If you’ve visited this website in the past week, chances are you were here to read and/or comment on the blog post Attempts at Assessing Chaser Contributions to the Warning Process. Wow. Talk about a passionate response — on all sides! That entry has prompted the biggest response, in terms of comments, in quite some time. I have read every comment posted, but am too busy preparing for the Hazardous Weather Testbed’s Experimental Forecast Program to post a long response to every one. Instead I thought I would act as my own ombudsman and address some of the reoccurring themes that keep appearing in the aforementioned post’s comments.

When reading below, one may ask themselves why I haven’t removed the offending post. The reason is multi-faceted. First, as soon as I do that I subject myself to (in my opinion) a more severe criticism of writing somewhat of a hit piece and then taking it down when I didn’t like the response. I personally do not think that is right and so in that regard, the post must stay. If I’m going to write something that I know is going to be controversial, I must be prepared to accept the negative comments that result. My philosophy is that as long as a comment is not spam, not profane, nor attacking myself or others personally, I’ll never remove it. No matter how much I may disagree, it is only in hearing all sides of an issue that I can expand my horizons. Secondly, there have been some good discussions that have resulted from the post, both for my position and against it. As long as these conversations refrain from snarks, I see no reason why they should not be allowed to be seen.

Now, for my thoughts on the post…

Let me begin by saying that this certainly was not my best post in terms of scientific content. I made a fundamental flaw in the post that quite a few commenters picked up on, and I’ll admit that I did it. No matter what these data presented suggest, they cannot prove one way or another the intention of a person. At best, with better data than presented, one might be able to assess chaser impact, but not motives. I tried to be cognizant of this fact in some aspects of the blog post (such as not titling the post some variant on “Are Chasers Chasing to Save Lives”), but failed miserably in others (using the data shown to justify the line “Please don’t insult my intelligence by claiming to chase to ‘save lives’.”). This is something I must make sure I do not do again, and if I do, I trust you will hold me accountable.

Next, some commenters accused me of doing “bad science”. In response to at least one of these comments I responded that I never claimed to be doing science. However, after a couple days of thinking about this issue I believe that the original comment and my response both miss the point. This isn’t “bad science”, nor is it “not science”. It’s “unfinished science”. If I left things as is, said that the matter was closed, and closed my mind to differing points of view, then it most certainly would be “bad science”. Instead, I tried to go out of my way to imply that my view points were far from definitive. I wrote things such as “circumstantial evidence”, “Attempts at Assessing”, and “To summarize, I believe…”. I posted these ideas on a blog website, not a scientific journal for a reason, they are initial ideas and certainly would not hold up in a court of law nor a scientific journal.

What the post tried to do, and admittedly failed miserably at doing, was attempt to objectively assess the contributions chasers have to the warning process. I put forth an idea, people attacked it and poked holes in it. If I am to act like the scientist I would like to think I am, I should not take these criticisms personally, but rather use them to continue to evaluate my idea(s), refine the idea(s), and try again. This is how science is supposed to work! At the end of the process the final idea(s) will be stronger and more refined than anything initially proposed.

Assessing chaser impact on the warning process is an extremely complex problem as there are many variables and many signals. As several commenters suggest, I did allow myself to fall into the “confirmation bias” trap — I saw what I wanted from irrelevant and/or inconclusive data. But, by putting my thoughts out in the open, people were quick to point out the idea’s flaws, which will allow me (in time) to do better analyses with differing datasets and strengthen my position. Again, this is how I believe science should work. Putting this data and ideas on the website wasn’t the mistake, but intertwining my personal opinions so strongly was. And for that, I do have regrets; I’ll be better about that moving forward.

However, removing my personal beliefs, this is the first attempt, to my knowledge, that tries to objectively assess the contributions chasers have on the warning process. Due to the complex nature of the problem, and the fact I did this as sort of a “back-of-the-envelope” calculation, I merely looked at aggregate measures using simple NWS performance metrics. Possible ideas that could be done were suggested in the comments, and when I have time, I’ll certainly try and investigate some of these. (Aside, if a reader would like to do this, I’m more than happy to share my datasets.) There are a lot of other potential impacts, both negative and positive, that need to be assessed as well. As it stands now, a lot of anecdotal stories are offered by those on either side of this issue, but do we really have any idea what the actual impact is? From a chaser point of view, being able to demonstrate a positive impact in the warning process could help counter the negative perceptions current circulating in several news outlets and improve interactions with emergency response officials. From an emergency response official perspective, knowing chaser impacts might lead to a new respect for chasers, or more clout in trying to regulate them. But then again, maybe both sides would rather not know…

None of the comments have changed my underlying assumptions that most chasers chase for personal reasons, not the noble reasons of saving lives and doing it for the NWS often offered by chasers when interviewed by the media. However, we are (I am?) a long ways off from being able to assess this objectively. My previous post was a first attempt at this. I’m sure it won’t be my last. And I’m sure that there will always be someone out there challenging my views. That’s the way it is supposed to be.

Today I gave a version of my presentation on Tornado Warnings. This presentation was originally given earlier this month at the University of Alabama at Huntsville. We recorded today’s presentation so that others could see it, but I will warn you that my delivering the presentation this time did not go as smoothly as it did in Huntsville. (I stumbled over my words a couple of times and missed a few points I wanted to make.) But as a good sport, and someone who wants to see the conversation continue, I’m posting the link to the recording so that others may watch it and contribute feedback on what they thought.

When watching the presentation, a couple of questions I would love for you to keep in mind:

• I am not an operational forecaster. No matter what I may say; no matter what I may think, I have never been in the position of actually having to issue a warning. Until I am in that position, everything I say should be considered my opinion. This seminar is in no way an attack on operational forecasters. They do a tremendous job under extremely stressful situations. This seminar is aimed at fostering a discussion on policy, not on specific actions a forecaster should or should not take.
• Current Tornado Warning metrics center around Probability of Detection, False Alarm Ratio, and other contingency table measures. However, not every detection and not every false alarm are created equal. Are there better metrics that could be used to measure tornado warning performance? If so, what would they look like?
• As mentioned above, not all false alarms are created equal. Furthermore, issues such as areas within the warning not being impacted by a severe event and broadcast meteorologists interrupting regular programming to cover warnings within demographic areas all give rise to the notion of perceived false alarm ratio. How can we adequately measure this, and maybe more importantly, is there anything we as a community can do to address issues arising from this?
• Warning philosophies (severe and tornado) vary from office to office, leading to the sometimes asked question, “Do we have a single National Weather Service or 122 Local Weather Services?” Are these differing warning philosophies a good thing or a bad thing? If it is a good thing, how can we better communicate the different philosophies to users, or is that even necessary? If it is a bad thing, how do determine which philosophy(ies) do we standardize around? Or, is there a third option here that we’re (I’m) missing?
• Should warnings be meteorology centric or people centric? Although population centers appear to show up in the datasets, is this a reflection of being people centric or merely a reflection that radar locations tend to be co-located with population centers and our understanding of thunderstorm phenomena are inherently tied to radars?
• Instead of moving toward an Impact Based Warning paradigm, or a tiered warning paradigm, is it time to consider including probabilities or other means of communicating certainty/uncertainty information into the warning process? If so, how do we go about doing this in a manner that does not leave the users of these products behind? In other words, how do we move toward an uncertainty paradigm in which average citizens can understand?

I firmly believe that the warning system in place has undoubtedly saved thousands of lives throughout it’s history. However, I do believe that it has problems and stands to be improved. However, I cannot put into words what the problem(s) is(are). I believe that it will require community efforts to address these problems. This includes all of the severe weather community: research meteorologists, operational meteorologist, NWS management, emergency managers, broadcast meteorologists, and, maybe the most overlooked piece, social scientists.

Lastly, I must apologize to Greg Blumberg for coming across much harsher than I intended to when addressing a comment he made during the presentation. My response was intended in jest since I know Greg, but that didn’t come across to everyone in the audience, which tells me I shouldn’t have said it. Greg, my sincerest apologies, and I hope you understand that my response was entirely in jest.

With that said, I hope you enjoy the presentation, and I look forward to hearing your ideas!

I am going to begin this post by saying upfront, in bold font.

I merely have a problem with people justifying their chase activities by saying they do it to “help the NWS” or to “save lives”. I have no problem with people who chase, just be honest with why you do it. In fact, since I know I’m going to take a lot of heat from certain groups within the chaser community, I’m going to repeat it.

OK, now that the disclaimer is out of the way, what is the purpose of this post?

In the wake of last weekend’s tornado outbreak, several news agencies (LA Times, USA Today, and Detroit Free Press just to name a few) have written stories on the (exponential?) increase in the number of storm chasers on the roadways. It is not my intention to discuss the complaints of emergency managers and other local government officials. (That will have to be left for a future post.) Nor do I intend to discuss whether chasing is morally wrong and/or should it be outlawed. Instead, I want to stick to the all-too-often used storm chaser justification for chasing, “Storm chasers save lives.” I’ve seen this justification used in the numerous stories I’ve read this week. I’ve heard storm chasers complain about the “bad rap” they are getting; that people are focusing on the actions of a few and forgetting that “chasers save lives”.

Storm chasing really began to take off in the mid-1990s with the advent of the first Verification of the Origin of Rotation in Tornadoes Experiment (VORTEX) and the subsequent movie Twister. Since these two events, the number of people who identified themselves as chasers has been on the rise (at least according to my perceptions). In recent years, television shows on storm chasing, software such as GRLevel3, ThreatNet, and Spotter Network, as well as the increased cell-phone bandwidth, among many other things, has resulted in what I perceive as an almost exponential growth in the number of storm chasers. If these storm chasers really were chasing to “save lives” as many or most claim, I would hope to see some sort of reflection of this in the fatality counts from tornadoes. After all, more people out saving lives should result in more lives being saved.

Below is a figure courtesy of Dr. Harold Brooks. It shows the annual number of tornado fatalities in the United States in terms of deaths per million people. Examining tornado fatalities in this context attempts to account for the fact that the population is increasing, and thus there are more people susceptible to losing their life in a tornado.

What should be very obvious is that from about 1950 through about 2000 the trend is decisively downward. However, since around 2000, the trend is approximately flat, meaning that the odds of dying from a tornado is roughly the same now as it was back in 2000. This figure is one of my absolute favorites as it contains a lot of information and leads to a lot of tough questions for the severe weather community. One question that is often asked is, why does the trend seem to flatten out in the 2000s? There could be a lot of reasons why this is, and we’ll leave those to another post. My point here is that with the explosion in the number of chasers, I would expect to see some reflection of this in the number of fatalities resulting from tornadoes. However, the data do not seem to suggest that storm chasers have had that much influence in saving lives.

“But, Patrick, storm chasers provide a valuable service to the National Weather Service by providing real-time information to aid in the warning process. You can’t use a single figure to negate all the contributions of chasers to the warning process!” Fair enough. If chasers do provide a significant impact in the warning process, we should see some reflection of their contributions in the various tornado and tornado warning metrics, so let’s take a look.

Above is a figure that breaks down the number of reported tornadoes by year and by F/EF rating. As with the figure before it, there is a lot of information behind the data going into this figure, but we’ll leave that for another post as well. For the current purpose, you’ll notice that for the most part, most of the data have remained unchanged. The exception being the number of EF-0 tornadoes, and to a lesser extent the number of EF-1 tornadoes, dramatically increases starting around 1990. This increase coincides with the advent and widespread adoption of Doppler radars. With the increased information Doppler radar provided meteorologists, weaker tornadoes were more easily detected, and thus, the number of reported weaker tornadoes has increased. If chasers had a significant impact in the number of tornadoes observed, I would expect to see some sort of change to the trend-lines beginning in the mid-1990s as the increased number of chasers saw more of the tornadoes that meteorologists missed. The truth is, the impact of chasing is circumstantially less than the impact due to adoption of Doppler radar.

To illustrate this even further, lets consider the probability that a tornado will occur in a tornado warning probability that a tornado that is occurring has been or will be warned. (In verification parlance, this is known as Probability of Detection, or POD.) As the figure below indicates, the Probability of Detection has increased consistently, albeit slowly, since around 1990, which is roughly when the increase in weaker tornadoes began. Taking these two pieces of information together, it would suggest that the Probability of Detection has increased as a result of detecting the weaker (F/EF-0 and F/EF-1) tornadoes, and not the result of chasers making reports.

Considering the Probability of Detection aspect of the problem lead me to consider, well maybe chasers have an impact on the tornado warning lead time. With chasers calling in those “rotating wall cloud” reports, maybe the lead time has increased. The mean lead time for all tornado warnings is shown below.

Now, this figure requires a bit of explanation. The National Weather Service defines lead time in a quirky way. First, lead time is merely the amount of time elapsed from the issuance of a tornado warning to the first report of a tornado. Sounds simple enough, right? Well, it gets tricky when you consider a tornado that occurs before a tornado warning. In this case, one would expect a negative lead time. This negative lead time would approach negative infinity in the case that a tornado is never warned. However, this is not how the National Weather Service reports lead time. The NWS assigns a lead time of 0 for all tornadoes that occur before or without the issuance of a tornado. Thus, if you have a low Probability of Detection Thus, if a tornado does not have a warning, or warnings are not issued until after the a tornado is reported, you would expect to have a low lead time because of all the zero lead times that would be averaged in.

What we see in the figure above is that the tornado warning lead time has increased fairly consistently since around 1990. It has increased from about roughly 5 minutes to roughly 15 minutes. Once again, however, this increase in lead time does not appear to be related to storm chasers. In fact, it appears to be directly related to the increase in Probability of Detection.

Plotting the Probability of Detection (multiplied by 20) on the same plot as Tornado Warning Lead Time (above), it becomes pretty obvious that the two are highly correlated. In fact, if we were to plot the linear trend lines of the two metrics (below), we would find that the slope is almost the exact same!

For completeness, I’ve plotted both the raw values and the trend lines on the same plot above. Once again, I do not see any significant impact from storm chasers on these metrics. I firmly believe that they are all attributable to better forecaster training and widespread adoption of Doppler Radar.

Maybe chasers have an impact on the False Alarm Ratio, or the number of times a tornado warning is issued and a tornado fails to develop. Certainly chasers have had an impact here, right? After all, with the number of chasers out there reporting what the see in real time back to the NWS, the NWS is issues fewer tornado warnings where no tornado occurs. Unfortunately, as the figure below indicates, the False Alarm Ratio, or FAR, has not changed since 1986. It has remained fairly constant around 75%

Yes, I admit that all of this “evidence” is circumstantial; maybe the improvements since 1990 are the result of chasers and not Doppler Radar. (Maybe we should ask NWS forecasters if they would rather have Doppler Radar or chasers?) I also admit that there could be some chasers who really do chase with intention of “saving lives” or “aiding the warning process”, but I think these chasers are few and far between. Instead, what I really see, and believe, are chasers who are interested in chasing for their own reasons (adrenaline, fame, money, etc) and try to pass it off as a noble cause. If people really did chase for the sole reason of helping others, there would be no need for still and video cameras and no need for chasers to get as close as possible. Please don’t insult my intelligence by claiming to chase to “save lives”. I do not see paramedics and firefighters setting up tripods at the scenes of fires and accidents, so why are chasers unless there is another motivation? At least be honest with yourselves and with those around you, and say you, and most other chasers, chase for personal reasons, and if you remember to help the NWS out while doing it, you try to do so.

To summarize, I believe chasers have a limited impact on the warning process, and don’t appear to directly “save lives”. With that said, I don’t have no problem with people who want to chase. My problem lies with people who chase and then are dishonest about, or at least misrepresenting, their intentions.

Lastly, I want to make a distinction between “spotters” and “chasers”. Spotters have been around for a long time, dating back to the days of World War II (and possibly longer). The role of spotters used to be to warn military bases about approaching thunderstorms so that people could be removed from munition depots on the off-chance that lighting stuck the munitions. Spotters tend to be tied to local communities and have relationships with the local officials involved in decision making process. I suspect that spotters have a much greater role in the warning process than chasers, although, it appears (at least circumstantially) that this contribution is still at the margins.

Again, these are my interpretations of the data; I’m sure others will interpret the data in other ways, and I have no doubt that those of you who disagree with my interpretation will let me know.

On Saturday evening a the National Weather Service in Wichita was receiving reports of a large, violent tornado moving toward the city of Conway Springs, KS. Based on the protocol of the Impacts Based Warnings pilot program, the local NWS office issued a Tornado Emergency for Conway Springs. (The text of this warning, including the line

...TORNADO EMERGENCY FOR CONWAY SPRINGS...

Above is the image from the NWS Wichita, KS homepage. (Original image can be found on this page). It clearly shows that Conway Springs was spared from damage from the tornadoes. Based on how I have defined tornado emergencies in my research, this (potentially) would be considered a successful Tornado Emergency, as a tornado occurred within the bounds of the tornado polygon. (It’s “potential” depending on what tornado intensity threshold you want to use for a tornado emergency.)

However, since the Severe Weather Statement that announced the Tornado Emergency specifically stated,

...TORNADO EMERGENCY FOR CONWAY SPRINGS...

I’ll reiterate my position:

The current warning system has problems, but works remarkably well for a vast majority of the population, despite these problems. At the same time, no one in the severe weather community understands, let alone can articulate, what these problems actually are. Before attempting to implement solutions to address what the problems are perceived to be, the severe weather community needs to seek to understand what the problems are. Only then, once the problems are known and understood, can solutions can be proposed, tested, revised, and tested again before being implemented into a warning system that has undoubtedly saved tens of thousands of lives throughout the course of it’s use.

The text of the Severe Weather Statement issuing a Tornado Emergency for Conway Springs, KS.

815
WWUS53 KICT 150236
SVSICT

SEVERE WEATHER STATEMENT
NATIONAL WEATHER SERVICE WICHITA KS
936 PM CDT SAT APR 14 2012

KSC191-150300-
/O.CON.KICT.TO.W.0029.000000T0000Z-120415T0300Z/
SUMNER KS-
936 PM CDT SAT APR 14 2012

...A TORNADO WARNING REMAINS IN EFFECT FOR NORTHERN SUMNER COUNTY
UNTIL 1000 PM CDT...

...TORNADO EMERGENCY FOR CONWAY SPRINGS...

AT 932 PM CDT...A CONFIRMED LARGE...VIOLENT AND EXTREMELY DANGEROUS
TORNADO WAS LOCATED 5 MILES SOUTHWEST OF CONWAY SPRINGS...AND MOVING
NORTHEAST AT 35 MPH.

THIS IS A PARTICULARLY DANGEROUS SITUATION.

HAZARD...DEADLY TORNADO.

SOURCE...SPOTTER CONFIRMED TORNADO.

IMPACT...THIS IS A LIFE THREATENING SITUATION. YOU COULD BE KILLED IF
         NOT UNDERGROUND OR IN A TORNADO SHELTER. COMPLETE
         DESTRUCTION OF ENTIRE NEIGHBORHOODS IS LIKELY. MANY WELL
         BUILT HOMES AND BUSINESSES WILL BE COMPLETELY SWEPT FROM
         THEIR FOUNDATIONS. DEBRIS WILL BLOCK MOST ROADWAYS. MASS
         DEVASTATION IS HIGHLY LIKELY MAKING THE AREA UNRECOGNIZABLE
         TO SURVIVORS.

LOCATIONS IMPACTED INCLUDE...
WELLINGTON...CONWAY SPRINGS...BELLE PLAINE...WELLINGTON AIRPORT AND
RIVERDALE.

PRECAUTIONARY/PREPAREDNESS ACTIONS...

TO REPEAT...A LARGE...EXTREMELY DANGEROUS AND POTENTIALLY DEADLY
TORNADO IS ON THE GROUND. TO PROTECT YOUR LIFE...TAKE COVER NOW. MOVE
TO AN INTERIOR ROOM ON THE LOWEST FLOOR OF A STURDY BUILDING. AVOID
WINDOWS. IF IN A MOBILE HOME...A VEHICLE OR OUTDOORS...MOVE TO THE
CLOSEST SUBSTANTIAL SHELTER AND PROTECT YOURSELF FROM FLYING DEBRIS.

TORNADOES ARE DIFFICULT TO SEE AND CONFIRM AT NIGHT. TAKE COVER NOW.

&&

LAT...LON 3748 9781 3747 9715 3730 9715 3706 9780
TIME...MOT...LOC 0235Z 213DEG 30KT 3735 9768

TORNADO...OBSERVED
TORNADO DAMAGE THREAT...CATASTROPHIC
HAIL...2.50IN

$$

Today, the Storm Prediction Center has issued a Day 2 High Risk for only the second time in it’s history. When was the previous one? 06 April 2006 for the vicinity affected by the 27 April 2011 outbreak. For those interested, the SPC DID NOT issue a Day 2 High Risk for the 27 April 2011 outbreak.

Below is a figure that plots the SPC forecast probabilities for the Day 2 High Risk. (The area contained in the 60% area is what was defined to be the High Risk.) In addition to plotting the forecast probabilities, the verifying storm reports from the National Weather Service publication Storm Data are overlaid. You may notice in the report legend (lower right), the total number of reports of that type and the total number of significant reports of the given type as included. In case you do not know, a significant report is:

• Wind Gust great than or equal to 64 kts
• Hail greater than or equal to 2 inches in diameter
• Tornadoes greater than or equal to F/EF2 strength

So, since the SPC did not issue a Day 2 High Risk for 27 April 2011, does this mean that the SPC is expecting an even bigger severe weather event across the central US tomorrow? Not necessarily. On 26 April 2011, most meteorologists knew a significant tornado outbreak was going to occur the following day. In fact, most everyone I know expected the SPC Day 1 outlook to have a High Risk on 27 April 2011. The reason the 26 April 2011 SPC Day 2 outlook did not include a High Risk is because there were lingering uncertainties as to where the greatest concentration of severe weather would occur. So, even though today’s Day 2 outlook contains a High Risk, it does not necessarily mean that SPC is expecting an even bigger outbreak than 27 April 2011 tomorrow. It just means that SPC has a higher confidence on the region to be affected.

In light of all the discussion surrounding today’s Day 3 Moderate Risk issued by the Storm Prediction Center, one question that is on a lot of people’s mind is, “What do Day 3 Moderate Risks Become?” Well, here’s the answer…

Note: this post is a follow up to the original SPC Day 3 Moderates in Context from 2011.

The month of February saw me take the written portion of my general examination and the first half of March saw me prepare for and defend the oral portion of the general examination. A future blog post will describe in more detail what the general exam process was like, as well as provide a copy of my written question, written response, and oral presentation. I write this as a feeble defense as to why things have been quiet here. That’s about to change…

I won’t go into all the details of everything coming up, but I do want to pass along that I will be visiting the University of Alabama at Huntsville on 4-6 April 2012. The purpose of this visit is to give a seminar (12:45PM on 5 April 2012) titled, “Tornado Warnings: Past, Present, and Future“. In addition to the reasoning listed in the abstract (below), a lot of this seminar has grown out of my interest in the tornado warning process and the work previously posted on this blog. If you are in the area and are interested in attending the seminar, please contact me and I’ll put you in contact with the University. I’m hoping that EMs, Media, and NWS people can attend. As is my policy, the presentation will be posted online after giving it. Furthermore, in support of open science and reproducible research, I will be making available all of my code and data. I hope to do this for all of the research presented in this blog. My philosophy, “Don’t take my word for it; do it yourself, and here’s how you can get started.” (More on this in future posts.)

As I state in the abstract, I don’t pretend to have all the answers. I’m merely offering my opinions as a means of starting a conversation — a conversation I truly believe needs to take place. I hope you will participate.

Here is a link to download my presentation on Tornado Emergencies from today’s National Severe Weather Workshop. I firmly believe the NWS is standing on a precipice, and the entire meteorological community needs to take a moment and figure things out before it’s too late. This presentation is designed to start a conversation; let the discussion begin!

A Review of NWS Tornado Emergencies

This morning as I was making my rounds on social media before my day began, I came across a tweet from a good friend, and even better meteorologist, Ryan Vaughan. Apparently overnight some hail producing thunderstorms had rolled through his area after his forecast mentioned that the trough responsible for the thunderstorms would remain south of the area. Unfortunately, his forecast was off by about 100 miles, which is really good when you consider he’s attempting to forecast something that is “invisible”. But as someone who takes pride in his work, he was a tad disappointed. Ryan’s tweet simply stated,

“God sure has sent me a couple of slices of humble pie lately when it comes to forecasting. As I’ve said, sometimes we forecast, he laughs.”

After seeing this tweet, and thinking about how humbling forecasting can be, I came across a thread on a message board that was discussing the recently released AccuWeather Seasonal Tornado Forecast. In the thread it was brought up just how bad AccuWeather’s 2011 – 2012 Seasonal Winter Weather forecast had been (image below).

This forecast was particularly atrocious when you consider the November 2011 – January 2012 average temperature and precipitation maps shown below. As you can see, the area on the AccuWeather forecast that was to experience cold and snow conditions has actually observed temperatures that are well above normal and precipitation amounts largely below what is expected. In fact, places such as Midland, TX have received just as much, or more, snow than places across the midwest. Northeast Arkansas had received more snow by November (~10 inches in places) than Chicago, IL and Buffalo, NY had received by mid-January.

But this discussion isn’t about AccuWeather per se. To prove my point, here are the forecasts from the NOAA/NWS Climate Prediction Center. They are slightly better than AccuWeather on the temperatures across the south, but still call for cooler weather (than normal) across the north. The precipitation forecasts are almost 180 degrees out of phase from what happened in reality.

Don’t get me wrong; seasonal forecasting is hard. There is a reason why the severe convective hazards research community has resisted making seasonal tornado outlooks for so long — there is just too much intra-seasonal variability! But what really bothers me is when people use a forecasting philosophy of persistance on the seasonal scale. The idea behind persistance is that the atmosphere is in a relatively stable state and what’s previously happened will continue to “persist”.

In the southern plains, the first half of February 2011 was a winter nightmare. Two major winter storms traversed the area in the span of two weeks, with a comparatively “minor” winter weather event in between. (Note, that this “minor” winter weather event would have been considered a fairly significant one during a “normal” winter — whatever that is!) In addition to heavy snow, these winter storms were also accompanied by bitter cold. In fact, during this approximately two week span, Oklahoma set it’s all-time record low temperature and Arkansas reported it’s greatest 24-hour snowfall accumulation in history.

What people failed to remember was that this occurred in the midst of an extremely warm and dry winter. As cold as it was in Oklahoma during the first half of February 2011, the second half was even warmer than the first half was cold! Remember the record low set? Less than a week later the temperature had warmed over 100 degrees Fahrenheit at the same location! In fact, Oklahoma finished the month either near “normal” or slightly above normal in terms of average temperature.

During the winter of 2011-2012 2010-2011, Earth was experiencing a La Nina pattern. Namely, the waters of the central Pacific were cooler than normal. This has profound impacts downstream (i.e., over the United States). Typically during a La Nina, the CONUS is drier and milder than average, but due to atmospheric processes I won’t discuss here, is subject to extreme cold outbreaks. If one of these extreme cold outbreaks encounters moisture, then the recipe for a winter storms is well on it’s way to completion — as was the case in early February 2011.

To illustrate just how dry the winter of 2010-2011 2011-2012 was for the southern United States, below are a series of images from the U.S. Drought Monitor. It shows that in October 2010 (before the 2010-2011 winter) that much of the southern plains was experiencing normal to slight drought conditions. Fast forward 4 months and that picture had changed (Second image: February 2011 — after the barrage of winter storms). Pretty much everywhere in the southern plains was experiencing a drought. A drought that would persist through the Summer, culminating in the worst category of drought by October 2011 (Third Image).

When seasonal forecasts called for a repeat performance of La Nina this winter (not that La Nina’s are “repeatable”), a lot of those in the weather business called for a repeat of last winter’s conditions, which to a 0th order is reasonable expectation. So imagine my surprise when people tended to remember and focus on the two week period of extreme cold and heavy snow, rather than the season-long drought and mild conditions. I nearly fell out of my chair when I heard a local television meteorologists talk about how since we had X last winter and we got a lot of cold and snow, and X is expected this winter it should be a cold and snowy winter. How quickly we tend to forget! Out perceptions of what happened is affected more by extreme, unusual, and disrupting events, rather than the long-term mundane average. So, how did the forecast for a cold, snowy winter pan out? Well as you can see above, it’s been wetter and warmer than normal. It’s been so wet in fact, we’ve made substantial progress in overcoming a large part of our drought (below). Although, we still have a long ways to go.

So, what’t the take away point? Seasonal forecasting is hard. At its current optimum, it is slightly better than an educated guess (although, some might argue that all forecasting is this way!). So, when you hear the prognosticators try to spin their poor seasonal forecasts, you should know better than to fall for it. And when they offer you their next “highly accurate” and “highly detailed” seasonal forecasts, you’ll know exactly what to do with it. Take it with a grain of salt.

Last July I wrote about software I was developing for displaying forecast soundings. Unfortunately, after discussing what I already had done in preparation for last year’s Hazardous Weather Testbed (HWT) Experimental Forecast Program (EFP), my schedule prevented me from devoting any time toward this project.

In the days before Christmas I realized that I needed to revisit SHARPpy (SkewT and Hodograph Analysis and Research Program in Python) if I was going to have anything for my presentation at the American Meteorological Society’s Annual Meeting in New Orleans, LA. So, the last two weeks has been devoted to frantic code writing to put together some form of SHARPpy in time for my presentation. When I sat down and looked at my old work, I couldn’t understand, nor could I remember, what I had been doing. I decided to throw out my old work and begin anew.

SHARPpy has been completely overhauled. The visual aesthetics are modeled after the Storm Prediction Center’s sounding analysis tool, NSHARP, and the underlying numerical routines are based on SHARP95. SHARPpy is written completely in pure Python — no Numpy, Scipy, or Matplotlib. In other words, once Python is installed on a computer, you can install and run SHARPpy — there are absolutely no additional dependencies to install! The motivation for sticking with pure Python, and sacrificing the speed Numpy, Scipy, and Matplotlib offer, was to allow for simple integration into the National Weather Service’s data visualization software package (Advanced Weather Information Processing System II — AWIPSII), which is currently under development. (Note, SHARPpy 2.0 will most likely be refactored to make use of Numpy, Scipy, and Matplotlib.)

SHARPpy is written in such a manner that the file handing and data management, graphical displays, and numerics are all separate. This greatly increases SHARPpy’s utility. Inside SHARPpy, all calculations are done on a custom data structure, called a Profile Object. The Profile Object consists of 6 data arrays: Pressure, Height, Temperature, Dewpoint, U-component of wind, and V-component of the wind, as well as some meta-data and helper functions to identify things such as the index of the surface layer. (Alternatively, one could provide the Wind Direction in degrees and Wind Speed and the Profile Object will convert these to the U-, V-components on the fly.) The benefit of using the Profile Object is that SHARPpy knows the structure of the data on which it will operate and/or draw. Thus, in order to add support for additional data types (observational, BUFKIT format, raw models, etc) all one has to do is create a wrapper to put the data into the Profile Object. (The Profile Object has helper functions to create itself. All one does is pass the 6 arrays!) Also, since the drawing is separate from the numerics, SHARPpy can be used to compute thermodynamic and kinematic parameters for model output — without having to actually draw individual soundings!

Below are a smattering of sample images created this evening.

The first image is tonight’s sounding from Miami, FL. The temperature trace is in red, the dewpoint trace is in green. The blue trace corresponds to the wet-bulb temperature. The yellow-traces (there are more than one, they just overlap!) are the parcel trajectories for a Surface-Based Parcel, 100-hPa Mixed Layer Parcel, and the Effective-Inflow-Layer Mixed Parcel. In the upper-right, the hodograph is displayed with white dots indicating each 1km AGL interval. (Note, the program goes out to the web and downloads the data, lifts all the parcels, and draws the display in about 1-1.5 seconds!)

In addition to computing the visual SkewT and Hodograph, SHARPpy can compute kinematic variables and parameters. Below are just a sample of the fields that can be computed. Wind information is displayed in a format of U-, V-component, Wind Direction @ Wind Speed. Helicity information is provided positive+negative helicity, positive helicity, and negative-helicity. Again, this takes less than 0.5 seconds to compute and display. (These are for the Miami, FL sounding displayed above.)

Below is a small sample of the thermodynamic variables and parameters that can be computed. All five parcels (Surface, Mixed-Layer, Most-Unstable, Forecast Surface, and Effective Inflow Layer) are computed. This routine takes about 0.5 seconds to run. (These are for the Miami, FL sounding displayed above.)

Lastly, I’ve incorporated preliminary support for ensemble soundings. Below are five, 4-km storm-scale ensemble member forecasts for Birmingham, Alabama. These model simulations were created in support of last year’s HWT EFP. They were initialized at 00 UTC 27 April 2011 and are valid for 21 UTC 27 April 2011. Each forecast member has over 1100 sounding locations, with 37 forecast soundings at each location. These data are stored in a text file that is approximately 150MB per member! SHARPpy can read these text files, parse out the correct soundings, compute all the parameters, and draw the sounding in less than 5 seconds!

What is displayed are the temperature, dewpoint, wet-bulb temperature, and hodograph for each of the 5 members. The thicker lines are from the “control member” and the other lines are from various perturbations. I should also point at that the wind barbs plotted on the right of the skewt are from the control member, as well.

I still have a lot of work left ahead of me (such as fixing up some of the displays and incorporating the text output on the main graphical display), but SHARPpy is coming along nicely. If you will be attending the AMS Annual Meeting later this month, please be sure to stop by my talk! It’s in the Python Symposium and will take place Tuesday morning at 11:15 AM. After my presentation, I hope to release SHARPpy to the open-source community. This will give people the ability to download and test SHARPpy while it is still under development, provide feedback, and even help develop new features! Some features that I’m interested in including are time-height cross-sections, more winter weather support, and whatever else might come to mind! It is my hope that SHARPpy can become a community supported sounding analysis package that the meteorological community can coalesce around!

And, for my international friends, if you aren’t fond of SkewTs, SHARPpy can also make STUVEs!

Please let me know what you think!