{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:46:41Z","timestamp":1770043601091,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000142012449"],"award-info":[{"award-number":["N000142012449"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["42275010"],"award-info":[{"award-number":["42275010"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["#NA21OAR4320204"],"award-info":[{"award-number":["#NA21OAR4320204"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["N000142012449"],"award-info":[{"award-number":["N000142012449"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42275010"],"award-info":[{"award-number":["42275010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["#NA21OAR4320204"],"award-info":[{"award-number":["#NA21OAR4320204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NOAA\/Office of Oceanic and Atmospheric Research","award":["N000142012449"],"award-info":[{"award-number":["N000142012449"]}]},{"name":"NOAA\/Office of Oceanic and Atmospheric Research","award":["42275010"],"award-info":[{"award-number":["42275010"]}]},{"name":"NOAA\/Office of Oceanic and Atmospheric Research","award":["#NA21OAR4320204"],"award-info":[{"award-number":["#NA21OAR4320204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 \u03bcm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 \u03bcm mid-level troposphere water vapor channel and 11.2 \u03bcm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast.<\/jats:p>","DOI":"10.3390\/rs15122998","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T01:32:34Z","timestamp":1686274354000},"page":"2998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms"],"prefix":"10.3390","volume":"15","author":[{"given":"Huanhuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, OK 73072, USA"},{"name":"University of Chinese Academy of Sciences, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8658-9068","authenticated-orcid":false,"given":"Qin","family":"Xu","sequence":"additional","affiliation":[{"name":"NOAA\/OAR\/National Severe Storms Laboratory, Norman, OK 73072, USA"}]},{"given":"Thomas A.","family":"Jones","sequence":"additional","affiliation":[{"name":"Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, OK 73072, USA"},{"name":"NOAA\/OAR\/National Severe Storms Laboratory, Norman, OK 73072, USA"}]},{"given":"Lingkun","family":"Ran","sequence":"additional","affiliation":[{"name":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"ref_1","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 Radiances in the NCEP SSI Analysis System","volume":"126","author":"Derber","year":"1998","journal-title":"Mon. Weather Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1256\/qj.04.171","article-title":"The Assimilation of AIRS Radiance Data at ECMWF","volume":"132","author":"McNally","year":"2006","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.1175\/MWR-D-15-0445.1","article-title":"All-Sky Microwave Radiance Assimilation in NCEP\u2019s GSI Analysis System","volume":"144","author":"Zhu","year":"2016","journal-title":"Mon. Weather Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1175\/BAMS-86-8-1079","article-title":"Introducing the next-generation Advanced Baseline Imager on GOES-R","volume":"86","author":"Schmit","year":"2005","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1175\/WAF-D-15-0043.1","article-title":"Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments","volume":"30","author":"Wheatley","year":"2015","journal-title":"Weather Forecast."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1175\/WAF-D-15-0107.1","article-title":"Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part II: Combined Radar and Satellite Data Experiments","volume":"31","author":"Jones","year":"2016","journal-title":"Weather Forecast."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1175\/MWR-D-17-0280.1","article-title":"Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 mm) Radiances into a Warn-on-Forecast System","volume":"146","author":"Jones","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"3363","DOI":"10.1175\/MWR-D-18-0062.1","article-title":"Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction","volume":"146","author":"Zhang","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_11","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."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1002\/qj.3466","article-title":"An Adaptive Background Error Inflation Method for Assimilating All-Sky Radiances","volume":"145","author":"Minamide","year":"2019","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"D02114","DOI":"10.1029\/2007JD008624","article-title":"Time-Expanded Sampling for Ensemble-Based Filters: Assimilation Experiments with a Shallow-Water Equation Model","volume":"113","author":"Xu","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1175\/2007MWR2185.1","article-title":"Time-Expanded Sampling for Ensemble Kalman Filter: Assimilation Experiments with Simulated Radar Observations","volume":"136","author":"Xu","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1175\/MWR-D-18-0009.1","article-title":"On the Use of Cost-Effective Valid-Time-Shifting (VTS) Method to Increase Ensemble Size in the GFS Hybrid 4DEnVar System","volume":"146","author":"Huang","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1175\/MWR-D-21-0148.1","article-title":"Using a Cost-Effective Approach to Increase Background Ensemble Member Size within the GSI-Based EnVar System for Improved Radar Analyses and Forecasts of Convective Systems","volume":"150","author":"Gasperoni","year":"2022","journal-title":"Mon. Weather Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s00376-010-0021-4","article-title":"Time-Expanded Sampling for Ensemble-Based Filters: Assimilation Experiments with Real Radar Observations","volume":"28","author":"Lu","year":"2011","journal-title":"Adv. Atmos. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1175\/WAF-D-14-00108.1","article-title":"Time-Expanded Sampling for Ensemble-Based Data Assimilation Applied to Conventional and Satellite Observations","volume":"30","author":"Zhao","year":"2015","journal-title":"Weather Forecast."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Gao, J., Xu, Q., and Ran, L. (2023). Applying Time-Expended Sampling to Ensemble Assimilation of Remote Sensing Data for Short-Term Predictions of Thunderstorms. Remote Sens., 15.","DOI":"10.3390\/rs15092358"},{"key":"ref_20","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., and Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3."},{"key":"ref_21","unstructured":"Liu, H., Hu, M., Ge, G., Zhou, C., Stark, D., Shao, H., Newman, K., and Whitaker, J. (2018). User\u2019s Guide Version 1.3-Compatible with GSI Community Release v3.7, NOAA\/OAR\/Global Systems Laboratory, Developmental Testbed Center. Available online: https:\/\/dtcenter.org\/community-code\/ensemble-kalman-filter-system-enkf\/documentation."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1175\/2007JAS2112.1","article-title":"Advances in Radiative Transfer Modeling in Support of Satellite Data Assimilation","volume":"64","author":"Weng","year":"2007","journal-title":"J. Atmos. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1175\/2007MWR2018.1","article-title":"Ensemble Data Assimilation with the NCEP Global Forecast System","volume":"136","author":"Whitaker","year":"2008","journal-title":"Mon. Weather Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1175\/MWR-D-15-0242.1","article-title":"A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh","volume":"144","author":"Benjamin","year":"2016","journal-title":"Mon. Weather Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1175\/2009JAS2965.1","article-title":"Simulated Electrification of a Small Thunderstorm with Two-Moment Bulk Microphysics","volume":"67","author":"Mansell","year":"2010","journal-title":"J. Atmos. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1002\/qj.49712555417","article-title":"Construction of Correlation Functions in Two and Three Dimensions","volume":"125","author":"Gaspari","year":"1999","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/BAMS-D-14-00174.1","article-title":"Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities","volume":"97","author":"Zhang","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1175\/BAMS-D-14-00173.1","article-title":"Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities","volume":"97","author":"Smith","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1175\/2008JTECHA1156.1","article-title":"Additive Noise for Storm-Scale Ensemble Data Assimilation","volume":"26","author":"Dowell","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_30","unstructured":"Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, Academic Press. [3rd ed.]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/2998\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:50:56Z","timestamp":1760125856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/2998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,8]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15122998"],"URL":"https:\/\/doi.org\/10.3390\/rs15122998","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,8]]}}}