@article{fdi:010071364,
  title = {{G}lobal assessment of tropical cyclone intensity statistical-dynamical hindcasts},
  author = {{N}eetu, {S}. and {L}engaigne, {M}atthieu and {M}enon, {H}. {B}. and {V}ialard, {J}{\'e}r{\^o}me and {M}angeas, {M}organ and {M}enk{\`e}s, {C}hristophe and {A}li, {M}. {M}. and {S}uresh, {I}. and {K}naff, {J}. {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his paper assesses the characteristics of linear statistical models developed for tropical cyclone ({TC}) intensity prediction at global scale. {T}o that end, multilinear regression models are developed separately for each {TC}-prone basin to estimate the intensification of a {TC} given its initial characteristics and environmental parameters along its track. {W}e use identical large-scale environmental parameters in all basins, derived from a 1979-2012 reanalysis product. {T}he resulting models display comparable skill to previously described similar hindcast schemes. {A}lthough the resulting mean absolute errors are rather similar in all basins, the models beat persistence by 20-40% in most basins, except in the {N}orth {A}tlantic and northern {I}ndian {O}cean, where the skill gain is weaker (10-25%). {A} large fraction (60-80%) of the skill gain arises from the {TC} characteristics (intensity and its rate of change) at the beginning of the forecast. {V}ertical shear followed by the maximum potential intensity are the environmental parameters that yield most skill globally, but with individual contributions that strongly depend on the basin. {H}indcast models built from environmental predictors calculated from their seasonal climatology perform almost as well as using real-time values. {T}his has the potential to considerably simplify the implementation of operational forecasts in such models. {F}inally, these models perform poorly to predict intensity changes for {C}ategory 2 and weaker {TC}s, while they are 2-4 times more skilful for the strongest {TC}s ({C}ategory 3 and above). {T}his suggests that these linear models do not properly capture the processes controlling the early stages of {TC} intensification.},
  keywords = {tropical cyclone ; statistical model ; multiple linear regression ; intensity forecast ; atmospheric predictors ; {ATLANTIQUE} ; {PACIFIQUE} ; {OCEAN} {INDIEN} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{Q}uarterly {J}ournal of the {R}oyal {M}eteorological {S}ociety},
  volume = {143},
  numero = {706},
  pages = {2143--2156},
  ISSN = {0035-9009},
  year = {2017},
  DOI = {10.1002/qj.3073},
  URL = {https://www.documentation.ird.fr/hor/fdi:010071364},
}