Cloudrun is a SaaS platform for the Weather Research and Forecasting (WRF) model. WRF is by far the most widely used numerical weather prediction model, but it currently requires access to supercomputers and domain expertise. Our goal is to make custom weather prediction possible for those without access to these resources.
This is our first MVP and we're working on making it more accessible for non-modelers. We greatly appreciate any feedback on how to make this service easier to use!
Bad joke aside: I'm curious, what is the "business" need and market/end-users?
Congrats and good luck Milan and Josh.
Our end-users are currently mostly students and those working in academia. For these gals and guys, it can be a PITA to use supercomputers to run weather models. Milan can speak more to this.
However, the potential user is anyone who needs custom weather prediction. Renewable energy, commodities trading, event planning (sports, concerts, etc.) to name a few.
We're hearing about new potential industries all the time! Ideas are welcome!
I'd presume that mountain bikers, kayakers, climbers, and really anyone else who is into sports that are dependent on weather may be interested in similar information.
Delivering the information to a community like that seems like it would be too niche for your service, but for the people that already offer services to those people, perhaps there'd be interest.
At this point, we are targeting existing WRF users and newcomers into the field. These are either academic meteorologists without access to big hardware, or SMBs with focus on weather intelligence for agriculture, commodities and energy trading, and similar.
At a later stage, we will probably move toward operational weather prediction, and expand to ocean circulation (ship routing, search & rescue) and surface wave models (custom surf forecasts).
As Josh said, though, we've just started and learning every day about potential use cases.
How about all the other configurations of WRF (HWRF, WRF-DA, WRF-CHEM, Global)?
What sizes can you scale to? What type of hardware are you running on, if you don't mind me asking?
All runs go on Intel Xeon Platinum 8168. We currently restrict the execution to shared-memory mode while we fine-tune the scaling in distributed memory. If you try to submit a run too big (either for RAM or scalability), Cloudrun will not let you proceed.
Do you have preference for MPAS Atmosphere, Ocean, or both?
My preference would be for MPAS-A.
Also, great to know about your use case! We have been looking into applying this for snowfall prediction for ski resorts, but I wasn't thinking about mountain expeditions in general.
What is the typical forecast time window that is important for your use case? Is it < few days or out to a week?
The shortcomings of the existing systems is that it is cumbersome to get elevation properly into the model. SpotWx for example, gives you the forecast for the elevation of the square you clicked on, which is the average of the surrounding elevation. This may be irrelevant if you're walking a ridge vs a valley bottom.
SpotWx, however, does something very right, which is very good hourly breakdown. The avalanche forecasts are typically for a very large area, and the timing of precipitation isn't important, but heating due to temperature change or sun is very important.
We are already (internally) experimenting with 1 km and sub-1-km grid resolutions using HRRR as initial and boundary conditions. Creating custom forecast domains at fine resolutions is something that we are currently developing and should be ready in the next 2-3 months.
Thank you very much for your input, it's super helpful!
I don't know weather models; is there something about WRF that allows people without domain knowledge to get sensible results? That's not the normal experience with complex models -- sometimes even with people that are supposed to be domain experts.
> What's the advantage of this is over running, say, at Penguin scalably on Infiniband nodes if you need the compute?
POD gives you the nodes and a secure shell and you take it from there. In contrast, we are building a SaaS, so the hardware and the configuration of the model (from compiling the MPI libraries to running the model) are abstracted away. The user provides input (files for now, parameters in the near future), and gets output as a result. We scale in parallel also, but don't use Penguin under the hood.
> I wonder how weather researchers work if they don't have access to HPC facilities
Many meteorologists only analyze existing model data, and not necessarily run the models.
> is there something about WRF that allows people without domain knowledge to get sensible results?
Yes, in the sense that the output data represents 3-dimensional future snapshots of the atmosphere. So you don't necessarily need to understand the internals of the model (and definitely not the labor intensive setup) to interpret the results. It really depends on what you're after. If you study atmospheric dynamics, you're likely already a meteorologist. However an energy trader knows how to interpret temperature, wind power, or insolation trends, but does not need to know about how cloud microphysics are implemented internally.
In particular for the input files, 'namelist.input' seems like a list of parameters I could google for/tweak, but I'm not sure where I'd go about sourcing data for the initial/boundary condition files 'wrfinput_d01' and 'wrfbdy_d01.
In the meantime, you can email us at firstname.lastname@example.org, let us know your region of interest and time window, and we will happily create the input files for you.
We considered not launching the Beta before the input file creation was done, but we wanted to get feedback as early as possible. I really appreciate your interest and feedback!
If by any chance you decide to go creating these files yourself, the process is tedious but well documented here: http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/users_...
We do plan to start writing short tutorials on setting up simulations and geared toward non-experts.
I've been looking to find a way to get short term localised rainfall forecasts in Australia (for sporting events) and excited to see how this goes!
i've always wanted to learn about weather simulation models so happy to stumble into this domain
Cloudrun's purpose is to run WRF. Most runs are computationally expensive enough that they could take days to run on your home computer. By then, your weather predictions are less relevant.
Because of this, WRF is typically run on supercomputers, but even people with access to these tell us they can be difficult to work with.
The other goal of Cloudrun is to make weather modeling accessible to the layman. It can be a pretty complicated process to run WRF. Let us know how your journey goes!
If there's anything we can help you with, don't hesitate to contact us anytime: https://cloudrun.co/help/contact-us