Seasonal Carbonate Chemistry Dynamics on Southeast Florida Coral Reefs: Localized Acidification Hotspots From Navigational Inlets

Details

• Journal Title:
  Frontiers in Marine Science
• Personal Author:
  Enochs, Ian C. ; Manzello, Derek P. ; Jones, Paul R. ; Stamates, S. Jack ; Carsey, Thomas P.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; AOML (Atlantic Oceanographic and Meteorological Laboratory) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)
• Description:
  This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble-variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18-24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP (National Centers for Environmental Prediction) global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.
• Source:
  Frontiers in Marine Science, 6(160).
• DOI:
  https://doi.org/10.3389/fmars.2019.00160
• Format:
  PDF
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:302bcda15f2978b775bb57090bfbe8bfa12b3503248d85f09aac48c905018d2ee4d1f02742e3ebb31069d94509c62ae78f3be987fa138c3d9eff3cf6c547a479
• Download URL:
  https://repository.library.noaa.gov/view/noaa/20116/noaa_20116_DS1.pdf
• File Type:
  [PDF - 3.93 MB ]