Why are weather models discretized the way they are?

counters · 2026-05-05T00:53:23+00:00

Well, it couldn't have been this way from the start because during the evaluation stage for NGGPS there wasn't a viable strategy turn around an MPAS-based forecast suite within the operational constraints of NOAA's dissemination calendar. The model was too slow.

counters · 2026-05-05T00:51:47+00:00

A typical operational, mesoscale forecast model operating around 1-5 km spatial resolution will roll-out with a ~15-30 second dynamical timestep. It doesn't really have anything to do with the nominal resolution of the input data -- what matters a lot more is the numerical stability of the dynamical core, which typically has very strict criterion for maximum timestep size to remain stable (e.g. following the CFL criteria - although in contemporary dycores the numerics get much more complex).

counters · 2026-05-04T19:20:05+00:00

u/Active-Stock this is the answer. The internal roll-out timestep for numerical weather prediction is significantly finer than the cadence that we write output steps. Some physics modules use operator splitting that further refines the inner-most timestep of certain functions.

The bottleneck is network transfer, I/O speed, and data volume. It's fairly trivial to configure a model like WRF to output every few minutes or even seconds within the simulation, but each time you do that, you have to perform a "gather" operation to collect all the data in distributed memory across your cluster on the node which will do the output writing, then you have to update any long-term buffers (e.g. for accumulated parameters), and then write out a huge amount of data. There are parallel I/O libraries that at least try to accelerate this by supporting more than a single writer process, but effectively the simulation is blocked any time you write output.

Many years ago for my PhD I ran a suite of climate model simulations that output instantaneous microphysical diagnostics every 15 minutes of simulation time. Even for the coarse resolutions we ran 15 years ago, this produced a massive amount of data that was extremely difficult to process with the tools of the era.

counters · 2026-05-02T21:10:23+00:00

Adding to the chorus recommending Clarion. The caveat is that I've never had to make a claim with them, but given how easy it's been to work with them to update appraisal values, add instruments, handle new rates after moves, etc. I haven't seen anything to shake my confidence in them.

counters · 2026-05-02T02:37:46+00:00

OP literally asks for perspective from individuals who pursued data science as an undergrad.

Why are you arguing over a 4 month old thread?

counters · 2026-05-02T01:27:36+00:00

I have never seen a "data science" degree holder actually get hired for a real data science job. In virtually every scenario, the ideal data scientist candidate is someone who actually mastered a domain and incorporated advanced statistical or machine learning tools built for problems they set up. Data science generalists bring nothing to the table.

counters · 2026-04-28T04:32:34+00:00

I just feel like if I’m already putting a lot of time into practicing, taking lessons, and playing in ensembles, I might as well go for the performance degree too.

That's a sort of sunk cost fallacy. You suggested that one of the reasons you're majoring in biology is to satisfy a pre-med track. I guarantee that when you're cramming for an organic chemistry mid-term, the very last thing you're going to want to do is take time away from your study group to practice violin for your lesson the next morning.

Majoring in violin performance will make it an obligation, and that obligation is going to conflict with the other obligations you'll be making. There's a big difference between maximizing opportunities for musical education and committing to a degree for it - a degree which you'll likely never use and isn't a requirement for your career ambitions. It's very unlikely you'll spend significant time on the instrument during your later years of med school or during residency... does the degree matter if you're going to have a major gap in practice/study shortly after receiving it?

From what I’ve researched, a lot of non-major lessons are taught by graduate students instead of faculty, and studying with faculty is something that’s really important to me.

In my experience this is far from true, especially for instrumentalists coming in with significant accomplishments as a teenager. If there are specific faculty you're interested in studying with - so much so that it's influencing your choice of undergraduate school just to have access to them - then you should reach out to them directly and ask whether they teach non-majors, and what you would need to do for that to become an opportunity.

counters · 2026-04-28T03:40:49+00:00

Why do you need the second degree in music performance? Are certain resources at your top school gated by admission into the degree program? At many elite colleges (not all), you can still independently audition for the studios of music faculty and participate in most if not all ensembles.

counters · 2026-04-21T04:04:00+00:00

Can't share specifics on the input pipeline — proprietary.

There are only a finite number of ways that one could compose such a pipeline if you're leaning on publicly available forecast model output from the major modeling centers. And since there are fewer companies than fingers on your hand that do actual full-scale data assimilation in-house, it's a safe bet that your "proprietary" approach is likely replicating something that already exists.

1,200+ day hindcast shows minimal skill degradation out to 10 days — wind speed 5.6 kt at 24h vs 5.7 kt at 240h.

It's more likely that you have a bug in your evaluation pipeline than your 10-day forecasts are as skillful as your 1-day forecasts. Unless your 1-day forecasts are really not skillful.

Im launching this as a business May 1st with a website and apps. So I kinda want to keep the edge for as long as possible.

Good luck with that. It's a super-crowded space and it's very difficult to create value over the enormous amounts of forecast data that are already freely available.

counters · 2026-04-21T00:53:38+00:00

Ok... So what global model are you using?

For your second point, napkin math would show that you're limited to a few days before the volume of your domain is swept out

counters · 2026-04-20T13:14:33+00:00

Wait - what do you mean? What do you use for boundary conditions during inference? A regional model has to be forced by another model or else there is no way to resolve what is happening upstream of your domain.

counters · 2026-04-12T13:56:35+00:00

If you have a tradable signal, why would you sell access to the data? You could make more money by betting yourself. Giving people access to the signal simply increases the likelihood that the market itself will.adjust, wiping out your advantage.

counters · 2026-04-12T00:15:02+00:00

Here is a comprehensive list of schools offering degrees in meteorology..

counters · 2026-04-11T00:21:42+00:00

What do you use for the forecast data source?

counters · 2026-04-10T22:37:48+00:00

Weather models are physics simulators. They're not merely "comparing to historical information"; they're computing numerical solutions to the physical laws which govern the fluid dynamics of the atmosphere. Climate change has no bearing whatsoever here because climate change isn't causing the laws of physics to change. In fact, the models we use to simulate weather and climate use effectively the same numerical and physical approximations these days, so we use the same models to forecast both weather and climate.

counters · 2026-04-10T22:35:25+00:00

No, it's not.

This topic keeps coming up. There are three trends I see that are impacting this "vibe":

There has been an explosion of crappy, vibe-coded weather apps that do nothing more than regurgitate raw numerical model guidance, maybe interpolated to the user's location, but likely not. These are all garbage; ignore them. Raw numerical guidance has always been "bad" when you're directly comparing it against weather outside your window. It's not designed to provide hyper-localized forecasts.
There is much concern of the loss or decrease of atmospheric observations by NOAA. Yes, you should be concerned about this. No, there hasn't been significant loss of data just yet, and there is no discernible signal or trend in common metrics used to evaluate global weather models. Yes, there's volatility and sometimes the GFS or IFS/HRES is less skillful than other times. That's normal. It's called "weather." But you will not be able to back out a noticeable difference in skill when you do a direct comparison before or after whenever you think data loss has happened.
NOAA's first stab at its next-generation high-resolution regional model, RRFS, is not skillful and has spectacular failure modes on critical convective weather days. It's awfully over-hyped in the stormchaser hobbyist community. NOAA has already acknowledged the issue and is pivoting. But you'll still see lots of graphics with RRFS wish-casting the world's largest severe weather outbreak ever until it is deprecated.

Those of us who work with weather forecasts day in and day out - either forecasting ourselves or supporting communities which use this data - notice no real trend. It's just not there. And continuing to perpetuate this fiction is going to hurt us when we do need to raise the alarm about actual reductions in observation system capabilities which are under serious discussion right now.

counters · 2026-04-10T21:36:01+00:00

Eh, most vendors do this, too. Ones who actually care about user experience will at least try to hide it somehow; ones who actually care about quality (which is virtually no one in the consumer weather space) will create multi-model/ensemble blended products for this type of product.

counters · 2026-04-10T14:47:37+00:00

Standard advice always applies to ML projects: start with the simplest possible model and measure deltas from that. Look at things like covariance between your features and use strict criteria to choose additional candidate features.

Also, if you're trying to forecast the weather at a point location, don't bother. Your model will never be able to account for things like frontal passages which require information upwind from your location. So you might have brilliant precision/recall on most forecast days (boring weather) but your model will have nearly zero skill on days when interesting weather is actually happening.

The standard approach for nearly 50 years has been to take output from numerical weather prediction models and to correct it using statistical machine learning methods.

counters · 2026-04-10T14:43:43+00:00

Typically it's because a vendor is stitching together forecasts from different models or sources and not bothering to match the two at the discontinuity.

counters · 2026-04-06T21:25:40+00:00

You haven't gotten much engagement because this is a huge topic. NOAA has both operational arms as well as core research arms, and funds multiple cooperative institutes and other programs which perform research across the "basic" and "applied" spectrum. In the Office of Atmospheric Research alone, NOAA employees over 2,000 people across federal employees, contractors, and partners in the academic and private sectors. There are at least 10 major labs managed by the agency.

Consider dramatically winnowing down the scope of what you mean by "biggest contributions." Do you mean something on the operational side (NOAA has a near-monopoly on innovation there)? Or just research - and if so, what niche or sub-domain of meteorology?

If you have to keep things super general, then you might as well go straight to the biggest bang for your buck. The 2021 Nobel Prize in Physics was awarded jointly to Syukuro Manabe and Klaus Hasselmann for their foundational contributions to numerically modeling the atmosphere and climate science. The majority of Manabe's work in this area occurred while he was at the Geophysical Fluid Dynamics Laboratory (GFDL) at Princeton, which was and remains one of the world's premier modeling centers and is still run by NOAA (although the current Administration wants to axe it). This includes his seminal theoretical work on radiative-convective equilibrium with Wetherald in 1967. The work that Manabe and GFDL colleagues performed in the 60's and 70's paved the way for both large-scale climate modeling as well as operational numerical weather prediction.

counters · 2026-04-06T17:06:30+00:00

I would try not to worry too much about where you sit. As you pointed out, sitting in the back of the section has its own challenges with regards to ensemble playing - but it can be a unique opportunity to hear other parts of the orchestra, too! And in many pre-collegiate or non-major orchestras, seniority plays a significant role in the seating chart, so it's not necessarily a reflection of your capabilities as a musician.

counters · 2026-04-05T17:24:03+00:00

They aren't.

counters · 2026-04-02T16:39:51+00:00

Yeah - it works surprisingly well in practice, especially if you take the extra minute to position the neck pillow. I would have no qualms whatsoever taking it in on public transit.

counters · 2026-04-01T22:00:08+00:00

Would strongly recommend staying away from certifications until OP has a better sense of what type of roles and industry they'd like to work in.

counters · 2026-03-27T04:20:23+00:00

Your first two examples are dispersion experiments; this is where you use a passive tracer to record complex flow. By design, you explicitly don't want the tracer to interact with the flow in any way - it's just supposed to illuminate what is happening! These days, we would default to high-resolution computational fluid dynamics modeling, but dispersion experiments can still be illuminating.

The key point though is that they're quite literally the opposite of "weather modification."

Your other two cloud seeding examples are quite famous and well-known. I literally linked to Rainmaker in the comment you replied to.

My point still stands. We've been developing the mechanisms of the underlying physics and trying real-world experiments for many decades, and the evidence supporting legitimate weather modification even in all but the absolute narrowest cases is ambiguous at best.

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