bluewave

I was talking about coastal sections from NJ out across LI. I agree about a heavy wet snow potential somewhere on the more inland side of that heavy rainfall zone in SE MA. Just a little latitude in a marginal setup makes a big difference.

The flow was ENE here near the coast so it would have pulled in too much warm air at the start. The lack of strong deepening would have meant that the coast couldn’t cool down enough at the tail end of the storm for a significant shift to snow. But interior areas would have done well.

The issue with the last coastal is that it remained strung out with two weaker low centers. So even during the peak of winter it still probably would have been more of a high elevation interior event. Not enough deepening to prevent mixing issues near the coast.

I had my first frost on Saturday morning but was very patchy.

Even before we get to the cold season these strong -NAOs are still finding a way to link up with the Southeast ridge or WAR. The storm for this week was originally forecast to be a coastal storm track. But it has shifted to a warmer cutter track to our west. In the old days a -NAO +PNA would nearly always result in a classic coastal storm track since there wouldn’t have been a Southeast ridge or WAR. New run -NAO linking up with the WAR Old run no WAR

This has been the pattern over the last decade. Early start to the season followed by a lull from late August into early September. Followed by a very strong late September to late October. So very challenging to get a low ACE season anymore with the record Atlantic SSTs and these historic RI and so many Cat 4-5 hurricanes.

It’s really interesting how we also had great 11 year runs for snowfall in December and March also. OCT-NOV 2008 to 2018…DEC 2000 to 2010….MAR 2009 to 2019. Following each of those runs it was like someone shutting off a switch during the respective months. OCT and NOV haven’t featured any early season snows since 2018. Our last historic December snowstorm was Boxing Day in 2010. Interior sections near BGM in 2020 got their historic December snowstorm near 40” since the storm hugged the coast too close near ACY for us to get the really heavy totals. Then the big cutter a week later on Christmas with all the damage and flooding at the ski resorts. Then our least really big March with 30” on Long Island was 2018 and some areas did well in March 2019. Since then coastal sections have had nearly no March snow in the last 6 years.

That was a great run of early season snowfall from 2008 to 2018. The higher elevation event later in October 2008 followed by the historic late October 2011 snowstorm. Then the post Sandy snowstorm in 2012 and the SWFE in November 2018.

What I have been saying is that the faster Northern Stream of the Pacific Jet driven by the gradient formed between the record SSTs over the WPAC and Siberian cold has lead to the dominant cutter, hugger, and suppressed Southern Stream since 2018-2019 producing the record low 7 year snowfall totals along the I -95 corridor. My early guess for this winter is that average DJF temperatures will be warmer than last winter was based on the tropical forcing that we are currently experiencing. I was able to identify the factor last October which lead to the colder winter which I discussed in the main forum. This feature isn’t there this October. But since the snowfall was so low last winter, can’t be sure whether we see something similar again or we get a short term favorable MJO leading to higher snowfall totals than last winter. Since we could beat last winters snowfall totals with a few good storm tracks. But the December 4” rule will probably be in effect again for La Ninas. Nearly all La Niña winters since 1991 with under 4” at places like NYC and Newark in December finished the season below. And all above this level went into snowy seasons. Last December we came in under which lead to my December guess for another below average snowfall season which worked out.

Each event is constrained by the overall pattern and not the other way around.

This has been one of the model forecast error patterns for years now of underestimating the WAR or Southeast ridge in the longer range. It’s what has been happening with such a strong Pacific Jet and near record SST warmth over the Gulf Stream and Atlantic. The models were originally forecasting the main low to be a coastal track to our east. But now the -NAO is linking up with the Greenland block again so the low is cutting to our west. So we haven’t any really deep lows taking a benchmark track since January 2022 during the winters. Even the coastal a few weeks ago were two weaker lows instead of one consolidated one. Our primary storm track since 2018-2019 has been cutter, hugger, and suppressed Southern Stream leading to the record 7 year low snowfall totals along the I-95 corridor. I am hoping one of these winters we can get a great MJO 8 month like January 2022 again with the strong benchmark tracks. But each of the times the MJO is approaching phase 8 it has weakened and reloaded into the warmer phases again. So this will probably be a short term modeling surprise if it happens again and not really seen from weeks out. New run stronger WAR Old run weaker WAR

Tom Skilling and Matt Lanza have a nice in depth discussion here on what the lack of balloon soundings may mean for the accuracy of weather model forecasts. https://www.facebook.com/TomSkilling/posts/weather-forecast-models-produce-forecasts-of-varying-accuracy-interestingly-some/1319935109491429/ With weather balloon observations regularly going off line at a whole series of key observation stations, we're moving into uncharted territory and hoping forecast models still behave. The concern is legitimate. Weather satellite and in flight aircraft data may help fill the void--but the data they provide is different than the more thorough balloon measurements. We may not see issues with model accuracy each day. But it's been established in some research work--and we know for a fact---that the upper air observations gathered by weather balloons carry disproportionate weight in influencing model accuracy. That's because their measurements of the atmosphere are in many ways more complete than other sources--such as satellite or in flight aircraft. NASA runs a global weather forecast model--the GEOS model. Among its roles is to look at the impacts of and distribution of gases and particulates in the atmosphere. It's also been useful in studying greenhouse gases and also the movement of dust and other particulates off the Sahara Desert westward out over the Atlantic and all the way to South and North America. It could also be used, were, God forbid, biological agents released into the atmosphere or a nuclear accident sent a huge amount of radioactivity into the atmosphere to track these agents and where they might go. So the NASA GEOS model isn't exclusively used for weather forecasting alone. But it IS an atmospheric forecast model and the data fed into it is comparable to the data fed into the full range of weather forecast models. You might have asked when you hear discussions of the fact different models come up with varying forecasts at times---"why is it they employ multiple models in the weather forecast process? Why not just one??" It's a reasonable question--and the answer is quite simple. It's because NO single model is perfect. Nor are our measurements of the atmosphere. Having multiple solutions from multiple models, each making slightly varied assumptions on how to handle the physics of the imperfectly measured atmosphere, offers human forecasters a range of possible forecasts scenarios--each valid yet some more likely to work out than others. When a common set of forecast solutions appears from varying models, it makes it easier to "cull" the less likely forecasts from the mix settling on the most accurate of the varied forecasts. NASA modelers have spent time looking at the observations fed into their model. By studying how the model behaves when certain data is flawed or missing, they've produced a ranking of which of the 210-million observations which go into many global forecasts based on their importance to the forecasts these models generate. A graphic I'm posting with this post lays out what they found. And interestingly, radiosonde data--in other words, weather balloon data--comes out as 2nd most influential of the different data sets which go into computer weather forecast models. This places weather balloon data in a uniquely powerful position. Eliminating balloon data from a set of stations, which has begun happening in greater numbers in recent months due to budget cuts, won't be something which messes up model predictions every day or in the same way. What it DOES suggest is that unexpected, hard to identify impacts may well occur. And who's to say that won't happen on a day when tornadoes or a snowstorm is a occurring and you need an accurate model read on how the extreme weather situation is evolving----or, equally scary, when a hurricane is approaching. That unpredictable and varying accuracy is introduced into the weather forecast environment is very concerning. Also, since many of the stations which have been pulled off line by budget cuts are in tornado and severe weather prone areas, the uniquely accurate 3-D measurements of the atmosphere weather balloons provide is worrisome. ----------------------------------------------- One other period provided meteorological forecast modelers a unique opportunity to check out the impact of losing upper air data from weather balloons--and it resulted in a measurable loss in computer model accuracy. The world lost Russian weather balloon soundings during an outage in 2015. European Centre researchers studied the impact on the loss of these balloon reports during that outage. This was a much broader loss of weather balloon data than what is happening now. But the outage led to a significant loss in model forecast accuracy. Because global weather is part of a vast, interconnected system, the loss of a critical data set produces forecast accuracy issues upstream and downstream of the regions where such data is usually taken. The 2015 outage provided a rare glimpse of what happens when weather balloon data is lost. That was a much different era in meteorological science so it's difficult to precisely anticipate the sort of problems such an out would produce today. But the loss of data as critical as that which is provided by our radiosonde (i.e. weather balloon) network can't be dismissed. ---------------------------------- https://theeyewall.com/weather-balloon-launch-cuts-an-honest-look-at-how-it-should-impact-forecasts/

12z Euro doesn’t quite connect all the way to the offshore hurricane moisture. But still has some spots going 2”+ with a decent looking squall line as the low cuts to our west. Maybe with some luck we can get a little more of a moisture connection in later runs to push the rainfall potential higher.

The Canadian is the only model with hurricane so far west. The Euro and GFS are further east. So a nice compromise would be a deepening low going to our west opening a moisture channel with the hurricane staying offshore like a PRE or IVT to enhance the rainfall potential.

We need the rains so hopefully the low tracking to the west now can tap some moisture from the hurricane. If we can actually verify a soaking rainfall, then maybe we can begin putting the drought behind us. All of our long range forecasts have verified drier than originally forecast. If the 12z Euro comes in really juicy, then it could be the beginning of a change to wetter. We will see…

Warmer storm track on the latest EPS with the low now tracking to our west instead of the east in earlier runs. We have seen this quite a bit in recent years. Have been getting warmer storm tracks even with -NAO and +PNA patterns. The WAR is a little stronger than earlier runs. New run stronger WAR linking up with the Greenland block Old run weaker WAR

The only way to determine whether a lack of weather ballon data is causing a specific model error is to do a data denial experiment which is impossible without the data. https://www.cnn.com/2025/10/14/weather/alaska-storm-weather-balloons-trump-cuts-nws-climate How big of a difference the missing balloon data made, though, may never be known. The best way to determine that would be to run computer models with weather balloon data fed into them and without it, in what is known as a data denial experiment — impossible to do without the data itself.

Nino 1.2 continues to run warmer than 3.4. This has been the recent theme with the EWBs not able to penetrate closer to the SA coast. It all began with the record WWB near the SA coast back in March 2023.

Pretty strong signal for a little cooler than average trough in the Northeast into early November.

Can’t really lean on any AI data unless you know the integrity of the data used to train the AI.

The last time a Greenland Block worked in our favor with a KU here along the 1-95 corridor was February 2021. The MJO 8 in January 2022 was more of a Pacific driven snowfall pattern. Most other Greenland blocks like December 2022, March 2023, and February 2025 were Southeast ridge link ups with Great Lakes cutters or I-78 to I-84 hugger tracks. Prior to 2019 there Southeast ridge link ups with Greenland blocks were rare. Like in December 2012 and the late 1990s. But those were usually followed up by more favorable Greenland blocking patterns and KU snowstorms in the next months or years which hasn’t happened yet.

If I had to draw up a set of maps now, would probably just use the EPS mean from days 3-7 to leave enough wiggle room for changes in later model runs.

Each of the models for next week have a different solution. The two upper lows and the hurricane are all in different positions at day 5. Plus the strong block to the north. So we are getting the windshield wiper effect with a new solution every run. There is even a 3rd upper low now showing up later in the forecast period dropping down from Canada to complicate matters.

We may never know for sure but the loss of weather balloon data is probably having some effect on the model performance. https://theconversation.com/typhoon-leaves-flooded-alaska-villages-facing-a-storm-recovery-far-tougher-than-most-americans-will-ever-experience-267423 But as the storm approached Alaska, everything went sideways. The weather model forecasts changed, reflecting a faster-moving storm, and Halong shifted to a very unusual track, moving between Saint Lawrence Island and the Yukon-Kuskokwim Delta coast. Unlike Merbok, which was very well forecast by the global models, this one’s final track and intensity weren’t clear until the storm was within 36 hours of crossing into Alaska waters. That’s too late for evacuations in many places. Did the loss of weather balloon data canceled in 2025 affect the forecast? That’s a question for future research, but here’s what we know for sure: There have not been any upper-air weather balloon observations at Saint Paul Island in the Bering Sea since late August or at Kotzebue since February. Bethel and Cold Bay are limited to one per day instead of two. At Nome, there were no weather balloons for two full days as the storm was moving toward the Bering Sea. Did any of this cause the forecast to be off? We don’t know because we don’t have the data, but it seems likely that that had some effect on the model performance.

If we could find a way to replicate the cooler high temperature patterns that we have been getting in May recently at other times of the year, then we would be onto something. Islip , NYPeriod of record: 1963-09-05 through 2025-10-23DateLowest maximum temperatures (degrees F)Top Record 2nd Record 3rd Record 5/1 49 in 2016 52 in 1978 53 in 2019 5/2 51 in 1995 53 in 1988 53 in 1975 5/3 49 in 1985 53 in 2016 53 in 1986 5/4 47 in 1987 51 in 2016 54 in 1978+ 5/5 44 in 1978 46 in 1987 53 in 2016 5/6 50 in 1978 52 in 2016 52 in 1967 5/7 44 in 1967 50 in 2022 52 in 1965 5/8 53 in 1971 54 in 1992 54 in 1989+ 5/9 49 in 2020 49 in 1972 50 in 1977 5/10 52 in 1989 54 in 2024 55 in 1995+ 5/11 54 in 1998 54 in 1989 55 in 1995+ 5/12 48 in 2010 51 in 2008 55 in 1966 5/13 51 in 2019 51 in 2017 51 in 2002 5/14 52 in 2019 57 in 2006 57 in 1984+ 5/15 53 in 1978 55 in 1973 56 in 1967 5/16 53 in 1984 53 in 1978 54 in 1996 5/17 56 in 1978 57 in 1994 58 in 1966 5/18 49 in 2007 53 in 2002 56 in 1968 5/19 51 in 1976 53 in 1994 55 in 1993 5/20 51 in 2000 54 in 2005 55 in 2008+ 5/21 49 in 1990 54 in 2000 56 in 2025 5/22 53 in 2025 54 in 2003 57 in 2000 5/23 54 in 2003 54 in 1982 57 in 1973 5/24 56 in 1982 57 in 2003 57 in 1969 5/25 49 in 1967 51 in 2013 51 in 2005 5/26 52 in 1967 53 in 2003 57 in 1973 5/27 56 in 1974 57 in 1973 58 in 1983+ 5/28 60 in 2014 60 in 2009 60 in 1996+ 5/29 51 in 2021 58 in 2017 60 in 1996+ 5/30 52 in 2021 60 in 2017 61 in 1965 5/31 55 in 1984 61 in 1967 63 in 1992