NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format)
d337000
| DOI: 10.5065/Z83F-N512
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This dataset has been cited 51 times. See a list of published works that have cited this dataset Published works that have cited this dataset:
2026
Liu, W., Y. Xue, X. Ling, C. Li, B. He, and L. Han, 2026: Study on CO <sub>2</sub> concentration assimilation by integrating a multi-source satellite fusion dataset based on the WRF-Chem model. International Journal of Remote Sensing, 1-36, https://doi.org/10.1080/01431161.2026.2612845
Zhang, Z., Y. Liu, X. Ma, Z. Li, P. Xu, J. Zhang, M. Min, D. Di, B. Li, and J. Li, 2026: A 1 km hourly high-resolution 3D wind field dataset over the Yangtze River Delta incorporating dynamical downscaling, observational assimilation, and land use updates. Earth System Science Data, 18(3), 1683-1701, https://doi.org/10.5194/essd-18-1683-2026
2025
Chen, H., T. Sun, K. Zhao, Y. Chen, A. Zhou, and C. Tong, 2025: Dual-Polarization Radar Data Assimilation Based on Hydrometeor Classification and Its Impact on Severe Weather Prediction. Journal of Geophysical Research: Atmospheres, 130(12), https://doi.org/10.1029/2024JD042797
Deng, Y., X. Wang, X. Fu, N. Wang, H. Yang, S. Zhao, X. Guo, J. Lang, Y. Zhou, and D. Chen, 2025: Enhancing Forecasting Capabilities Through Data Assimilation: Investigating the Core Role of WRF 4D-Var in Multidimensional Meteorological Fields. Atmosphere, 16(11), https://doi.org/10.3390/atmos16111286
Do, P., J. S. Haase, I. H. Baños, P. Hordyniec, and B. Cao, 2025: Impact of Airborne Radio Occultation Observations on Short Term Precipitation Forecasts of an Atmospheric River. Geophysical Research Letters, 52(13), https://doi.org/10.1029/2025GL115639
Doglioni, G., S. Serafin, M. Weissmann, G. Ferrari, and D. Zardi, 2025: Impact of the Assimilation of Surface Observations on Limited-Area Forecasts Over Complex Terrain. Meteorological Applications, 32(5), https://doi.org/10.1002/met.70107
Sun, T., J. J. Guerrette, Z. Liu, J. Ban, B. Jung, I. Hernandez Banos, and C. Snyder, 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. Geoscientific Model Development, 18(22), 8569-8587, https://doi.org/10.5194/gmd-18-8569-2025
Xu, D., T. Song, H. Li, J. Min, J. Luo, and F. Shen, 2025: Four-Dimensional Variational Assimilation of Precipitation Data With the Large-Scale Analysis Constraint in the 21.7 Extreme Rainfall Event in China. Journal of Geophysical Research: Atmospheres, 130(7), https://doi.org/10.1029/2024jd042522
Zheng, Y., C. Lu, Z. Wu, Z. Guan, J. Li, Z. Wang, and C. Liu, 2025: Assimilation of high-resolution GNSS tropospheric delays and its effects on a severe convective event nowcasting. Atmospheric Research, 314, 107785, https://doi.org/10.1016/j.atmosres.2024.107785
2024
Bray, M. t., and S. m. Cavallo, 2024: Investigating Arctic Cyclone-Tropopause Polar Vortex Interactions with Idealized Observing System Simulation Experiments. Monthly Weather Review, 152(7), 1445-1467, https://doi.org/10.1175/MWR-D-23-0215.1
Chandragiri, M. K., S. Dubey, S. B. Roy, and J. P. George, 2024: Optimization of hybrid data assimilation for cases of very heavy rainfall events over the Indian region. Bulletin of Atmospheric Science and Technology, 5(1), https://doi.org/10.1007/s42865-024-00087-6
Jung, B., B. Ménétrier, C. Snyder, Z. Liu, J. J. Guerrette, J. Ban, I. H. Baños, Y. G. Yu, and W. C. Skamarock, 2024: 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). Geoscientific Model Development, 17(9), 3879-3895, https://doi.org/10.5194/gmd-17-3879-2024
Kim, D., and H. M. Kim, 2024: Adjoint-based observation impact on meteorological forecast errors in the Arctic. Quarterly Journal of the Royal Meteorological Society, 150, 5403-5421, https://doi.org/10.1002/qj.4876
Kim, D., and H. M. Kim, 2024: Design of buoy observation network over the Arctic Ocean. Cold Regions Science and Technology, 218, https://doi.org/10.1016/j.coldregions.2023.104087
Kim, D., and H. M. Kim, 2024: Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic. Royal Society Open Science, 11(9), https://doi.org/10.1098/rsos.240594
Marvasti, A., and S. Dakhlia, 2024: Moral hazard and selection bias in insurance markets: Evidence from commercial fisheries. Southern Economic Journal, 90(3), 682-700, https://doi.org/10.1002/soej.12666
Munsi, A., A. P. Kesarkar, J. N. Bhate, and V. S. Tallapragada, 2024: Helicity: A Possible Indicator of Negative Feedback Initiation of Tropical Cyclone-Ocean Interaction. Earth and Space Science, 11(1), https://doi.org/10.1029/2023EA003211
Munsi, A., A. P. Kesarkar, and J. Bhate, 2024: Sensitivity of simulation of rapidly intensified tropical cyclones to local planetary boundary layer scheme. Modeling Earth Systems and Environment, 10, 3881-3896, https://doi.org/10.1007/s40808-024-01984-7
Qin, L., Y. Chen, D. Meng, X. Cheng, and P. Zhang, 2024: Variational All-Sky Assimilation Framework for MWHS-II With Hydrometeors Control Variables and Its Impacts on Analysis and Forecast of Typhoon Cases. Journal of Advances in Modeling Earth Systems, 16(10), https://doi.org/10.1029/2023MS004153
Tai, S., Z. Feng, J. Marquis, and J. Fast, 2024: Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm. Journal of Geophysical Research: Atmospheres, 129(19), https://doi.org/10.1029/2024JD041004
Teng, H., Y. Kuo, and J. M. Done, 2024: Contribution of radio occultation data to atmospheric river landfall forecasts in the eastern North Pacific. Quarterly Journal of the Royal Meteorological Society, 151, https://doi.org/10.1002/qj.4906
Xu, D., X. Zhang, J. Min, and F. Shen, 2024: Impacts of Assimilating All-Sky FY-4A AGRI Satellite Infrared Radiances on the Prediction of Super Typhoon In-Fa During the Period With Abnormal Changes. Journal of Geophysical Research: Atmospheres, 129(11), https://doi.org/10.1029/2024jd040784
Xu, H., Y. Zhao, D. Zhao, Y. Duan, and X. Xu, 2024: Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model. Environmental Research Letters, 19(5), https://doi.org/10.1088/1748-9326/ad41f0
2023
Cherubini, T., P. Antonelli, S. Businger, and P. Scaccia, 2023: Assimilation of Transformed Retrievals From Satellite High-Resolution Infrared Data Over the Central Pacific Area. Journal of Geophysical Research: Atmospheres, 128(21), https://doi.org/10.1029/2022JD038153
Guerrette, J. J., Z. Liu, C. Snyder, B. Jung, C. S. Schwartz, J. Ban, S. Vahl, Y. Wu, I. H. Baños, Y. G. Yu, S. Ha, Y. Trémolet, T. Auligné, C. Gas, B. Ménétrier, A. Shlyaeva, M. Miesch, S. Herbener, E. Liu, D. Holdaway, and B. T. Johnson, 2023: 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. Geoscientific Model Development, 16(23), 7123-7142, https://doi.org/10.5194/gmd-16-7123-2023
Honda, T., 2023: Development of a Polar Mesocyclone and Associated Environmental Characteristics During the Heavy Snowfall Event in Sapporo, Japan, in Early February 2022. Journal of Geophysical Research: Atmospheres, 128(12), https://doi.org/10.1029/2022jd037774
Kim, D., and H. M. Kim, 2023: Evaluation of observation impact on the meteorological forecasts associated with heat wave in 2018 over East Asia using observing system experiments. Heliyon, 9(12), https://doi.org/10.1016/j.heliyon.2023.e23064
Lin, H., J. Sun, T. M. Weckwerth, E. Joseph, and J. Kay, 2023: Assimilation of New York State Mesonet Surface and Profiler Data for the 21 June 2021 Convective Event. Monthly Weather Review, 151(2), 485-507, https://doi.org/10.1175/MWR-D-22-0136.1
Noh, Y., Y. Choi, H. Song, K. Raeder, J. Kim, and Y. Kwon, 2023: Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)-RTTOV (v12.3). Geoscientific Model Development, 16(18), 5365-5382, https://doi.org/10.5194/gmd-16-5365-2023
Peng, Z., L. Lei, Z. Tan, M. Zhang, A. Ding, and X. Kou, 2023: Dynamics-based estimates of decline trend with fine temporal variations in China's PM<sub>2.5</sub> emissions. Atmospheric Chemistry and Physics, 23(22), 14505-14520, https://doi.org/10.5194/acp-23-14505-2023
Teng, H., Y. Kuo, and J. M. Done, 2023: Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific. Geophysical Research Letters, 50(5), https://doi.org/10.1029/2021GL096750
2022
Boyaj, A., H. P. Dasari, Y. V. R. Rao, K. Ashok, and I. Hoteit, 2022: Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India. Meteorological Applications, 29(6), https://doi.org/10.1002/met.2103
Ha, S., 2022: Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme. Geoscientific Model Development, 15(4), 1769-1788, https://doi.org/10.5194/gmd-15-1769-2022
Huang, Y., J. Wei, J. Jin, Z. Zhou, and Q. Gu, 2022: CO Fluxes in Western Europe during 2017-2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations. Remote Sensing, 14(5), 1133, https://doi.org/10.3390/rs14051133
Koshin, D., K. Sato, M. Kohma, and S. Watanabe, 2022: An update on the 4D-LETKF data assimilation system for the whole neutral atmosphere. Geoscientific Model Development, 15(5), 2293-2307, https://doi.org/10.5194/gmd-15-2293-2022
Li, X., J. B. Roberts, J. Srikishen, J. L. Case, W. A. Petersen, G. Lee, and C. R. Hain, 2022: Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018. Geoscientific Model Development, 15(13), 5287-5308, https://doi.org/10.5194/gmd-15-5287-2022
Li, Z., Z. Ma, P. Wang, A. H. N. Lim, J. Li, J. A. Jung, T. J. Schmit, and H. Huang, 2022: An Objective Quality Control of Surface Contamination Observations for ABI Water Vapor Radiance Assimilation. Journal of Geophysical Research: Atmospheres, 127(15), https://doi.org/10.1029/2021jd036061
Liu, Z., C. Snyder, J. J. Guerrette, B. Jung, J. Ban, S. Vahl, Y. Wu, Y. Trémolet, T. Auligné, B. Ménétrier, A. Shlyaeva, S. Herbener, E. Liu, D. Holdaway, and B. T. Johnson, 2022: 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. Geoscientific Model Development, 15(20), 7859-7878, https://doi.org/10.5194/gmd-15-7859-2022
Yang, E., H. M. Kim, and D. Kim, 2022: Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis. Earth System Science Data, 14(4), 2109-2127, https://doi.org/10.5194/essd-14-2109-2022
2021
Kirthiga, S. M., B. Narasimhan, and C. Balaji, 2021: A multi-physics ensemble approach for short-term precipitation forecasts at convective permitting scales based on sensitivity experiments over southern parts of peninsular India. Journal of Earth System Science, 130(2), https://doi.org/10.1007/s12040-021-01556-8
Lam, M., and J. C. Fung, 2021: Model Sensitivity Evaluation for 3DVAR Data Assimilation Applied on WRF with a Nested Domain Configuration. Atmosphere, 12(6), 682, https://doi.org/10.3390/atmos12060682
Zhu, B., Z. Pu, A. W. Putra, and Z. Gao, 2021: Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra. Remote Sensing, 14(1), 42, https://doi.org/10.3390/rs14010042
2020
Balasubramanian, S., D. M. McFarland, S. Koloutsou-Vakakis, K. Fu, R. Menon, C. Lehmann, and M. J. Rood, 2020: Effect of grid resolution and spatial representation of NH<sub>3</sub> emissions from fertilizer application on predictions of NH<sub>3</sub> and PM<sub>2.5</sub> concentrations in the United States Corn Belt. Environmental Research Communications, 2(2), https://doi.org/10.1088/2515-7620/ab6c01
Koshin, D., K. Sato, K. Miyazaki, and S. Watanabe, 2020: An ensemble Kalman filter data assimilation system for the whole neutral atmosphere. Geoscientific Model Development, 13(7), 3145-3177, https://doi.org/10.5194/gmd-13-3145-2020
Lin, L., and Z. Pu, 2020: Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land-Atmosphere Data Assimilation with GSI-EnKF. Monthly Weather Review, 148(7), 2863-2888, https://doi.org/10.1175/MWR-D-19-0370.1
Strauss, L., S. Serafin, and M. Dorninger, 2020: Skill and Potential Economic Value of Forecasts of Ice Accretion on Wind Turbines. Journal of Applied Meteorology and Climatology, 59(11), 1845-1864, https://doi.org/10.1175/JAMC-D-20-0025.1
2019
Lin, L., and Z. Pu, 2019: Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah. Monthly Weather Review, 147(12), 4345-4366, https://doi.org/10.1175/MWR-D-19-0017.1
2018
Gao, X., S. Gao, and Y. Yang, 2018: A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast. Atmosphere, 9(9), 346, https://doi.org/10.3390/atmos9090346
Wanik, D. W., E. N. Anagnostou, M. Astitha, B. M. Hartman, G. M. Lackmann, J. Yang, D. Cerrai, J. He, and M. E. B. Frediani, 2018: A Case Study on Power Outage Impacts from Future Hurricane Sandy Scenarios. Journal of Applied Meteorology and Climatology, 57(1), 51-79, https://doi.org/10.1175/JAMC-D-16-0408.1
2017
He, J., D. W. Wanik, B. M. Hartman, E. N. Anagnostou, M. Astitha, and M. E. B. Frediani, 2017: Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network. Risk Analysis, 37(3), 441-458, https://doi.org/10.1111/risa.12652
Xue, T., J. Xu, Z. Guan, H. Chen, L. S. Chiu, and M. Shao, 2017: An assessment of the impact of ATMS and CrIS data assimilation on precipitation prediction over the Tibetan Plateau. Atmospheric Measurement Techniques, 10(7), 2517-2531, https://doi.org/10.5194/amt-10-2517-2017