Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8569-2025
© Author(s) 2025. 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-18-8569-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 Nov 2025
Development and technical paper |
| 14 Nov 2025
| 14 Nov 2025
All-sky AMSU-A radiance data assimilation using the gain-form of Local Ensemble Transform Kalman filter within MPAS-JEDI-2.1.0: implementation, tuning, and evaluation
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Jonathan J. Guerrette
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
now at: Tomorrow.io, Golden, CO 80401, USA
Zhiquan Liu
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Junmei Ban
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Byoung-Joo Jung
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Ivette Hernandez Banos
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Chris Snyder
NSF National Center for Atmospheric Research, Boulder, CO 80301, USA
Related authors
Tao Sun, I-Han Chen, Craig S. Schwartz, Zhiquan Liu, Hugh Zhang, and Dale Barker
EGUsphere, https://doi.org/10.5194/egusphere-2026-2476, https://doi.org/10.5194/egusphere-2026-2476, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We implemented an effective way to use weather radar observations in a modern weather analysis and forecasting system. By linking radar information with temperature, moisture, and rainfall processes, the method enables the model to adjust these conditions more consistently. Case studies show clear improvements in the location and intensity of rainfall forecasts over the deep tropics.
Tao Sun, I-Han Chen, Craig S. Schwartz, Zhiquan Liu, Hugh Zhang, and Dale Barker
EGUsphere, https://doi.org/10.5194/egusphere-2026-2476, https://doi.org/10.5194/egusphere-2026-2476, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We implemented an effective way to use weather radar observations in a modern weather analysis and forecasting system. By linking radar information with temperature, moisture, and rainfall processes, the method enables the model to adjust these conditions more consistently. Case studies show clear improvements in the location and intensity of rainfall forecasts over the deep tropics.
Tzu-Chin Tsai, Jen-Ping Chen, Zhiquan Liu, Siou-Ying Jiang, Rong Kong, Ying-Jhang Wu, Junmei Ban, Ling-Feng Hsiao, Yu-Shuang Tang, Pao-Liang Chang, and Jing-Shan Hong
EGUsphere, https://doi.org/10.22541/essoar.177248713.30739498/v1, https://doi.org/10.22541/essoar.177248713.30739498/v1, 2026
Short summary
Short summary
Weather radars observe rain and ice inside clouds, but models often cannot fully use this information because their cloud physics schemes do not describe the particle properties needed to simulate radar signals. This study develops a new cloud microphysics scheme directly linked to a radar operator that uses the same particle information. Tests using an idealized and a real case show that TCWA2 can reproduce observed radar features, supporting improved radar-based weather forecasting.
Junmei Ban, Zhiquan Liu, Byoung-Joo Jung, Ivette Hernandez Banos, Benjamin Ruston, and Andrew Collard
EGUsphere, https://doi.org/10.5194/egusphere-2026-1047, https://doi.org/10.5194/egusphere-2026-1047, 2026
Short summary
Short summary
The assimilation of all-sky radiances in MPAS–JEDI, the data assimilation system for the Model for Prediction Across Scales–Atmosphere (MPAS-A) based on the Joint Effort for Data Assimilation Integration (JEDI), has been extended in this study to incorporate ATMS observations. Month-long cycling experiments demonstrate consistent and encouraging improvements in dynamical, thermodynamic, and moisture-related fields resulting from the assimilation of all-sky ATMS radiances.
Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
Geosci. Model Dev., 19, 731–754, https://doi.org/10.5194/gmd-19-731-2026, https://doi.org/10.5194/gmd-19-731-2026, 2026
Short summary
Short summary
Ground-based microwave radiometers (GMWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from GMWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
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.
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.
Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma
Atmos. Chem. Phys., 21, 13747–13761, https://doi.org/10.5194/acp-21-13747-2021, https://doi.org/10.5194/acp-21-13747-2021, 2021
Short summary
Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
Cited articles
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, Bull. Amer. Meteor. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009.
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Bauer, P., Geer, A. J., Lopez, P., and Salmond, D.: Direct 4D-Var assimilation of all-sky radiances, Quart. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659, 2010.
Berner, J., Shutts, G. J., Leutbecher M., and Palmer, T. N.: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system, J. Atmos. Sci., 66, 603–626, https://doi.org/10.1175/2008JAS2677.1, 2009.
Bishop, C. H., Whitaker, J. S., and Lei, L.: Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization, Mon. Weather Rev., 145, 4575–4592, https://doi.org/10.1175/MWR-D-17-0102.1, 2017.
Bonavita, M., Geer, A. J., and Hamrud, M.: All-Sky Microwave Radiances Assimilated with an Ensemble Kalman Filter, Mon. Weather Rev., 148, 2737–2760, https://doi.org/10.1175/MWR-D-19-0413.1, 2020.
Bowler, N. E., Clayton, A. M., Jardak, M., Lee, E., Lorenc, A. C., Piccolo, C., Pring, S. R., Wlasak, M. A., Barker, D. M., Inverarity, G. W., and Swinbank, R.: Inflation and localization tests in the development of an ensemble of 4D-ensemble variational assimilations, Q. J. R. Meteorol. Soc., 143, 1280–1302, https://doi.org/10.1002/qj.3004, 2017.
Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteor. Soc., 125, 2887–2908, https://doi.org/10.1002/qj.49712556006, 1999.
Campbell, W. F., Bishop, C. H., and Hodyss, D.: Vertical covariance localization for satellite radiances in ensemble Kalman filters, Mon. Weather Rev., 138, 282–290, https://doi.org/10.1175/2009MWR3017.1, 2010.
Chan, M.-Y., Zhang, F., Chen, X., and Leung, L. R.: Potential impacts of assimilating all-sky satellite infrared radiances on convection-permitting analysis and prediction of tropical con-vection, Mon. Weather Rev., 148, 3203–3224, https://doi.org/10.1175/MWR-D-19-0343.1, 2020.
Chandramouli, K., Wang, X., Johnson, A., and Otkin, J.: Online nonlinear bias correction in ensemble Kalman filter to assimilate GOES-R all-sky radiances for the analysis and prediction of rapidly developing supercells, Journal of Advances in Modeling Earth Systems, 14, e2021MS002711, https://doi.org/10.1029/2021MS002711, 2022.
Cucurull, L., Derber, J. C., and Purser, R. J.: A bending angle forward operator for global positioning system radio occultation measurements, J. Geophys. Res.-Atmos., 118, 14–28, https://doi.org/10.1029/2012JD017782, 2013.
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/10.1002/qj.493, 2009.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. R. Meteorol. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003.
Fertig, E., Baek, S. J., Hunt, B., Ott, E., Szunyogh, I., Aravéquia, J., Kalnay, E., Li, H., and Liu, J.: Observation bias correction with an ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography, 61, 210–226, https://doi.org/10.1111/j.1600-0870.2008.00378.x, 2009.
Frolov, S., Shlyaeva, A., Huang, W., Sluka, T., Draper, C., Huang, B., Martin, C., Elless, T., Bhargava, K., and Whitaker J.: Local volume solvers for Earth system data assimilation: Implementation in the framework for Joint Effort for Data Assimilation Integration, Journal of Advances in Modeling Earth Systems, 16, e2023MS003692, https://doi.org/10.1029/2023MS003692, 2024.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999.
Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation, Q. J. R. Meteorol. Soc., 137, 2024–2037, https://doi.org/10.1002/qj.830, 2011
Geer, A. J., Baordo, F., Bormann, N., Chambon, P., English, S. J., Kazumori, M., Lawrence, H., Lean, P., Lonitz, K., and Lupu, C.: The growing impact of satellite observations sensitive to humidity, cloud and precipitation, Q. J. R. Meteorol. Soc., 143, 3189–3206, https://doi.org/10.1002/qj.3172, 2017.
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu, E. H., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N., Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data assimilation at operational weather forecasting centres, Q. J. R. Meteorol. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202, 2018.
Gilleland, E., Hering, A. S., Fowler, T. L., and Brown, B. G.: Testing the Tests: What Are the Impacts of Incorrect Assumptions When Applying Confidence Intervals or Hypothesis Tests to Compare Competing Forecasts?, Mon. Weather Rev., 146, 1685–1703, https://doi.org/10.1175/mwr-d-17-0295.1, 2018.
Grody, N., Weng, F., and Ferraro, R.: Application of AMSU for obtaining water vapor, cloud liquid water, precipitation, snow cover and sea ice concentration. Preprints, 10th Int. TOVS Study Conf., Boulder, CO, Int. ATOVS Working Group, 230–240, https://pubs.ssec.wisc.edu/research_Resources/publications/pdfs/ITSC10/grody01_ITSC10_1999.pdf (last access: 17 October 2025), 1999.
Grody, N., Zhao, J., Ferraro, R., Weng, F., and Boers, R.: Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit, J. Geophys. Res., 106, 2943–2953, https://doi.org/10.1029/2000JD900616, 2001.
Guerrette, J. J., Liu, Z., Snyder, C., Jung, B.-J., Schwartz, C. S., Ban, J., Vahl, S., Wu, Y., Baños, I. H., Yu, Y. G., Ha, S., Trémolet, Y., Auligné, T., Gas, C., Ménétrier, B., Shlyaeva, A., Miesch, M., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T.: Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations, Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, 2023.
Ha, S., Snyder, C., Skamarock, W. C., Anderson, J. L., and Collins, N.: Ensemble Kalman filter data assimilation for the Model for Prediction Across Scales (MPAS), Mon. Weather Rev., 145, 4673–4692, https://doi.org/10.1175/MWR-D-17-0145.1, 2017.
Healy, S. B. and Thépaut, J. N.: Assimilation experiments with CHAMP GPS radio occultation measurements, Q. J. R. Meteorol. Soc., 132, 605–623, https://doi.org/10.1256/qj.04.182, 2006.
Honda, T., Miyoshi, T., Lien, G., Nishizawa, S., Yoshida, R., Adachi, S. A., Terasaki, K., Okamoto, K., Tomita, H., and Bessho, K.: Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015), Mon. Weather Rev., 146, 213–229, https://doi.org/10.1175/MWR-D-16-0357.1, 2018.
Houtekamer, P. L., Lefaivre, L., Derome, J., Ritchie, H., and Mitchell, H. L.: A system simulation approach to ensemble prediction, Mon. Weather Rev., 124, 1225–1242, https://doi.org/10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2, 1996.
Hunt, B. R., Kostelich, E. J., and Szunyogh. I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D: Nonlinear Phenomena, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007.
Ingleby, B.: Global assimilation of air temperature, humidity, wind and pressure from surface stations, Q. J. Roy. Meteor. Soc., 141, 504–517, https://doi.org/10.1002/qj.2372, 2014.
Johnson, B. T., Dang, C., Stegmann, P., Liu, Q., Moradi, I., and Auligne, T.: The Community Radiative Transfer Model (CRTM): Community-Focused Collaborative Model Development Accelerating Research to Operations, Bull. Amer. Meteor. Soc., 104, E1817–E1830, https://doi.org/10.1175/BAMS-D-22-0015.1, 2023.
Joint Center for Satellite Data Assimilation and National Center for Atmospheric Research: JEDI-MPAS Data Assimilation System v2.1.0 (2.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.15201032, 2023.
Jung, B.-J., Ménétrier, B., Snyder, C., Liu, Z., Guerrette, J. J., Ban, J., Baños, I. H., Yu, Y. G., and Skamarock, W. C.: Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta), Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, 2024.
Kalnay, E., Ota, Y., Miyoshi, T., and Liu, J.: A simpler formulation of forecast sensitivity to observations: Application to ensemble Kalman filters, Tellus, 64A, 18462, https://doi.org/10.3402/tellusa.v64i0.18462, 2012
Kotsuki, S., Ota, Y., and Miyoshi, T.: Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere, Q. J. R. Meteorol. Soc., 143, 2001–2015, https://doi.org/10.1002/qj.3060, 2017.
Lei, L., Whitaker, J. S., and Bishop, C.: Improving assimilation of radiance observations by implementing model space localization in an ensemble Kalman filter, Journal of Advances in Modeling Earth Systems, 10, 3221–3232, https://doi.org/10.1029/2018MS001468, 2018.
Lei, L., Whitaker, J. S., Anderson, J. L., and Tan, Z.: Adaptive localization for satellite radiance observations in an ensemble Kalman filter, Journal of Advances in Modeling Earth Systems, 12, e2019MS001693, https://doi.org/10.1029/2019MS001693, 2020
Liu, J. and Kalnay, E.: Estimating observation impact without adjoint model in an ensemble Kalman filter, Q. J. Roy. Meteor. Soc., 134, 1327–1335, https://doi.org/10.1002/qj.280, 2008.
Liu, Z., Snyder, C., Guerrette, J. J., Jung, B.-J., Ban, J., Vahl, S., Wu, Y., Trémolet, Y., Auligné, T., Ménétrier, B., Shlyaeva, A., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T.: Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation, Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, 2022.
Lonitz, K. and Geer, A. J.: New screening of cold-air outbreak regions used in 4D-Var all-sky assimilation. Technical Report 35, EUMETSAT/ECMWF Fellowship Programme Research Report Shinfield Park, Reading, https://www.ecmwf.int/node/10777 (last access: 17 October 2025), 2015.
Migliorini, S. and Candy, B.: All-sky satellite data assimilation of microwave temperature sounding channels at the Met Office, Q. J. Roy. Meteor. Soc., 145, 867–883,https://doi.org/10.1002/qj.3470, 2019.
Minamide, M. and Zhang, F.: Assimilation of All-Sky Infrared Radiances from Himawari-8 and Impacts of Moisture and Hydrometer Initialization on Convection-Permitting Tropical Cyclone Prediction, Mon. Weather Rev., 146, 3241–3258, https://doi.org/10.1175/MWR-D-17-0367.1, 2018.
Miyoshi, T., Sato, Y., and Kadowaki, T.: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system, Mon. Weather Rev., 138, 2846–2866, https://doi.org/10.1175/2010mwr3209.1, 2010.
NASA: Goddard Earth Sciences Data and Information Services Center (GES DISC) OPeNDAP data access portal, NASA Goddard Space Flight Center [data set], https://sounder.gesdisc.eosdis.nasa.gov/opendap, last access: 17 October 2025.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format), Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/Z83F-N512, 2008.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GDAS Satellite Data 2004–continuing, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/DWYZ-Q852, 2009.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D65D8PWK, 2015.
Nerger, L.: On serial observation processing in localized ensemble Kalman filters, Mon. Weather Rev., 143, 1554–1567, https://doi.org/10.1175/MWR-D-14-00182.1, 2015.
NOAA: Global Ensemble Forecast System (GEFS), NOAA [data set], https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast, last access: 17 October 2025.
Okamoto, K., Sawada, Y., and Kunii, M.: Comparison of assimilating all-sky and clear-sky infrared radiances from Himawari-8 in a mesoscale system, Q. J. R. Meteorol. Soc., 145, 745–766, https://doi.org/10.1002/qj.3463, 2019.
Okamoto, K., Ishibashi, T., and Okabe, I.: All-sky infrared radiance assimilation of a geostationary satellite in the Japan Meteorological Agency's global system, Q. J. R. Meteorol. Soc., 149, 2477–2503, https://doi.org/10.1002/qj.4516, 2023.
Park, J., Xue, M., and Liu, C.: Implementation and testing of radar data assimilation capabilities within the Joint Effort for Data Assimilation Integration framework with ensemble transformation Kalman filter coupled with FV3-LAM model, Geophysical Research Letters, 50, e2022GL102709, https://doi.org/10.1029/2022GL102709, 2023.
Rennie, M. P.: The impact of GPS radio occultation assimilation at the Met Office, Q. J. R. Meteorol. Soc., 136, 116–131, https://doi.org/10.1002/qj.521, 2010.
Shahabadi, M. B. and Buehner, M.: Implementation of All-Sky Assimilation of Microwave Humidity Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System , Mon. Wea. Rev., 152, 1027–1038, https://doi.org/10.1175/MWR-D-23-0227.1, 2024.
Shao, H., Derber, J., Huang, X.-Y., Hu, M., Newman, K., Stark, D., Lueken, M., Zhou, C., Nance, L., Kuo, Y.-H., and Brown, B.: Bridging research to operations transitions: Status and plans of community GSI, Bull. Amer. Meteor. Soc., 97, 1427–1440, https://doi.org/10.1175/BAMS-D-13-00245.1, 2016.
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and Ringler, T. D.: A Multiscale Nonhydrostatic Atmospheric Model Using Centroidal Voronoi Tessellations and C-Grid Staggering, Mon. Weather Rev., 140, 3090–3105, https://doi.org/10.1175/mwrd-11-00215.1, 2012.
Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146, 3445–3460, https://doi.org/10.1175/mwr-d-18-0155.1, 2018.
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490, https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003.
Tong, M., Zhu, Y., Zhou, L., Liu, E., Chen, M., Liu, Q., and Lin, S.: Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS, Mon. Weather Rev., 148, 2971–2995, https://doi.org/10.1175/MWR-D-19-0231.1, 2020.
Trémolet, Y. and Auligné T.: The Joint Effort for Data Assimilation Integration (JEDI), JCSDA Quarterly Newsletter, 66, 1–5, https://doi.org/10.25923/RB19-0Q26, 2020.
Wang, X., Chipilski, H. G., Bishop, C. H., Satterfield, E., Baker, N., and Whitaker, J. S.: A Multiscale Local Gain Form Ensemble Transform Kalman Filter (MLGETKF), Mon. Weather Rev., 149, 605–622, https://doi.org/10.1175/MWR-D-20-0290.1, 2021.
Weston, P., Geer, A. J., and Bormann, N.: Investigations into the assimilation of AMSU-A in the presence of cloud and precipitation. Technical Report 50, EUMETSAT/ECMWF Fellowship Programme Research Report, https://doi.org/10.21957/ewahn9ce, 2019.
Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002.
Whitaker, J. S. and Hamill, T. M.: Evaluating Methods to Account for System Errors in Ensemble Data Assimilation, Mon. Weather Rev., 140, 3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
Zhang, F., Snyder, C., and Sun, J.: Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter. Mon. Weather Rev., 132, 1238–1253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2, 2004.
Zhang, F., Minamide, M., and Clothiaux, E. E.: Potential impacts of assimilating all-sky infrared satellite radiances from GOES-R on convection-permitting analysis and prediction of tropical cyclones, Geophys. Res. Lett., 43, 2954–2963, https://doi.org/10.1002/2016GL068468, 2016.
Zhang, F., Minamide, M., Nystrom, R. G., Chen, X., Lin, S.-J., and Harris, L. M.: Improving Harvey forecasts with next-generation weather satellites: Advanced hurricane analysis and prediction with assimilation of GOES-R all-sky radiances, Bull. Amer. Meteor. Soc., 100, 1217–1222, https://doi.org/10.1175/BAMS-D-18-0149.1, 2019.
Zhu, L., Xue, M., Kong, R., and Min, J.: Direct Assimilation of All-Sky GOES-R ABI Radiances in GSI EnKF for the Analysis and Forecasting of a Mesoscale Convective System, Mon. Weather Rev., 151, 737–760, https://doi.org/10.1175/MWR-D-21-0293.1, 2023.
Zhu, Y., Liu, E., Mahajan, R., Thomas, C., Groff, D., Delst, P. V., Collard, A., Kleist, D., Treadon, R., and Derber, J. C.: All-Sky Microwave Radiance Assimilation in the NCEP's GSI Analysis System, Mon. Weather Rev., 144, 4709–4735, https://doi.org/10.1175/MWR-D-15-0445.1, 2016.
Zhu, Y., Gayno, G., Purser, R. J., Su, X., and Yang, R.: Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP, Mon. Weather Rev., 147, 2603–2620, https://doi.org/10.1175/MWR-D-18-0228.1, 2019.
Short summary
We evaluated a new ensemble data assimilation system that uses satellite observations in all weather conditions for global weather forecasts. The results show that including cloud- and precipitation-affected satellite data improves forecasts of moisture, wind, and clouds, especially in the tropics. This work highlights the potential of this new ensemble data assimilation system to enhance global weather forecasts.
We evaluated a new ensemble data assimilation system that uses satellite observations in all...
