The multi-model ensemble approach makes sense — single provider apps are only as good as that one API. Curious how much the ML bias correction actually improves accuracy in practice versus the raw ensemble average. Do you have any before/after metrics on that?
Hi, the exact improvement heavily depends on the specific location and the weather patterns at the time. Generally though, we see roughly a 10% improvement in day-ahead forecasts for core variables like temperature and cloudiness compared to the raw ensemble average. The difference is even bigger if you compare it to the standard single-model approach that most weather APIs use.
That's a meaningful improvement — 10% on day-ahead forecasts is actually significant when you compound it across a week of planning. Makes sense that
location variance would be high though; I'd imagine coastal vs inland vs mountainous locations behave very differently for your correction models.
The multi-model ensemble approach makes sense — single provider apps are only as good as that one API. Curious how much the ML bias correction actually improves accuracy in practice versus the raw ensemble average. Do you have any before/after metrics on that?
Hi, the exact improvement heavily depends on the specific location and the weather patterns at the time. Generally though, we see roughly a 10% improvement in day-ahead forecasts for core variables like temperature and cloudiness compared to the raw ensemble average. The difference is even bigger if you compare it to the standard single-model approach that most weather APIs use.
That's a meaningful improvement — 10% on day-ahead forecasts is actually significant when you compound it across a week of planning. Makes sense that location variance would be high though; I'd imagine coastal vs inland vs mountainous locations behave very differently for your correction models.
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I just subscribed. The temperature legend on the map is in deg C even though I have selected deg F units.