{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:24:59Z","timestamp":1780367099030,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42105126"],"award-info":[{"award-number":["42105126"]}]},{"name":"National Natural Science Foundation of China","award":["BK20210662"],"award-info":[{"award-number":["BK20210662"]}]},{"name":"National Natural Science Foundation of Jiangsu Province","award":["42105126"],"award-info":[{"award-number":["42105126"]}]},{"name":"National Natural Science Foundation of Jiangsu Province","award":["BK20210662"],"award-info":[{"award-number":["BK20210662"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding boundary-layer atmospheric temperature and moisture is essential for advancing our knowledge of the Earth system. This study adopts a one-dimensional variational (1DVAR)-based technique to integrate spaceborne measurement and ground-based observations for improving the retrieval of low-level atmospheric profiles. The performance of the algorithm under different atmospheric and observational scenarios, such as surface-air and skin temperature differences (\u2206T), surface pressure (Ps), and satellite zenith angle, respectively, has been systematically evaluated using the Geosynchronous Interferometric Infrared Sounder (GIIRS) on board the Fengyun-4A satellite as an example. Through theoretical information analysis, using both simulated and actual data experiments, this study demonstrates that incorporating ground-based temperature and moisture observations significantly enhances retrieval accuracy with 1DVAR, particularly over elevated terrain. The new algorithm is more effective in low-level temperature retrievals when air temperatures are colder relative to surface-skin temperatures, and it also shows greater benefit for water-vapor retrievals when the temperature difference between the air and the skin is minimal. However, as the zenith angle increases to 55\u00b0, the accuracy of temperature retrievals deteriorates, although this is mitigated by the combination of surface-air temperature observations. Notably, the positive impact of surface observations extends to approximately 100\u2013200 hPa above the surface, underscoring the importance of accurate ground-based measurements in conjunction with spaceborne data for atmospheric profiling.<\/jats:p>","DOI":"10.3390\/rs16244634","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T06:44:05Z","timestamp":1733899445000},"page":"4634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Effect of Surface Observations on Enhancing the GIIRS Thermodynamic Profile Retrieval"],"prefix":"10.3390","volume":"16","author":[{"given":"Chuanhai","family":"Deng","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0475-9690","authenticated-orcid":false,"given":"Di","family":"Di","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5504-9627","authenticated-orcid":false,"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1175\/JAMC-D-14-0299.1","article-title":"AIRS, IASI, and CrIS Retrieval Records at Climate Scales: An Investigation into the Propagation of Systematic Uncertainty","volume":"54","author":"Smith","year":"2015","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"803","DOI":"10.5194\/amt-11-803-2018","article-title":"A Simulated Observation Database to Assess the Impact of the IASI-NG Hyperspectral Infrared Sounder","volume":"11","author":"Guidard","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature14956","article-title":"The Quiet Revolution of Numerical Weather Prediction","volume":"525","author":"Bauer","year":"2015","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/qj.3654","article-title":"Assimilation of Satellite Data in Numerical Weather Prediction. Part I: The Early Years","volume":"146","author":"Eyre","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1002\/qj.4228","article-title":"Assimilation of Satellite Data in Numerical Weather Prediction. Part II: Recent Years","volume":"148","author":"Eyre","year":"2022","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10546-020-00551-8","article-title":"The Persistent Challenge of Surface Heterogeneity in Boundary-Layer Meteorology: A Review","volume":"177","author":"Anderson","year":"2020","journal-title":"Bound. Layer Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rold\u00e1n-Henao, N., Yorks, J.E., Su, T., Selmer, P.A., and Li, Z. (2024). Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data. Remote Sens., 16.","DOI":"10.3390\/rs16173252"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, H., Ma, X., Jia, G., Mi, Z., and Ji, H. (2022). Synergistic Retrieval of Temperature and Humidity Profiles from Space-Based and Ground-Based Infrared Sounders Using an Optimal Estimation Method. Remote Sens., 14.","DOI":"10.3390\/rs14205256"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E1999","DOI":"10.1175\/BAMS-D-22-0129.1","article-title":"Synthetic Observations of the Planetary Boundary Layer from Space: A Retrieval Observing System Simulation Experiment Framework","volume":"104","author":"Kurowski","year":"2023","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13.1","DOI":"10.1175\/AMSMONOGRAPHS-D-18-0020.1","article-title":"100 Years of Progress in Forecasting and NWP Applications","volume":"59","author":"Benjamin","year":"2019","journal-title":"Meteorol. Monogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1175\/BAMS-D-16-0293.1","article-title":"Satellite-Based Atmospheric Infrared Sounder Development and Applications","volume":"99","author":"Menzel","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"971","DOI":"10.5194\/amt-11-971-2018","article-title":"Single-Footprint Retrievals of Temperature, Water Vapor and Cloud Properties from AIRS","volume":"11","author":"Irion","year":"2018","journal-title":"Atmos. Meas. Tech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4600","DOI":"10.1002\/2016JD024867","article-title":"Geostationary Satellite-based 6.7 \u039cm Band Best Water Vapor Information Layer Analysis over the Tibetan Plateau","volume":"121","author":"Di","year":"2016","journal-title":"JGR Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6940","DOI":"10.1002\/jgrd.50548","article-title":"Combining Ground-based with Satellite-based Measurements in the Atmospheric State Retrieval: Assessment of the Information Content","volume":"118","author":"Ebell","year":"2013","journal-title":"JGR Atmos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1175\/JAMC-D-18-0155.1","article-title":"Near-Real-Time Surface-Based CAPE from Merged Hyperspectral IR Satellite Sounder and Surface Meteorological Station Data","volume":"58","author":"Bloch","year":"2019","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2020EA001402","DOI":"10.1029\/2020EA001402","article-title":"Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis","volume":"8","author":"Ma","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.5194\/gmd-11-2717-2018","article-title":"An Update on the RTTOV Fast Radiative Transfer Model (Currently at Version 12)","volume":"11","author":"Saunders","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1175\/1520-0450(2000)039<1248:GSOTAF>2.0.CO;2","article-title":"Global Soundings of the Atmosphere from ATOVS Measurements: The Algorithm and Validation","volume":"39","author":"Li","year":"2000","journal-title":"J. Appl. Meteor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0273-1177(97)00915-0","article-title":"Information Content and Optimisation of High Spectral Resolution Remote Measurements","volume":"21","author":"Rodgers","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rodgers, C.D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific.","DOI":"10.1142\/9789812813718"},{"key":"ref_21","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. Wea. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1007\/s00376-010-0145-6","article-title":"Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data","volume":"28","author":"Lee","year":"2011","journal-title":"Adv. Atmos. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4102210","DOI":"10.1109\/TGRS.2021.3078829","article-title":"Geostationary Hyperspectral Infrared Sounder Channel Selection for Capturing Fast-Changing Atmospheric Information","volume":"60","author":"Di","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Borbas, E.E., Seemann, S.W., Huang, H.-L., Li, J., and Menzel, W.P. (2005, January 25\u201331). Global Profile Training Database for Satellite Regression Retrievals with Estimates of Skin Temperature and Emissivity. Proceedings of the XIV International ATOVS Study Conference, Beijing, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e2021GL093672","DOI":"10.1029\/2021GL093672","article-title":"Impact of High Temporal Resolution FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case With GRAPES Global 4D-Var Assimilation System","volume":"48","author":"Yin","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_26","first-page":"12,583","article-title":"Enhancing the Fast Radiative Transfer Model for FengYun-4 GIIRS by Using Local Training Profiles","volume":"123","author":"Di","year":"2018","journal-title":"JGR Atmos."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s13351-017-6161-z","article-title":"Developing the Science Product Algorithm Testbed for Chinese Next-Generation Geostationary Meteorological Satellites: Fengyun-4 Series","volume":"31","author":"Min","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8827","DOI":"10.1109\/TGRS.2019.2923247","article-title":"Intercomparisons of Cloud Mask Products Among Fengyun-4A, Himawari-8, and MODIS","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Shuai, H., Chunxiang, S., Bin, X., Tao, Z., Lipeng, J., Xiao, L., and Shuai, S. (2019). Development and Progress of High Resolution CMA Land Surface Data Assimilation System (HRCLDAS), ResearchGate."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xu, Y., Han, S., Shi, C., Tao, R., Zhang, J., Zhang, Y., and Wang, Z. (2023). Comparative Analysis of Three Near-Surface Air Temperature Reanalysis Datasets in Inner Mongolia Region. Sustainability, 15.","DOI":"10.3390\/su151713046"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.5194\/acp-19-3097-2019","article-title":"From ERA-Interim to ERA5: The Considerable Impact of ECMWF\u2019s next-Generation Reanalysis on Lagrangian Transport Simulations","volume":"19","author":"Hoffmann","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.5194\/hess-24-2527-2020","article-title":"Evaluation of the ERA5 Reanalysis as a Potential Reference Dataset for Hydrological Modelling over North America","volume":"24","author":"Tarek","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Di, D., Xue, Y., Li, J., Bai, W., and Zhang, P. (2020). Effects of CO2 Changes on Hyperspectral Infrared Radiances and Its Implications on Atmospheric Temperature Profile Retrieval and Data Assimilation in NWP. Remote Sens., 12.","DOI":"10.3390\/rs12152401"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e2022GL101628","DOI":"10.1029\/2022GL101628","article-title":"Inter-Calibration of Geostationary Imager Infrared Bands Using a Hyperspectral Sounder on the Same Platform","volume":"50","author":"Di","year":"2023","journal-title":"Geophys. Res. Lett."},{"key":"ref_35","first-page":"275","article-title":"Bias Characteristics and Bias Correction of GIIRS Sounder Onboard FY-4A Satellite for Data Assimilation","volume":"46","author":"Liu","year":"2022","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1175\/1520-0450(2002)041<0144:ARNNAF>2.0.CO;2","article-title":"A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument","volume":"41","author":"Aires","year":"2002","journal-title":"J. Appl. Meteor."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"629","DOI":"10.5194\/amt-13-629-2020","article-title":"A Channel Selection Method for Hyperspectral Atmospheric Infrared Sounders Based on Layering","volume":"13","author":"Chang","year":"2020","journal-title":"Atmos. Meas. Tech."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:52:11Z","timestamp":1760115131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,11]]},"references-count":37,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244634"],"URL":"https:\/\/doi.org\/10.3390\/rs16244634","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,11]]}}}