{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:40:24Z","timestamp":1766486424550,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Provincial Basic and Applied Basic Research Fund, the Regional Joint Fund","award":["2021A1515110944","GRMC2022M22"],"award-info":[{"award-number":["2021A1515110944","GRMC2022M22"]}]},{"name":"Guangdong Meteorological Bureau project","award":["2021A1515110944","GRMC2022M22"],"award-info":[{"award-number":["2021A1515110944","GRMC2022M22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A\/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O\u2212B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O\u2212B and Gaussian distributions when assimilating all-sky data from FY-4A\/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A\/AGRI observations and improves the Gaussianity of the O\u2212B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A\/AGRI observations in a uniform manner without cloud detection.<\/jats:p>","DOI":"10.3390\/s24082572","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T09:15:20Z","timestamp":1713345320000},"page":"2572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Error Model for the Assimilation of All-Sky FY-4A\/AGRI Infrared Radiance Observations"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3068-4071","authenticated-orcid":false,"given":"Dongchuan","family":"Pu","sequence":"first","affiliation":[{"name":"School of Environment, Harbin Institute of Technology, Harbin 150006, China"},{"name":"School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3257-6058","authenticated-orcid":false,"given":"Yali","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong-Hong Kong-Macao Greater Bay Area Weather Research Center for Monitoring Warning and Forecasting (Shenzhen Institute of Meteorological Innovation), Shenzhen 518016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1175\/2009JTECHA1248.1","article-title":"High-Spectral- and High-Temporal-Resolution Infrared Measurements from Geostationary Orbit","volume":"26","author":"Schmit","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, D., Liu, R., and Shen, F. (2022). Impacts of FY-4A AGRI Radiance Data Assimilation on the Forecast of the Super Typhoon \u201cIn-Fa\u201d (2021). Remote Sens., 14.","DOI":"10.3390\/rs14194718"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4","volume":"98","author":"Guo","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1175\/MWR-D-22-0024.1","article-title":"Characteristic Differences of CrIS All-Sky Simulations of Brightness Temperature with Different Microphysics Parameterization Schemes","volume":"150","author":"Li","year":"2022","journal-title":"Mon. Weather Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1007\/s00376-022-1380-3","article-title":"Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the \u201c21\u00b77\u201d Henan Extremely Persistent Heavy Rainfall","volume":"40","author":"Xu","year":"2022","journal-title":"Adv. Atmos. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1002\/qj.56","article-title":"Adaptive bias correction for satellite data in a numerical weather prediction system","volume":"133","author":"McNally","year":"2007","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1029\/2006JD008191","article-title":"Coupling of water vapor convergence, clouds, precipitation, and land-surface processes","volume":"112","author":"Betts","year":"2007","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.5194\/amt-12-4903-2019","article-title":"All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud","volume":"12","author":"Geer","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/qj.3202","article-title":"All-sky satellite data assimilation at operational weather forecasting centres","volume":"144","author":"Geer","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1002\/qj.2102","article-title":"The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model","volume":"139","author":"Stengel","year":"2013","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"ES6","DOI":"10.1175\/2011BAMS3224.1","article-title":"Next-Generation Numerical Weather Prediction: Bridging Parameterization, Explicit Clouds, and Large Eddies","volume":"93","author":"Hong","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1002\/qj.3022","article-title":"Evaluation of IR radiance simulation for all-sky assimilation of Himawari-8\/AHI in a mesoscale NWP system","volume":"143","author":"Okamoto","year":"2017","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1175\/MWR-D-16-0357.1","article-title":"Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015)","volume":"146","author":"Bessho","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00376-021-1088-9","article-title":"Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives","volume":"39","author":"Li","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1002\/qj.830","article-title":"Observation errors in all-sky data assimilation","volume":"137","author":"Geer","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"4481","DOI":"10.1175\/MWR-D-19-0133.1","article-title":"Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System","volume":"147","author":"Otkin","year":"2019","journal-title":"Mon. Weather Rev."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1007\/s13351-020-9122-x","article-title":"Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan","volume":"34","author":"Xiao","year":"2020","journal-title":"J. Meteorol. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1002\/qj.2776","article-title":"Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system","volume":"142","author":"Harnisch","year":"2016","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.atmosres.2023.106898","article-title":"All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event","volume":"293","author":"Xu","year":"2023","journal-title":"Atmos. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.scitotenv.2018.01.155","article-title":"Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery","volume":"627","author":"Xu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"154045","DOI":"10.1016\/j.scitotenv.2022.154045","article-title":"Inferring vertical variability and diurnal evolution of O3 formation sensitivity based on the vertical distribution of summertime HCHO and NO2 in Guangzhou, China","volume":"827","author":"Hong","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_24","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_25","unstructured":"Matricardi, M. (2005). The Inclusion of Aerosols and Clouds in RTIASI, the ECMWF Fast Radiative Transfer Model for the Infrared Atmospheric Sounding Interferometer, ECMWF."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104912","DOI":"10.1016\/j.atmosres.2020.104912","article-title":"Improving forecasts of a record-breaking rainstorm in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR","volume":"239","author":"Wu","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_27","first-page":"340","article-title":"A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR\/TN-475+STR. June 2008. Mesoscale and Microscale Meteorology Division. National Center for Atmospheric Research","volume":"475","author":"Skamarock","year":"2008","journal-title":"Boulder"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cageo.2015.06.014","article-title":"Parallel GPU architecture framework for the WRF Single Moment 6-class microphysics scheme","volume":"83","author":"Huang","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1175\/1520-0493(2004)132<0103:ARATIM>2.0.CO;2","article-title":"A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation","volume":"132","author":"Hong","year":"2004","journal-title":"Mon. Weather Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1899","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_31","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1175\/1520-0493(2001)129<0569:CAALSH>2.0.CO;2","article-title":"Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity","volume":"129","author":"Chen","year":"2001","journal-title":"Mon. Weather Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1175\/MWR-D-11-00056.1","article-title":"A Revised Scheme for the WRF Surface Layer Formulation","volume":"140","author":"Dudhia","year":"2012","journal-title":"Mon. Weather Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1029\/2018JD029643","article-title":"Assimilating Every-10-minute Himawari-8 Infrared Radiances to Improve Convective Predictability","volume":"124","author":"Sawada","year":"2019","journal-title":"J. Geophys. Res. Atmos."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:29:22Z","timestamp":1760106562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/8\/2572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,17]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24082572"],"URL":"https:\/\/doi.org\/10.3390\/s24082572","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,4,17]]}}}