Global Identification of Previously Undetected Tropical Cyclone Candidates in NOAA/CIRES 20th Century Reanalysis Data

Prior to the advent of the satellite era, limited in situ synoptic observations led to an indefinite number of tropical cyclones (TCs) remaining undetected in tropical basins globally. These cyclones were not identified operationally and therefore are not a part of the consensus TC climatology. While prior studies (Vecchi and Knutson 2008) suggest that this historical undersampling exists, there remains considerable disagreement regarding its true magnitude. This disparity has led to difficulties in interpreting long-term trends in TC activity. Previous research presented a scheme to identify previously unknown Atlantic Basin potential cyclones in the pre-satellite era (Truchelut and Hart 2011). This was accomplished through compositing the mean thermodynamic structure of historical TCs in the NOAA/CIRES 20th Century Reanalysis and identifying similar signatures in the reanalysis that did not correspond to known Best-Track cyclones. Synoptic verification using historical surface observations showed the technique effectively identified around 1.5 such cases per year for the 1951-1958 Atlantic Basin hurricane seasons.

This research expands the scope of the original methodology by presenting a filtering algorithm that dramatically improves the efficiency and speed with which candidate events are identified and applying this scheme to the Atlantic, Pacific, and Indian Ocean Basins for the years 1891-1979. Quantitative and objective estimates of global TC candidate counts for several decades prior to the advent of formal climatological records are presented, including track and longevity data for each candidate, along with an estimated probability that the event was actually a TC. While synoptic verification has not yet been performed on each of the candidate events identified, preliminary findings suggest that reanalysis models are a promising means of efficiently adding new information to the TC climatological record and are a promising basis upon which to improve our understanding of long-term trends in activity.