lett., 37, L03104, https://doi.org/10.1029/2009GL041492, 2010. a
Immel, T., E., S., England, S., Henderson, S., Hagan, M., Mende, S., Frey, H., Swenson, C., and Paxton, L.: Control of equatorial ionospheric morphology by atmospheric tides, Geophys. Res. Lett., 33, L15108, https://doi.org/10.1029/2006GL026161, 2006. a, b
Jin, H., Miyoshi, Y., Pancheva, D., Mukhtarov, P., Fujiwara, H. J., and Shinagawa, H.: Response of migrating tides to the stratospheric sudden warming in 2009 and their effects on the ionosphere studied by a whole atmosphere-ionosphere model GAIA with COSMIC and TIMED/SABER observations, J. Geophys. Res., 117, A10323, https://doi.org/10.1029/2012JA017650, 2012. a
Kuhl, D., Rosmond, T., Bishop, C., McLay, J., and Baker, N.: Comparison of hybrid ensemble/4DVAR and 4DVAR within the NAVDAS-AR data assimilation framework, Mon. Weather Rev., 141, 2740–2758, https://doi.org/10.1175/MWR-D-12-00182.1, 2013. a
Laprise, R.: The resolution of spectral models, B. Am. Meteorol. Soc., 73, 1453–1455, 1992. a
Laundal, K. M. and Richmond, A. D.: Magnetic Coordinate Systems, 206, 27–59, https://doi.org/10.1007/s11214-016-0275-y, 2017. a
Liu, H.-L.: Variability and predictability of the space environment as related to lower atmosphere forcing, Space Weather, 14, 634–658, https://doi.org/10.1002/2016SW001450, 2016. a, b
Liu, H.-L., Sassi, F., and Garcia, R.: Error growth in a whole atmosphere climate model, J. Atmos. Sci., 66, 173–186, https://doi.org/10.1175/2008JAS2825.1, 2009. a
Liu, H.-L., Foster, B., Hagan, M., McInerney, J., Maute, A., Qian, L., Richmond, A., Roble, R., Solomon, S., Garcia, R., Kinnison, D., Marsh, D., Smith, A., Richter, J., Sassi, F., and Oberheide, J.: Thermosphere extension of the Whole Atmosphere Community Climate Model, J. Geophys. Res., 115, A12302, https://doi.org/10.1029/2010JA015586, 2010. a, b
Liu, H.-L., Yudin, V., and Roble, R.: Day‐to‐day ionospheric variability due to lower atmosphere perturbations, Geophys. Res. Lett., 40, 665–670, https://doi.org/10.1002/grl.50125, 2013. a
Liu, H.-L., Bareedn, C., Foster, B., Lauritzen, P., Liu, J., Lu, G., Marsh, D., Maute, A., McInerney, J., Pedatella, N., Qian, L., Richmond, A., Roble, R., Solomon, S., Vitt, F., and Wang, W.: Development and validation of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X 2.0), J. Adv. Model. Earth Sy., 10, 381–402, https://doi.org/10.1002/2017MS001232, 2018 (data available at: https://www.cesm.ucar.edu/models/cesm2/download, last access: 1 April 2022). a, b
McCormack, J., Hoppel, K., Kuhl, D., de Wit, R., Stober, G., Espy, P., Baker, N., Brown, P., Fritts, D., Jacobi, C., Janches, D., Mitchell, N., Ruston, B., Swadley, S., Viner, K., Whitcomb, T., and Hibbins, R.: Comparison of mesospheric winds from high-altitude meteorological analysis system and meteor radar observations during the boreal winters of 2009–2010 and 2012–2013, J. Atmos. Sol.-Terr. Phy., 154, 132–166, https://doi.org/10.1016/j.jastp.2016.12.007, 2017 (data available at: https://map.nrl.navy.mil (cd to map/pub/nrl/navgem-ha/2013) in netCDF format, last access: 1 April 2022). a, b, c
McDonald, S., Sassi, F., and Mannucci, A.: SAMI3/SD-WACCM-X simulations of ionospheric variability during northern winter 2009, Space Weather, 13, 568–584, https://doi.org/10.1002/2015SW001223, 2015. a, b
McDonald, S., Sassi, F., Tate, J., McCormack, J., Kuhl, D., Drob, D., Metzler, C., and Mannucci, A.: Impact of non-migrating tides on the low latitude ionosphere during a sudden stratospheric warming event in January 2010, J. Atmos. Sol.-Terr. Phy., 171, 188–200, https://doi.org/10.1016/j.jastp.2017.09.012, 2018. a, b
Millward, G. H., Muller-Wodrag, I. C. F., Aylward, A. D., Fuller-Rowell, T. J., Richmond, A. D., and Moffett, R. J.: An investigation into the influ- ence of tidal forcing on F region equatorial vertical ion drift using a global ionosphere-thermosphere model with coupled electrodynamics, J. Geophys. Res., 106, 24733–24744, https://doi.org/10.1029/2000JA000342, 2001. a
Pedatella, N. and Maute, A.: Impact of semidiurnal tide on the midlatitude thermospheric wind and ionosphere during sudden stratospheric warmings, J. Geophys. Res., 120, 10740–10753, https://doi.org/10.1002/2015JA021986, 2015. a
Pedatella, N., Reader, K., Anderson, J., and Liu, H.-L.: Application of data assimilation in the whole atmosphere community climate model to the study of day-to-day variability in the middle and upper atmosphere, Geophys. Res. Lett., 40, 4469–4474, https://doi.org/10.1002/grl.50884, 2013. a
Pedatella, N., Liu, H.-L., Marsh, D., Reader, K., Anderson, J., Chau, J., Goncharenko, L., and Siddiqui, T.: Analysis and hindcast experiments of the 2009 sudden stratospheric warming in WACCMX+DART, J. Geophys. Res., 123, 3131–3153, https://doi.org/10.1002/2017JA025107, 2018. a
Pedatella, N., Liu, H.-L., Marsh, D., Reader, K., and Anderson, J. L.: Error growth in the mesosphere and lower thermosphere based on hindcast experiments in a whole atmosphere model, Space Weather, 17, 1442–1460, https://doi.org/10.1029/2019SW002221, 2019. a
Richmond, A.: Ionospheric electrodynamics using magnetix APEX coordinates, J. Geomag. Geoelec., 47, 191–212, https://doi.org/10.1002/2017JA025107, 1995. a
Rideout, W. and Coster, A. J.: Automated GPS processing for global total electron content data, GPS Solut., 10, 219–228, https://doi.org/10.1007/s10291-006-0029-5, 2006. a
Rishbeth, H.: Thermospheric winds and the F-rgion: A review, J. Atmos. Sol.-Terr. Phy., 34, 1–47, https://doi.org/10.1016/0021-9169(72)90003-7, 1972. a
Sassi, F., Liu, H.-L., Ma, J., and Garcia, R.: The lower thermosphere during the northern hemisphere winter of 2009: A modeling study using high-altitude data assimilation products in WACCM-X, J. Geophys. Res., 118, 8954–8968, https://doi.org/10.1002/jgrd.50632, 2013. a
Sassi, F., McCormack, J., and McDonald, S.: Whole atmosphere coupling on intraseasonal and interseasonal time scales: a potential source of increased predictive capability, Radio Sci., 54, 913–933, https://doi.org/10.1029/2019RS006847, 2019. a, b
Sassi, F., McCormack, J., Tate, J., Kuhl, D., and Baker, N.: Assessing the impact of middle atmosphere observations on day-to-day variability in lower thermospheric winds using WACCM-X, J. Atmos. Sol.-Terr. Phy., 212, 105486, https://doi.org/10.1016/j.jastp.2020.105486, 2020. a, b, c, d, e, f, g, h, i
Solomon, S. and Qian, L.: Solar extreme-ultraviolet irradiance for general circulation models, J. Geophys. Res., 110, A10306, https://doi.org/10.1029/2005JA011160, 2005. a
van der Meeren, C., Laundal, K. M., Burrell, A. G., Lamarche, L. L., Starr, G., Reimer, A. S., and Morschhauser, A.: aburrell/apexpy: ApexPy Version 2.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.7818719, 2023. a
VanZandt, T. E., Clark, W. L., and Warnock, J. M.: Magnetic Apex Coordinates: A Magnetic Coordinate System for the Ionospheric F2 Layer, J. Geophys. Res., 77, 2406–2411, https://doi.org/10.1029/JA077i013p02406, 1972. a
Wang, H., Fuller-Rowell, T., Akmaev, R., Hu, M., Kliest, D., and Iredell, M.: First simulations with a whole atmosphere data assimilation and forecvast system: The January 2009 major sudden stratospheric warming, J. Geophys. Res., 116, A12321, https://doi.org/10.1029/2011JA017081, 2011. a, b
Weimer, D.: Models of high-latitude electric potentials derived with a least error fit of spherical harmonic coefficients, J. Geophys. Res., 100, 19595–19607, https://doi.org/10.1029/95JA01755, 1995. a
Weimer, D.: Improved ionospheric electrodynamics models and application to calculating Joule heating rates, J. Geophys. Res., 110, A05306, https://doi.org/10.1029/2004JA010884, 2005. a
Wu, T.-Y., Liu, J.-Y., Chang, L., Lin, C.-H., and Chiu, Y.-C.: Equatorial ionization anomaly response to lunar phase and stratospheric sudden warming, Sci. Rep., 11, 14695, https://doi.org/10.1038/s41598-021-94326-x, 2021. a
Editor-in-chief
This paper gives an outlook on what will happen to the predictability of the ionosphere and upper atmosphere in the case we no longer should have relevant measurements in the mesosphere. It shows how important global measurements in the mesosphere are for the predictability of the variability of the ionosphere and thus for our communication and navigation systems. This is particularly important because the satellites that currently provide us with observations of the mesosphere have all already far exceeded their originally planned lifetimes (e.g. SABER, MLS, ...).
This paper gives an outlook on what will happen to the predictability of the ionosphere and...
Short summary
This study shows how middle-atmospheric data (starting at 40 km) affect day-to-day ionospheric variability. We do this by using lower atmospheric measurements that include and exclude the middle atmosphere in a coupled ionosphere–thermosphere model. Comparing the two simulations reveals differences in two thermosphere–ionosphere coupling mechanisms. Additionally, comparison against observations showed that including the middle-atmospheric data improved the resulting ionosphere.
This study shows how middle-atmospheric data (starting at 40 km) affect day-to-day ionospheric...