@InCollection{SatterfieldWKHHEMMFISMHJLMELBYLBRSMMTM:2021:ApMeRa,
               author = "Satterfield, Elizabeth A. and Waller, Joanne A. and Kuhl, David D. 
                         and Hodyss, Dan and Hoppel, Karl W. and Eckermann, Stephen D. and 
                         McCormack, John P. and Ma, Jun and Fritts, David C and Iimura, 
                         Hiroiyuki and Stober, Gunter and Meek, Chris E. and Hall, Chris 
                         and Jacobi, Christoph and Latteck, Ralph and Mitchell, Nicholas J. 
                         and Espy, Patrick J. and Li, Guozhu and Brown, Peter and Yi, Wen 
                         and Li, Na and Batista, Paulo Prado and Reid, Ian and Sunkara, 
                         Eswaraiah and Moffat-Griffin, Tracy and Murphy, Damian and 
                         Tsutsumi, Masaki and Marino, John",
                title = "Statistical Parameter Estimation for Observation Error Modelling: 
                         Application to Meteor Radars",
            booktitle = "Data Assimilation for Atmospheric, Oceanic and Hydrologic 
                         Applications (Vol. IV)",
            publisher = "Springer",
                 year = "2021",
               editor = "Park, S. K. and Xu, L.",
                pages = "185--213",
             keywords = "meteor radar, data assimilation.",
             abstract = "Data assimilation schemes blend observational data, with limited 
                         coverage, with a short term forecast to produce an analysis, which 
                         is meant to be the best estimate of the current state of the 
                         atmosphere. Appropriately specifying observation error statistics 
                         is necessary to obtain an optimal analysis. Observation error can 
                         originate from instrument error as well as the error of 
                         representation. While representation error is most commonly 
                         associated with unresolved scales and processes, this term is 
                         often considered to include contributions from pre-processing or 
                         quality control and errors associated with the observation 
                         operator. With a focus on practical operational implementation, 
                         this chapter aims to define the components of observation error, 
                         discusses their sources and characteristics, and provides an 
                         overview of current methods for estimating observation error 
                         statistics. We highlight the implicit assumptions of these 
                         methods, as well as their shortcomings. We will detail current 
                         operational practice for diagnosing observation error and 
                         accounting for correlated observation error. Finally, we provide a 
                         practical methodology for using these diagnostics, as well as the 
                         associated innovation-based observation impact, to optimize the 
                         assimilation of meteor radar observations in the upper 
                         atmosphere.",
          affiliation = "{U.S. Naval Research Laboratory} and {Met Office} and {U.S. Naval 
                         Research Laboratory} and {U.S. Naval Research Laboratory} and 
                         {U.S. Naval Research Laboratory} and {U.S. Naval Research 
                         Laboratory} and {U.S. Naval Research Laboratory} and CPI and GATS 
                         and GATS and GATS and {University of Saskatchewan} and {University 
                         of Tromsų} and {University of Leipzig} and {University of Rostock} 
                         and {University of Bath} and {Norwegian University of Science and 
                         Technology} and {Chinese Academy of Sciences} and {University of 
                         Western Ontario} and {University of Science and Technology of 
                         China} and {China Research Institute of Radiowave Propagation} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {The 
                         University of Adelaide} and {Chungnam National University} and 
                         {British Antarctic Survery} and {Australian Antarctic Division of 
                         Sustainability} and {National Institute of Polar Research} and 
                         {University of Colorado Boulder}",
                  doi = "10.1007/978-3-030-77722-7_8",
                  url = "http://dx.doi.org/10.1007/978-3-030-77722-7_8",
                 isbn = "9783030777227",
                label = "lattes: 2306964700488382 28 
                         SatterfieldWKHHEMMFISMHJLMELBYLRSMMTMB:2021:ApMeRa",
             language = "en",
                  url = "https://link.springer.com/book/10.1007/978-3-030-77722-7",
        urlaccessdate = "05 maio 2024"
}