Forecasting rapid intensity changes in tropical cyclones (TCs) is hard as the factors responsible span many scales. External and internal dynamical and thermodynamical variables act simultaneously in a nonlinear fashion, either complementing, amplifying, inhibiting or not impacting the TC intensity at all. We try to address the following question: What is the relative importance of the external and vortex-scale variables that influence rapid intensity changes within a TC? Further, which of these variables must be prioritized from an observational standpoint? To answer these questions, a systematic analysis was conducted on a large number of representative TCs to make statistically significant conclusions using discriminant analyses of wavenumber (WN) - filtered fields, with a principal component analysis to detect over-fitting and identify the subset of variables (from the environment and the vortex) consistently correlated with rapid intensity change. Our analyses indicate that a small number of variables wield the most influence on TC rapid intensity changes. The most important variables within the vortex are the WN 0 of precipitation within the radius of maximum winds, the amplitudes of WN 1 of precipitation and the mid-level horizontal moisture flux convergence in the rain band region. Likewise, the most important environmental variables are the angle of the driest air from the shear vector and the magnitude of environmental wind shear. These variables must be prioritized in future observational and consequent data assimilation efforts.
Satellite-based scatterometers, for historical reasons, have been used mainly to derive the wind forcing term for oceanography applications in the form of the near-surface wind field. However, the scatterometer is sensitive to the surface roughness, which is related to the wind stress field, which is in turn related to the wind field at the bottom of the troposphere but not just at 10 meters above the surface { indeed, in organized systems such as tropical cyclones, the surface roughness is highly correlated with the wind at altitudes much higher than 10 meters. We show how to assimilate this data as a function of the vertical principal components of the wind rather than the oversimplified alternative. We derive the empirical correlations between simulated scatterometer observations and underlying columns of wind produced by a numerical weather prediction model and derive an observation operator based on these correlations. We then present the results of the subsequent assimilation.
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