87storms

The Wizards and Terps are as difficult as an Alberta Clipper.

Hopefully it’s wrong, mostly because our chance of actually getting a snowstorm beyond mid March is probably lower than the Terps making the ncaa tourney.

That would be really stupid.

This weather is on a different level.

Overrunning with a high pressure sliding off the coast seems like a snow/mix to rain setup. That’s more fun in December than early March. Maybe Richmond can pad its airport totals with the initial stuff.

Emotionally, it’s absolutely a C- tops. I mean, our flagship storm was several hours of snow followed by mixed precip, and the last storm was an absolute joke here. Objectively, cold matters in the grading system and we had an anomalous stretch. I saw ppl playing ice hockey on a frozen pond in Baker Park and it was frozen for weeks. The biggest issue this winter was a lack of precip…an issue that’s been prevalent the last few years up this way. That said, I drove thru Gambrill today and they still have a couple inches up there at 1200’+, but that’s also a Frederick microclimate.

And now you all know why I kvetch about the lack of snow (admittedly more than I should and not in the banter thread often enough). This winter gets no higher than a B- imby. Glad the snow mounds are on their way out and spring is on its way in.

I’m hyped.

The weather today and tomorrow has premium potential.

Yea, might be bright banding. I’m on the northern edge, but hasn’t been pouring or anything.

That is true. Gfs tried lol

The current radar is making me think I should delete this post.

Was just noticing that lol. Is the gfs the new model overlord?

I’m looking forward to a pattern that actually favors a consistent return of gom moisture. With the nor’easter, the Atlantic finally said, “I’ll do the job”, though that’s not our best path to victory here. The lack of a stj and gulf influence is by far the number 1 reason for the northern md snow drought. I expect that once El Niño settles we won’t have so much of this nickel and dime, hit or miss stuff.

The bias/variance trade-off in ML is probably one of the most important things anyone can learn when building models. I imagine it’s even more of a factor as model resolution improves and training sets get larger. Would be an interesting topic to explore.