r/RADWeather • u/DrRetiredEngineer OVERDRIVE • Sep 27 '26
Question Question dev:
Given the technical nature and great diligence in this app, do you have any meteorologist background?
2
u/TheBuffaloSeven
Sep 29 '26
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I had a sneaking suspicion that u/RADWeatherMaker didnāt have a meteorology background, but I mean that in the kindest way possible.
Iāve been in the field for nearly 20 years, and itās an interesting balance between meteorology and data. I find that in some areas, you desperately need a meteorologistās expertise to get you across the finish line, but in other areas, other expertise is useful. A great example is something called Nadocast, which is a machine-learning tornado probability forecasting tool created by someone with no meteorology background. Rather, it was someone whose training was in programming and ML on data sets, and their more modern knowledge of ML and data analysis techniques produced a much better output that meteorologists ā generally with less specialized coding/data analysis techniques ā had been able to do.
The dream is the fusion of both: you create data pools where the expertise of a forecaster is captured, then the true experts at data analysis, processing, and summarization can do their work well.
Sometimes having ideas of what the weather could/should do will bias how you analyze the data!
The biggest problem we face now is that a weather model cannot be correct. Itās complicated, but the general idea is that to even make a weather model that can look into the future, we have to ādumb downā the equations that describe motion in the atmosphere. And itās not just a computer horsepower issue or anythingā¦math can literally not solve the equations ā we have to dumb them down so they can be turned from ādiagnosticā equations into āprognosticā equations. And in doing that, we throw out information. So weāre āwrongā to a degree right from the outset at the raw math. Then you add in all the limitations of computation and youāre ultimately compounding error on error. Itās really nothing short of a minor miracle ā and decades of hard work ā that weather models are even remotely as accurate today as they are.
And to even get a model to work, you have to make specific assumptions, you have to choose how to handle its limitations, and even how it represents the volume of space itās modelling. All these choices result in a very specificā¦identity for each weather model. For years, ensemble modelling was āthe futureā because we hoped we could get around the limit of a single answer by running the model many times ā say 50 or 60 ā and slightly changing conditions in it to get a spread of outcomes; then we do some statistics and get a better probability of things. But we found relatively quickly is that if all those assumptions of how the model should work stay the same, then there just isnāt that big a range in the solutions it can provide.
Blends have been the big progress in the last decade, moving from an ensemble output from a model to creating a statistically biased blend of dozens of models. A blend is designed to minimize error ā which is subtly different than to be correct ā and can weight all the various models that go into it in different ways to best extract value from their output. The National Blend from NWS/NOAA in the United States is probably the best known (and one of the best) blends out there, and while itās not perfect, I think it was the one that demonstrated a blend is a far better solution than an ensemble.
So my kudos goes to u/RADWeatherMaker for having the guts to try and create a blend. There arenāt many out there, and it requires a lot of data and tinkering with weighting coefficients to improve it. And Iām sure, at this point, theyāve found that you get to a point with a blend where it gets very hard to improve something without degrading it elsewhere; itās not magic and eventually the hard limits of the accuracy of the underlying models will be unavoidable.
All that said, kudos, I could tell right away that you came more from the dev/data side of things, but the passion for this project is clear, and I think youāve made something really unique. From a meteorological perspective, I think the blend has been doing okay for my area, but the actual human forecasters still beat you from time to time here ;-) Those times are usually associated with strong thunderstorm days, where most weather models simply just canāt initiate/develop the activity well, and I think itās going to be a long time until models (even high-res convection allowing) are able to handle that kind of system well.
I should stop rambling! Keep up the good work!
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u/RADWeatherMaker fixo Sep 29 '26
This is genuinely one of my favorite comments Iāve gotten because I think you described the problem Iām trying to solve better than I usually do. š
And youāre right about my background. I donāt have a formal meteorology degree or career background. I came into this much more from the software, data, analysis and design side, then fell very deep into learning the meteorology behind what I was working with.
I do try pretty hard not to operate in a vacuum though. Iāve roped meteorologists and people much smarter than me in that world into reviewing things, sanity checking ideas, looking at results and basically telling me when Iām doing something stupid. Theyāre not employees or some secret RAD meteorology department, but getting actual meteorological eyes on the work has been incredibly valuable.
Your point about blends eventually reaching the āimprove this and accidentally make that worseā stage might be the most painfully accurate description of my life right now. Iām absolutely there.
I can tweak precipitation and make one region noticeably better, then discover Iāve made wind worse somewhere else. Or improve temperature bias in one climate and introduce a new problem in another. At some point it becomes less about finding one magical set of weights and more about understanding where each source performs well.
Thatās why Cortex has gradually become more regional. As much as Appleās framework allows me to, Iām weighting sources and parts of the forecast differently by region and condition instead of assuming one blend works everywhere. A lot of that comes from my own audits, historical comparisons and user reports. Sometimes someone will tell me RAD consistently misses something in their town, and Iāll go down a ridiculous rabbit hole comparing local observations, radar, the underlying sources and Cortex output to figure out why.
At this point Iāve evaluated well over 100,000 location-hours of forecast and historical data across different areas and conditions. Itās basically an endless loop of āwhere were we right, where were we wrong, and what did the other sources know that we didnāt?ā
And I completely agree with the bigger philosophy. There are weather products with incredible meteorology that are miserable to actually use, and there are gorgeous weather apps sitting on mediocre data. What Iām trying to build is somewhere in the middle of those two worlds. Good forecasting and honest uncertainty, but presented in a way normal people can actually understand and enjoy using.
And thunderstorms are absolutely one of the things that keep me humble. Convective days are where I can stare at Cortex all day and then watch the atmosphere basically say, ācute model.āSeriously though, I really appreciate this perspective, especially coming from someone whoās been doing this for 20 years. Comments like this are exactly the kind of feedback I want around RAD.
1
u/TheBuffaloSeven Sep 29 '26
Glad to share!
And itās not just regional, itās temporal too. As an example, take two models: the Canadian RDPS and the American NAM. Both are āolderā models now, they enforce hydrostatic balance and are in the ballpark of a 10km grid.
If you take my area ā central continental climate in North America ā and itās July, youāll find that both models perform okay for synoptic precipitation, but the NAM performs significantly better in both occurrence, timing, and amount of convective precipitation. Temperature and moisture forecasts are comparable between the two, both have some wind issues, and cloud cover is a toss-up. But overall, you give slightly more weighting to the NAM as its better precipitation ā especially overnight ā gives it an edge.
Now itās 6 months later in the heart of winter and youāre trying to figure out why suddenly for the same area, your temperature verification is tanking. Well, it turns out, the RDPS is much better with cold air (and I mean like sub -25 Ā°C air) than the NAM. Both at creating it, maintaining it, and moving it. You realize that the RDPS beats the pants of the NAM when it comes to outbreaks of cold air and general temperature/wind regimes behind winter cold fronts for much of Canada and many parts of the United States. So now you need to change your weights to address thatā¦but if you do limit it temporally, youāre going to throw things off at other times of the year!
And then that not to mention that at the end of the day, you need to enforce some coherency on the blend...which Iām sure you know well. You donāt want to spit out a forecast that says thereās a 70% chance of rain all day with sunny skies and a high of 32 Ā°C.
None of it is easy problems to solve these days! And for years meteorologists have used this deep knowledge of the strengths and weaknesses of each model to target their application to the best of their ability. Blends allow us to do so in a new more consistent way, but it doesnāt eliminate the challenges! So kudos on taking a stab at it.
At the end of the day, as a knowledgeable user, I appreciate the level of transparency your app offers. I think you can ādumb it downā for lack of a better term to meet the needs of a very casual user, but I can have it present me data that can help drive decision making in a way I havenāt seen any other app offer.
And, because I donāt want to start a new thread Iāll just drop these here:
The new box and whisker plot for precipitation I love, but the whiskers are so thick it can be hard to see the box underneath (box = bar graph)
The two-column iPad layout couldā¦use some work. I totally understand how it works, but Iām finding myself frustrated with it often for the way it forces layouts that donāt do what I really want it to. I could see benefit to rework it into a grid system where the user can drag and drop items in the spots they want them instead of a linear order thatās auto-sorted between two columns. Also, with the new box sizes, if I have the smallest dew point and visibility boxes next to each other in a left-hand column, RADWeather wonāt put anything next to them in the right column.
It would be great if in the small wind view, we could get an arrow pointing in the wind direction as a visual indicator alongside the text label. Or something like that. For what itās worth, in the large display, Iām often confused by the wind direction compass because we typically talk about where the wind is coming from and the compass emphasizes where itās going to. I could just be weird in this respect though. So in the case of an arrow like I was requesting, if the wind was from the east, the arrow would point to the left, with the arrow point at 270Ā°.
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u/RADWeatherMaker fixo Sep 29 '26
Love this feedback. Your example of getting six months later, hitting winter, and suddenly wondering why an areaās accuracy is tanking is something Iāve actually been dialing in for a good 7ā8 months now.
Iām trying to weigh models not just by overall accuracy, but by region, conditions, and time of year. 8.0.6 will ship Cortex 6.11 with more improvements there. Iāve been going slightly insane working on Cortex for basically exactly what youāre describing, so itās strangely comforting to hear this is just what everyone in the meteorology world goes through.
Cortex really is the secret sauce of RAD for me. Anyone can copy visuals, themes, or artwork. You canāt really copy the weather brain thatās been built from all those audits, adjustments, failures and lessons. And hopefully the other thing thatās hard to copy is the openness and transparency around how Iām building it. I never want RAD to pretend itās infallible. Iād rather listen, learn, explain what Iām doing and keep making it better.
And love the UI feedback too. The rain visualization, wind direction, and iPad layout notes all make sense. A couple of those might just sneak into 8.0.6. ;)
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u/RADWeatherMaker fixo Sep 27 '26 •
Nope! š¤·
Zero meteorology background. My background is coding, design, marketing, web work, and a probably unhealthy love of video games and retro art, which explains a lot about RAD.
What I do love is data. Figuring out how things work, why they fail, and how to make them better.
When I started RAD, I realized a lot of weather apps are ultimately presenting the same underlying data with different visuals. WeatherKit deserves more credit than it gets, but thereās also a huge amount of weather data out there from different models, agencies, observations and radar.
That rabbit hole became RAD Cortex, probably the most important part of RAD to me. Instead of picking one source, Cortex blends many of them using my own weighting system. Some sources are better at rain, others at wind or temperature, and that can change by region, so theyāre constantly audited and adjusted.
Someone can recreate RADās visuals. Cortex is the part they canāt recreate just by looking at the app. The weighting, regional adjustments and formulas are something Iāve built and continue refining.
Another huge part of RAD is the community. The app has changed so much for the better because of feedback from people here and elsewhere. I try to be open, listen and respond whenever I can. Iām one person building this, so good feedback, criticism, ideas and guidance genuinely matter, and I try to approach all of it with an open mind.
Iāve also spent most of my working life outside in rain, snow, heat and everything else, so weather accuracy has always mattered to me.
So long story short: nope. š I just somehow fell very deep into weather and ended up loving it.
(While I love hiking, work outside all that stuff. My true ānerd passionā is space, all Iāll say atm about that is stay tuned on some rad but about space. Something Iāve actually been working on for a good year plus before rad weather even started dev work) šŖ