scholarly journals Persistent northward North Atlantic tropical cyclone track migration over the past five centuries

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Lisa M. Baldini ◽  
James U. L. Baldini ◽  
Jim N. McElwaine ◽  
Amy Benoit Frappier ◽  
Yemane Asmerom ◽  
...  
2020 ◽  
Vol 101 (12) ◽  
pp. E2058-E2077
Author(s):  
Feifan Zhou ◽  
Zoltan Toth

AbstractThe success story of numerical weather prediction is often illustrated with the dramatic decrease of errors in tropical cyclone track forecasts over the past decades. In a recent essay, Landsea and Cangialosi, however, note a diminishing trend in the reduction of perceived positional error (PPE; difference between forecast and observed positions) in National Hurricane Center tropical cyclone (TC) forecasts as they contemplate whether “the approaching limit of predictability for tropical cyclone track prediction is near or has already been reached.” In this study we consider a different interpretation of the PPE data. First, we note that PPE is different from true positional error (TPE; difference between forecast and true positions) as it is influenced by the error in the observed position of TCs. PPE is still customarily used as a proxy for TPE since the latter is not directly measurable. As an alternative, TPE is estimated here with an inverse method, using PPE measurements and a theoretically based assumption about the exponential growth of TPE as a function of lead time. Eighty-nine percent variance in the behavior of 36–120-h lead-time 2001–17 seasonally averaged PPE measurements is explained with an error model using just four parameters. Assuming that the level of investments, and the pace of improvements to the observing, modeling, and data assimilation systems continue unabated, the four-parameter error model indicates that the time limit of predictability at the 181 nautical mile error level (n mi; 1 n mi = 1.85 km), reached at day 5 in 2017, may be extended beyond 6 and 8 days in 10 and 30 years’ time, respectively.


2017 ◽  
Vol 145 (3) ◽  
pp. 1107-1125 ◽  
Author(s):  
Christopher Velden ◽  
William E. Lewis ◽  
Wayne Bresky ◽  
David Stettner ◽  
Jaime Daniels ◽  
...  

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).


1999 ◽  
Author(s):  
Scott R. Fulton ◽  
Nicole M. Burgess ◽  
Brittany L. Mitchell

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