Space vision

Some cool voyages to space occurred in the last week or so. The Cygnus spacecraft launched and after a bit of trouble docked with the ISS. A major milestone on the second flight for this company. SpaceX launched a new and improved Falcon9. They redesigned the engine structure on the 1st stage, built the second stage to refire to put satellites into higher orbits, modified the first stage to refire to control re-entry, and to fire again to slow the vehicle down for splashdown - what will eventually become a soft landing on launchpad!  The Russian proton rocket came roaring back to life after a failed flight previously.

Get in, Get Down, Cover Up!

Summary - Remember to shelter in place. Put as many walls between you and the tornado as possible. Get in, Get Down, Cover Up!

Verification continued

Yesterday I showed the full period (roughly a day 1 convective outlook) of the SSEO. Since we will test the ensemble at finer time scales, down to 3 hour periods, here are the three 3hr periods under consideration.
18-21 UTC

The figures presented here should be used with caution. The models do not always produce reliable or skillful forecasts of the initiation or evolution of convection or convective mode. Thus using them as I did before depends crucially on being good enough in the time period under consideration to provide useful but not specific guidance.

This first thing you may notice is how circular all the probabilities look due to the use of a gaussian smoother. Thus there are both very few reports and model simulated reports in these areas. There is very little overlap between reports and model reports.
21-00 UTC

In this second period, model reports increase dramatically for some members. The amount of overlap increases for NSSL-WRF, NMMB, and 3/4 of the HRW members. Not bad. I would hope that the members can capture some of the severe weather scenario that played out, including getting close to the proper location. NSSL-WRF does well in this period in SW OK as do the HRW-NMMs. If you look to NE MO then those members plus an HRW-ARW member cover that maximum pretty closely.
00-03 UTC

In the 3rd period, the ARW's, NSSL-WRF, and NMMB and HRW-NMM all contribute (glancing blow for some of them) in some way to the maxima in SW OK. The same holds for MO.

For this case at least, the models appear to be able to simulate at least 3 hour probabilities of total severe weather. As I have indicated elsewhere, the use of UH to match against all severe reports appears, again for this case, justified. Such will be one of my foci for the upcoming Hazardous Weather Testbed Spring Forecasting Experiment as part of the Experimental Forecast Program. Testing this out for a bunch of cases this year, and extending it back in time will be a goal of mine moving forward.

Verification issues: severe wx and fronts

I have been computing total severe probabilities from the SSEO and the event on the 17th offered a nice opportunity to do "verification". The idea is to use object based Hourly Maximum Updraft Helicity and treat local maxima as storm reports. This method allows us to extract information from the 4km pseudo convection allowing ensemble in a way that is comparable to storm reports (at least at the level of the grid used for verification, wind reports are scrutinized for speed).

Here is the verification, and of course this is all experimental:

Getting attention

What seems to garner attention these days? It certainly isnt success. More often than not it is failure. Failure it seems, is quite the motivating force. Politicians use the perception of failure in concise talking points. Sometimes they use absolute failure but more often than not they never let the truth get in the way of a good story. This attracts both kinds of attention: The people that agree with you love your refreshing honesty and the people that disagree with repeat your talking point and explain why it is wrong in some nuanced fashion. Double the message at half the price!

Even in social media on a personal level. Speaking about something calmly and rationally gets some attention. But making a scene, getting "facts" "wrong", well that is an entirely engaging flame war for the masses. Everyone wants to take their turn at the pinata that you just happened to leave near a giant baseball bat.

The other ways of getting attention usually involve solid communication skillz. Relating on a personal level, unleashing some passion, being vulnerable, expressing yourself on a deep emotional level. These garner a different kind of attention, perhaps. They seem to be places that resonate from within, in a way that is just as expressive but can also be deeply constructive. The "I have been there" attitude, "I know what that is like".  You know these conversations. they occur on topics outside of controversy, or perhaps we practice them outside of controversy and dont bring them to the flame war party.

We have to choose to behave like this. To attract attention to those things we care about on a personal level, to treat other people as if we share a perspective and thus can relate. To become "friends" before we hammer out our discourse. After all it isnt the perspective that blocks us from acheiving this, its the pollution from "I am right and you are wrong" attitude. A contamination of the communication environment. Its hard to make any point when you have to absorb all the nastiness in your first breathe before you get your words out.

So ask yourself: Are you contributing to the pollution? Are you selling your point of view or are you collecting information to update your perspective or perception?

We cant be perfect in every conversation. So dont expect other people to be. But at least give a listen and try to understand. Make them identify their assumptions and you should identify yours. After all the point isnt to get attention in the first place. Its to communicate.

Risk A Vision

Enough nonsense crosses my path that I have been thinking about the ways in which innovation happens. Its usually an outsider, a person who doesnt think too hard about rules, a rebel. Someone who implicitly knows THE assumptions of the day are actually holding progress hostage. The intuitive feeling for most people would be to simply press ahead, work from within the system, to effect incremental change.

Yet innovation is almost always something larger than incremental. It opens up possibilities where there were none before. It naturally makes you consider other perspectives, options, etc. It comes through revolution -  overthrowing a system of control that was suffocating. It is always a counter intuitive example.

Yet somehow, as a society, we have found ways to control or manage such things. That some process completely understood by people could lead one to innovate. Yet with all this control, we seem to be rather adept at running things into the ground. We keep rediscovering what it means to fail and yet not understanding how total control, perceived or otherwise, led to failure. Some might have you believe that this type of failure is actually a part of the process of success.

What I think marks innovation is perspective not clouded by limitation. Sure you have to fail before you succeed. Failure isnt the goal however. Its the knowledge gained, the perspective altered or rearranged, and the process sharpened. Innovation is very much a step function improvement. A rare event.

But you can not control for it or put rules around it or dedicate a fraction of time to it. Those rules are meant for focus, iterative development, and incremental advances. Maybe you want to challenge me here at this point. "Doesnt that sound like innovation?" It is perhaps counter intuitive but no it isnt innovation. Innovation is all about changing that process from making some thing to exploring the vision of that idea. The result of innovation might be a thing, but that thing was born out of a new way of thinking.

Most of our scientific organizations rely on this. Your proposal to NSF might require you change the way the problem is thought about. To develop new ways of thinking. To tackle problems that are elusive and demand new solutions; to push the envelope of knowledge.

Innovation seldom comes from resource constrained environments. In fact I might argue that it necessarily does not come from there. It will evolve externally, never even making its way into that environment ... why would it? It would be killed in that hostile environment. We speak about topics like critical mass, institutional memory, great people that drive innovation. At times this is true. But seldom does this come at a time when the belts are tightened. It almost always happens in spurts when there is aplenty. In fact it doesnt even have to mean money. It could be people or resources. Any constrained environment, cut off from perspective becomes stagnant.

But the major factor in innovation, and probably the most important player: Risk. Identifying it, seeking it and taking it. The people that innovate arent good at taking risk, they are good at ignoring the risk they are taking. In fact, I think when people look back at it, they say "oh yeah we were taking a big risk but...".  The biggest risk is that you dont take a risk. And that is what our culture has nurtured. We identify the risk and play it safe ... so called risk aversion. More like innovation aversion.

4/7/13 - All about the details

Today is the beginning of multi-day severe weather episode. Thus, it is a good opportunity to take a look at the Storm Scale Ensemble of Opportunity (SSEO) and do a #science. The SSEO is a 7 member WRF-ARW (NSSLWRF 4km, 2 5.1 km HiResWindow) , WRF-NMM (1 CONUS, 2 HiResWindow all 4km) , and NMMB (New NAM 4km) convection allowing ensemble. My recent work (unpublished) suggests that the NMMB is similar in effective resolution to the 5.1 km ARW membership.

Todays forecast features the not-so-mythical cap: that love-to-hate it feature which keeps storms from forming OR makes them isolated and pristine to photograph. So it is not surprising that the first take-home message is that not all members even initiate storms in western OK. NSSL-WRF has a hard time generating storms for the southern half of OK. It appears it develops at least isolated, shallow convection: the models equivalent to turkey towers. Two NMMs develop convection further south, one of which has an isolated storm down by the Altus area. And this is the second take-home message: This behavior in the individual members suggests the models are picking up on storms that they themselves cannot resolve (i.e. storm coverage is low). In situations like this it may mean that isolated storms will be the only mode, and that they will be short lived the further south you go. It is my interpretation that at least one good storm will develop off the dryline by the surface low in SW OK, though it might be short-lived by our standards, long enough to pose a solid hail threat if it is supercellular (sometimes it takes time to ramp up these storms in tough to thrive in capped environments).

The result of this limited storm coverage is reduced probabilities for severe weather proxies. In this case, we use model derived Updraft Helicity as a signal for storm intensity. Our sensible threshold for stronger than most is UH > 25 m2s-2. To calibrate you, storms that get up to 100 are considerably stronger. Storms in NSSL-WRF have generated upwards of 400, but those tend to be single point maxima.

The forecast UH > 25 m2s-2 probabilities (3 hour variety) show the maximum chance for severe weather in the models is northern OK and southern KS. Quickly tonight the threat shifts to MO for any line segments along a front.

Storm motion estimates (southern OK) for right moving supercells are slow and to the southeast, while left moving storms will be quick to the northeast. For the north, somewhat slow and to the east, with left splits quicker to the northeast.

So the uncertainty is relatively large in OK, but I guess I would say that is normal in a capped environment. That models have any signal at all suggests I should favor it. But being wrong by one or two storms means ... well it may mean that nothing happens (i.e. risk is there but not realized) or that something does indeed happen (but is no severe because it doesnt last long enough).

Best chances for today appear to be in KS, with isolated supercells, eventually merging along that boundary and moving eastward. No doubt KS has the bigger tornado threat but you cant rule it out in OK.

The bottom line is that, on a day like today, it is highly unlikely that I could draw for you what the Radar Screen would look like. The devil is in the details and today is ALL about the details in OK.

UPDATED 4/8/13:

 The above is verification for yesterday from my experimental processing of the SSEO relying on Updraft Helicity tracks essentially using 4 passes. The strongest tracks (UH > 75) get the new fancy magenta contours (same levels as the color bar). Looking back at the evolution in the NSSL WRF this morning, it was apparent that it did a fantastic job with both mode, location, and evolution of the storms. It had some flaws in timing across northeast KS and overdid the MO MCS. I also looked at the experimental 12UTC version of the NSSL WRF and it had some differences but generally looked pretty similar. I guess that means I have to start processing the 12Z experimental SSEO version!

The ensemble mean probabilities takes on the shape of the dominant contributing members of the NMM variety but does bot beat the skill of the NSSL WRF at the 15 percent probability level. Looking carefully, each member overlaps with, and thus contributes to, at least one of the storm report local maxima.

Learning organization

Anyone who knows anything about modeling knows that you cant just be the best at any one modeling system. You have to be in an environment of learning, with a clear vision of not only how to improve your models via feedback from customers, but also agile to know the limits of your modeling system and move on when the problems dont match the techniques. This is hard to do.

The ECMWF has chosen to do one model and do it well, organizing a myriad of people to attack fundamental problems. They have focused resources, a clear vision, and well defined goals. In the US we have many modeling systems, a robust set of testbeds, and as a result a diversified community of modelers. We have a broader learning organization that is by definition less focused due to diversity. We have goals but our problem is the lack of agility despite this diversity. Diversity can be equally paralyzing because there are too many options and we become distracted and governed by our perceived priorities and focus heavily on our limitations. This results in quick approaches that pay off in the short term but not in the long term.

In essence we will ask our best people to always be playing catch up and not give them enough time to be forward thinking. The time spent reading papers, going to conferences, making connections and exchanging ideas is well worth the cost of travel. It provides an outlet, an entry into the creative process through collaboration, and a realization of perspective that you cant get internally (e.g. the purpose of achieving new perspectives is for growth which can quickly stagnate in low morale environments). It is effectively a way to get other people to invest in your problems without using your money. We all benefit when these folks make successful, mutually beneficial collaborations and has been a hallmark in the aerospace industry!

This is precisely about creating a learning organization that has agility and flexibility of choosing good methods without being hampered by custom or culture to implement good ideas. This is all about interaction between researchers and forecasters, to make sure that the best methods, the best techniques, and the best mutually beneficial interactions occur to get the job done. This is an iterative process, and is a hallmark of a learning organization. But learning organizations have well defined, forward looking goals that equate to a solid vision and they have tactical support to implement, refine, and iterate.

Bogging down the system prevents even good intentioned people from creating a vision, crafting a strategy and developing a tactical approach to get the job done. Hiring freezes when organizations are already short staffed, or cutting corners on travel and interactions only serve to amplify the long term impacts. And when those impacts surface they will be unbearable.

#SciComm

I really like using hashtags (i.e. obsessed). They are short and sweet communication tools laced with sarcasm and emotion amongst other things. The computer version of Kevin Nealon's Mr Subliminal (from SNL for you young people #spoiledkids). Subtle, sharp, confrontational (thank you yes I am), and honest. It resonates because people think it but dont say it. Its a glimpse into someone elses reality. An attempt to drop social norms and just be you, the you in your head. #vulnerable

Which brings me to science and communication. Communication is effective when blunt and honest but not jam packed with facts (or you know #science). Science is not communication. Science informs communication, communication may inform science, and there is even a field of the science of science communication.  People get this confused. #includingme We train our brains to think in terms of science but we have not trained ourselves to translate that information into someone elses perspective. Obviously that is one of the hardest things to do. To be cognizant of someone elses perceptions, views, maybe even feelings, gut instincts and be sensitive to their level of comfort with risk.

The risks evolve with time sometimes without us knowing or realizing. Such is the case with weather and climate. We are adaptive beings and as such have adapted to that which is most familiar. In all cases we have adapted to seasons. #notlookingatyoufloridians #70 #coatwx  We have not adapted well to longer term drought (lasting more than 2 years); of course I dont have direct evidence but some OK towns are rumored to run out of water in the next 10 months. #tornadosummit

#meanwhileatthemesoscale So I attended a great seminar about real people #shudder caught in flash floods in France. The framework established by Isabelle Ruin was all about space and time scales of actions (e.g. real people making decisions, estimating risk, perceiving risk, and experiencing danger). The cool part was estimating the time at which people had to anticipate and react to the threat, based on the phenomena. My interpretation of this was a sort of internal or personal calibration to the threat. When the people react or take action to mitigate a risk we hope they are doing such things at a pace faster than the phenomena. in that case they will be in mitigation, any slower and they will be reacting. In both circumstances, people can make poor decisions whether they be well informed or not.  It was concluded that lead time particularly its suggested increase may not be any good. This has been eluded to in other work by colleagues so it isn't much of a surprise to me.

The notion that we should provide more information to help people make decisions is right on. But that doesnt mean more or better information of the scientific variety. It may mean, for now, cues that resonate with people to help them personalize the risk. To make it real. #itcanhappen #itIShappeningtoyou Extreme events happen suddenly and put you in a state of shock and awe. #surreal You can prepare for it, but until it happens, you really dont know how prepared you should have been. #doesthatevenmakesense

More importantly we need to slow down how we change the current system. Everyone has solutions but not everyone knows the unintended consequences, or else we wouldnt call them unintended consequences. The point of going through the motions of evaluating any potential solutions is to identify those consequences, those times where things wont work well, and those times that they do. But also to ensure that we establish learning along the way. Iteration is the key to success. #lightbulbs And iteration requires failure. If all you do is derive #solutions, then you have succeeded in failing to learn. And you implicitly acknowledge that your intent is to be done with this for a while once you get a result that is "good enough" or "close enough".

The challenge we all face is to inform, organize, plan, and react based on the risk. Solid communication and solid science can get us there, but we have to be truly organized and informed to make that a reality. To accomplish this we will have to be vulnerable, blunt, and honest in our communication tactics, and we will need to be accessible in our vision, strategy, and tactics to keep people well informed. And that is the goal of scientific communication: To keep people well informed.

Tornado Summit

I had the chance to attend the Tornado Summit with attendees from emergency management (EM), physical and social scientists, and Insurance representatives. I couldnt be everywhere so I missed all the Insurance breakout sessions. I usually enjoy seeing a different perspective but there simply was no time. My main mission was to conduct short surveys of EM professionals for our NOAA grant relating to Social and behavorial influences on weather driven decisions. That mission was accomplished and I got to meet some interesting and dedicated professionals.

Being consistent

Consistency is key. Consistency from forecast to forecast*. Consistency from big event to big event**.  This is all about being credible, accessible if you will. Getting the benefit of the doubt via past experience. It also means being measured. Taking risks only when you have to. Its playing long-ball. It is very easy to lose credibility and much harder to get it back once you lose it.