Passive-Microwave-Enhanced Statistical Hurricane Intensity Prediction Scheme

Abstract

The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SSTrelated predictors. The addition of the microwave predictors produces a 2%-8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

A western North Pacific tropical cyclone intensity prediction scheme

“…Similar phenomena exist for other lead times. The MAEs of the total samples are 2.6, 4.2, 5.5, 6.1, 6.1, and 6.3 m s −1 from 12 to 72 h, respectively, showing a similar leveling-off of the errors at longer lead times to that described by Jones et al (2006). It should be pointed out that a fair comparison among different prediction schemes for TC intensity can only be made based on a unique best-track dataset.…”

“…Efforts are being continuously made to improve the skill of these techniques by including the passive microwave information (Jones et al, 2006), considering inland decay factors , or proposing consensus/ensemble schemes .…”

Passive Microwave Quantification of Tropical Cyclone Inner-Core Cloud Populations Relative to Subsequent Intensity Change

“…Lonfat et al (2004) evaluated spatial asymmetries in TRMM Microwave Imager (TMI) rain rates for global TCs over 1998-2000, noting a preference for rainfall in the forward quadrants of the TC relative to its motion while tropical storms exhibited greater asymmetries than hurricanes. Jones et al (2006) incorporated a PM component across the TC's innermost 100 km into the Statistical Hurricane Intensity Prediction Scheme (SHIPS) using the T b mean, maximum, minimum, and standard deviation, with standard deviation found to be a skillful predictor for the east Pacific. Overall, Jones et al (2006) note PM inclusion in SHIPS improved intensity prediction by 4%-8%, with the greatest improvement found for TCs undergoing an intensity change of 610 kt (1 kt 5 0.5144 m s 21 ) over 24-48 h.…”

High-Definition Sounding System (HDSS) for Atmospheric Profiling

“…Widely-used Tropical Cyclone (TC) models include regional air-sea coupled dynamical models such as COAMPS-TC (Hao, et al, 2013), HWRF (Tallapragada, et al, 2014;Kim, et al, 2014) and GFDL (Bender et al, 2007;Gall, et al, 2011), global dynamical models such as GFS and ECMWF, and statistical-dynamical intensity-prediction models such as SHIPS, STIPS, L-GEM, RII (DeMaria and Kaplan, 1999;DeMaria, et al, 2005;Knaff et al, 2005;Jones, et al, 2006;DeMaria, 2009;DeMaria, 2010;Kaplan, et al, 2010). All of these models benefit from improved 3-D spatial observational plus enhanced temporal resolution, i.e.…”