{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:33Z","timestamp":1760146593800,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program of Shanghai Academic\/Technology Research Leader","award":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"],"award-info":[{"award-number":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"]}]},{"name":"National Natural Sciences Foundation of China","award":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"],"award-info":[{"award-number":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"]}]},{"name":"National Key R&amp;D Program of China","award":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"],"award-info":[{"award-number":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"]}]},{"name":"National Key R&amp;D Program of China","award":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"],"award-info":[{"award-number":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"]}]},{"name":"Youth Innovation Team for New Technologies and Assimilation Application of Satellite Microwave Data Processing, China Meteorological Administration","award":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"],"award-info":[{"award-number":["21XD1404500","42475168","22YFC3080500","22YFC3080500","CMA20240N10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Despite the well-known importance of radar data assimilation, there are limited studies on landfalling typhoons in terms of directly assimilating radar reflectivity data, especially using a reflectivity operator based on double-moment microphysics. In this study, radar reflectivity data assimilation experiments are conducted with an ensemble Kalman filter (EnKF), using simulated observations in an Observing System Simulation Experiment (OSSE) framework for the landfalling typhoon In-Fa. With an OSSE, it is convenient to analyze the impact of assimilation of radar reflectivity on analysis and forecast for various state variables, especially for hydrometeors. Our results show that the direct assimilation of radar reflectivity with EnKF does not introduce non-physical hydrometeors and is able to adjust well, not only to hydrometers, but also to some large-scale variables which are not directly related to reflectivity, especially in terms of temperature and vertical velocity. Though the most notable reduction in the Root Mean Square Errors (RMSEs) is observed through mixing the ratio of rainwater and snow, the analysis of other variables is also significantly improved with the accumulation of assimilation cycles. The correlation analysis reveals the strongest correlation between radar reflectivity data and hydrometeor-related variables as well as the correlation with certain large-scale variables, indicating that these cross-variables are updated well through the reliable multivariate ensemble covariance in the EnKF. As a result, an obvious improvement in typhoon intensity and precipitation forecast is obtained in the data assimilation experiment. The impact of assimilation on radar reflectivity can last for up to 15\u201316 h.<\/jats:p>","DOI":"10.3390\/rs16224286","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T06:06:54Z","timestamp":1731996414000},"page":"4286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Direct Assimilation of Radar Reflectivity Data with a Two-Moment Microphysics Scheme for a Landfalling Typhoon in an OSSE Framework"],"prefix":"10.3390","volume":"16","author":[{"given":"Ziyue","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China"},{"name":"Zhejiang Institute of Meteorological Science, Hangzhou 310051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyao","family":"Luo","sequence":"additional","affiliation":[{"name":"Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China"},{"name":"Key Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China"},{"name":"Key Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China"},{"name":"Key Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6638-1865","authenticated-orcid":false,"given":"Rui","family":"He","sequence":"additional","affiliation":[{"name":"Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China"},{"name":"Key Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1175\/BAMS-D-11-00263.1","article-title":"Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges","volume":"95","author":"Sun","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1175\/MWR3092.1","article-title":"3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part I: Cloud Analysis and Its Impact","volume":"134","author":"Hu","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1175\/MWR3093.1","article-title":"3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR","volume":"134","author":"Hu","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_4","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_5","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."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2009GL038658","DOI":"10.1029\/2009GL038658","article-title":"Assimilation of Coastal Doppler Radar Data with the ARPS 3DVAR and Cloud Analysis for the Prediction of Hurricane Ike (2008)","volume":"36","author":"Zhao","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1175\/MWR2898.1","article-title":"Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments","volume":"133","author":"Tong","year":"2005","journal-title":"Mon. Weather Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.46267\/j.1006-8775.2020.024","article-title":"Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting","volume":"26","author":"Min","year":"2020","journal-title":"J. Trop. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1175\/MWR-D-18-0033.1","article-title":"Direct Assimilation of Radar Reflectivity Data Using 3DVAR: Treatment of Hydrometeor Background Errors and OSSE Tests","volume":"147","author":"Liu","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1175\/2007MWR2071.1","article-title":"Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments","volume":"136","author":"Tong","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1175\/JTECH1835.1","article-title":"An OSSE Framework Based on the Ensemble Square Root Kalman Filter for Evaluating the Impact of Data from Radar Networks on Thunderstorm Analysis and Forecasting","volume":"23","author":"Xue","year":"2006","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1175\/2008MWR2691.1","article-title":"A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses","volume":"137","author":"Aksoy","year":"2009","journal-title":"Mon. Weather Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1175\/2009MWR3086.1","article-title":"A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts","volume":"138","author":"Aksoy","year":"2010","journal-title":"Mon. Weather Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2020GL090179","DOI":"10.1029\/2020GL090179","article-title":"Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble-Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand-Alone Regional FV3 Model at a Convection-Allowing Resolution","volume":"47","author":"Tong","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1175\/MWR-D-16-0162.1","article-title":"Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation","volume":"145","author":"Putnam","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Luo, J., Li, H., Xue, M., and Zhu, Y. (2022). Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019). Remote Sens., 14.","DOI":"10.3390\/rs14163987"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1175\/1520-0450(1975)014<1156:RRFCIN>2.0.CO;2","article-title":"Radar Reflectivity Factor Calculations in Numerical Cloud Models Using Bulk Parameterization of Precipitation","volume":"14","author":"Smith","year":"1975","journal-title":"J. Appl. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1175\/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2","article-title":"Bulk Parameterization of the Snow Field in a Cloud Model","volume":"22","author":"Lin","year":"1983","journal-title":"J. Clim. Appl. Meteorol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1175\/MWR-D-16-0231.1","article-title":"Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell","volume":"145","author":"Wang","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.1175\/2007MWR2083.1","article-title":"Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables","volume":"136","author":"Jung","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5095","DOI":"10.1175\/2008MWR2387.1","article-title":"Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization","volume":"136","author":"Thompson","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1175\/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2","article-title":"Ensemble Data Assimilation without Perturbed Observations","volume":"130","author":"Whitaker","year":"2002","journal-title":"Mon. Weather Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2008JD009944","DOI":"10.1029\/2008JD009944","article-title":"Radiative Forcing by Long-lived Greenhouse Gases: Calculations with the AER Radiative Transfer Models","volume":"113","author":"Iacono","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_24","unstructured":"Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M. (2008). A Description of the Advanced Research WRF Version 3, UCAR\/NCAR."},{"key":"ref_25","unstructured":"Tewari, M., Wang, W., Dudhia, J., LeMone, M.A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. (2004, January 12\u201316). Implementation and Verification of the United 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_26","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1175\/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2","article-title":"The Kain\u2013Fritsch Convective Parameterization: An Update","volume":"43","author":"Kain","year":"2004","journal-title":"J. Appl. Meteorol."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"7183","DOI":"10.1029\/2000JD900719","article-title":"Summarizing Multiple Aspects of Model Performance in a Single Diagram","volume":"106","author":"Taylor","year":"2001","journal-title":"J. Geophys. Res. Atmos."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4286\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:34:03Z","timestamp":1760114043000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,17]]},"references-count":28,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224286"],"URL":"https:\/\/doi.org\/10.3390\/rs16224286","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,11,17]]}}}