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A new model still needs an old question
On 12 May 2026, the European Centre for Medium-Range Weather Forecasts implemented version two of its artificial-intelligence ensemble forecasting system. Its implementation record describes a coordinated change with the physics-based Integrated Forecasting System, including a new wave component and changes to the machine-learning training procedure. Crucially, it also explains that the older AI ensemble performed worse when initialized from the new physics-based system's experimental analyses. Updating the forecast engine without attending to its starting data was not enough. ECMWF's AIFS ENS v2 implementation record.
That dependency is a useful entry into ensemble forecasting. A forecast is not simply an algorithm presented with the world. It is an algorithm presented with an estimate of the world, built from measurements, earlier calculations and decisions about uncertainty. An ensemble then offers several possible developments of that estimated state. The scientific challenge is not merely to make the developments look different. It is to make their differences informative about what may actually happen.
ECMWF first put its AI ensemble into operations on 1 July 2025, alongside its physics-based forecasts. The launch announcement described 51 forecast members. This was an operational milestone, not a claim that uncertainty had been eliminated, nor evidence that every individual member should be treated as equally credible for every conceivable application. ECMWF's July 2025 announcement.
The question that survives changes in computing architecture is remarkably concrete: when a forecast says an event has a particular probability, does the event occur at the corresponding frequency across suitable comparable forecasts? A second question immediately follows. Does the system distinguish the situations in which that probability should be higher from those in which it should be lower?
Those are different achievements. A forecast can be cautious but uninformative, detailed but overconfident, or accurate on average while systematically missing a rare category. To understand the value of an ensemble, the reader has to move beyond the picture of many colored lines spreading across a map. The lines are a sample of possibilities. What matters is the relationship between that sample, the question being asked and the observations eventually available to test it.
The starting atmosphere is an estimate
Data assimilation combines observations with a short-range forecast to estimate the atmospheric state from which another forecast begins. ECMWF describes the previous short forecast as the background and the updated state as the analysis. Its observing inputs come from systems including satellites, weather stations, ships and buoys; both the measurements and the model carry uncertainty. An ensemble of assimilation calculations helps characterize uncertainty in the starting state. ECMWF's account of data assimilation.
Imagine, as a deliberately simplified example, that two nearby observations imply slightly different positions for a temperature boundary. The analyst cannot insert both positions as a single perfectly known line. Nor is choosing the midpoint necessarily an adequate description of the information. The uncertainty might concern the boundary's location rather than its strength. It could also be related to the winds that will move it. Treating each number independently would discard those relationships.
That distinction explains why an ensemble is more than a collection of random disturbances. A coherent atmospheric state connects pressure, temperature, moisture and motion. An arbitrary numerical alteration can create an implausible state or a disturbance the model rapidly removes. Useful perturbations need to represent uncertainty that the forecasting system cannot already resolve, including uncertainty capable of affecting the subsequent evolution.
A second uncertainty concerns the evolution itself. Processes smaller than the model's resolved scales have to be represented approximately. Different representations, parameter settings or stochastic treatments can lead to different trajectories even from similar starting conditions. ECMWF's historical account distinguishes initial-condition uncertainty from model uncertainty and describes the introduction of techniques addressing both. Its dates and configurations are a history of the system, not a specification of every model running today. Thirty years of ensemble forecasting at ECMWF.
For interpretation, the important point is that agreement among members is conditional on how the ensemble was constructed. Fifty runs that share an inadequate representation of a process can agree with one another and still be wrong together. The visible width of the forecast distribution does not automatically include every source of error. More agreement can mean stronger evidence, but only if past verification supports that interpretation for the relevant variable, place and forecast range.
A member is a trajectory, not a vote from an independent witness
Suppose an invented ensemble has 50 equally weighted members and 14 predict a specified temperature threshold will be exceeded. The raw member fraction is 28 percent. That calculation is straightforward. The interpretation is harder: it is a probability estimate produced by this ensemble, not an observation that nature has placed exactly 14 winning tickets in a bag of 50.
Members share observations, numerical assumptions and often much of their computational machinery. They are not 50 independent research teams confirming the same result. Even when random draws are independent conditional on a model, they remain draws from that model's representation of uncertainty. Independence of random numbers cannot repair a missing physical mechanism or a systematic error in the initial analysis.
There is a useful distinction between sampling a distribution more densely and making the distribution better. In a hypothetical distribution with a ten percent chance of an event, one hundred perfectly generated samples need not contain exactly ten occurrences. Additional samples can stabilize the estimate. But if the forecasting distribution mistakenly assigns a one percent chance, drawing ten thousand samples more precisely estimates the wrong number.
This distinction becomes more important as machine learning makes very large ensembles computationally attractive. A 2025 research paper on spherical Fourier neural operators developed a system intended to support much larger ensembles and assessed it using mean, spectral and extreme-event diagnostics. It distinguished the realism of individual members from the behavior of their average and acknowledged remaining spatial blurring. These are research checks on a particular system, not proof that a large member count alone validates an ensemble. The primary study on huge ensembles.
A forecaster therefore needs two descriptions of an ensemble: how many realizations it contains and what uncertainties those realizations represent. The first is a count. The second requires scientific documentation and experience against observations. A beautifully populated distribution can still be narrow in the wrong dimension. It may explore small differences in temperature while failing to represent an alternative storm track that would dominate the local outcome.
The average storm may never exist
Consider a toy forecast with two equally likely scenarios for a narrow band of strong wind. In one, the band passes west of a town; in the other, it passes east. Averaging the wind field at every location can produce a broad, weaker pattern. That average is not a third scenario in which a weak band follows a central path. It is a mathematical summary of two different possibilities.
An ensemble mean can still be valuable. It answers a particular numerical question about the distribution and can reduce errors that cancel across members. The problem begins when a map of that mean is treated as a physically complete future. The mean of several displaced features need not preserve any one feature's shape, timing or peak intensity.
A simple invented temperature example makes the distinction visible without a map. Half the members predict 8 degrees Celsius and half predict 18. The mean is 13. Another ensemble has every member at 13. Both have the same mean, but the first represents two sharply separated possibilities and the second represents near-total agreement. A display containing only 13 conceals the information needed to distinguish them.
The same issue appears in time. Imagine a three-hour strong-wind episode arriving either early in the afternoon or late in the evening. An average time series can spread that episode over a longer period and lower its apparent peak. Someone examining each member can see concentrated episodes with uncertain arrival. Someone examining only the mean might infer a prolonged moderate event.
Neither presentation should be declared universally superior. A question about expected aggregate exposure may legitimately use an average. A question about whether a short operational window overlaps a concentrated event needs the distribution of timing. The necessary transformation depends on the question, not on which chart looks most decisive.
This is one reason probabilistic forecasting cannot be reduced to surrounding a single line with an arbitrary shaded band. Some uncertainty is approximately symmetric around a central estimate. Other uncertainty consists of distinct branches. Preserving those branches can be more informative than compressing them into one central trajectory and one measure of spread.
Define the event before counting it
The National Weather Service defines probability of precipitation in terms of a measurable amount at a given point during a specified period. Its Juneau forecast-terms page identifies measurable precipitation as at least 0.01 inch, including the water equivalent of frozen precipitation. That is a definition of an event, not a prediction of how much of the period will be wet. NWS forecast terminology.
An ensemble probability needs equally explicit boundaries. Is the quantity instantaneous temperature, daily maximum temperature, or temperature averaged over a day? Does a wind threshold refer to a sustained speed or a gust? Is precipitation accumulated over six hours or twenty-four? A number without those definitions is not yet a testable forecast.
Consider two hypothetical days, both receiving twelve millimeters of rain. On the first, it falls evenly over twelve hours. On the second, it falls in one hour. A forecast of the daily total could verify equally well in both cases, while a forecast of hourly intensity would not. The difference arises before any argument about model quality: the two forecasts ask different questions.
Spatial definitions matter just as much. The chance that some location within a large region exceeds a threshold is generally not the same as the chance at a particular location. In a constructed example, ten equally likely scenarios each place one shower in a different, non-overlapping part of a region. Every scenario contains a shower somewhere, while any one of those ten parts receives a shower in only one scenario. The regional occurrence probability is one hundred percent; the probability for a specified part is ten percent.
This does not make either number misleading by itself. Misleading communication occurs when the regional number is labeled as a local probability or the local number is read as a guarantee that the rest of the region stays dry. The event definition determines the denominator and the observation needed for verification.
Even the word exceed requires care. Greater than a threshold and greater than or equal to it are different events when values are rounded or when a distribution has substantial mass at a boundary such as zero precipitation. Operational products need consistent definitions so that a forecast is not credited or penalized through an unnoticed change in bookkeeping.
Calibration is a relationship across cases
A reliability diagram groups forecasts by predicted probability and compares those probabilities with observed event frequencies. Calibration concerns that correspondence. Resolution, in the verification sense, concerns the ability to distinguish situations with different observed frequencies; it does not mean the spacing of points on a model grid. ECMWF's statistical guide discusses these concepts together while distinguishing them from the tendency to issue more concentrated or extreme forecasts. ECMWF's statistical concepts guide, archived version.
Here is an entirely invented verification exercise. A service issues a thirty percent probability on one hundred occasions, and the event occurs thirty times. For that set, the single probability category is perfectly calibrated. Yet the forecast never distinguishes one occasion from another. It could be no more informative than stating the long-run frequency every day.
Now suppose the one hundred occasions secretly comprise two equally sized groups. The event occurs ten times in one group and twenty times in the other. The combined thirty percent forecast still matches the combined thirty percent frequency. Within the groups, however, it overpredicts the first frequency and underpredicts the second. Aggregation has hidden a conditional problem.
This is why an apparently reassuring national reliability diagram does not automatically establish calibration for a coastal station, a mountain valley or a particular season. More specific groupings can uncover meaningful errors. They can also become statistically fragile: a bin containing only a few forecasts provides a noisy estimate of event frequency. Splitting data indefinitely does not create unlimited knowledge.
A convincing assessment needs to navigate that tension. It should show enough conditioning to reveal important variation without pretending that tiny samples determine precise probabilities. The useful questions include how many cases contribute to each category, how categories were selected, whether neighboring cases are related and whether an apparent deviation persists in independent data.
A single outcome cannot settle calibration. If an event assigned a twenty percent probability occurs, that is compatible with a calibrated forecast. It is also compatible with a poor forecast. The information arrives through repeated comparisons, not through declaring that the probability was wrong because the less likely outcome happened. The forecast is accountable, but it must be held accountable for the quantity it actually supplied.
An invented scorecard with all the arithmetic exposed
For a binary event, the Brier score averages the squared difference between the predicted probability and an outcome coded as one or zero. Lower is better. The statistical guide cited above gives this definition and its decomposition into reliability, resolution and uncertainty components. The following numbers are a separate, constructed example, not results from any weather service.
Take one hundred cases with twenty observed events. A constant forecast of twenty percent receives a squared error of 0.64 on each event and 0.04 on each non-event. Its total is twenty times 0.64 plus eighty times 0.04, or sixteen. Dividing by one hundred gives a mean score of 0.16.
Now invent a more informative forecast. It assigns five percent to eighty cases, among which four events occur. It assigns eighty percent to the remaining twenty cases, among which sixteen events occur. Both groups are exactly calibrated in this constructed sample. The overall event count remains twenty, so the comparison uses the same outcomes rather than an easier collection of days.
For the low-probability group, four events contribute four times 0.95 squared, or 3.61. Its seventy-six non-events contribute seventy-six times 0.05 squared, or 0.19. That group's total is 3.80. In the high-probability group, sixteen events contribute sixteen times 0.20 squared, or 0.64; four non-events contribute four times 0.80 squared, or 2.56. Its total is 3.20.
Across the hundred cases, the second forecast's score is therefore 7 divided by 100, or 0.07. Relative to the constant forecast's 0.16, the proportional reduction is 56.25 percent. This is the Brier skill score for this invented comparison, not an operational performance claim.
The improvement is not a reward for confidence by itself. The more extreme probabilities earned their place because they separated groups with genuinely different observed frequencies. Randomly assigning the same five and eighty percent values to cases would preserve their outward decisiveness without preserving that ability.
The arithmetic also explains why a few dramatic failures do not tell the entire story. The high-probability forecast is strongly penalized on its four non-events, yet still improves the overall score because it correctly distinguishes many more event-prone cases. Conversely, a forecast that avoids all strong probabilities can look less embarrassing on selected days while discarding useful information across the full record.
A probability distribution needs more than one threshold
A binary score tests one event definition. A temperature distribution makes claims about many possible thresholds, not just whether tomorrow exceeds a single chosen value. The continuous ranked probability score, or CRPS, evaluates a forecast's cumulative distribution against the observed value. Jordan, Krüger and Lerch's methods paper presents its integral definition and a form that can be calculated directly from ensemble values. It also explains why proper scoring rules encourage reporting the predictive distribution rather than strategically distorting it. The primary scoringRules paper.
For an equally weighted empirical ensemble, CRPS can be calculated as the average absolute distance from members to the observation, minus half the average absolute distance between all ordered pairs of members, including self-pairs. That subtraction matters: merely averaging member errors would evaluate uncertainty differently and encourage collapse toward a single value. The expression scores the empirical distribution, not just its mean.
Consider an invented two-member temperature ensemble, 10 and 14 degrees, followed by an observation of 12. The average distance to the observation is two degrees. The four ordered member pairs have separations zero, four, four and zero, giving an average of two. Half of that average is one, so the CRPS is one degree.
If both members had been 12, the score for this one case would be zero. If both had been 10, it would be two. Those results do not prove that narrow distributions are always better. Across cases in which the observation varies, an unjustifiably narrow distribution will accumulate errors. Proper scoring is a statement about expected performance over the relevant uncertainty, not a promise that the best distribution wins every individual occasion.
Units are informative here. For temperature expressed in degrees Celsius, this CRPS has temperature units; for a precipitation amount, it has precipitation units. It would make little sense to compare an unnormalized temperature score with a rainfall score as if their numerical sizes identified the better forecast system.
Comparisons also need a consistent treatment of finite ensemble size. A small collection is an imperfect sample even when its generating distribution is good. The WMO lead-centre methodology for subseasonal predictions explicitly uses finite-member adjustments for several skill scores and warns that differing hindcast periods complicate comparisons between systems. That is a documented example from subseasonal verification, not a claim that every medium-range product uses the same protocol. WMO lead-centre verification methodology.
A flat histogram is not a certificate
Another diagnostic orders the members from lowest to highest and records where the observation falls among them. Repeating this over many forecasts produces a rank histogram. In an idealized exchangeable setting with continuous values, all ranks are equally likely. Excess observations outside the ensemble range can be a warning about insufficient spread; persistent asymmetry can indicate bias. ECMWF teaching material explicitly cautions that a uniform rank histogram is necessary but not sufficient for reliability. Hagedorn's ensemble-prediction lecture.
The count of possible ranks is easy to overlook. Three ordered members create four positions for an observation: below all of them, between the first two, between the second and third, and above all of them. Being outside a finite ensemble is therefore not automatically evidence that the generating forecast was impossible or dishonest.
In that ideal three-member example, two of the four equally likely positions are outside the member range. With fifty members there are fifty-one positions, of which two are outside. The corresponding fraction is about 3.92 percent. These are mathematical consequences of the stated ideal assumptions, not empirical coverage estimates for a named operational ensemble.
Ties complicate the picture for quantities such as rainfall, where many members and observations may be zero. A rank calculation has to handle those ties consistently. Otherwise a visible feature in the histogram may partly reflect the sorting procedure rather than a meteorological deficiency.
More fundamentally, ranks discard distances. An observation just above the highest member and an observation far above it occupy the same outer rank. The histogram cannot by itself say how severe an outlying error was. Nor does an aggregate histogram necessarily reveal that one class of situations is biased high and another biased low.
This makes the diagnostic useful but incomplete. It can expose an obvious mismatch between a distribution and observations, while a flat result leaves other questions open. The quality of a probabilistic forecast is not something that one attractive plot can certify. Different diagnostics deliberately discard different information; using several helps identify which information remains unchecked.
Calibration changes a product, not the past
Statistical postprocessing learns a relationship between model output and observations. In a familiar temperature formulation, the predictive mean is a fitted function of ensemble forecasts and the predictive variance depends on ensemble spread. Gneiting's 2014 technical memorandum describes this nonhomogeneous regression approach, also called ensemble model output statistics, and distinguishes the resulting distribution from the raw member values. Calibration of Medium-Range Weather Forecasts.
Imagine a hypothetical station where a particular forecast configuration has tended to run two degrees too cold in a relevant training sample. Adding two degrees to a new forecast is a simple candidate correction. It is not automatically justified in a different season, at another lead time, or after the model changes. A successful correction represents a repeatable relationship, not a license to repaint any uncomfortable result.
Spread requires separate attention. A correction that shifts every member equally leaves their separation unchanged. If the ensemble has also been too narrow, adjusting the mean alone cannot fix that problem. Conversely, widening an ensemble without correcting a persistent shift can create a broad distribution that contains more observations while remaining systematically misplaced.
The choice of distribution matters too. A Gaussian temperature model permits values on both sides of its mean. Applying the same unconstrained distribution to precipitation would permit negative amounts unless the formulation addressed that boundary. Zero rainfall and positive rainfall also pose different statistical questions: whether precipitation occurs and how much falls when it does.
An ECMWF study published in its winter 2014/15 newsletter examined postprocessing for temperature, precipitation, wind and cloud cover using station observations. It found improvements under its tested configuration, while noting that training based on earlier operational forecasts could become inconsistent after model upgrades. Its results are evidence for the value and limits of that particular calibration exercise, not a numerical promise for today's systems. The primary ECMWF calibration study.
The central distinction is between repairing a repeatable statistical relationship and recovering information the model never represented. Postprocessing can learn that a certain pattern usually implies warmer local conditions. It cannot establish a missing alternative weather scenario merely by making a confidence interval wider. A calibrated local distribution and a dynamically coherent set of future weather maps are related products, but they are not interchangeable.
Reforecasts are experiments with a version number
Reforecasts run a forecast model for dates in the past, providing a history against which current output can be interpreted. ECMWF's explanatory documentation distinguishes fixed reforecast collections from collections generated alongside real-time forecasts and emphasizes consistency between the model version used for reforecasts and the operational version being calibrated. ECMWF's explanation of reforecasts.
This is different from simply collecting whatever forecasts happened to be issued years ago. An operational archive records a succession of evolving systems. A reforecast experiment can ask what one specified system would have produced across a historical sample. Both records are valuable, but they answer different questions about continuity and performance.
A hypothetical upgrade illustrates the danger of mixing them carelessly. Suppose an older model has a persistent cold bias and a correction trained on that model adds two degrees. Engineers then remove the cold bias in the model itself. Keeping the old correction unchanged can introduce a warm bias into the final product. The correction did not become mathematically invalid; its learned relationship stopped matching the system it was applied to.
Evaluation has another boundary: information available during development versus information reserved for testing. If the same unusual events are repeatedly used to choose a calibration scheme and to advertise its performance, the assessment is no longer a clean test of future use. A model can be adapted to quirks in its development sample without acquiring the claimed general ability.
Weather makes this separation subtle because nearby dates are related. Randomly dividing individual hours into training and test sets can leave different portions of the same evolving weather system on both sides. A test meant to represent future seasons must account for that relationship rather than treating every archived row as independent evidence.
A versioned evaluation record should therefore preserve more than a model name. It needs initialization dates, lead times, observation references, calibration training periods and the treatment of missing forecasts. Those details determine what has been tested. Without them, a claimed improvement can reflect a new model, a new observing sample, a different season or a changed target without making clear which change deserves the credit.
The observation has a footprint too
A thermometer reports conditions at its location and exposure. A model value represents a quantity on its own spatial and temporal framework. Comparing them requires a matching operation, whether through interpolation, averaging or a site-specific observation model. A disagreement can contain both forecast error and a mismatch between the quantities being compared.
Consider a hypothetical grid cell that includes a ridge and a sheltered valley. A station in the valley is not a random representative of the entire terrain. A model's smoothed elevation may differ from the station elevation. A repeated difference at that station may be highly relevant to a local forecast product, while not demonstrating that every part of the modeled air mass has the same error.
Rain provides a different illustration. A small displacement of a narrow rain band can generate two penalties in a point-by-point map comparison: rain predicted where it did not occur and rain observed where it was not predicted. A neighborhood approach can instead ask whether a forecast captured the amount of threshold-exceeding area within a surrounding region.
The fractions skill score is one such spatial measure. Antonio and Aitchison's study examines how its interpretation depends on the reference used to declare skill and develops an alternative reference. Their work is a reminder that changing the scale of verification also changes the question being answered; a convenient reference criterion is not automatically a universal definition of a useful forecast. The authors' full paper on fractions skill.
For a hypothetical one-grid-cell displacement, a broad neighborhood may show strong agreement in total rainy fraction even though the forecast fails at an individual address. That is not a contradiction. The forecast can be useful at the broad scale and inaccurate at the narrow one. Reporting only the broad-scale score would hide the local failure; reporting only exact point agreement could hide useful information about the larger pattern.
This is particularly important when a system's computational grid becomes finer. More detailed output is not automatically more predictable output. Verification should establish which structures improve, at what spatial scales, and for which lead times. A sharp-looking map is a representation. Skill is a relationship demonstrated against an appropriate reference.
Extremeness is not a probability printed in another color
ECMWF's Extreme Forecast Index compares the ensemble distribution with a model-climate distribution, giving additional emphasis to differences in the tails. Its documentation also notes a limitation: the index does not describe how far forecast values extend beyond the model-climate limits, one reason for the complementary Shift of Tails concept. ECMWF's EFI documentation.
An index describing unusualness should not be read as the probability of a particular outcome. A value of 0.8 is not, just by sharing a decimal format, an eighty percent chance that a specified local threshold will be crossed. To obtain that probability, the event must be defined and its forecast distribution evaluated accordingly.
The baseline also matters. In an invented comparison, 28 degrees may be unusual for one place and season but ordinary for another. An anomaly-oriented product can highlight the former even if the latter has the higher absolute temperature. Neither ranking determines consequences on its own. The statistical rarity of weather and the response of a particular population, organism or piece of equipment are separate quantities.
Rare-event verification demands patience with denominators. A hypothetical archive containing ten thousand matched forecasts sounds large. If only ten meet an extreme-event definition, there are still only ten observed examples in that category. Ten thousand simulations of one starting state would not create ten thousand independent historical extremes against which the model had been validated.
The goal is not to dismiss tail information because it is difficult to verify. It is to prevent simulated detail from being mistaken for observational evidence. Larger ensembles can explore the tails of a model's distribution more thoroughly. Longer, well-characterized observations and carefully designed tests are needed to establish how that distribution relates to rare events in the world.
A useful forecast is not one universal decision
Forecast quality and forecast value are related but distinct. Richardson's account of predictability and economic value uses a simplified cost-loss framework to examine why the same probability forecast can have different value for different users. Its economic assumptions are part of the model, not a complete description of public safety or every weather-sensitive decision. Predictability and economic value.
Consider a purely hypothetical, risk-neutral operation choosing whether to protect a replaceable item. Protection costs ten units and, by assumption, prevents all of a potential hundred-unit loss. Without protection, a twenty percent event probability implies an expected loss of twenty units. Under those deliberately restrictive assumptions, paying ten units is preferable. If protection instead costs thirty units, the same forecast does not justify it on expected cost alone.
Nothing about the atmosphere changed between those comparisons. Neither user needs the forecast probability to be relabeled. Their choices differ because their costs differ. A single yes-or-no weather symbol would have to conceal that distinction or silently choose one user's threshold for everyone.
The example also exposes its own limitations. If protection only partly prevents damage, the relevant comparison changes. If the item cannot be replaced, if losses are not adequately represented by money, or if actions themselves create risks for other people, the simplified arithmetic is insufficient. It is an illustration of information use, not an instruction for a real hazardous situation.
Probabilities retain value precisely because they separate the description of uncertain weather from the consequences of a decision. Verification then checks the weather claims, while a separate evaluation asks whether a particular use improved outcomes under stated assumptions. Confusing those evaluations can make a useful probability look wrong because one action was unsuccessful, or make a poor forecast look excellent because a user happened to avoid a loss.
Read a benchmark in its own tense
The GenCast paper was published online on 4 December 2024. In its 2019 evaluation, it reported better CRPS performance than the comparison ENS system on 97.2 percent of 1,320 variable, lead-time and vertical-level combinations. The paper explicitly says those targets are not independent tasks. It also explains its training and test separation and excludes precipitation from its main results because of concerns about the ERA5 precipitation reference. These are essential boundaries around the result. The primary GenCast paper.
A percentage of scorecard targets is not a percentage of individual weather forecasts that were correct. Nor does a comparison with an operational system from a historical evaluation automatically establish superiority over later versions. The study's publication date, test period and comparator configuration occupy different places in the evidence record.
There is nevertheless real information in a carefully bounded benchmark. It can demonstrate that a method represents uncertainty better under specified tests, identify variables where it struggles and provide a reproducible starting point for improvement. The mistake would be to replace those details with a universal ranking that the experiment never attempted to establish.
A useful comparison also separates marginal predictions from joint scenarios. Two systems might give equally good temperature distributions at each of two locations while differing in whether they predict both locations will be hot together. A user interested in a regional aggregate needs that dependence. A collection of individually calibrated local probabilities does not uniquely determine it.
To see why, imagine two hypothetical towns, each with a fifty percent probability of exceeding a threshold. In one joint forecast, both exceed together in half the scenarios and neither exceeds in the rest. In another, exactly one town exceeds in every scenario. The local probabilities are identical. The probability that both exceed is fifty percent in the first system and zero in the second.
That simple construction shows why realistic member trajectories remain important even when the headline product is a probability. They carry information about relationships across places, times and variables. A forecast system earns trust not by presenting more possible futures, but by demonstrating which features of those futures are supported: the event frequencies, the distinctions between situations, the spatial structure and the dependencies that matter for the question at hand.
The strongest ensemble forecast is therefore neither the one with the narrowest range nor the one with the largest stack of members. It is the one whose uncertainty has been represented, tested and communicated at the right level. Its achievement is not escaping uncertainty. It is making uncertainty specific enough to examine, compare and use without pretending that a possible future has already become a fact.
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