Impressive Jet streak off the Pac coast

The new radiosondes with GPS winds really have made a difference, though there has been little talk or even research on this. The difference has really been the availability of upper tropospheric winds in strong jets. I did a little work in this area to look into where and when the old radiosondes would lose the wind data. I will have to dig it up at some point.

The inspiration for this post came as I checked my email this evening and was made aware that the RUC model failed in its initial attempt at a forecast today. The reason was a strong jet entering the Pac Coast. Here is the 250 hPa map:

And the sounding for MFR:

167 knots or 86 m s-1! And there was still wind data all the way to 7 hPa! These speeds are not too much higher than this morning with 157 and 149 knots at SLE and MFR.

Upon further reflection though, October 500 hPa temperatures at -25 C seem a little extreme. And thats not even in the core of the extratropical cyclone! Maybe it will come ashore tomorrow near UIL.

Portland has thunderstorms in the forecast and there is lightning along the coast.

I will have to dig into how extreme these temperatures are for the Pacific Coast in October.

UPDATE: 195 knots from MFR this morning!

Interesting Null tornado event

The tornado watch appeared promising. Plenty of shear with a bulk wind difference near 25 m s-1, CAPE was solid at 1000-1500 J kg-1 and storms had initiated in proximity to the dry line outflow boundary intersection. The storms stayed small and very early on began to split with small left movers that dissipated rapidly north of the outflow boundary.

Yet, despite having supercell like qualities they faded into obscurity after 2-3 hours.

Missing from the closest observed sounding (taken AFTER the storms and somewhat well removed from the INITIAL area, but was later just south of an area of again small storms) was a layer of steep lapse rates, and a cap. Seemingly the near isothermal area around 500 hPa was a CAPE robber.

This sounding is intriguing only when we look at the overall 12 hour changes  in the area of interest. The 850, 700, 500 temperatures had cooled at OUN. At FWD, 850 and 700 T was down 1 degree C, but the 500 T had warmed by 3 C while at 601 T had cooled by 2.6C! The upper low which showed a jetlet rounding the base of the trough at 500 hPa had weakened by 5 m s-1.

Water vapor imagery doesn't show this 500-600 presumed frontal zone as the upper levels were quite moist. It appears a subtropical jet/ shortwave was influencing the action over Texas.  Maybe this "front" was a small scale circulation. I found it about the same pressure level, same potential temperature at DRT 12 hours prior albeit weaker.

Nothing at the surface really jumps out. Dew points held in the 59-62 F range  in advance of the storms. Something mesoscale was happening for sure but its difficult to understand what. Model simulations anyone?

Sidenote: I did happen to see a wave-like feature that had passed over the CI area just prior to CI on IR imagery that interacted with some small storms in OK. Easily seen on Visible as cirrus.

Comments on Climate Change adaptation

http://freakonomics.blogs.nytimes.com/2010/10/18/matthew-kahn-answers-your-climatopolis-questions/

A couple of comments:
1. "Most people live in cities". This may be True but there are alot of people who live on the coastline.

2. "If the world had 1 bald guy there would be no Rogaine." I don't think that one follows logically from the other. rather, there was a need to invent Rogaine because 1 guy didn't like being bald or figured out bald people don't like being bald. This is capitalism. Not adaptation. Adaptation would have been the bald guy figuring out that some chicks dig dudes with no hair or wearing a nice hat. After all Rogaine was a solution to a problem, whereas adaptation is learning to live with the problem or (more my way of thinking) not treating your lack of hair as a problem.

3. "Free market  capitalism will protect us from climate change." The free market built up our coastlines while hurricanes went slightly dormant for a 20 year spell. And when they raged back alot of people lost their homes on the coastline ... see point 1.

4. "People update their probabilities as new information arises." And Monkeys fly out of my ass... People are generally good at assessing their risk. People are generally bad about acting timely on that risk because they misjudge based on imperfect information. Its why people in New Orleans and East decided to stay put. They had good intel. BIG storm surge, powerful hurricane, strong winds. They had heard this and the evacuation orders. Some still stayed citing previous experience with hurricanes. Some 1700 people died. They updated their probabilities but we will never know how much weight they assigned to those probabilities.

5. I like the eternal optimist: "Educate the citizens about heat waves." Perhaps you are more likely to survive if you can afford, and consequently purchase an air conditioner. But first, after I update my probabilities, I realize that a heat wave just happened and wont return for at least a while so i will wait. I will wait because my first priority will be to put food on the table instead of waiting 1,2,5,10 years before the next heat wave hits.

6. "In Climatopolis, I assume that such a dramatic event would occur gradually." Rarely has drought been gradual. It is sudden and lasts for a while. The impacts are slow because first you realize you are in a drought after the drought has started (reminds me of a recession, or a post-recession). Then you realize you have find some method of rationing which is gradual and increases as the length of the drought increases. Afterall predicting how, where, when, and why a drought is relieved is no easy business.

But this is the point. These opinions make certain assumptions about the rapidity of discrete events. They also make assumptions about the rapidity of innovation to discrete events. Maybe you can take Einstein as an example: from mathematics failure to patent guy teaching himself mathematics. That took time and experience and a whole host of other factors. With climate there may not be sudden achievable advances with which to adapt. rather we will have to know not just our next set of circumstances but the ones after that. We will have adapted in some ways, over time, but we are not built to continually adapt (henceforth accelerated adaptation).

Certainly the community (academic, industrial, manufacturing) we have built is adapting and it shows. Bio-diesel from corn, algae or whatever. But that is one maybe two steps forward but no less realizable on a global scale. The ideas will come out, with this I agree. But, will the time it takes to realize these ideas come faster and faster as the need, not just arises, but indeed accelerates?

This is why climate change science needs to be done. We need to understand the possible outcomes, or impacts of regional climate change. People will have to be making decisions in advance, with limited information. Only this will apply to those who have the means to afford those decisions in advance. Some of us live month to month, day to day and simply cannot afford to make decisions without some sense of certainty (picking up and moving to a new area without the means to support oneself with a job all based on uncertain information with no timetable of when "climate change" will occur.

Pondering perspective in ensemble modeling

When forecasting anything, one must always consider the perspective one has. This is not easily achieved since our point of view is necessarily biased, either by a previous forecast, previous experience, analogs, or instinct.

Perspective "is the choice of a context or a reference (or the result of this choice) from which to sense, categorize, measure or codify experience, cohesively forming a coherent belief, typically for comparing with another." - From wikipedia.

Note the implied bias: "typically for comparing with another".

This is why ensembles are so neat in modeling the weather. The whole point of an ensemble is provide perspective or perhaps more appropriately predictability and by extension certainty (or uncertainty). This is particularly true even if the range of solutions does not cover the phase space of what is possible. In most instances, the mean of the ensemble is better than any individual member.

In the case where the outlier has the most value (no matter how wrong it is), the forecasters perspective may be the only real clue that it is even remotely likely for it to be correct. That is the value of the human forecaster and their experience is most likely to recognize the value of an outlier. There is significant risk associated with favoring an outlier. For one, you are going against what all the other members of the ensemble are trying to convey. So, you have to have good reasoning and great perspective on why so many members could be wrong.

In my opinion, this is why so many BIG forecast failures have occurred. It is difficult to trust an outlier, in a timely manner, because it takes a long time to discount a lot of members AND analyze the outlier in question in great detail such that you trust the solution. This is a major issue in severe storms research since the forecast period is short, the lifetime of some storms and their hazards are even shorter, and the models we use are shrouded in uncertainty (initial data, model spin up time, resolution, physics, dynamics).

Let us not forget that even ensembles have difficulty in predicting the certainty. Just because all the members are similar does not mean the forecast is certain. The issues we face now are just as much technical as they are scientific. Navigating the world of coarse ensembles and fine resolution ensembles will be fascinating.