What Weather Routing Actually Buys
Download the full methodology (PDF) · Interactive report with route maps
A note on currents (why this matters)
ERA5 carries no ocean currents, so an initial wind/wave-only run understated value on the current-dominated routes, riding the favorable current / dodging the counter-current is a major real-world lever. We therefore add CMEMS GLORYS surface currents to the replay path (they apply to SOG and fuel), and Agulhas + N.Atlantic were re-run current-aware. As predicted, the missing value was structural (β), not tactical (α): on Agulhas the structural term rose from β ≈ 0.1% (blind) to β = +1.1% (current-aware), while the tactical term was unchanged (α ≈ 0.9%, an upper bound). Currents are quasi-stationary, so they enter the forecast-free β, strengthening the “structural > tactical” conclusion, not overturning it. All figures below are current-aware unless noted.
TL;DR
Weather-routing vendors quote “we save X %”. This study asks which layer the benefit lives in and against what baseline, and the honest answer is much smaller than the headline. We pre-registered a four-stage ROI estimator and ran it over a basket of four regimes (a storm+current corridor, a North Atlantic crossing, a trade-wind negative control, and a short coastal leg), each as a paired counterfactual under the same ERA5-replay weather.
The gain decomposes into geometry + β + α, and α (the only forecast-dependent part) is < 1 % of fuel. All current-aware, % of planned-route fuel, bootstrap 95 % CI:
route Geometry G β (seasonal + current) α (forecast, hindcast UB) total Agulhas 11.9 % +1.1 % [0.46, 1.89] 0.9 % [0.03, 1.85] 14.0 % N.Atlantic 5.5 % −1.4 % [−2.25, −0.64] 0.7 % [0.30, 1.19] 4.8 % Geometry (a one-time route-design correction any shortest-path tool delivers) and β (the climatologically knowable corridor + riding the quasi-stationary current, free from a routeing/current chart) dominate. The tactical α, the marginal value of reacting to this departure’s forecast, which is all a real-time service adds, is under 1 % of fuel, and that is a hindcast upper bound (the operational figure is lower).
Two ROI numbers, because the baseline is the whole game. Against a ship’s planned route the optimiser appears to save 14.0 % (Agulhas) / 4.8 % (N.Atlantic). Against the constraint-respecting geodesic, the honest no-weather-routing baseline, the physics-ceiling ROI is small or negative: Agulhas +2.3 % [0.9, 3.9], N.Atlantic −0.7 % [−1.6, −0.05]. Even with currents, the optimiser barely beats (Agulhas) or loses to (N.Atlantic) the straight shortest path; the headline is mostly route-design + structure.
The negative control passes. On the benign trade-wind route the optimiser’s weather term is −0.2 % (CI [−0.31, −0.02], ≤0), it does not manufacture a saving where there’s no weather to exploit. Unbiased.
North Atlantic is the sharpest result. Even current-aware, the optimiser’s storm/current-dodging swings (a corridor 3 to 6× the model grid, genuine tactical routing) add more distance than they save, so the optimised route is worse than the straight geodesic (physics-ceiling negative). Real-time spot optimisation adds little-to-negative value here, in hindcast (= the upper bound).
Everything here is an upper bound. Replay gives the optimiser the true future; the operational (forecast-error) figure is strictly lower (~44 % of benefit lost to forecast error in the literature). Surface currents are now included (CMEMS GLORYS); they entered β (structural), confirming the conclusion.
The honest reframing the evidence supports: weather routing = a cheap, robust seasonal-structural core (route design G + climatology/current β) + a small, tail-concentrated tactical premium α that real-time services sell, and the marketed headline conflates all of it against a flattering baseline.
1. The four-stage ROI estimator (why two ROI numbers)
A ship’s planned route departs from the great circle for reasons unrelated to weather: hard law (coast- distance minima, TSS) and soft law (the master’s prudence). Crediting the optimiser for undoing those is double-counting. So we report two numbers per voyage:
- operational ROI = optimised vs the planned RTZ (soft-law corridor), the deliverable number.
- physics-ceiling ROI = optimised vs the constrained geodesic (shortest path subject to land + hard law), the honest ceiling, route-design slack removed.
The constrained geodesic is built by a land-masked A* (using the optimiser’s own land mask, so both respect the same constraints, constraint symmetry) + a shore-aware string-pull. Every saving is split exactly into a distance term and a per-mile weather term; only the weather term is genuine weather-routing ROI.
2. Headline: two ROI numbers, the weather term, and the distance/weather split
All current-aware (CMEMS GLORYS); bootstrap 95 % CIs, 10 000 resamples, seed 20260524, paired by departure.
| route | regime | operational ROI (vs planned) | physics-ceiling ROI (vs geodesic) | weather term (% fuel) | distance term |
|---|---|---|---|---|---|
| Agulhas | storm + Agulhas current | 14.0 % [12.3, 15.7] | +2.3 % [0.9, 3.9] | 6.9 % [5.0, 8.8] | 7.1 % |
| N.Atlantic | depressions + Gulf Stream | 4.8 % [3.7, 6.0] | −0.7 % [−1.6, −0.05] | 4.2 % [2.9, 5.6] | 0.6 % |
| Trade-wind (neg. control) | benign trades, no current | −0.8 % [−1.1, −0.5] | −0.8 % (= op; planned ≡ geodesic) | −0.2 % [−0.31, −0.02] | −0.6 % |
Even with currents, the physics-ceiling ROI is small or negative. On Agulhas the geodesic rounds the Cape through the favorable current, so the optimiser’s edge over it is only +2.3 %. On N.Atlantic the optimiser loses to the straight geodesic (−0.7 %): its storm/current-dodging detours add distance that does not pay off. The weather term (genuine wind/wave + current routing, vs the planned route) is 4 to 7 %; the 14 % Agulhas headline is the mechanism by which a least-distance baseline inflates “weather skill” into route-shortening.
3. Layer 2: structural vs tactical (corridor-width test)
Current-aware route spread (geodesic-Fréchet), within- vs across-season:
| route | within-season | across-season | ratio | route band / grid Δ |
|---|---|---|---|---|
| Agulhas | 89.6 nm | 96.0 nm | 0.93 | 1.4× (near-single corridor) |
| N.Atlantic | 123.5 nm | 134.5 nm | 0.92 | 3.1× (wide, weather-dependent) |
| Trade-wind | 36.1 nm | 33.2 nm | 1.09 | 1.0× (one corridor) |
Within-season ≈ across-season everywhere (ratio ~0.9): the optimised route barely depends on the season, it is a near-invariant structure. Agulhas and the trade control sit close to the grid limit (structurally predictable from climatology, no forecast needed); N.Atlantic swings wider (3× the grid), this is real tactical routing, but those swings don’t pay off (§4).
4. Layer 3: the tactical premium (the money number)
α is exactly the tactical premium: fly each season’s medoid optimised route through the month’s actual weather and currents, and compare to the spot-optimal. Current-aware: - Agulhas: α = 0.9 % of fuel [0.03, 1.85], tail-concentrated, austral winter (JJA) ~2.5 %, calm shoulder months near zero or slightly negative. The forecast-reactive value is real but lives only in the stormy tail. - N.Atlantic: α = 0.7 % of fuel [0.30, 1.19], roughly flat across seasons, even on the canonical storm route, reacting to each departure’s specific weather buys under 1 % of fuel, in hindcast. In both cases α is small, and it is a hindcast upper bound: with real forecast error the operationally realisable tactical value is lower still. The tactical layer, where real-time services live, is not where the fuel is.
5. Layer 1: seasonal dispersion & the driver
Baseline fuel on the fixed route, and how well a weather-severity index explains it: - Agulhas: mean 193.2 t, CV 4.1 %, driver R²(fuel ~ severity) 0.88. - N.Atlantic: mean 196.7 t, CV 10.5 % (big winter depressions), driver R² 0.97. - Trade-wind: mean 126.2 t, CV 2.3 % (benign), driver R² 0.65. Routing’s value scales with the severity the route is exposed to, confirming “when does routing earn its keep”: in the stormy tail, not the median voyage.
6. Production stability: convergence (#10), waypoint stress test, resolution
- Convergence: after a distance-scaled time-step fix, the isochrone engine converged 12/12 on every basket route (48/48 isochrone runs), vs 2/8 before the fix on the long Agulhas route. Reliable convergence is the precondition for any ROI claim, now met across regimes and route lengths.
- Waypoint stress test (#10): on Agulhas (Jul departure), the baseline machinery handled 6→200 waypoints with zero self-intersections, zero land crossings, bounded RSS (1.9→2.7 GB), stable wall-time; the isochrone produced identical output (169.24 t) for every corridor waypoint count 6→50, it robustly finds its own path regardless of input density (the 50-waypoint schema cap is a sound guard). Engines are stable in production as waypoints multiply.
- Resolution: isochrone fuel/distance are
identical at 0.5° and 0.25° (all 12 months), the
isochrone is wavefront-based, not grid-based, so
grid_resolution_degdoesn’t perturb it (grid engines would be sensitive, but are too slow to replay-bench).
7. Charter weather evidence (#12)
Beaufort/Douglas passage distributions + % passage time above the
charter exclusion thresholds (Bft 5 / Douglas 4): - N.Atlantic:
87.5 % of passage time above exclusion (Bft 7 to 9 reached,
Douglas 5 to 7 common), a genuinely heavy-weather route. -
Trade-wind: 90.5 %, but for a different
reason: it sits steadily at Douglas 4 (73 % of the
time). The trades are benign for routing (low
variability, nothing to dodge) yet mostly above a Douglas-3
good-weather threshold. The exclusion metric measures absolute sea
state, not variability, a distinction a charterer should understand.
One-pagers: beaufort_douglas_{natl,trade_satl}.pdf (+
Agulhas).
8. Honest scope and the follow-on
This is a hindcast study (perfect foresight = an
upper bound) of wind/wave routing (ERA5 has no currents, N.Atlantic’s Gulf-Stream value is unrepresented) on baseline +
isochrone (the Cython A* engines are forecast-only;
astar/dijkstra appear replay-compatible but
are too slow to bench). A method caveat: the geodesic/planned baselines
use voyage_calculate while the optimised route uses the
optimiser path, so a small code-path component may sit in the
physics-ceiling number, the operational number and
the weather term are computed within consistent paths and are
the robust headline. The operational (forecast-error)
ROI, the real number, is lower and needs an
archived forecast aligned to historical dates, which we do not have, so
it is specced, not run (we do not fabricate forecasts).
The current-riding β (CMEMS climatology) is likewise a specced
follow-on.
What the confirmatory evidence supports, against the pre-registered protocol: the defensible weather-routing ROI is a few percent of fuel, lives mostly in route design + season, and is materially smaller than the headline once measured against the right baseline, with the tactical, real-time layer adding little-to-negative value in hindcast on the routes tested. The decision rule is met for production trust on convergence/stability (#10 ✓) and on the negative control (unbiased ✓); it counsels against marketing real-time spot optimisation as the source of the saving.
Method note. The estimator runs each voyage in-process through a paired ERA5 + CMEMS-GLORYS replay (the same realized weather and surface currents for every baseline), benchmarked against a constraint-respecting geodesic rather than the planned route, with bootstrap confidence intervals over twelve monthly departures and a pre-registered protocol fixed before any run. Hindcast figures are upper bounds on the operationally realisable, forecast-limited value; the operational (forecast-error) arm and an out-of-sample multi-year climatology are the specced next steps. Full methodology in the PDF; interactive route maps and the decomposition in the report.