{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:38:50Z","timestamp":1765233530650,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T00:00:00Z","timestamp":1684972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A4A1032646","2022R1A2C1012361"],"award-info":[{"award-number":["2021R1A4A1032646","2022R1A2C1012361"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (\u00b1250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.<\/jats:p>","DOI":"10.3390\/rs15112760","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T02:00:19Z","timestamp":1685066419000},"page":"2760","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4207-1520","authenticated-orcid":false,"given":"Miranti Indri","family":"Hastuti","sequence":"first","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Republic of Korea"},{"name":"Kualanamu Meteorological Station, The Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Jl. Tengku Heran, Beringin, Deli Serdang 20552, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6133-6040","authenticated-orcid":false,"given":"Ki-Hong","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Republic of Korea"}]},{"given":"Ji-Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dance, S.L., Ballard, S.P., Bannister, R.N., Clark, P., Cloke, H.L., Darlington, T., Flack, D.L.A., Gray, S.L., Hawkness-Smith, L., and Husnoo, N. (2019). Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project. Atmosphere, 10.","DOI":"10.3390\/atmos10030125"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1002\/qj.3737","article-title":"Hourly 4D-Var in the Met Office UKV operational forecast model","volume":"146","author":"Milan","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1002\/qj.3977","article-title":"Radar reflectivity assimilation using hourly cycling 4D-Var in the Met Office Unified Model","volume":"147","author":"Simonin","year":"2021","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1175\/WAF-D-14-00095.1","article-title":"Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations","volume":"30","author":"Min","year":"2015","journal-title":"Weather Forecast."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1175\/JAM2248.1","article-title":"Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case","volume":"44","author":"Xiao","year":"2005","journal-title":"J. Appl. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1175\/JAS-D-11-0162.1","article-title":"Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification","volume":"69","author":"Gao","year":"2012","journal-title":"J. Atmos. Sci. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1175\/JAMC-D-12-0120.1","article-title":"Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events","volume":"52","author":"Wang","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, J.-W., Min, K.-H., Lee, Y.-H., and Lee, G. (2020). X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area. Remote Sens., 12.","DOI":"10.3390\/rs12050893"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106062","DOI":"10.1016\/j.atmosres.2022.106062","article-title":"Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast","volume":"270","author":"Lee","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1175\/WAF-D-17-0108.1","article-title":"A Scheme to Assimilate \u201cNo Rain\u201d Observations from Doppler Radar","volume":"33","author":"Gao","year":"2018","journal-title":"Weather Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1175\/JTECH-D-17-0081.1","article-title":"Assimilation of Radar Radial Velocity and Reflectivity, Satellite Cloud Water Path, and Total Precipitable Water for Convective-Scale NWP in OSSEs","volume":"35","author":"Pan","year":"2018","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1175\/MWR-D-18-0418.1","article-title":"A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction","volume":"148","author":"Yang","year":"2020","journal-title":"Mon. Weather Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3319\/TAO.2000.11.1.157(COSMIC)","article-title":"Vandenberghe. Assimilation of GPS radio occultation data for numerical weather prediction","volume":"11","author":"Kuo","year":"2000","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1175\/MWR-D-19-0286.1","article-title":"The Impact of GPS RO Data on the Prediction of Tropical Cyclogenesis Using a Nonlocal Observation Operator: An Initial Assessment","volume":"148","author":"Chen","year":"2020","journal-title":"Mon. Weather Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s44195-022-00004-4","article-title":"Impact of assimilating Formosat-7\/COSMIC-II GNSS radio occultation data on heavy rainfall prediction in Taiwan","volume":"33","author":"Chang","year":"2022","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2103","DOI":"10.1002\/met.2103","article-title":"Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India","volume":"29","author":"Boyaj","year":"2022","journal-title":"Meteorolol. Appl."},{"key":"ref_17","first-page":"1","article-title":"GPS Radio Occultation Data Assimilation in the AREM Regional Numerical Weather Prediction Model for Flood Forecasts","volume":"2018","author":"Cheng","year":"2018","journal-title":"Adv. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2002.808226","article-title":"Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS","volume":"41","author":"King","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1002\/qj.3463","article-title":"Comparison of assimilating all-sky and clear-sky infrared radiances from Himawari-8 in a mesoscale system","volume":"145","author":"Okamoto","year":"2019","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s00376-020-0219-z","article-title":"Assimilating all-sky infrared radiances from Himawari-8 using the 3DVar method for the prediction of a severe storm over North China","volume":"38","author":"Xu","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1175\/MWR-D-19-0163.1","article-title":"Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms","volume":"147","author":"Zhang","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, L., Guan, J., and Zhang, M. (2020). Evaluation of combined satellite and radar data assimilation with POD-4DEnVar method on rainfall forecast. Appl. Sci., 10.","DOI":"10.3390\/app10165493"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1175\/MWR-D-19-0379.1","article-title":"Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System","volume":"148","author":"Jones","year":"2020","journal-title":"Mon. Weather Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, K., and Guan, P. (2023). The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts. Atmosphere, 14.","DOI":"10.3390\/atmos14020375"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1175\/MWR-D-13-00023.1","article-title":"Andersson. Influence of assimilating satellite derived atmospheric motion vector observations on numerical analyses and forecasts of tropical cyclone track and intensity","volume":"142","author":"Wu","year":"2014","journal-title":"Mon. Weather Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2021MS002484","DOI":"10.1029\/2021MS002484","article-title":"Impact of assimilating high-resolution Atmospheric Motion Vectors on convective scale short-term forecasts: 1. Observing system simulation experiment (OSSE)","volume":"13","author":"Zhao","year":"2021","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1175\/JTECH-D-17-0093.1","article-title":"Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia","volume":"35","author":"Kim","year":"2018","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2","article-title":"A three-dimensional variational data assimilation system for MM5: Implementation and initial results","volume":"132","author":"Barker","year":"2004","journal-title":"Mon. Weather Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1175\/BAMS-D-11-00167.1","article-title":"The weather research and forecasting (WRF) model\u2019s community variational\/ensemble data assimilation system: WRFDA","volume":"93","author":"Barker","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.2151\/jmsj1965.75.1B_181","article-title":"Unified notation for data assimilation: Operational, sequential and variational","volume":"75","author":"Ide","year":"1999","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1175\/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2","article-title":"The National Meteorological Center\u2019s spectral statistical-interpolation analysis system","volume":"120","author":"Parrish","year":"1992","journal-title":"Mon. Weather Rev."},{"key":"ref_32","unstructured":"Han, Y., Van Delst, P., Liu, Q., Weng, F., Yan, B., and Han, Y. (2006). JCSDA Community Radiative Transfer Model (CRTM)-Version 1, NESDIS. NOAA Technical Report."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1002\/qj.493","article-title":"Variational bias correction of satellite radiance data in the ERA\u2013Interim reanalysis","volume":"135","author":"Dee","year":"2009","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4017","DOI":"10.1175\/MWR-D-12-00083.1","article-title":"Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area Ensemble Kalman Filter","volume":"140","author":"Liu","year":"2012","journal-title":"Mon. Weather Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1002\/qj.2233","article-title":"Enhanced radiance bias correction in the National Centers for Environmental Prediction\u2019s Gridpoint Statistical Interpolation data assimilation system","volume":"140","author":"Zhu","year":"2014","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1175\/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2","article-title":"The use of TOVS cloud-cleared radiance in the NCEP SSI analysis system","volume":"126","author":"Derber","year":"1998","journal-title":"Mon. Weather Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1002\/qj.2242","article-title":"Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects","volume":"140","author":"Okamoto","year":"2014","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1002\/qj.3022","article-title":"Evaluation of IR radiance simulation for all-sky assimilation of Himawri-8\/AHI in a mesoscale NWP system","volume":"143","author":"Okamoto","year":"2017","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1029\/JA073i005p01819","article-title":"On the radio occultation method for studying planetary atmosphere","volume":"73","author":"Phinney","year":"1968","journal-title":"J. Geophys. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"39","DOI":"10.6028\/jres.050.006","article-title":"The constants in the equation for atmospheric refractivity index at radio frequencies","volume":"50","author":"Smith","year":"1953","journal-title":"J. Res. Natl. Inst. Stand. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s00376-015-4092-0","article-title":"Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm","volume":"32","author":"Ye","year":"2015","journal-title":"Adv. Atmos. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4600","DOI":"10.1002\/2016JD024867","article-title":"Geostationary satellite-based 6.7 \u03bcm band best water vapor information layer analysis over the Tibetan Plateau","volume":"121","author":"Di","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_43","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., and Powers, J.G. (2005). A Description of the Advanced Research WRF Version 2, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research. National Center for Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Emanuel, K.A., and Raymond, D.J. (1993). The Representation of Cumulus Convection in Numerical Models. Meteorological Monographs, American Meteorological Society.","DOI":"10.1007\/978-1-935704-13-3"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1175\/2009MWR2968.1","article-title":"Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models","volume":"138","author":"Lim","year":"2010","journal-title":"Mon. Weather Rev."},{"key":"ref_46","unstructured":"Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M.A., Mitchell, K., and Cuenca, R.H. (2004, January 12\u201316). Implementation and verification of the unified NOAH land surface model in the WRF model. Proceedings of the 20th Conference on Weather Analysis and Forecasting\/16th Conference on Numerical Weather Prediction, Seattle, WA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1175\/MWR3199.1","article-title":"A new vertical diffusion package with an explicit treatment of entrainment processes","volume":"134","author":"Hong","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3077","DOI":"10.1175\/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2","article-title":"Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model","volume":"46","author":"Dudhia","year":"1989","journal-title":"J. Atmos. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.1002\/qj.4144","article-title":"Examination of all-sky infrared radiance simulation of Himawari-8 for global data assimilation and model verification","volume":"147","author":"Okamoto","year":"2021","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1175\/MWR-D-16-0257.1","article-title":"Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance","volume":"145","author":"Minamide","year":"2017","journal-title":"Mon. Weather Rev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2760\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:42:17Z","timestamp":1760125337000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,25]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112760"],"URL":"https:\/\/doi.org\/10.3390\/rs15112760","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,25]]}}}