{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:10:44Z","timestamp":1772831444733,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202105330044"],"award-info":[{"award-number":["202105330044"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["41705133"],"award-info":[{"award-number":["41705133"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["202105330044"],"award-info":[{"award-number":["202105330044"]}]},{"name":"National Natural Science Foundation of China","award":["41705133"],"award-info":[{"award-number":["41705133"]}]},{"name":"Key Laboratory of Space Ocean Remote Sensing and Application","award":["202105330044"],"award-info":[{"award-number":["202105330044"]}]},{"name":"Key Laboratory of Space Ocean Remote Sensing and Application","award":["41705133"],"award-info":[{"award-number":["41705133"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26\u201327%. This may be useful information to the meteorological agencies and researchers.<\/jats:p>","DOI":"10.3390\/rs16020430","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:36:41Z","timestamp":1705923401000},"page":"430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8918-7857","authenticated-orcid":false,"given":"Changliang","family":"Shao","sequence":"first","affiliation":[{"name":"China Meteorological Administrator Meteorological Observation Centre, Beijing 100081, China"},{"name":"Alfred-Wegener-Institute, Helmholtz-Zentrum f\u00fcr Polar-und Meeresforschung (AWI), 27570 Bremerhaven, Germany"}]},{"given":"Lars","family":"Nerger","sequence":"additional","affiliation":[{"name":"Alfred-Wegener-Institute, Helmholtz-Zentrum f\u00fcr Polar-und Meeresforschung (AWI), 27570 Bremerhaven, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.1002\/qj.4331","article-title":"Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA\u2019s global forecast system","volume":"148","author":"Garrett","year":"2022","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1175\/MWR-D-22-0332.1","article-title":"An Investigation on Joint Data Assimilation of a Radar Network and Ground-Based Profiling Platforms for Forecasting Convective Storms","volume":"151","author":"Huo","year":"2023","journal-title":"Mon. Weather Rev."},{"key":"ref_3","first-page":"1","article-title":"Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometre-scale ensemble data assimilation system","volume":"72","author":"Tobias","year":"2020","journal-title":"Tellus A Dyn. Meteorol. Oceanogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1002\/qj.49711247414","article-title":"Analysis methods for numerical weather prediction","volume":"112","author":"Lorenc","year":"1986","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Song, L., Shen, F., Shao, C., Shu, A., and Zhu, L. (2022). Impacts of 3DEnVar-Based FY-3D MWHS-2 Radiance Assimilation on Numerical Simulations of Landfalling Typhoon Ampil (2018). Remote Sens., 14.","DOI":"10.3390\/rs14236037"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.5194\/amt-16-2691-2023","article-title":"The impacts of assimilating Aeolus horizontal line-of-sight winds on numerical predictions of Hurricane Ida (2021) and a mesoscale convective system over the Atlantic Ocean","volume":"16","author":"Feng","year":"2023","journal-title":"Atmos. Meas. Tech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.tcrr.2023.06.001","article-title":"Recent Advancements in Aircraft and In Situ Observations of Tropical Cyclones","volume":"12","author":"Holbach","year":"2023","journal-title":"Trop. Cyclone Res. Rev."},{"key":"ref_8","unstructured":"Pan, S. (2023). Improving Short-Term Forecast of Severe and High-Impact Weather Events Using a Weather-Dependent Hybrid Ensemble-Variational Data Assimilation System with Radar and Satellite Derived Observations. [Ph.D. Thesis, University of Oklahoma]."},{"key":"ref_9","unstructured":"Pena, I.I. (2023). Improving Satellite-Based Convective Storm Observations: An Operational Policy Based on Static Historical Data. [Ph.D. Thesis, Stevens Institute of Technology]."},{"key":"ref_10","first-page":"259","article-title":"The Impact of Doppler Wind Lidar Measurements on High-Impact Weather Forecasting: Regional OSSE and Data Assimilation Studies","volume":"3","author":"Pu","year":"2017","journal-title":"Data Assim. Atmos. Ocean. Hydrol. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1002\/qj.4511","article-title":"Comparison of temperature and wind observations in the Tropics in a perfect-model, global EnKF data assimilation system","volume":"149","author":"Li","year":"2023","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1175\/MWR-D-12-00203.1","article-title":"The impact of covariance localization for radar data on EnKF analyses of a developing MCS: Observing system simulation experiments","volume":"141","author":"Sobash","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s00703-011-0130-3","article-title":"The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter","volume":"112","author":"Dong","year":"2011","journal-title":"Meteor. Atmos. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"585","DOI":"10.2151\/jmsj.2014-605","article-title":"Optimal localization for ensemble Kalman filter systems","volume":"92","author":"Reich","year":"2014","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1175\/MWR-D-11-00102.1","article-title":"A unification of ensemble square root filters","volume":"140","author":"Nerger","year":"2012","journal-title":"Mon. Weather Rev."},{"key":"ref_16","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., and Barker, D.M. (2021). A Description of the Advanced Research WRF Model Version 4.3, National Center for Atmospheric Research. No. NCAR\/TN-556+STR."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.cageo.2012.03.026","article-title":"Software for Ensemble-based Data Assimilation Systems-Implementation Strategies and Scalability","volume":"55","author":"Nerger","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Todorova, T. (2020). Diminishing Marginal Utility and the Teaching of Economics: A Note, ZBW\u2014Leibniz Information Centre for Economics.","DOI":"10.14505\/jres.v12.14.02"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.eneco.2019.06.017","article-title":"Increasing marginal costs and the efficiency of differentiated feed-in tariffs","volume":"83","author":"Kira","year":"2019","journal-title":"Energy Econ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shao, C., and Nerger, L. (EGUsphere, 2023). WRF-PDAF v1.0: Implementation and Application of an Online Localized Ensemble Data Assimilation Framework, EGUsphere, preprint.","DOI":"10.5194\/egusphere-2023-2311"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Li, Y., Cong, Z., and Yang, D. (2023). Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter. Remote Sens., 15.","DOI":"10.3390\/rs15071852"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.5194\/acp-22-1773-2022","article-title":"Data assimilation of volcanic aerosol observations using FALL3D+PDAF","volume":"21","author":"Mingari","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.5194\/gmd-13-4305-2020","article-title":"Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM","volume":"13","author":"Nerger","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.5194\/gmd-13-3607-2020","article-title":"An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution","volume":"13","author":"Zheng","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0924-7963(97)00109-7","article-title":"A singular evolutive extended Kalman filter for data assimilation in oceanography","volume":"16","author":"Pham","year":"1998","journal-title":"J. Mar. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.physd.2006.11.008","article-title":"Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter","volume":"230","author":"Hunt","year":"2007","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3385","DOI":"10.1256\/qj.05.108","article-title":"Diagnosis of observation, background and analysis-error statistics in observation space","volume":"131","author":"Desroziers","year":"2005","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1175\/MWR-D-17-0336.1","article-title":"On the Selection of Localization Radius in Ensemble Filtering for Multiscale Quasigeostrophic Dynamics","volume":"146","author":"Ying","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"064019","DOI":"10.1088\/1748-9326\/9\/6\/064019","article-title":"Dependence of US hurricane economic loss on maximum wind speed and storm size","volume":"9","author":"Zhai","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_31","first-page":"675","article-title":"A preliminary study on the benefit assessment of track and intensity forecast of landfall tropical cyclones","volume":"33","author":"Wu","year":"2017","journal-title":"J. Trop. Meteorol."},{"key":"ref_32","first-page":"1945","article-title":"Unified Ensemble Mean Forecasting of Tropical Cyclones Based on the Feature-Oriented Mean Method","volume":"36","author":"Zhang","year":"2021","journal-title":"Weather Forecast."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"493","DOI":"10.5194\/npg-10-493-2003","article-title":"Conditional nonlinear optimal perturbation and its applications","volume":"10","author":"Mu","year":"2003","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1175\/2008MWR2640.1","article-title":"A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: Conditional Nonlinear Optimal Perturbation","volume":"137","author":"Mu","year":"2009","journal-title":"Mon. Weather Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s00376-022-2136-9","article-title":"Effects of dropsonde data in field campaigns on forecasts of tropical cyclones over the Western North Pacific in 2020 and the role of CNOP sensitivity","volume":"40","author":"Qin","year":"2022","journal-title":"Adv. Atmos. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/430\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:17Z","timestamp":1760104037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020430"],"URL":"https:\/\/doi.org\/10.3390\/rs16020430","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]}}}