JP3.41 The utility of the ERA40 Cyclone Phase Space in Trend Diagnosis and North Atlantic Tropical Cyclone Reanalysis (2008

JP3.41

The utility of the ERA40 Cyclone Phase Space in Trend Diagnosis and North Atlantic Tropical Cyclone Reanalysis

Danielle Manning, Florida State University, Tallahassee, FL; and R. Hart

As understanding of tropical cyclone (TC) evolution both during and

beyond the tropical phase improves, forecasting and analysis

techniques are adjusted accordingly. While these changes hopefully

lead to more accurate forecasts, they introduce inconsistencies into

best track datasets. The tropical cyclone reanalysis project was

started in an attempt to remove such inconsistencies and biases

(Landsea et al. 2004). It is important to understand biases within

best track datasets before studies of long term trends can be

meaningfully completed. Here, the strengths and limitations of the

ECMWF reanalysis data (ERA40; Uppala 2005) are examined within the

cyclone phase space (CPS; Hart 2003).

This process included using the CPS to quantify biases and evolving

trends in North Atlantic TC representation within the ERA40. It is

found that TCs are poorly resolved even beyond what is expected given

the ERA40's grid size. By binning data into three temporal groups it

is found that the introduction of satellite data results in a drastic

improvement in the representation of ERA40 TCs. Not surprisingly, TC

size seems to have the most profound effect since the ERA40 grid size

is 1.125° (Uppala 2005). It is also found that location (specifically

longitude) also has a marked effect on TC representation within the

ERA40, but it should be noted that location is linked to data

density.

Despite these inconsistencies, the ERA40 CPS can be used to

scrutinize historical structural classification of some TCs, provided

that the TCs are represented much better than the mean (usually by

more than one standard deviation). Within this study, it is found

that some TCs may require refined timing of extratropical transition.

Others may need refined structural classification at the beginning or

in the middle of their HURDAT tracks. Finally, there are some TCs for

which the ERA40 and HURDAT classifications agree throughout the

duration of the TC's lifecycle. Within the study, cases from each of

these subsets of TCs are presented and one potential addition to the

best track dataset is examined. It should be noted that potential

revisions are suggested only when evidence outside of the ERA40 CPS

can be obtained since no singular source should be the basis of

revisions to the best track dataset.