CycloneCenter Participation for August 2014

August saw a bounce back from the July slump. In August, we surpassed 350,000 classifications, which is a nice milestone to pass this summer.

Also in August, Read More…

Spawns of El Nino? Hurricanes Iselle and Julio Aim For Hawaii

Cyclone Center is tracking two storms as we classify this afternoon.

It has been quite a remarkable week in the eastern and central Pacific that has culminated in two hurricanes taking aim at the Hawaiian Islands today.  Hurricane Iselle has shown herself to be quite resilient as she has maintained her hurricane strength despite moving over cooler ocean waters.  Hurricane warnings are out for the big island as residents prepare for a significant event.  Meanwhile, Hurricane Julio is following close behind, continuing to intensify despite his movement over cooler waters.  The graphic below from the Central Pacific Hurricane Center shows the likelihood of significant winds over the next few days in the islands:

 

 

Probability of experiencing tropical storm force (>=34 kt) or greater winds from Iselle

Probability of experiencing tropical storm force (>=34 kt) or greater winds from Iselle

The black areas on the graph show where strong winds are certain.  The black area on the left is from Iselle, while the one to the right is from Hurricane Julio.  Unlike his big sister, Julio is not expected to track directly over the big island – but rather miss a bit to the north.

We’ve written about significant Hawaiian hurricanes before.  Most occur during El Nino years, which is a time when the ocean temperatures in the central and eastern Pacific are warmer than normal.  These warmer waters provide additional energy into the tropical cyclones, allowing them to maintain their strength for longer periods of time and to move across wider areas of the ocean.  Usually, ocean temperatures near Hawaii are near 24-25 degrees Celsius (C), which is barely warm enough to sustain a minimal tropical cyclone.  Current temperatures are between 26 and 27 degrees C, making for a much better experience for these tempests.

Head on over the Cyclone Center (cyclonecenter.org) today and join our “2005” campaign as we move towards classifying the intensity of all tropical cyclones that formed during that El Nino year.

Hurricane Iselle heads for Hawaii

Hurricane Iselle heads for Hawaii

- Chris Hennon is part of the Cyclone Center Science Team and Associate Professor of Atmospheric Sciences at the University of North Carolina at Asheville

CycloneCenter Participation for July 2014

Well, June was a tough month to follow. While July saw a drop in numbers, we did have a lot of new names in the top 10.

In July, Read More…

CycloneCenter Participation for June 2014

June kicked off the North Atlantic and Eastern Pacific Hurricane season. Your participation really showed. This was the most active month since December 2012.

Also, we’re developing a couple of presentations for the American Meteorological Society’s Annual meeting in Phoenix, Arizona. The meeting isn’t until January, but the planning and preparation begins now, so please perform classifications to help make the presentations a success.

In June, Read More…

Kulab says “Please Classify Me!”

Not much has been happening in recent weeks in the tropics (with the notable exception of the extreme western Pacific), so allow me to try generate some fake excitement by highlighting one of our four featured storms – KULAB.  Why Kulab?  Well, I don’t know.  It didn’t do anything extraordinary, unless you were a fish or an unfortunate fisherman.  But it was there.  And even though Kulab wasn’t a typhoon, didn’t make landfall, and has essentially been forgotten in the annals of history, we here at Cyclone Center want to see it analyzed because that is what we do.  We do not discriminate on the basis of color, size, location, or weirdness of name.  Every tropical cyclone has something to contribute, so we’re going to push for the completion of Kulab, because quite frankly nothing else is going on right now!

So usually when we have a featured storm, we can tell you a little bit about its name and history.  I have no idea what a Kulab is.  A quick check of Wikipedia turns up several leads.  Apparently Kulab is a village in west central Iran with a population of 61 (!).  I wonder if any of those villagers are classifying on Cyclone Center – can you imagine them logging on and seeing their village on the front page?  What’s next, Typhoon Tehran?  But wait, there is yet another Iranian village of Kulab in the eastern part of the country – this one with a population of 92!  Google also tells me that Kulab is the “new West Flemish campus of the University of Leuven, created from the integration of the academic programs of VIVES North (formerly KHBO) and KU Leuven“.  That would be in Belgium.  I’m pretty sure we have some Belgian classifiers – maybe someone from Kulab?

I am sure that has gotten you very excited to classify Kulab.  But just in case you still need some convincing, I give you kulab.org, the “Research Extension Page of Dr.K.Ulaganathan’s laboratory, Centre for Plant Molecular Biology,  Osmania University, Hyderabad, Andhra Pradesh, INDIA.  Basically, they really like plants there and study them a lot.  I’m certain that if you visited, they would have posters of Tropical Cyclone Kulab up all over the place.

So let me conclude by making this plea.  Sometimes in life you have to trudge through the mundane (Kulab) to get to the good stuff (about any other storm).  Storms have feelings too.  Kulab has been sitting out there for a few weeks now wondering if anyone will click on him (her?).  Sure, he gets a couple here and there, but then you leave him for the more exciting times at Cafe Sonca and Nesat.  So lets show Kulab some love and get him some clicks so we can respectfully retire him.  Are you with me?

Please classify me!  I know I'm not as cute as the other ones, but I have some interesting features too!

Please classify me! I know I’m not as cute as the other ones, but I have some interesting features too!

- Chris Hennon is part of the Cyclone Center Science Team and Associate Professor of Atmospheric Sciences at the University of North Carolina at Asheville

A Tropical Cyclone Nursery

The next four storms on CycloneCenter are new ones from the Western Pacific basin. They represent four storms that each start in a small region of the Pacific Ocean, but follow very different paths.

Ever wondered what happened to the baby that was shared time with you in the hospital nursery when you were born? Born in the same hospital on the same day, you have likely taken very different paths (unless you’re a twin).

CycCen-WP-StartingPoint

Chalk it up to chaos (remember this wacky definition of it?) or something else, but it is interesting that — like babies in a hospital — tropical cyclones with similar origins take different paths as well. These storms — Kulap, Roke, Sonca and Nesat — formed in roughly the same location of the western Pacific Ocean in 2005 however they took very different paths.

Help us better understand their lifetime by classifying the Four Storms.

http://www.cyclonecenter.org/

Also thanks for your help on the fours storms from 2004. They were a great success and the initial results look very good.

Early results for Charley and Frances

What a week we had! We had envisioned many classifications, but received so many more! So far we have received more than 11,000 classifications from nearly 2000 users in June. These storms had never been analyzed on CycloneCenter and Hurricane Charley was completed on the first day! Hurricane Frances is nearly complete now. We will likely have more completely new storms this month.

Learning algorithms

There are numerous crowdsourced science projects out there and each have the same goal: To better understand an issue (hurricanes, bats, animal populations, etc.) based on input from numerous clicks and selections from citizen scientists. In addition to the Zooniverse, there are other crowdsourced projects. The concept of learning from a crowd is not new. There are many mathematical and statistical papers available that provide a means to accurately learn the best possible answer based on everyone’s input.

In our analysis, we have used an approach to estimate a probability of a selection based on the selections from individuals, given what those individuals tend to select. It is a pretty complex algorithm that took me a while to understand, so I won’t belabor the point, but provide some links to the papers below. The method described by Raykar et al. is an Expectation Maximization (E-M) algorithm.

Our initial analysis is looking at what type of storm is the cyclone based on the broad categories available: No storm, Curved band, Embedded Center, Eye, Shear or Post tropical. Later, we plan to use this information to estimate of the storm’s intensity.

Hurricane Charley

Hurricane Charley was relatively short-lived: only 6 days so only about 48 images. This means it was completed relatively quickly, contrast that with Frances which has nearly 150 images.

The following graphically denotes the basic selections for Hurricane Charley. The selections (or votes) by citizen scientists are denoted in the lower graph. Each column  is the selections for a given image of a storm. The percentages show what fraction of the citizen scientists selected for an image. The upper graph denotes the probability of the image type based on the selections and the tendencies of the citizen scientists. These are most often 100% of one type, but can sometimes be a “toss-up” (i.e., no clear winner such as the case in the first two images of Charley).

Early results - Charley

Early results of citizen scientist votes and the combined storm type aggregation from a learning algorithm for Hurricane Charley (2004).

Also, there is quite a bit of variance in the selections and no clear time period when the storm had an eye. This is partly an artifact of the satellite imagery. Each pixel is about 8km while operational data available to forecasters can be as high as 1 km for each pixel. Such resolution helps identify small eyes.

Hurricane Frances

Even while Hurricane Frances is available for classifying, the early results are very good. They show a bit more consistency in the selections. Since it isn’t done yet, there are some images with less than 10 classifications, but it looks consistent so far.

Same as above, except for Hurricane Frances (2004).

Same as above, except for Hurricane Frances (2004).

The graph shows large agreement in storm type at various stages of hurricane development. The storm rapidly developed an eye by about day 3. It maintained an eye more most of the time between day 4-9. Then the primary type became embedded center with some selections of other types (e.g., shear). By day 12, the storm had begun to dissipate and was largely being classified as post-tropical or No storm.

Summary

Most of the users this month are new so these results certainly aren’t final. The learning algorithm needs lots more samples from all the new classifiers to more accurately understands their tendencies. As time goes on and those who were active on these storms classify other storms, the E-M algorithm will refine this storm.

Nonetheless, the results are very encouraging. In fact, we’ve made more than 180 of these plots for all storms that are complete (or nearly complete). The next step will be to further analyze the results and see how best to estimate storm intensity from these classifications.

Bibliography

The following papers were crucial in our initial analysis of the CycloneCenter data.

Learning from crowds 2010: VC Raykar, S Yu, LH Zhao, GH Valadez, C Florin, L Bogoni, L Moy, The Journal of Machine Learning Research 11, 1297-1322

This article is the basis for our current algorithm.  At first I used the binary approach to determine which images had eyes. Then I applied the multi-class approach (section 3) for all storm types.

Supervised learning from multiple experts: whom to trust when everyone lies a bit, 2009:VC Raykar, S Yu, LH Zhao, A Jerebko, C Florin, GH Valadez, L Bogoni, L Moy, Proceedings of the 26th Annual international conference on machine learning
This is basically the same method but with a bit more explanation for some aspects of the algorithm. Also, it has a great title.

A Quiet Hurricane Season in the Atlantic?

The official start of the hurricane season in the North Atlantic was June 1 and most experts are predicting a relatively quiet season, pointing to relatively cool water temperatures in place and a developing El Nino in the Pacific.   El Nino can be thought of as a substantial warming of ocean water in the central and/or eastern Pacific which in turn alters global weather patterns.  Atlantic hurricanes typically encounter more hostile atmospheric conditions during El Nino events, limiting their potential to develop and strengthen.  Most of the inactive seasons in the Atlantic over the past 20 years have occurred during El Nino events.

Assuming that the seasonal forecasts are correct (which can be a leap of faith sometimes, especially since El Ninos can be difficult to forecast), an inactive season does not mean that the U.S. is significantly less vulnerable to a major hurricane landfall. The last major (Category 3 or higher on the Saffir-Simpson scale) hurricane to make a direct landfall in the U.S. was Hurricane Wilma nearly 9 years ago (Hurricane Sandy was not a major hurricane at landfall);   this in an era of relatively high activity.  Conversely, Hurricane Andrew (1992) was a Category 5 hurricane that devastated south Florida; there were only 6 tropical storms or hurricanes that year!  Much has been written on this major hurricane landfall drought – most attribute it to luck.

Hurricane Ivan (2004)

Hurricane Ivan (2004) is one of the four storms being featured on Cyclone Center this month

To mark the beginning of the Atlantic season, this month Cyclone Center is going back 10 years to a time when major hurricanes seemed to come at the U.S. coastline every week.  We will begin the month by featuring four storms that caused substantial problems for the state of Florida:  Charley, Frances, Ivan, and Jeanne.  34 people lost their lives and almost $19 billion in damages was attributed to these storms.    As the month progresses and our classifiers do their thing, we will cycle in other major hurricanes for classification.

Remember, your classifications will help us to improve our estimates of the strength of these storms.  This in turn will help scientists to understand how tropical cyclones have been changing over the last three decades.  Head on over to Cyclone Center and work on these major 2004 storms today.

- Chris Hennon is part of the Cyclone Center Science Team and Associate Professor of Atmospheric Sciences at the University of North Carolina at Asheville

Eyes on Cyclone Center

With 15,000+ citizen scientists contributing to CycloneCenter.org, we have more than thirty thousand eyes searching through satellite data.

So far, everyone has provided input on almost 50,000 images. As we begin to sift through all the responses, one task is to determine the storm type (eye, shear, embedded center or curved band) of each image from all the responses.

The eye images seem to make up about 8% of our images so far. The image below is a collection of some of the images identified as eye scenes by the citizen scientists. This is only a small portion of what we have, but it shows great progress.

Images identified as Eye scenes by citizen scientists.

Images identified as Eye scenes by citizen scientists.

This contains only 391 of the ~4500 eye images identified. So, 30,000 human eyes have found 4500 storm eyes.

CycloneCenter Participation for April 2014

This month, we are naming the monthly award for the top classifier as the “Baja award” in honor of our consistent participant baha23 and retiring the name in recognition of the consistent contribution to the project from the monthly stat board (so that others can also be recognized for their effort). Likewise, the overall project activity list is now the “Bretarn board” in honor of our longtime contributor, bretarn (who also was our 300,000th classification).

Question of the Month: What is the best time to classify storms? Do you classify during your downtime? Answer in the comments below.

For March 2014, we had 7,964 classifications of 364 storms from 254 citizen scientists.

Top 10 most active citizen scientists for April 2014.

Classifications Scientist
3386 baha23
611 peterthorne
576 bretarn
483 Atms345_kdg
464 skl6284
228 tdw1203
180 TheNerd
141 shocko61
131 WIDEnet
79 andrewlania

Baja Award: Most active citizen scientists each month.

Month Number User
Sep 2012 658 atomic7732
Oct 2012 3667 chrisotahal
Nov 2012 3276 bretarn
Dec 2012 2747 bretarn
Jan 2013 2555 shocko61
Feb 2013 1714 shocko61
Mar 2013 1998 bretarn
Apr 2013 1474 ATMS103LGB
May 2013 1451 astroboyOW
Jun 2013 1084 bretarn
Jul 2013 976 Geeklette
Aug 2013 1051 skl6284
Sep 2013 431 tdw1203
Oct 2013 2733 baha23
Nov 2013 3737 baha23
Dec 2013 500 Atms345_ssc
Jan 2014 3064 baha23
Feb 2014 3455 baha23
Mar 2014 4610 baha23
Apr 2014 611 peterthorne

The Bretarn Board: Most active citizen scientists overall.

Classifications Scientist
22,582 bretarn
21,907 baha23
10,690 shocko61
5006 astroboyOW
4544 peterthorne
3988 chrisotahal
3471 cch001
3172 tpatch
3030 skl6284
2711 velthove

Postscript

Did you notice that peterthorne moved up a spot on the all time list. It turns out that work travel is a great time to classify storms. So why not use Airport wifi to classify storms while waiting in the airport??

Also, what do you think of the Baja award?

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