eduggs

To me based on the current radar it looks like parts of the LHV and WCT could exceed a foot.

Orange County went from 12-15" on the 12z 3km NAM to a dusting to an inch. That's brutal. The south trend might not be over.

3-5 hour period of moderate to heavy snow and out. Still starts as rain for most.

This storm will probably meet the latitude-dependent definition of bombogenesis but not the 24mb drop in 24 hours. It's also forming in a relatively low pressure environment, so the absolute pressure is not as impressive as if there were a strong HP in the vicinity. It could be a fun few hours on Tue. morning! Vertical lift is impressive. Deep saturated column, favorable mid-level tracks. Other than the warm antecedent conditions, the biggest limitation (and slight disappointment) is the short duration. This thing will likely be in and out in less than 12 hours. Accumulating snow for less than that. And heavy snow for an even shorter period.

When you average heights across 5 days, the composite often looks better than it really is. A trof axis too far west followed by cold NW mid-level flow often produces a great averaged 500mb anomaly. But that's a rain to dry scenario.

So then just say you'll be satisfied with 1. You can't get 1-3 inches of snow. That's a forecast range from 1980s local news.

I suspect it's radar reflectivity vs surface precipitation. If you compare 700mb RH on the GFS I bet it matches up reasonably well with the NAM radar reflectivity. But below 700mb and especially below 925 is pretty dry.

Heisy makes a really good point about interpreting 500mb height anomalies. It goes to the heart of why some people have a horrendously bad track record of identifying "promising patterns" over the past 2 winters. It's not just bad luck. Using height snapshots is risky. The details matter. Mean QPF distribution, mean 850mbs temps, and evolution of the height field are also important. But... I also agree there is reason for muted excitement for a significant storm from the 13th or so on.

I liked the 12z CMC a little better, but both the 12z and 0z CMC are pretty decent... much more threatening than the GFS. I don't love the trof along the west coast at the end of the run, however.

I think you've hit on why it has a poor track record in terms of assessing storm threats in the LR. There are a couple problems: 1. "Longwave pattern" is a very general concept. It's continental-scale - meaning correlations with regional weather are weak - particularly in a predictive sense. Yes significant snow is well correlated to characteristic "patterns," but since there are far more of these "patterns" than significant snowstorms, we know that these features are necessary but not sufficient for big snowfall. 2. Significant uncertainty exists in ensemble modeling forecasts beyond 10 days. Even if aspects of a 500mb height field are well predicted over parts of the globe, other areas are poorly predicted. It's usually not possible to know which regions will be well modeled. This keeps overall confidence in LR pattern recognition relatively low. 3. The sub-continental scale details only resolvable inside 10 days largely determine whether a global scale longwave pattern can be productive for regional wintry precipitation or not. This would be true even if you knew the precise longwave pattern in advance. Add these up and the argument is that using low confidence LR anomaly charts to try to identify general "patterns" is not very effective for LR regional storm threat identification. At the very same time you are able to start determining if a nearing "pattern" is a head-fake or not you are just starting to pick out the finer scale mid-and upper level details. Some of the same models are used for both objectives (pattern ID and details), and they both start to clarify at the far end of the mid-range simultaneously. That's why I believe multi-model ensemble QPF and 850mb temperature charts inside 10 days are the better starting point for threat identification. Followed by looping the raw 500mb heights with vorticity of both the ensembles and operational models inside about 8 days. It's funny how people are so quick to come up with excuses for why a sure-fire pattern change advertised on the models failed to materialize. It's usually something random like a bridging ridge, west or east-based something, one of the indices... AO, EPA, NAO offset ENSO. What those excuses are really a reflection of is that modeled LR "patterns" are not causally connected to future outcomes. One does not cause the other. They are both simultaneous reflections of the state of the atmosphere-ocean system at a given time.

January is always the most likely month of the year for cold and snow in the Mid-Atlantic coastal plain, regardless of the state of climate indices. So predicting that there will be cold periods in January and maybe some snow is not a bold call. But there is also reason to be concerned that this historically bad stretch of winters might be systemic. The tendency for ULLs to tilt and deepen west of us over the mid-continent but flatten and dampen to our east could be somehow related to a changing climate. Our already too-small-sample size of seasonal analogs may be increasingly useless as we move forward in time. The possibility that this winter could be another ratter is a legitimate concern. All we can reasonably see out into the weather future is about 10 days, and this upcoming period has at least as much potential as any so far this winter. But no, I do not agree that patience is warranted. There is less time left in winter than it would seem.

Everything depends on the evolution and orientation of the height fields. Individual shortwave interactions make or break regional weather outcomes. The 10 days 12z GFS vs CMC charts illustrate that. The colors (anomalies) being in the "right" places can't tell us much beyond 10 days. Even worse if the height anomalies are time-averaged in addition to being ensemble-averaged. That degree of smoothing completely masks the critical details. By the time those "colors" are usefully predictive, the mid range models can already start working out finer-scale details including shortwave interactions. Looking for periods where the LR anomalies are favorable is completely backwards IMO. It mistakenly assumes an unknown future "flow state" can have a causal impact on a future weather outcome. The reality is that height anomalies and future regional weather are correlated but NOT sequentially causally connected. They only appear to be that way in hindsight and when performing reanalysis. Years of poor performance of this forecasting strategy should have encouraged a shift towards the Walt Drag method. But people see what they want to see. They crave understanding if they don't have control. And they squint to see a light at the end of a dark tunnel.

No snow on Tug Hill and northern Maine is weird to see at the end of December.

It's actually possible to have a significant snowstorm before or after a "bad" Pacific flow pattern and get that 5-day average look. Heck even jumbled, partially interfering shortwaves could blunt the Pacific influence and still produce a time-smoothed result to match that graphic. LR multi-day-averaged anomaly maps are ensemble-and time-averaged. That produces a very low resolution, continental-scale overview. I think it's important to understanding what we're looking at before we try to interpret it.

The 18z GFS has a parade of between 6 and 10 successive shortwaves (depending on how you distinguish them) that partially interfere with each other over the next 10 days to prevent any significant local storm development. This highlights one of the problems of using LR time-blended height anomalies to try to identify favorable or active "periods." The averaged anomalies look interesting over the next week, but as usual, everything comes down to the evolution and orientation of the height fields. The actual weather could end of being quite boring depending on the fine details of wave interaction. I prefer Walt Drag's method of threat identification mostly keeping inside of 10 days using a mid-range multi-model super-ensemble focusing on QPF and temperature distributions. To my knowledge Walt doesn't mention climate indices or height anomalies. And he doesn't frequently trigger annoyed disappointment with a lot of LR false alarms.

I was merely pointing out that even when MR or LR model ensemble forecasts verify a high degree of accuracy with respect to the general continental-scale height field, there is typically too much uncertainty at that range to make regional weather forecasts. This was in reference to someone suggesting a 5-day old GEFS chart matched tomorrow's height field pretty well... and also references from a week ago suggesting this period could produce a wintry event. Snapshot anomaly charts should never be used by themselves for synoptic forecasting. IMO they are massively overused and the result of an increase in interest in climate indices and LR forecasting.

That's why anomaly charts are overrated. People assume blue always means good. I can't count the number of times with all of my fingers and toes over the past two winters that 10 day+ anomaly charts gave a false impressions of a favorable period. It's much better to simply loop the raw 500mb heights with vorticity to observe the progression. But people have developed this bad habit of obsessing over the anomalies.

The "trace" threshold makes the southern fringes look quite a bit inflated

I like your write up. I also lived through the 80s and 90s. I remember several frustratingly low snow years. I agree from a snow perspective it's impossible to know for sure how much influence a changing base state has vs. just being in a bad stretch. One thing that does stand out, however, about recent years is the warmth. The 80s had cold periods even when it didn't snow. Outside of 2015 it hasn't been cold recently. Ice is forming later (if at all) and melting sooner. Growing seasons are lengthening. Many places are exceeding 99th percentile frequency statistics for warmth parameters. While we can't know for sure how much our average weather has been affected by a changing climate, I'm personally convinced that it is now observable over our lifespans.

Those bins are a little too general IMO. Some periods straddle the boundary and in any given season we go through phases of each state. Regardless, they are not causing our weather, they are reflections of it. What is likely as our climate continues to warm is that we will increasingly observe atmospheric circulation patterns in "warm phases." We will likely incorrectly attribute these warm phases to other geophysical parameters such as el nino etc. But in reality, what is more likely is that both el nino and other warm state indices are both correlated to a warmer base climate state as opposed to one physically causing the other. This is classic causal fallacy and it is common in amongst hobbyist meteorologists.

Brutal for ski areas. I'm still holding out hope for the Jan. 1-3 period. It's just far enough out into the fuzzy period of modeling that if we squint we can imagine a snow treat.

But it also happened last winter.

The binarily defined parameters el nino and la nina are drastically too simplistic to explain continental-scale weather patterns by themselves. There are literally dozens of confounding variables, some already identified, some not. And in truth, the state of the coupled atmosphere and ocean system at any given moment is unique. It has never been before and never will again be in exactly this state. Efforts to characterize and lump together numerical indices to understand and predict these systems cannot fully capture their uniqueness and variability. To base a forecast months into the future based on what happened decades ago during an "el nino" is laughably simplistic.

I think I mostly agree, but with caveats. If you go back 10 days and compare the ensemble forecasted 10-day 500mb chart to, say, last night's 6hr GFS 500mb chart (or the actual 500mb interpolated analysis) you'll see some of the features match up well and others not so well. Whether or not we can say a model correctly forecasted a "pattern" is completely subjective and dependent on the spatial scale in question, criteria for defining a "pattern," and reference thresholds for accuracy. People living in regions where the ensemble 10-day 500mb heights were poorly forecast would disagree that a model nailed a "pattern." In these areas, the airmass and surface features are drastically different than predicted 10-days ago. Since we never know for sure in advance which areas will be more accurately modeled and which less, it's very difficult to have confidence in even general "pattern" features at this range. What I have observed for many years on this forums is that posters (including meteorologists) confidently proclaim a particular "pattern" coming 10-15 days or more in advance but the realization rate of those prediction is much less than would be warranted based on the confidence in the original claim. People instinctively clamor for understanding and predictability. There is desperation to see the light at the end of the tunnel. We cling to a simplistic understanding of the relationships between climate and regional weather. But we're collectively just not (yet) as good at seeing into the future as we think we are. And we rationalize it away instead of using honest assessment to understand our limitations.

Totally agree about the weather apps. In terms of forecasting temperatures 40 days out. In general it's basically a coin flip whether a particular day will be warmer or colder than "average." Average is of course a moving target. In recent years I would always hedge warmer than average for future forecasts. So maybe 60-40 warmer than average bet. A "snow supporting column" might be slightly easier to forecast in advance than surface temperatures, but at 40 days out it's essentially impossible to accurately predict.