This is a quite cynical take on how weather forecasting works written by someone that quite clearly does not know one single weather forecaster.

First of all there are only two agencies on the planet that do the number crunching to work out some reasonable forecast data that encompasses the whole globe. These are the NWS and the UK Met Office. As well as having to have a lot of big computers these agencies also need source data, this data - observations - comes from airports and plenty of other places where things like wind speed, precipitation temperature and so on is actually measured. At times the observations are wrong - imagine the baking tarmac of that big airport and how that differs from the tranquil yet noisy houses close to a nearby river.

The NWS differs from the Met Office in that they don't charge for the GRIB data. The tax payer has paid for it already in the USA so they don't have to pay for it again. Hence the proliferation of things like The Weather Channel that use NWS rather than Met Office data.

One thing that outsiders to weather forecasting do not realise is what it is that weather forecasters actually do. They imagine them to be very scientific - which they are - but they don't realise that they are essentially in the 'betting shop' business. To take an automotive example, if you had perfect knowledge of every car that is entering tomorrow's F1 race and you had perfect knowledge of the well-being of every single driver, mechanic and tea lady involved in the event, can you actually predict which of the 22 drivers is going to win? Will it be the guy on pole? The guy who has one most of the races so far? The guy who consistently comes second? Or some random outsider?

The GRIB data is far from perfect knowledge, it is a forecast of what is going to happen and the accuracy depends on the time window going into the future. The data is fully 3 dimensional, think of it as lots of onion layers going around the whole planet. Data points are on a grid - what happens if your town is next to some huge mountain with 'your' data point on that grid being several thousand feet higher than where your town is? The GRIB data for your town is not actually for your town, it is for the mountain. A meteorologist will have rules of thumb plus the science to arrive at a more accurate guess than the GRIB gives - this is interpretation of the data, not some sixth sense, however, it is still nonetheless a gamble/guess.

As well as the GRIB data there are things like satellite images - from lots of different flavours of satellite - plus there is radar data. This can all be layered up on top of GRIB data and pretty maps to create an interpreted forecast. The 'wet bias' is more likely to be rookie mistake meteorology rather than devious ploy to get viewers watching. Look at any satellite image and see the low level haze from things like jet plane 'contrails' plus coastal fog etc. There is an awful lot of it on satellite images and it is very easy to permanently be predicting rain from seeing such cloudy greyness. Hence at the local weather station this is more likely to happen. On the Weather Channel where they have excellent interpretation tools for their forecasters this is less likely to happen, not so much because of the tools but because of the forecasters - they are more experienced gamblers.

The other thing to remember with weather forecasting is that today's predictions can be checked against tomorrow's observations. Things can be consistently wrong for a given town/area due to the way the GRIB data works (i.e. does not factor in local topology), and it can take a while before this error in the model is discovered and fixed. There may not be observation data available for smaller towns so some errors might never be fixed.

The weather prediction industry is fairly ripe for disruption. The tools that meteorologists have used to require big workstations to run, nowadays a Google Earth type of app would suffice, if someone could be bothered to write it.

Amongst themselves meteorologists know a lot more about the current factors influencing the big picture of the weather. For instance, the storms that start off on the west coast of Africa, cross the Atlantic and 'bounce back' to the UK, losing energy on the way to end up as mere rain. Clearly such weather patterns take weeks to do there thing, however, for a gardener in the UK it would be good to know if rain was on its way over the next few weeks. Yet the demands of forecasting format mean that the forecaster has to tie that down to 'rain expected teatime next Tuesday' (or whenever). Returning to the 'app' idea, it would be great for everyone if they could explore the raw data and have these bigger events pointed out by an expert, so that the raw data can be interpreted in a meaningful way. Instead we have banal 'insights' such a this article (that probably did not intend to be banal or naive but that is the way things sometimes happen despite trying hard).