@Article{DinizTodlHerd:2020:BrAsIm,
               author = "Diniz, F{\'a}bio Luis Rodrigues and Todling, R. and Herdies, 
                         Dirceu Lu{\'{\i}}s",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and NASA and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A brief assessment of the impact of nearly 40 years of assimilated 
                         observations over the Amazon basin",
              journal = "Earth and Space Science",
                 year = "2020",
               volume = "7",
               number = "3",
                pages = "e2019EA000779",
                month = "mar.",
             abstract = "Adding a Forecast Sensitivity-based Observation Impact component 
                         to Version 2 of the Modern Era Retrospective-analysis for Research 
                         and Applications, the present study provides an assessment of the 
                         impact of nearly 40 years of observations on short-range (24-hr) 
                         forecasts over the Amazon basin. Under self-verification, forecast 
                         errors are found to slightly increase from the early data-sparse 
                         days to the more recent years, when data dramatically increase. 
                         Throughout the reanalysis, satellite radiances dominate in volume, 
                         but only before 1999 they dominate the impacts. Beyond 1999, over 
                         50% of forecast error reduction is associated with conventional 
                         observations (radiosondes). Atmospheric Motion Vectors are also 
                         found to be large contributors to error reduction, but their 
                         contribution reduces in dry periods. In opposition to Atmospheric 
                         Motion Vectors, satellite radiances tend to contribute more in the 
                         dry season. Results provide motivation for additional conventional 
                         observations and the use of all-sky treatment of radiances.Plain 
                         Language Summary Observations of atmospheric variables are of 
                         fundamental importance to allow for reliable weather predictions 
                         and to enable scientists to improve their modeling of the 
                         atmosphere. Conventional observing systems measure temperature, 
                         winds, humidity, and pressure directly. These amount to a small 
                         fraction of the global observing system, which is dominated by 
                         indirect satellite observations. Objective evaluation for how 
                         different components of the observing systems contribute to 
                         improving weather predictions have become essential to help 
                         scientists understand how best to build future observing systems. 
                         The present study provides an evaluation of nearly 40 years of 
                         observations used in the context of a procedure called reanalysis, 
                         which essentially blends observations and model predictions in a 
                         carefully designed manner. Our particular work examines the impact 
                         of observations over the Amazon basin. In this region, 
                         conventional observations are found to still contribute most to 
                         reducing forecast errors, especially in the later years of the 
                         reanalysis, while satellite-derived winds are found to contribute 
                         most in the wet season. The work suggests that improving the 
                         treatment of other satellite observations allowing their use over 
                         cloudy and precipitating regions might change their ranking in 
                         comparison to conventional observations.",
                  doi = "10.1029/2019EA000779",
                  url = "http://dx.doi.org/10.1029/2019EA000779",
                 issn = "2333-5084",
             language = "en",
           targetfile = "diniz_brieg.pdf",
        urlaccessdate = "28 abr. 2024"
}