NHC cyclone forecasts issued Oct. 9, 8am (NY time) · 2 active storms: Jerry, Priscilla. Consolidates exposure estimates from ADAM, GDACS, and OCHA CHD.
Summary
| Storm | Country | Similar storms | Wind | Exposure [% pop.] | Return period |
|---|---|---|---|---|---|
| Jerry 2025 | Antigua and Barbuda | Maria 2017 Fiona 2022 Gonzalo 2014 | 34 kt | 94K (100%) | 2-year |
| 50 kt | 2K (2%) | 4-year | |||
| Guadeloupe (Fr.) | Bertha 2014 Dean 2007 Erika 2009 | 34 kt | 382K (100%) | 1-year | |
| Saint Barthélemy (Fr.) | Erika 2009 Erika 2015 Ernesto 2024 | 34 kt | 9K (91%) | — | |
| Dominica | Bertha 2014 Chantal 2013 Emily 2011 | 34 kt | 51K (89%) | — | |
| Montserrat (UK) | Dean 2007 Earl 2010 Erika 2009 | 34 kt | 5K (100%) | — | |
| Saint Kitts and Nevis | Erika 2009 Erika 2015 Ernesto 2024 | 34 kt | 49K (100%) | — | |
| Saint Martin (Fr.) | Irene 2011 Isaias 2020 Laura 2020 | 34 kt | 39K (100%) | — | |
| Bonaire (Neth.) | Dorian 2019 Erika 2009 Erika 2015 | 34 kt | 2K (6%) | — | |
| Anguilla (UK) | Ernesto 2024 Frances 2004 Irene 2011 | 34 kt | 13K (100%) | — | |
| Priscilla 2025 | Mexico | Trudy 2014 Andres 2009 Calvin 2017 | 34 kt | 283K (0%) | <1-year |
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Jerry 2025 Atlantic basin
Deterministic forecast
Probabilistic forecast
Reading the charts below: everything sits on one axis, population exposed. Dots are the current estimates by source (labelled beneath the axis) · vertical ticks are past storms since 2002, the most similar ones in bold · the shaded curve, where shown, is the exposure distribution of every storm to affect the country since 2002 — the same storms at every wind speed, so at 50 and 64 kt it concentrates near zero (most storms never bring those winds there) and a storm out in the tail is genuinely rare · the scale never runs past the country's total population · the coloured curves are the forecast probabilistic distribution of exposure from NHC's wind-speed probabilities, shown two ways: as a probability density, and as the chance that final exposure exceeds each value (starting at 100% and falling to zero) · a dashed line marks exposure already observed.
Antigua and Barbuda 4-year RP
Probability density view
Exceedance view — chance exposure exceeds each value
Probability density view
Exceedance view — chance exposure exceeds each value
Guadeloupe (Fr.) 1-year RP
Probability density view
Exceedance view — chance exposure exceeds each value
Saint Barthélemy (Fr.)
Probability density view
Exceedance view — chance exposure exceeds each value
Dominica
Probability density view
Exceedance view — chance exposure exceeds each value
Montserrat (UK)
Probability density view
Exceedance view — chance exposure exceeds each value
Saint Kitts and Nevis
Probability density view
Exceedance view — chance exposure exceeds each value
Saint Martin (Fr.)
Probability density view
Exceedance view — chance exposure exceeds each value
Bonaire (Neth.)
Probability density view
Exceedance view — chance exposure exceeds each value
Anguilla (UK)
Probability density view
Exceedance view — chance exposure exceeds each value
Priscilla 2025 Eastern Pacific basin
Deterministic forecast
Probabilistic forecast
Reading the charts below: everything sits on one axis, population exposed. Dots are the current estimates by source (labelled beneath the axis) · vertical ticks are past storms since 2002, the most similar ones in bold · the shaded curve, where shown, is the exposure distribution of every storm to affect the country since 2002 — the same storms at every wind speed, so at 50 and 64 kt it concentrates near zero (most storms never bring those winds there) and a storm out in the tail is genuinely rare · the scale never runs past the country's total population · the coloured curves are the forecast probabilistic distribution of exposure from NHC's wind-speed probabilities, shown two ways: as a probability density, and as the chance that final exposure exceeds each value (starting at 100% and falling to zero) · a dashed line marks exposure already observed.
Mexico <1-year RP
Probability density view
Exceedance view — chance exposure exceeds each value
