Publication Date

2014-04-07

Availability

Open access

Embargo Period

2014-04-07

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Meteorology and Physical Oceanography (Marine)

Date of Defense

2014-03-05

First Committee Member

Sharanya J. Majumdar

Second Committee Member

David S. Nolan

Third Committee Member

Brian E. Mapes

Fourth Committee Member

Ryan D. Torn

Abstract

This research is a study of the behavior of the observed processes leading up to genesis, and how ensembles can be used to assess their predictability. In the first part of this study, dropwindsonde observations of developing and non-developing tropical waves are examined from the 2010 Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) field campaign. Significant results include the development of positive temperature anomalies from 500-200 hPa two days prior to genesis in developing waves, which is not observed in the non-genesis mean. Progressive mesoscale moistening of the column is observed within 150 km of the center of circulation prior to genesis. The genesis composite is found to be significantly more moist than the non-genesis composite at the middle levels, while comparatively drier at low levels, suggesting that dry air is more detrimental to genesis when located at the middle levels. Time-varying tangential wind profiles reveal an initial delay in intensification, followed by an increase in organization 24 hours pre-genesis. The vertical evolution of relative vorticity, in addition to a warm-over-cold thermal structure, is more consistent with a top-down than a bottom-up genesis mechanism. Thereafter, several metrics are employed to evaluate predictive skill and attempt to quantify predictability using the ECMWF Ensemble Prediction System during the 2010 Atlantic hurricane season, with an emphasis on large-scale variables relevant to tropical cyclogenesis. These metrics include: (1) Growth and saturation of error; (2) Errors versus climatology; (3) Predicted forecast error standard deviation; and (4) Predictive Power. Overall, variables that are more directly related to large-scale, slowly-varying phenomena are found to be much more predictable than variables that are inherently related to small-scale convective processes, regardless of the metric. For example, 850-200 hPa wind shear and 200 hPa velocity potential are found to be predictable beyond one week, while 200 hPa divergence and 850 hPa relative vorticity are only predictable to about one day. Similarly, area-averaged quantities such as circulation are much more predictable than non-averaged quantities such as vorticity. Significant day-to-day and month-to-month variability of predictability for a given metric also exists, likely due to the flow regime. For wind shear, more amplified flow regimes are associated with lower Predictive Power (and thereby lower predictability) than less amplified regimes. Relative humidity is found to be less predictable in the early and late season when there exists greater uncertainty of the timing and location of dry air. Similarly, significant case-to-case variability is observed in the wave-relative analysis. For some genesis events, predictability of genesis appears to be directly related to the capability of the ensemble to predict an environment favorable for genesis. In other cases, predictability appears to be more directly associated with the strength and location of the initial disturbance in the model. By examining forecast joint distributions of variables, predicted relative humidity values at 700 hPa of less than 60% in the wave core (≤300 km of center of circulation) are found to be a strong limiting factor for genesis in the ensemble, and also tend to be correlated with weak 200 hPa divergence. Genesis is also found to occur in the presence of significant wind shear (~15 ms-1), but generally only when the core and environment of the wave are both very moist (~85% and 75% 700 hPa RH, respectively). Lastly, the ensemble demonstrates the potential to predict error standard deviation of variables averaged in 10° x 10° boxes, as well as within 300 km and 1000 km radii about individual tropical waves. Forecasts with greater ensemble standard deviation are on average associated with greater mean error, especially for forecasts with less than 168 h lead time. However, the ensemble also tends to be under-dispersive.

Keywords

tropical cyclones; ensembles; predictability; numerical models; dropwindsondes

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