text:
Blooming Groves Nursery
Russ Esposito, Owner
Donna Santoro, General Manager
Blooming Groves Nursery is a family owned and operated nursery established in 1991. We carry herbs all year with a larger selection during the winter months. From fall until spring, we carry vegetable plants and greens - some heirloom varieties. From spring until fall, we carry perennials including butterfly plants. We offer a large a large selection of succulents and cactus all year - including some rare and unusual plants.
We take pride in offering quality products at reasonable prices and all locally grown.
North Port, Florida


text:
27 October 2012
Recovery Run
Posted by Brad Hefta-Gaub under: Run.
I'm pretty skeptical of the HR readings from my HRM. My last couple runs have had a much higher than normal average HR reading, even though my Perceived Level of Exertion was low.
This was a casual/slowish run from dropping off a car we'd borrowed from a friend while our car is in the shop. Sunday, I will run a long run that ends at the car rental shop to pick up a rental for the next couple days.
- Type: Run
- Date: 10/27/2012
- Time: 15:22:00
- Total Time: 1:04:23.00
- Average Heart rate: 145
- Max Heart rate: 195
- Calories: 723
- Distance: 7.31 miles
- Average Pace: 8:48.63/mile
- Max Pace: 8:32.82/mile
- Ascent: 420 ft
- Descent: 575 ft
One Comment so far...
All Dietary Fat is Bad for You, Or is it? | What Is Omega 3 Says:
[...] the good fats. Start now on your way to a healthier lifestyle by adding omega 3 to your daily diet.When I was a child, my father was diagnosed as having extremely high triglyceride levels in his bloo…by us 4 children. Back then, omega 3 benefits from eating fish or omega 3 rich foods weren't yet [...]

subset: commoncrawl


text:
Free public domain sheet music from IMSLP / Petrucci Music Library
The list below includes all pages in the category "Romances". This includes vocal or instrumental ballades designated by their composers as a 'Romance' in a title or subtitle. See also Songs, Ballades.
Pages in category "Romances"
The following 200 pages are in this category, out of 1,094 total.
(previous 200) (next 200)
(previous 200) (next 200)


text:
Now more "fake pot" is illegal…
The Drug Enforcement Administration has made more "fake pot" (synthetic cannabinoids) illegal. That just happened today.
For the next two years, "UR-144," "XLR11″ and "AKB48″ are officially Schedule I illegal drugs under the Controlled Substances Act (CSA).
DEA officials report that these "fake pot" products "are falsely marketed and sold as 'herbal incense' or 'potpourri' products on the Internet and by a variety of retail stores."
The synthetic drugs have a similar effect on the brain as the main "psychoactive" chemical in marijuana. Products containing them have been marketed as being legal and are popular among teens and young adults, according to the DEA.
"The long-term physical and psychological effects of these substances and their associated products are unknown but are potentially severe, and psychotic and violent behavior has been observed in short-term users of these products," DEA officials said in a statement.
The DEA will be working with the U.S. Department of Health and Human Services to determine if the synthetic chemicals should be made permanently illegal, the DEA said.
Here's a copy of the final order.


text:
Woman accused of Facebook framingMarch 25,2013BENNINGTON Police in Bennington have accused a woman of creating a Facebook account in another womans name to frame her for violating a restraining order.
The Bennington Banner reported that Lindsey Baker of Bennington pleaded not guilty in court last week to identity theft, aggravated stalking, and other charges. Shes accused of creating the Facebook account in the name of Alyssa Parks, the girlfriend of Bakers former husband.
The 22-year-old Baker allegedly sent messages to her own Facebook account from the false Parks account and used them to get a restraining order against Parks. Parks has her own restraining order against Baker.
Parks told police she had deleted her Facebook account prior to the time Baker said the messages were sent.MORE IN This Just InBENNINGTON A Sandgate man with a previous conviction for a sex crime against a child in New... Full StoryThousands of Vermonters will have the chance to get their drivers licenses back starting Thursday. Full StoryA Castleton man, who police said has had several incidents at Fowler Services, denied striking... Full Story
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text:
On Feb 28, 4:26 am, "rajeshkank...@gmail.com"
> Hi all
> There are two programing platform java and .net which one is better
> for dicom.and why?
Neither one is better than the other. I work with DICOM in C++, Java
and C#. Each has there own advantages. Probably the only downside
to .NET languanges is that they only run on Microsoft Operating
Systems. Java and C++ can be used on Windows, Unix, and Mac OS X.


text:
In article <email@example.com>,
email@example.com says...
> I wasn't aware that the two versions *could* be swapped - Palm OS 5 uses ARM
> processor, and earlier versions use Motorola Dragonball CPU. You are
> correct, current PalmOne site says Palm OS 5 for that model. Is it a very
> early device?
Can't be that early - the Zire 31 is only, what?, 6 months old. Even the
original Zire didn't use PalmOS 3.
Remove removes from address to remove anti-spam measures.
Alan Gauton E-Mail agauton @ postmaster.co.uk
Never for me the lowered banner, never the last endeavour!
(Damon Hill - 16th June 1999)


text:
Kemp Post's Mike Ragozino dives into third base as Wanderers' Bill Rinker waits for a throw from his catcher.
Recognizing Mike as he makes his play during the Northampton County American Legion League baseball game in East Stroudsburg on June 2, 1987, was his brother Joe Ragozino. Kemp Post went on to win, 10-1.
Location, ST | website.com


text:
Bartleby the Scrivener
by Herman Melville
Bartleby the Scrivener Rules and Order Quotes
How we cite our quotes: Citations follow this format: (Paragraph)
Quote #1
All who know me consider me an eminently safe man. The late John Jacob Astor, a personage little given to poetic enthusiasm, had no hesitation in pronouncing my first grand point to be prudence, my next, method. (2)
This diagnosis is pretty clear – the Narrator is no wild rule-breaker. In fact, he's known for his steadfast adherence to rules and order.
Quote #2
After a few words touching [Bartleby's] qualifications, I engaged him, glad to have among my corps of copyists a man of so singularly sedate an aspect, which I thought might operate beneficially upon the flighty temper of Turkey and the fiery one of Nippers. (15)
The Narrator, who places great value upon order and respectability in the office, is initially invested in Bartleby's potential to instill order, rather than contribute to its breakdown.
Quote #3
Now, what was ginger? A hot, spicy thing. Was Bartleby hot and spicy? Not at all. Ginger, then had no effect upon Bartleby. Probably he preferred it should have none. (34)
Wow – even natural rules have no effect upon Bartleby. Somehow, the strength of his "preferences" manage to prevent even a physical sensation – the spiciness of ginger – from affecting him at all.


text:
PARKERSBURG - Wood County commissioners agreed to help fund Safetytown at Fairplains Elementary School, Williamstown business students attending a national competition and make a decision Monday on requested funding for a local golf tournament.
Students with Future Business Leaders of America told county commissioners 10 students from Williamstown and one from Parkersburg South High School won at the state level and can compete nationally in FBLA business leadership competition. The cost to go to the national competition, which is in Anneheim, Calif., is $11,000. The students said they have raised $5,600 so far, are planning more fundraisers and have contacted the school's Partner in Education to ask for help.
Evan Fowler, Alec McIntosh and Andrew Bond, accompanied by FBLA club adviser Williamstown teacher Jill Bryant, asked commissioners Monday for any help they could provide. The commissioners agreed to donate $1,000 to the students to help pay their expenses.
Article Photos
Photo by Pamela Brust
MaryAnn Osborne, director with Parkersburg Safetytown, along with numerous members of the North Parkersburg Kiwanis Club, which sponsors Safetytown, asked Wood County commissioners for help funding the event.
"I bet there would be some local businesses that would be willing to help, and you might also contact the chamber of commerce," said Commissioner Blair Couch.
Organizers of the Mid-Ohio Valley Golf Classic also requested funding. Proceeds from the tourney are distributed equally between Parkersburg High School's stadium and Erickson All Sports.
"Last year we tripled the number of golfers we had. We did have about 26 individuals from out of town and we feel we would qualify for Innovative Grant funding," said Steve Hussey. The innovative grant funding is made up of a portion of the county's hotel/motel tax revenue, to qualify for consideration, entities have to demonstrate/document overnight stays created by an event.
The golf tournament is scheduled for May 18-19 at Woodbridge. The officials asked the commission to consider contributing $5,000.
"This is our third year, every year we've grown. We've teamed up with the West Virginia Golf Association, and I think that's the draw," Hussey said.
Commission President Wayne Dunn said he wanted time to consider the request.
Couch told tournament officials funding for the innovative grant funding had already been set for the year, but there was the possibility of taking funds from video lottery proceeds. The commissioners discussed giving the tournament $1,000 from lottery proceeds, and said they would finalize a decision Monday.
Also Monday, MaryAnn Osborne and several members of the North Parkersburg Kiwanis Club asked commissioners for $2,000 to help fund Safetytown at Fairplains Elementary School, scheduled for the last two weeks in June. Last year the commission allocated $1,500.
This will be the 29th year for the event. Safetytown is for children who will be entering first grade in the fall. The program served 100 children last year teaching all aspects of safety. Osborne said there is capacity for 200. The program is free for the children.
Osborne said transportation for the children has been an ongoing issue and this year the program is partnering with Easy Rider Bus System.
"The children just need to go to the bus garage and sign up and they can be bused to the program. It's free for them," Osborne said.
The commissioners voted unanimously to donate $1,500 for the event this year. Dunn is a member of the North Parkersburg Kiwanis Club.


text:
Not sure if anyone has had this issue - a bug I've come across on a multi-site instance of WordPress 3.4.1 using Events Manager 5.2.3...
Whenever I save Settings for Events Manager, my selected "Events page" (Settings > Pages > Event List/Archives) throws a 404.
I tried resetting permalinks, changing the selected page and the template, and also enabling the default Twenty Eleven theme to ensure this wasn't a theme issue, but I was still getting a 404.
I temporarily resolved the issue by changing the slug of the desired page, but every time I saved settings in Events Manager I got the same issue, and would have to change the slug again - however, I noticed that the page was also a child page (not top level parent) - I set up a test with a top level parent page and that seemed to work fine. Is this is an issue with setting the "Events page" to a child page, perhaps?
Thought this might help out anyone else who is having similar issues, and give the plugin devs a heads-up.


text:
(This is how The NY Times covered the above Fortune Article)
Bill Gates as Anthropologist
By PAUL B. BROWN
Published: June 25, 2005
MARGARET MEAD. Louis Leakey. Bill Gates?
Grouping the founder of Microsoft
arketwatch.com/custom/nyt-com/html-companyprofile.asp&symb=MSFT> among
great anthropologists is not as strange as it first sounds, according to the
current issue of Fortune Small Business.
In an effort to grow ever closer to its customers, Microsoft has hired
numerous social scientists, including anthropologists, to help it understand
the natives, who in this case are the small-business owners who use its
The research is part of Microsoft's effort to produce its Office Small
Business Accounting software, although this approach to understanding
customers would seem applicable not only for other Microsoft products, but
also for just about every other company that sells a product.
Microsoft's idea behind going into the field to study customers is simple,
as Richard McGill Murphy writes in the article "Getting to Know You": the
better you understand how your customers work, the easier it becomes to
design products and services to meet their needs.
And the more you understand where the market is heading, the easier it is to
get there first.
Once upon a time, all Microsoft would have needed to do to understand the
small-business market would be to look inside its own offices in Redmond,
Wash. No more. Microsoft is now a company with $37 billion in annual sales
and 57,000 employees, and so the need to hire anthropologists.
Maybe Harrison Ford can play Mr. Gates in the movie.
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User Reviews & Ratings - Unguentine Plus topical
Read user comments about the side effects, benefits, and effectiveness of Unguentine Plus topical.
Overall User Ratings3 Total User Reviews
User Reviews
Learn about
User Reviews
Reviewer: Big Ernm8c2rn, 75 or over Male on Treatment for less than 1 month (Patient)
Ease of Use
I suffered a serious scalding burn on my foot after spilling boiling hot coffee on it. The burn was magnified by wool sports sock delaying the immediate removal of the heat. After cooling and cleaning affected area, Unguentine was applied, giving immediate pain relief and supplying antiseptic qualities. Used daily for 2 weeks it materially aided in burn healing.
found this review helpful.
Was this review helpful? Yes | No
Reviewer: Don T, 65-74 Male on Treatment for 10 years or more (Patient)
Ease of Use
This always was, and still is the best OTC burn medication ever produced.
found this review helpful.
Was this review helpful? Yes | No
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text:
With Instructables you can share what you make with the world, and tap into an ever-growing community of creative experts.
Bite-Sized Frozen Bananas made with cuteness and flavor!! I love chocolate covered frozen bananas but it's always an overwhelming amount of frozen banana to deal with, this is ...
You will need bananas! Chocolate. I used milk chocolate chips. Whipped cream out of the can. Fresh cherries! Sprinkles! Canola Oil. Paper Straws.
I peeled them, put them (one at a time), onto a paper plate and cut them. I wanted to make sure that they stood up on their own, ...
Have your chosen sticks or straws ready. I cut mine in half so that they were size-appropriate. I got my paper straws at Bed, Bath and Beyond. They ...
Okay, per every cup of chocolate chips, you need 1 Tbs of canola oil. Put your chocolate chips into the small pot, then your oil on top. Turn ...
Let your guests swarm your frozen mini treats and get all the flavor that they're looking for. For extra pizazz, you can spray some whipped cream on top ...
Banana Split Frittersby crapsoup
Frozen Bananasby zamrin
BANOFFEE BANANA SPLIT PUDDING IN CHOCOLATE BALLOON BOWLSby TreatFactory
Banana Ice Cream the Right Wayby PinchOfChili
Nerdy Bananasby lmnopeas
Join 2 million + to receive instant DIY inspiration in your inbox.
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© 2016 Autodesk, Inc.


text:
While the focus remained on the infamous postgame handshake Monday, the 49ers happily steered talk toward their continued transformation into an elite NFL football team.
Sure, the 49ers received ample airtime for the altercation between coach Jim Harbaugh and Detroit Lions counterpart Jim Schwartz, but there's a greater buzz being generated by the 49ers for their 5-1 start.
Veteran offensive tackle Joe Staley said Harbaugh has his players thinking anything is possible.
"The demeanor and attitude we have in this locker room is something I've never experienced in the NFL," Staley said. "I feel very, very confident, and everyone feels that way. We never feel like we're out of a game, no matter the deficit. ... He's been everything as advertised."
Harbaugh says he and the players feed off each other. That manifests itself in tough, physical play in games and lighthearted fun in the locker room.
"Now you see guys in here smiling, goofing around, basically not uptight," defensive tackle Ricky Jean Francois said. "They're loose. It's different from last year at 0-5, walking around with your head down and you're not really saying much.
"Now you're walking around and everybody's conversing. We're joking around with everybody ... It's just a better feeling."
Players now know what's expected of them, Staley said. Attention is paid to detail, practices are run with a purpose, meetings achieve the desired goals, and the team functions with a unified mindset.
It's enough to make Staley smile as he recounts how different things are under Harbaugh and his staff.
"We have a great buzz going on right now," Staley said.
Never mind all the attention being paid to the incident between Harbaugh and Schwartz, former coach Tony Dungy said.
The 49ers are a playoff-bound team and no longer a team everyone is waiting to come crashing down after a fast start.
Dungy singled out the 49ers as the biggest winner from Sunday's game. In doing so, he cited Harbaugh as the primary reason.
"(He's) the difference between last year's 49ers and this year's 49ers," Dungy said on NBC on Sunday night. "They're doing the same thing. They are running the ball, they're playing good defense, trying to protect Alex Smith. But they believe in this guy, this coach, Jim Harbaugh. He has changed the culture there."
The 49ers improved to 5-1 for the first time since 1998 by beating the Lions on Sunday.
That's where the focus should be, Harbaugh said. He wants people to shift the focus back on his players.
"I don't really want to detract any further from what our team did in that ballgame and the way this team is affecting each of the guys on this team," Harbaugh said, "because something special is happening."
Tight end Delanie Walker seems just fine with the 49ers being overlooked, though.
"It's a buzz," Walker said, "but we want to be slept on. We want to be known as sleepers who come in there and then bite. That's how we look at it. We don't want to be listed on top of the charts or anything like that. Just leave us where we're at and we'll keep doing what we're doing, making plays."


text:
Results 1 to 1 of 1
#1 Vince Flynn Lost His Battle With Cancer Last Night ...06-19-2013, 09:38 PM
The "Mitch Rapp" author of my favorite spy thrillers. Have read them all.
It made me cry even more, and ruined my eyemakeup as Mr. ABC and I were getting ready to go to a funeral today for one of his golf buddies, who also died of @#$%ing cancer.
Enough said ... RIP Vince! May God help those you left behind that loved you, to get through this dreadful time.
Forty-seven years old, is far too young to pass away!This above all: to thine own self be true, and it must follow, as the night the day, thou canst not then be false to any man.
~ William Shakespeare
|« Previous Thread | Next Thread »


text:
CHARLESTON -- The city of Charleston is looking for a new police chief.
Paul A. Johnson, director of the Charleston Department of Public Safety, turned in his written resignation Tuesday, according to City Manager Dave Brewer.
"It's effective Oct. 8," Brewer said. "We're just going to start the process of looking for a new chief."
Brewer said the process includes placing advertisements seeking applicants for the position in local newspapers and that they also plan to place an advertisement in the Missouri Municipal League's magazine.
As the next edition of the magazine won't be out until Sept. 17 or 18, Brewer said, he doesn't anticipate having the position filled before Johnson's last day as chief.
As for the department's leadership upon Johnson stepping down from the position, "we'll probably go with the chain of command that's there," Brewer said.
Capt. Bobby Hearnes is presently the highest ranking officer under Johnson.
Johnson became interim director for the department on June 13, 2002, following the departure of Richard Couch from the position. In August 2002, he was hired as the department's director.
A native of Scott County and a Scott County Central High School graduate, Johnson graduated from the Southeast Missouri State University Law Enforcement Academy in May 1993 and began his career in the Mississippi County Sheriff's Reserve.
In 1994, he was hired by the Scott County Sheriff's Department as a jailer-
dispatcher and worked his way up to be a road officer and investigator.
He joined the Charleston DPS as a detective in January of 2000.
Attempts to reach Johnson for comment on his resignation were unsuccessful as of presstime.
Brewer said Johnson did not give him a reason for the resignation.


text:
We are presently working on the SAP Banking suite , mainly on SAP Loans Management and SAP Collateral Management. We would now like to look into SAP Bank Analyzer. Can SAP Bank Analyzer be accessed in SAP ECC 6.0 ? If not, request you to clarify if this can be provided as an add-on or is it available in a different platform and if additional licences are required.
Moreover is there any training program and material on Bank Analyzer provided by SAP?
Shall appreciate a quick response.
Hi Subhobrata,
SAP Bank Analyzer has never been part of SAP ECC. It is shipped as part of Banking Services from SAP.
Banking Services is based on a different Netweaver stack (7.1X) than ECC (7.0X). Installation is only possible as separate instance and requires additional licensing.
An overview of trainings for analytical banking is available at: http://www1.sap.com/services/education/catalog/industry/curriculum.epx?context=%5b%5bBANK_ANAL%5d%5d%7c
Login to follow, like, comment, share and bookmark content.


text: Meta Battle Subway PokeBase - Pokemon Q&A Login Register All Activity Questions Unanswered Tags Users Ask a Question Rules Chat Which does more damage for crunch houndoom? 1 vote A houndoom with max 306 attack stat(no item) using crunch on lets say..slaking OR A houndoom with 240 attack stat WITH life orb using crunch on slaking? Im not too sure about the difference between move power and the attack stat damage houndoom crunch asked Feb 17, 2012 by HellDoom Your username is awesome commented Feb 20, 2012 by chandelure-gengar Please log in or register to add a comment. Please log in or register to answer this question. 1 Answer 0 votes Best answer It would have to be the same slaking due to damage calculation But the houndoom holding life orbs atk becomes 240x1.3=312 So life orb houndoom is the better one But the life orb boosts the move which in effect boosts its atk Because Mach punch is 40 power if you give it a choice band that boosts atk in effect you are boosting the move But if you give max atk houndoom life orb his atk becomes 306x1.3=397.8 which I think gets rounded up to 398 answered Feb 17, 2012 by Blobyolo edited Feb 20, 2012 by trachy Oh I get it thanks blob commented Feb 17, 2012 by HellDoom Please log in or register to add a comment. Related questions Bite, or Crunch? which move for blastiose? Why did the move reversal barely did any damage Curse or Crunch for Walrein? Crunch or a different move for my Durant? Crunch or Foul Play for Krookodile? Crunch or payback for tyranitar? Crunch or Ice Punch on Floatzel? Crunch vs sucker punch on mega-Khan? Useful Meta Posts How to change your username How to add a user avatar Why was my post hidden? The points system Question etiquette Flags vs Downvotes Anime FAQ


text:
Celebrate Worldwide Day of Play with Nickelodeon and NYRR!
On Worldwide Day of Play we want everyone to get out and get active. In New York City there are special events all week long that conclude with a huge celebration on September 21 in Brooklyn's Prospect Park. Click here for event details and free tickets. NYRR has a limited supply as well. Click here for availability.
Not in New York City? Host your own Worldwide Day of Play event!
NYRR's free games and activities can help you host a fun day that gets kids moving. Check out our event guides to plan a field day or fun run OR pick your favorite activities from NYRR's Running Start. We'll help you get started:
Other resources
2016 TCS New York City Marathon Event Gear is now online!


text:
Definition of stoolball in English:
Entry from British & World English dictionary
noun[mass noun]
(In the UK) a team game resembling cricket, with a board (originally a stool) as a wicket, played chiefly by women and girls.
Example sentences
- The earliest known reference to stoolball is in a 1330 poem by William Pagula, who recommended to priests that the game be forbidden within churchyards.
- All that we do know for a fact is that the terms baseball and stoolball were used interchangeably on many occasions.
- Originally, stoolball was a game played by milkmaids, as early as the 14th century.
For editors and proofreaders
Line breaks: stool|ball
What do you find interesting about this word or phrase?
Comments that don't adhere to our Community Guidelines may be moderated or removed.
= trending


text:
Sackings have followed Abramovich ever since he took over Chelsea. The axeing of incumbent manager Claudio Ranieri in 2004 was no big surprise - a billionaire oil baron was always going to want his own man in the top job at the club - but the disposal of the 'dead man walking' set a precedent of ruthless dismissals.
Ever since, the Russian has spared no expense getting some of the brightest managerial talent in world football to join his project in south-west London. He has had to spend a fair amount in pay-offs too.
Some of these men have gone on to shine in other jobs as their reputations have soared; others have found their careers nosediving after being chewed up and spat out by what appears to be one of the most politicised environments in sport.
- - - -
Jose Mourinho (June 2004-September 2007)
Before he joined Chelsea: The young manager who took Porto to domestic and European glory.
How did he do: Took a Chelsea side who had been improving for years and instantly elevated them to the next step as they won back-to-back titles. Also won two League Cups and an FA Cup, but always fell short in the Champions League.
Why he went: Left 'by mutual consent' after reported disagreements with Abramovich about the running of the club.
Since Chelsea: After nine months out of the game joined Inter Milan, who he guided to the Serie A title. A year later Mourinho's Inter won the treble of Scudetto, Coppa Italia and Champions League. He then moved to Real Madrid where he usurped Barcelona to win La Liga last season.
Record: Played 182 Won 123 Drawn 39 Lost 20 (a win ratio of 67.6 per cent).
Trophies: Premier League 2005, 2006; FA Cup 2007; League Cup 2005, 2007.
- - - -
Avram Grant (September 2007-May 2008)
Before he joined Chelsea: A total surprise choice to take over at Stamford Bridge, his only previous success had come in winning four Israeli league titles with Maccabi Tel Aviv and Maccabi Haifa.
How did he do: Slightly misunderstood his instructions to win as much silver as possible: Chelsea finished as silver medallists in the league, the Champions League and the League Cup.
Why he went: John Terry slipped, and missed, while taking the fifth penalty in the Champions League final shootout. Grant was fired three days later.
Since Chelsea: After 18 months or so out of the game joined Portsmouth, who were relegated but made the FA Cup final. He quit at the end of the season and joined West Ham a few weeks later, who he put on the path to relegation before getting fired. He quit Serbia's Partizan Belgrade in May.
Record: Played 54 Won 36 Drawn 13 Lost 5 (a win ratio of 66.7 per cent).
Trophies: None.
- - - -
Luiz Felipe Scolari (July 2008-February 2009)
Before he joined Chelsea: A World Cup winner with Brazil in 2002 who then took Portugal to the Euro 2004 final and 2006 World Cup semi-finals.
How did he do: Delivered what was at the time Chelsea's worst run of form in the Abramovich era as the relationship between the coach and his players disintegrated. Fired after just over half a season.
Since Chelsea: Returned to Brazil after a spell in Uzbekistan and was hired as manager of former club Palmeiras in 2010 years ago. However, Scolari was sacked in September of this year.
Record: Played 36 Won 20 Drawn 11 Lost 5 (a win ratio of 55.6 per cent).
Trophies: None.
- - - -
Guus Hiddink (February 2009-May 2009)
Before he joined Chelsea: A legendary boss in Dutch domestic football with six league titles and a European Cup title to his credit. Took both Netherlands and South Korea to the World Cup semi-finals, and led Russia to the Euro 2008 semi-finals.
How did he do: Took the job on a part-time basis while still Russia coach, and made a massive impact as the club lost just one more game during the season and lifted the FA Cup. As a rarity, he left Chelsea of his own accord.
Since Chelsea: Returned to the Russia job, but left after missing out on qualification for the 2010 World Cup. Became Turkey coach in 2010 but left after failing to qualify for Euro 2012. Signed an £8.3m-a-year deal to coach Russian side Anzhi Makhachkala in 2012.
Record: Played 22 Won 16 Drawn 5 Lost 1 (a win ratio of 72.7 per cent).
Trophies: FA Cup 2009.
- - - -
Carlo Ancelotti (June 2009-May 2011)
Before he joined Chelsea: Took AC Milan to the Serie A title and two Champions League victories.
How did he do: Won the double in his first season in the dugout at Stamford Bridge, but was fired a year later after a trophyless season despite being a firm favourite with the Blues players.
Since Chelsea: Took over as coach of big-spending, Qatari-backed Paris St Germain on a salary of £6m a year.
Record: Played 107 Won 67 Drawn 19 Lost 21 (a win ratio of 62.6 per cent).
Trophies: Premier League 2010; FA Cup 2010.
- - - -
Andre Villas-Boas (June 2011-March 2012)
Before he joined Chelsea: The young manager who took Porto to domestic and European glory. Like a certain Special One.
How did he do: Broke Scolari's record for producing Chelsea's worst run of form in the Abramovich era as the relationship between the coach and his players disintegrated. Fired after just over half a season. Stop us if you've heard that bit already as well.
Since Chelsea: Under pressure at Spurs following a difficult start to the season. Suffered a 5-2 loss at Arsenal at the weekend.
Record: Played 38 Won 18 Drawn 11 Lost 9 (a win ratio of 47.4 per cent).
Trophies: None.
- - - -
Roberto Di Matteo (March 2012-November 2012)
Before he joined Chelsea: Was assistant to AVB before taking the job initially on a caretaker basis. Previously had spells with MK Dons and West Brom on his CV, though hr was sacked by the Baggies after getting them promoted.
How did he do: Won both the Champions League and the FA Cup within three months of being in charge, but a mixed start to the new season, culminating with a 3-0 loss to Juventus, signalled the end of his reign.
Record: Played 42 Won 24 Drawn 9 Lost 9 (a win ratio of 57.1 per cent).
Trophies: FA Cup 2012; Champions League 2012.
- Soccer
- Sports & Recreation
- Champions League
- Abramovich


text:
Apple's announcement of the company's new iPhone 5S comes equipped with a fingerprint scanner has the potential to be a real game changer for personal device security – if it's done right. There are two factors that will determine the real success of this new feature, which has undeniable potential. First, reliability and second, security – though as a security researcher, I have to say it should really be security first.
There's a lot riding on the reliability factor. Will it work if I go for a swim and try to use my phone with raisin hands? What if it's cold outside and my fingers have shriveled a bit? Can I use my phone then? People aren't going to be happy if they're locked out of their phones because of environmental factors. As Apple well knows, if it's not both reliable and convenient, users will turn it off. Another question is about longevity. The Guardian posted a rumor this morning that there will be a 500-scan limit, which could be used up in six months. What will that do to my phone bill?
On the other side is security. Given Apple's history, we have to assume they've erred on the side of usability before considering security. Remember, this is the same company that just last year mistakenly posted all of our credentials in plaintext within our log files. And, hacking the iPhone 4 was child's play. It had a lot of issues with the security of its encryption key and could be easily cracked. But the iPhone 5 has a well-secured encryption key that is difficult, if not impossible, to crack. So maybe they have learned their security lesson?
What we need to know is how good a job did Apple actually do securing the biometric data. They say it's encrypted and not shared with other applications, but we'll have to wait and see how it works in practice. We also need to know if it's a single sign on approach. If a single fingerprint grants access to other services (particularly iCloud), that's a frightening prospect if Apple hasn't done a truly expert job at securing that local credential. Naturally, we'll continue to have more questions than answers until we can get our hands on some phones later this month to do some testing.
In reading the news before Apple's event, there was speculation about how secure a fingerprint really is. After all, we leave them quite literally everywhere and at a minimum, they're all over the phone. So how secure is it? If I lose my phone, could the person who picks it up use the fingerprints I've left behind to gain access? Again, this will depend on implementation, particularly on the quality of the sensor. A good fingerprint reader doesn't just look at the ridges – it looks at pore, temperature, pulse, and other factors. If it doesn't, it can be pretty easily cracked. As an example, a few years ago a company developed a mouse with an optical fingerprint scanner. If I breathed on the scanner to fog it up, it would recognize the fingerprint the previous user left behind and authenticate me. Scary to think that would work on a phone.
I also noticed somewhat tongue-in-cheek questions about whether a phone thief would need to take your finger too. While this does seem unlikely and creepy, it's not unheard of. Check out this (admittedly dated) article, in which the owner of a car protected by a fingerprint recognition system lost his finger as well as his car when thieves came calling.
Right now, all we have is the hype. We need the facts before we will truly know if the iPhone 5S biometric security system will be a game changer. I'll update this post with what I find.


text: Posted January 12, 2013 It is unfortunate that he is so high profile and a polarizing figure. More unfortunate is those that hate him just for being a Christian or popular or what ever reason morons on here have a stick up their **** for The Golden Calf of Bristol. No, he isn't the greatest passer out there but he ain't the worse either, and I've seen plenty of teams win with mediocre qbs. He is more of a leader than Cam ever thought about being, or will be. I hope the latter doesn't become true, of course. He won't be out of football, to the horror of all the haters, sorry. Keep dreamin. Now, bring on the haters. Lets see how much hate you got. 1 person likes this Share this post Link to post Share on other sites


text:
$9.00$6.75$6.75 / ea
Classic petals...delicate infusions of Apple Blossom kissed sweetly by Jasmine, floating upon the breath of truest Living Rose.
Strikingly romantic. As pretty as a rose. An intricately styled motif of In Love's beautiful smoke, earthen slate and white roses adorn this elegant Eau de Parfum bottle, perfect for a luxurious boudoir.
REVIEW SNAPSHOT®by PowerReviews
Reviewed by 1 customer
(3 of 3 customers found this review helpful)
Lovely Fresh Floral
By Sarah L.
from DeKalb, IL, USA
describe yourself Classic Style
- Clean & Fresh
- Great Smell
- Pricey / Poor Value
- Too Weak
Best Uses
Comments about Lollia In Love Little Luxe Eau de Parfum, Taupe, .13 oz:
I decided to try this fragrance even though I don't always have the best luck guessing what I'll like from the descriptions online. I was pleasantly surprised to find that the scent is really lovely, a delicate, fresh floral, classic, but not too sweet. Unfortunately, the scent seems to dissipate quite rapidly. I dislike heavy scents, but I do like to catch just a hint when I move, and I really can't smell this one at all after a couple of minutes. Considering how weak it is, it's overpriced.
- Fragrance Type:
- Floral
Bottom Line No, I would not recommend this to a friend
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text:
News 12 This Morning / Monday, June 18, 2012
AUGUSTA, Ga. -- A $3.4 million three-year grant from the U.S. Department of Health and Human Services helps provide Georgia Health Sciences with the tools to diagnose people with HIV.
During the month of June, the Ryan White Outreach Team is encouraging you to get tested and update your status.
It's a tiny packet that could hold big health results.
"We have a pretty big problem here, it's not something you see every day," said Thompson, the Community Outreach specialist with the Ryan White Team.
Thompson is with the Ryan White Outreach Team at GHSU. He says his clinic treats nearly 1,300 patients for HIV.
"The Augusta area tends to move between third and second in the state outside of Atlanta," he said.
Thompson himself was diagnosed in 1989 with the virus.
"When I was diagnosed in 1989, I thought I'd have five years, maybe 10," he said.
A lot has changed since then -- with rapid testing, a quick swab and a few minutes will give you a positive or negative result for HIV.
"Twenty minutes, a mouth swab and no needles. You know your result," Thompson said. "So they don't have to wait for weeks like they used to, to find out what's going on."
The fund from a federal grant also helps ensure patients get the treatment they need, even if they don't have insurance.
"The new medicine now have fewer side effects, fewer pills in fact, patients take one pill one time a day and they're done," Thompson said.
New treatments help people like Thompson lead normal lives. Thompson says not enough people always want to get tested.
"You shouldn't wait or get tested once a decade and just consider yourself OK," he added.
Thompson wants everyone to take the simple test at least once a year, and with most infections spreading through needles, razors or certain body fluids, no one is immune.
"It's something that pretty much all of us do, something that exposes us to the virus during the course of our life. That's why it's important everyone make sure," Thompson said.
Since early detection is key, he is hoping during this month's National Testing Day, more of you will take the test. National Testing day is on June 27. You can click here for times and locations.


text:
November 16, 2013, 9:45 am
The latest beers from Stone Brewing's Quingenti Millilitre Series have hit the metro. We will be talking about what they are and where you can find them on What's On Tap, live from the Keg...
Read More »
November 15, 2013, 10:45 am
Andrew and Ryan recap Iowa's Premiere Beer, Wine, and Food Expo, discuss the newest beers in the area and where you can find them, and talk about the crazy things you could get away with in...
June 14, 2013, 1:34 pm
This week's brew of the week comes to us from Tallgrass Brewing in Manhattan, KS. The VELVET ROOSTER is their Belgian style Tripel. Read more here ---> LINK
A little...
June 7, 2013, 11:31 am
Here's our brew of the week - a seasonal from Abita - Lemon Wheat.
Lemon Wheat
Lemon Wheat (May-September) is a crisp, refreshing beer made with lemon peel for a fresh citrus taste...
June 1, 2013, 11:18 am
Here's the link: http://www.greenflashbrew.com/our-beers.php
Imperial Red Rye
Bigger, bolder, and hoppier is what you get in Green Flash's Imperial Red Rye IPA. This is...
May 25, 2013, 11:11 am
Technically, not a specific beer...
The good news? It can be whatever you'd like!
May 17, 2013, 4:19 pm
What's better than one brew of the week? How about a dozen!
The Kegstand has a wide range of Sam Adams on tap right now - stop by and try one of them out!
May 10, 2013, 2:27 pm
This week's brew comes to us from the Windy City - courtesy of Goose Island, which is celebrating their 25th Anniversary this weekend.
A little video on our brew of the week:
May 3, 2013, 9:07 pm
This will make for fun coversation:
Coolest tap handles:
Here's some others:
Taps in...
May 3, 2013, 8:22 pm
Here's the Brew:


text:
Posted 5 years ago
(1 item)
I have these tiles on my kitchen walls. They are a french cat theme. Some have writing and others don't. The writing is whimsical as the tiles are labelled 'Chat Gall' (the tile is ainted to look like Chagall, the same for "Chat Plin" (Charlie Chaplin) etc.
Two plates which came with the kitchen (the old owners left them for us) are labeled De Simone. The tiles are De Simone as well. In all there are 43 of these painted tiles and 2 plates. Before I take them off the wall does anyone know if they are worth anything? Should I try to preserve them?
Many thanks for any information. There are also plain white matching tiles with a black line border. Not sure if I should preserve these either.
Any help would be greatly appreciated.


text:
Definition of pyrrolidine in English:
noun[mass noun] Chemistry
A pungent liquid made by the reduction of pyrrole.
- Chemical formula: C4H8NH.
Example sentences
- Mysliwy et al. reported the formation of NPYR from sodium nitrite and pyrrolidine in vivo in dog stomach.
- AAP motifs moderate the steric clash between the pyrrolidine ring and the O = C i - 4 carbonyl oxygen.
- These residues modulate the structural deformations caused by the pyrrolidine ring of Pro.
For editors and proofreaders
Line breaks: pyr¦roli|dine
Definition of pyrrolidine in:
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= trending


text:
A Proposed Methodology for Reconciling High-Resolution Numerical Modeling Guidance with Pattern Recognition to Predict Lake-Effect Snow
MICHAEL EVANS and RON MURPHY
NOAA/National Weather Service, Binghamton, NY
(Submitted 5 October 2007; in final form 11 March 2008)
Current methodologies for using numerical guidance to help with the forecasting of lake effect snow typically involve either 1) examination of numerical models with horizontal grid spacing small enough to explicitly forecast the structures associated with lake effect snow bands, or 2) examination of numerical models with relatively large horizontal grid spacing to forecast the meso-scale flow pattern, then application of experience or pattern recognition to predict the location, structure and intensity of the resultant snow bands. This paper demonstrates an application developed at the National Weather Service Forecast Office in Binghamton, New York, that aids forecasters with the pattern recognition aspect of lake effect snow forecasting. A forecasting process that combines pattern recognition with explicit output from a model with small horizontal grid spacing is demonstrated through examination of two case studies. Results from the case studies show that the pattern recognition application can be used to provide forecasters with useful information on the range of possible outcomes that could occur with a given meso-scale flow pattern. In addition, the case studies indicate that comparing forecasts from high-resolution models with output from the pattern recognition application can provide forecasters with increased confidence in the numerical guidance in some cases, and can help forecasters to improve on the numerical guidance in other cases.
1. Introduction
Lake-effect snow develops downwind of the Great Lakes in response to polar, continental or arctic air masses crossing the relatively warm waters of the lakes. A temperature difference (ΔT) of 13 ºC between the lake and 850 hPa has long been used as a minimum operational threshold for the development of lake-effect snow in the absence of synoptic-scale forcing (Rothrock 1960). Air masses associated with lake-effect snow are typically characterized by mid-tropospheric subsidence. As a result, the atmospheric wind and temperature profile associated with lake-effect snow outbreaks typically feature a surface-based mixed layer associated with strong vertical moisture and heat fluxes from the underlying lake surface, capped by a subsidence inversion. Favorable factors for intense, well-organized outbreaks of lake-effect snow include a deep surface-based mixed layer (Byrd et al. 1991), with small directional wind shear (Niziol et al. 1995). Orographic lift downstream of the lakes can also play a major role in intensifying lake-effect snows (Reinking et al. 1991). Finally, in some cases, snowfall associated with large-scale lifting can be enhanced when the large-scale lifting becomes superimposed on an environment characterized by cold air over a warm lake with a ΔT less than 13 ºC ("lake-enhanced" snowfall, Dockus 1985, Lackman 2001).
An idealized modeling study by Laird et al. (2003) indicated that large, single bands of lake-effect snow are favored downwind of elongated lakes in environments where the wind has a long over-water fetch, and the resultant over-water fetch to wind speed ratio is large. A more widespread coverage of narrow bands (multi-bands) is favored when the over-water fetch and resultant fetch to wind speed ratio decreases. This has also been shown observationally (Niziol et al. 1995). In central New York, operational experience indicates that boundary layer flows with a direction from 250º to 300º, roughly parallel to the long axis of Lake Ontario, are associated with long enough over-water fetch lengths to produce large, single bands of heavy snow over and downwind of the lake. Meanwhile, directions of greater than 300º typically reduce the over-water fetch sufficiently to produce widespread finer-scale multi-bands of lake-effect snow southeast or south of Lake Ontario.
Previous modeling studies have indicated that models with sufficiently small horizontal grid spacing can produce realistic simulations of large, single bands of lake-effect snow. For example, Ballentine et al. (1998) documented a simulation of a 15 km wide, intense west-east oriented single band of heavy snow over and downwind of Lake Ontario. The model used in the simulation had a 5 km horizontal grid spacing over eastern and central Lake Ontario, nested within a 15 km grid covering the entire area from Lake Huron through upstate New York. Precipitation distribution and amounts in the simulation matched well with observations during the first 12 hours of the simulation. Niziol (2003) documented a successful simulation of a band with similar dimensions east of Lake Erie, using an 8 km model. Smaller bands can also be simulated, however even smaller horizontal grid spacing is required. For example, Watson et al. (1998) successfully simulated smaller north-south oriented snow bands (widths approximately 5 km) south of the Finger Lakes in central New York, using a nested model with a 2.5 km inner nest centered over the Finger Lakes.
The current suite of National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Prediction (NCEP) operational forecast models are characterized by horizontal grid spacing near or slightly greater than what has been shown to produce realistic simulations of large lake-effect snow bands in the aforementioned studies, and are much too large to simulate smaller bands [the current horizontal grid spacing is 12 km for the North American Meso-scale (NAM) model (Rogers et al. 2001), and 13 km for the operational Rapid Update Cycle (RUC) model, (Benjamin et.al. 2004)]. Operational experience indicates that these models often produce realistic simulations of large single band snow events, although the intensity of the bands (based on quantitative precipitation forecasts (QPFs)) are typically under-forecast. Experience also shows that these models do not explicitly simulate the finer-scale reflectivity patterns that are typically observed in more widespread multi-band lake-effect snow events.
Given the resolution limitations of current operational models, forecasters making explicit forecasts of small-scale multi-bands of lake-effect snow are left with two options. The first option is to examine a local model with small enough horizontal grid spacing to explicitly simulate the smaller structures. One problem with this approach is that computing power typically available in an operational environment limits local models to grid spacing of 5 to 10 km, which represents a decrease in grid spacing compared to the NCEP operational models, but is still insufficient to fully simulate fine-scale bands. In addition, it has been shown that simply decreasing the horizontal grid spacing of a model does not automatically improve QPFs in a convective regime (Gallus 1999). The second approach is to use a model with larger horizontal grid spacing to forecast the meso-scale environment, then use experience and "rules of thumb" to deduce the structure, intensity and expected location of small-scale lake-effect snow bands. At our current stage, most forecasters combine pattern recognition from meso-scale patterns forecast by NCEP operational models with explicit output from models with larger horizontal grid spacing that may still not fully capture the smaller-scale bands. Forecasters typically finalize their forecasts by attempting to blend information from both approaches.
This paper consists of two case studies that will demonstrate how both approaches can be used, and how the results from both approaches can be blended to make forecasts for lake-effect snow. To demonstrate the utility of a model with small horizontal grid spacing, output will be shown from a 6-km implementation of the Weather Research and Forecasting (WRF) model (Zavisa, 2004). While this horizontal grid spacing is still not sufficient to explicitly simulate narrow multi-bands of lake-effect snow, it will be shown that this resolution is adequate to produce structures that are significantly more detailed than what is produced by the 12 km NAM. More details on the configuration of this model are given in section 2. Regarding the use of models with larger horizontal grid spacing and the subsequent application of pattern recognition, this paper will detail the use of an application developed at the National Weather Service Forecast Office (WFO) in Binghamton, NY (BGM) which aids forecasters by allowing them to compare output from real time NCEP models to data from a large database of historical lake-effect snow events. More details on this application are presented in section 2. Section 3 of this paper consists of the two case studies, and section 4 contains a summary and conclusion.
2. Methodology
Forecasts from models with small horizontal grid spacing shown in this paper are from a workstation version of the WRF model, using the Non-hydrostatic Mesoscale Model (NMM) dynamic core, run at the NWS forecast office in Buffalo, NY (BUF). The model was run with a 6 km horizontal grid spacing and 45 vertical levels over a domain covering the eastern and central Great Lakes area eastward through most of upstate New York (Fig. 1). The initial and boundary conditions for the model are provided by 12 km NAM grid tiles (the AWIPS 218 grid) at 6 hour temporal resolution. As such, an accurate depiction of air mass modification over the upwind western Great Lakes (i.e., Lakes Michigan and Superior) is dependent on the NAM forecast. (Sousounis and Mann (2000) have described the critical role that upstream lakes can play in the evolution of lake effect snow). The model is run with explicit convection, the new Ferrier microphysics scheme (Ferrier et. al. 2002), and the Mellor-Yamada-Janjic boundary layer scheme (Janjic 1996). Although 6 km is near the threshold of recommended use of explicit convection (i.e., Weisman et al. 1997), the shallow nature of the convective motions associated with lake-effect snow do not typically trigger deep convection in traditional convective parameterizations, thus explicit convection was used.
The pattern recognition application tool developed at BGM [available online at: http://www.lightecho.net/les/ ] works by comparing NAM model forecast sounding data to data in a historical database of NAM forecast sounding parameters. The database contains parameters from 6-hour NAM forecast soundings from over 300 cases that occurred from 2003-2007. Data were collected at times when lake-effect snow was occurring somewhere across central New York, or when the flow across the area had a trajectory that originated over the Great Lakes in combination with air temperatures that were low enough to potentially support lake-effect snow (based on a ΔT of at least 13 ºC.), yet no snow occurred (null events). At each time, BUFKIT visualization software (Mahoney and Niziol 1997; http://www.wbuf.noaa.gov/bufkit/bufkit.html ) was used to collect 6-hour forecast data at 3 points located downstream from Lake Ontario: Syracuse, NY (SYR), Ithaca, NY (ITH) and Binghamton, NY (BGM). Forecasters use the application by examining current model forecast output at one of these forecast points, then entering several, key corresponding forecast sounding parameters into a table. The application then performs a search of the historical database, and returns a list of the 5 historical events that are most similar to the current event, based on similarity of the sounding parameters. Note that the analogue cases are matched on a 6-h time resolution, therefore a historical match for a current case at 0600 UTC may or may not be the same match returned for the same case at 0000 UTC. Finally, the application provides forecasters access to web pages that contain information on these "most similar" events, including radar images, forecast soundings and text descriptions (including snowfall amounts). Forecasters can use this information to anticipate a range of possible evolutions for the current event. More details on the algorithm for determining the most similar, historical events are given in the appendix.
The key sounding parameters were chosen to best describe the attributes of a sounding that are most critical to the occurrence and evolution of lake effect snow, based on a review of previous research, plus local experience. The parameters are:
the boundary-layer mean wind speed and direction (where the boundary layer is defined as the
layer from the 3rd sigma layer of the model to the base of the subsidence inversion),
the 850 hPa temperature,
the ΔT between 850 hPa and Lake Ontario (based on lake temperature data from the Great Lakes Environmental Research Lab),
the height of the base of the subsidence inversion (an algorithm for determining the base of the inversion is given in the appendix),
the depth of the moist layer, as defined by all layers with a dew point depression of 3º C or less,
the maximum dew point depression below 900 hPa,
the depth of the dendritic snow growth layer, defined as any moist layers where temperatures are between -12º and -18º C,
the maximum upward vertical motion within the dendritic snow growth layer, and
the root mean square (RMS) of the wind in the boundary layer.
A parameter for event duration is also included, where events associated with boundary layer wind directional variation of 10º or less over a period of 12 hours or more are designated as "persistent". Finally, time of day is considered, allowing for the fact that afternoon and early evening lake-effect snow is often more cellular than during the late night or early morning, when more organized banded structures typically occur (Kristovich and Spinar 2004).
3. Case Studies
a. 16-17 January, 2007
Lake-effect snow showers developed downwind of Lake Ontario across central New York during the morning on 16 January 2007, as a cold northwest flow developed over the lake. Figs. 2-4 show output from the NCEP NAM model run at 12 UTC on the 16th. Cold northwest flow is evident over and downwind of the Great Lakes (Fig. 2), indicating a condition that would be favorable for the development of multi-band lake-effect snow across central New York. The flow was forecast to veer slightly through the period, but no pronounced wind shifts were indicated. Little organized upward vertical motion was indicated across central New York at 18 UTC (Fig. 3a), then a weak plume of upward vertical motion was forecast to develop downwind of Lake Ontario from 00 UTC through 06 UTC (Figs. 3b-c). The upward motion diminished by 12 UTC (Fig. 3d). In this case, the 12 km NAM only forecast a small area of very light precipitation between BGM and SYR during the period ending at 06 UTC (Fig. 4c), with no precipitation at any of the other time periods (Figs. 4a,b and d).
Forecast output from the 6 km workstation WRF run at BUF, is shown in Figs. 5a-d. Unlike the 12 km NAM, the 6 km WRF was able to explicitly forecast precipitation across much of central New York, at several different time periods between 12 UTC 16 January and 12 UTC 17 January . The implied precipitation was very light and disorganized through 00 UTC (Figs 5a, b), then organized into a distinct northwest-southeast oriented band just south of SYR by 06 UTC (Fig. 5c). The band shifted south toward BGM by 12 UTC (Fig. 5d). The total QPF from this model run through 12 UTC 17 January is shown in Fig. 6. Much of the area between SYR and BGM was forecast to receive some precipitation, with a maximum between 0.10 and 0.13 inches southwest of SYR.
Based on the model forecast data shown so far, a forecaster making a prediction during the morning on 16 January would expect some accumulating lake-effect snow between 12 UTC 16 January and 12 UTC 17 January,, across the area from SYR south to BGM. The fact that both the 12 km NAM and 6 km WRF indicated that the maxima of precipitation would occur just south of SYR would give the forecaster increased confidence of that location. However, the fact that the two models forecast significantly different amounts (a maximum of 0.03 inches for the 12 km NAM vs. a maximum of 0.13 inches for the 6 km WRF) would introduce uncertainty. At this point, it might be helpful to compare this event with some previous, similar events in order to 1) increase the forecaster's confidence on the expected location of heaviest snow, and 2) to give the forecaster some insight on how much snow similar events have historically produced.
Figs. 7a-d show forecast soundings from the NAM at SYR, valid on 16-17 January 2007 and displayed by BUFKIT visualization software. The display shows model forecast temperature, dew point and wind profiles on the right, and a map of the forecast area on the left, including a box outlining the most likely location for lake-effect snow based on the boundary layer mean wind direction and climatological studies done at BUF (Kolker 1978). The color of the box is dependent on the amount of wind shear in a user-defined boundary layer, with green indicating small shear, yellow indicating moderate shear, and red indicating large shear (Mahoney and Niziol 1997). Figs. 8a-d show output from the pattern recognition application, after forecast data from the 12 UTC NAM was entered, and the database search was executed. The left columns in each table contain sounding parameter values from each time for the current event, and the remaining columns contain data from the corresponding, historical, most similar events.
The data shown in Figs. 7 and 8 depict an event characterized by several hours of weakly sheared northwesterly flow. The base of the inversion was forecast around 800 hPa near the start of the event, lowering slightly by the end of the event. Moist layers were forecast to be quite shallow, and constrained to levels below the base of the subsidence inversion. Note that this event was characterized as "non persistent" at 18 UTC, and 12 UTC, but "persistent" at 00 UTC and 06 UTC. Recall that "persistent" events are defined as events with a boundary layer wind directional variation of 10º or less for a period of 12 hours or more. When an event is characterized as "persistent", the corresponding similar events are also more likely to be "persistent" and therefore are more likely to be associated with larger event total snowfalls.
Comparisons between 0.5º radar reflectivity imagery from the Binghamton WSR-88D Doppler radar (KBGM) at 18 UTC 16 January, and corresponding imagery from the top 3 "most similar" historical dates and times are shown in Fig. 9. The observed 0.5º reflectivity pattern at 18 UTC was characterized by a cellular snow shower pattern, with weak embedded banding across central New York (Fig. 9a). Radar imagery from the 3 most similar events (Figs. 9b-d) all indicated broken bands of light snow showers across the area from SYR south to BGM. Text summaries from these historical cases (available via the application, not shown), indicate that all 3 of these events produced only light snow showers between SYR and BGM, with no significant accumulations around the time that the imagery is shown.
Fig. 10a shows that the snowfall pattern at 00 UTC 17 January evolved into several, narrow northwest-southeast oriented snow bands between SYR and BGM. The most similar event was characterized by several northwest-southeast oriented bands across central NY and northeast Pa, with most of the coverage centered over the SYR area (Fig. 10b). The text summary for this historical event indicated that the snow bands in the SYR area persisted for nearly 48 hours, which resulted in heavy snow accumulations (greater than 10 inches). This result highlights a caveat when using this application for anticipating snow amounts. While both the current and historical event in this case were classified as "persistent", the current event was persistent for only about 12 hours, while the historical event was persistent for about 48 hours. As a result, the historical event may have been a good analogue for anticipating a "snap-shot" of the snow banding structure, but it was not a good analogue for anticipating event total snowfall. The 2nd event ( Fig. 10c) was characterized by weak, broad bands of light snow across central New York. Only light snow accumulations were occurring during the time of the image. The 3rd event was associated with several narrow, northwest to southeast oriented bands, mainly along and east of a line from SYR to BGM (Fig. 10d). The text discussion for this case indicated that these snow bands were associated with accumulations of 2 to 4 inches.
The snowfall pattern at 06 UTC 17 January was characterized by a single, narrow, northwest to southeast oriented band of snow near BGM, surrounded by weaker bands (Fig. 11a). The radar imagery from similar event # 1 indicated some narrow, broken bands in the SYR area (Fig. 11b). The text summary for this case indicated that the SYR area picked up from 2 to 5 inches of snow from this event, with less to the south. The second and 3rd events were associated with radar imagery that appeared to be quite similar to what was observed; namely several northwest to southeast oriented bands centered near BGM (Figs. 11c-d). The text summaries from both cases indicated that none of these bands produced more than 4 inches of snow in a 12 hour period. Fig. 12a shows that little if any snow was falling at 12 UTC 17 January. The radar imagery from similar event # 1 also indicated no snow (Fig. 12b). Events 2 and 3 were associated with weak multi-bands, producing little if any accumulation (Figs 12c-d).
In summary, the radar reflectivity patterns during this event transitioned from a cellular pattern of snow showers with weak embedded bands during the afternoon on 16 January, to weak-to-moderate, narrow northwest-southeast oriented bands from SYR south to near BGM during the evening through early morning hours on 17 January, to nothing by 12 UTC 17 January. The pattern recognition application shown in this paper returned events that would have given the forecaster strong indications for that type of evolution. Except for 1 event that persisted for nearly 48 hours, the historical event text summaries indicated that snowfall associated with the multi-bands were light (less than 5 inches in 12 hours). These types of accumulations would give a forecaster reasonable confidence in the liquid precipitation amounts of 0.01 to 0.13 inches forecast by the 6 km WRF, given that snow to liquid ratios for lake-effect snow events that are this cold are typically 20 to 1 or more (based on operational experience). The pattern recognition application in this case confirmed the high resolution numerical guidance that a small accumulation of snow could be expected between SYR and BGM through 12 UTC 17 January. Fig. 13 shows the observed snowfall from 12 UTC 16 January through 12 UTC 17 January. Snowfall occurred throughout the area between BGM and SYR, with amounts generally ranging between 1 and 3 inches, and isolated totals of 5 inches.
b. 17-18 March, 2007
Another, more significant lake-effect snow event developed southeast of Lake Ontario on 17 March, 2007. A cold northwest flow was indicated through the period, similar to the previous case (c.f., Figs. 2 and 14). Unlike the previous case, the northwest flow was co-located with a deep layer of near saturation in the lower and mid-troposphere, in the wake of a major east-coast cyclone moving northeast toward the Canadian Maritimes (not shown). A disorganized pattern of upward vertical motion east-southeast of Lake Ontario at 18 UTC was forecast to evolve into a broad northwest to southeast oriented band of ascent across central New York by 00 UTC 18 March (Figs. 15a-b). The band was forecast to persist through 06 UTC, then pull northward and weaken by 12 UTC (Figs. 15c-d). The resultant NAM QPF is shown in Figs. 16a-d. A broad area of light precipitation south of Lake Ontario was forecast through 00 UTC (Fig. 16a). The light precipitation expanded southeast toward northern Pennsylvania by 06 UTC, then diminished and pulled back to the north by 12 UTC (Fig 16b-c). The 24-hour QPF ending at 12 UTC 18 March indicated a large area of greater than 0.25 inches across central New York south of Lake Ontario, with a band of 0.10 inches extending south across portions of northeast Pennsylvania (Fig. 16d).
Figs. 17a-d show the same fields as Fig. 16, except that the data is from the 6 km WRF model. The same evolution indicated by the NAM was also indicated by the 6 km WRF. Specifically, a broad area of light precipitation south of Lake Ontario during the afternoon on 17 March (Fig. 17a) was forecast to evolve into an elongated band of precipitation extending southeast into northern Pennsylvania during the evening (Fig. 17b). The 24-hour QPF (Fig. 17d) indicated totals of greater than 0.5 inches across a broad area south of SYR, with a band of 0.1 to 0.25 inches extending southeast from near ITH west of BGM into northern Pennsylvania. Forecasters interpreting data from the NAM and 6 km WRF could conclude that a disorganized snow shower pattern during the afternoon on 17 March would evolve into a more organized pattern of significant northwest-southeast oriented snow bands during the evening hours. Several inches would be likely across the area just south of SYR, with a few inches likely in bands extending from ITH across the area west of BGM and into northern Pennsylvania. Lake-effect snow producing accumulations of several inches is a common occurrence in the SYR area, however lake-effect snow accumulations of more than 4 inches in 12 hours is rather uncommon across the southern tier of New York or northern Pennsylvania. With the NAM and the 6 km WRF indicating the development of significant snow bands across that area, forecasters were looking at the possibility of a relatively unusual event. In these situations, application of pattern recognition can be useful to confirm and refine the model forecasts, or to call the forecasts into question.
Figs. 18a-d show model forecast soundings at ITH from the 12 km NAM, valid at 18 UTC 17 March, 00 UTC 18 March, 06 UTC 18 March, and 12 UTC 18 March. As in the first case, a persistent northwest flow was indicated through the period. In contrast to the first case, the soundings indicated a persistent, deep layer of near saturation in the lower to mid troposphere. A weak inversion, located at about 850 hPa at 18 UTC was shown to dissipate by 00 UTC. The mean wind in the deep, well-mixed boundary layer backed gradually from around 330º early in the event to around 315º by 12 UTC, while retaining a nearly unidirectional shear profile. Wind directions greater than about 320 º are favorable for enhancement of lake-effect snow bands downstream of the Finger Lakes over southern New York (Sobash et al. 2005). Figs. 19a-d show the sounding parameters from each time shown in Fig. 18, after the appropriate data was entered into the application, and the database search was executed. The left column in each figure contains the sounding parameters from the case being examined, and the remaining columns contain data from the corresponding historical "most similar" events.
Figs. 20a-d show comparisons between KBGM 0.5º radar reflectivity imagery at 18 UTC and corresponding imagery from the top 3 "most similar" historical dates and times. Fig. 20a shows that the pattern at 18 UTC indicated a large area of light snow across central New York, with some heavier embedded cellular features located mainly north and west of BGM. The most similar event was characterized by a cellular pattern of snow showers, with accumulations of 2 to 3 inches across central New York north of BGM (Fig. 20b). The second event was characterized by a widespread area of light snow, with a few embedded snow bands extending from the Finger Lake area southeast toward northern Pennsylvania. The text summary for this event indicated that accumulations of 2 to 5 inches were widespread across central and southern New York. The third event also indicated a large area of light snow with a few embedded heavier snow showers near BGM southward across northern Pennsylvania. Snow accumulations with this event were less than one inch.
The 0.5º radar reflectivity at 00 UTC was characterized by a broad area of light snow across central New York, with a distinct band of heavier snow extending from the Finger Lakes near ITH southeast across south central New York (Fig. 21a). The most similar, historical event also showed a large area of light snow across central New York with an intense band extending from near ITH southeast across northeast Pennsylvania (Fig. 21b). Snowfall amounts with this most similar, historical event ranged from 2 to 6 inches across much of central New York and northern Pennsylvania, with a maximum of near 10 inches close to ITH. The second-most similar, historical event was also returned as a similar event to 18 UTC. Enhanced snow bands from the Finger Lakes near ITH can be seen extending southeast into northeast Pennsylvania, producing 2 to 5 inches of snow (Fig. 21c). The third most similar, historical event indicated a more disorganized, convective pattern, with some enhanced banding over central New York and northern Pennsylvania (Fig. 21d). Snow amounts during this period were 2 inches or less.
Fig. 22a shows that the 0.5º radar reflectivity at 06 UTC indicated a very intense snow band west of ITH, with other significant northwest-southeast bands located across central New York and northern Pennsylvania. The most similar, historical events all indicated significant snow bands extending from the Finger Lakes southeast toward northern Pennsylvania (Figs. 22b-d). The first two images were associated with 2 to 5 inch snow events for southern New York and northern Pennsylvania, and the next image was associated with the event that produced a widespread 2 to 6 inch snowfall across southern New York and northern Pennsylvania, with a maximum of 10 inches near ITH.
The 0.5º radar reflectivity at 12 UTC indicated a few narrow, moderately intense northwest to southeast snow bands persisting across central New York and northeast Pennsylvania (Fig. 23a). The bands had shifted northeast from their earlier position. The most similar, historical event returned by the application indicated a west-northwest to east-southeast oriented band located near BGM, surrounded by other, weaker bands (Fig. 23b). The text summary from this event indicated a 3 to 5 inch snowfall across the southern Finger Lakes area. The second historical event indicated a more widespread snow event than what was observed at 12 UTC, with snow amounts across the Finger Lakes and southern tier of New York ranging from 2 to 5 inches (Fig. 23c). The third event showed snow bands in a similar orientation as to what was observed at 12 UTC, however the bands were considerably weaker (Fig. 23d). All of the analog cases indicated that the bands would have an increasingly west-to-east orientation by 12 UTC, as opposed to the northwest-to-southeast orientation indicated earlier in the morning.
In summary, the pattern recognition application in this case correctly indicated that this would be an event characterized by significant snow bands extending from the Finger Lakes area near ITH southeast across northeast Pennsylvania. One of the similar events identified by the application was associated with a snowfall maximum of 10 inches near ITH, with most of the other events associated with amounts in the 2 to 5 inch range. The 24-hour observed snowfall for the period ending at 12 UTC 18 March is shown in Fig. 24. In this case, the pattern recognition application confirmed indications from the models that a significant snowfall would occur across the southern tier of New York and far northern Pennsylvania. The potential for up to 10 inches of snow indicated by the application was more than what was explicitly forecast by any of the guidance (assuming snow to liquid ratios of 20 to 1 or less), and should have signaled forecasters that an unusual event was about to occur.
4. Summary and Discussion
The increased availability of local models with small horizontal grid spacing has made their utilization increasingly popular during the past several years. At the same time, computational limits and operational needs have resulted in a 5 to 10 km horizontal grid spacing in many of these models. At these resolutions, narrow, multi-bands of lake-effect snow are not explicitly resolved. Therefore, there is still opportunity to apply forecaster experience and pattern recognition to improve over model guidance in the lake-effect snow forecasting process.
The main benefit of the pattern recognition application shown in this paper is to assist in expanding the experience database of all forecasters, by enhancing their ability to relate current events to historical events through pattern recognition. When experienced forecasters can confirm that output from a model with small horizontal grid spacing matches their experiential expectations given a particular flow regime, forecaster confidence increases, and it becomes easier for the forecaster to correctly anticipate significant or even extreme events. Conversely, if output from a model with small horizontal grid spacing does not match the forecasters expectations based on their experience, this could be taken as a signal to be skeptical of the numerical guidance. Therefore, this application should be particularly useful for less experienced forecasters.
One important aspect of any lake-effect snow event, that is not particularly well-addressed by the pattern recognition application shown in this paper, is event duration. The application is tuned to return "snap-shots" of expected 0.5º radar reflectivity images, given a particular set of expected environmental conditions. The forecaster does have the ability to enter whether or not the expected event will be "persistent," with the definition of a "persistent" event being any event with a mean wind direction varying by less than 10º through a period that is at least 12 hours long. However, this still leaves a wide range of possible event durations that are not accounted for. For example, recall from section 3a that the application once matched a "persistent" current event that remained nearly steady-state for around 12 hours with a historical event that remained nearly steady-state for 48 hours. In that case, the historical event produced much more snow than the current event, making it a poor analogue regarding storm total snowfall. Therefore, forecasters still need to carefully consider the effects of event persistence on total snowfall, since the degree of snow band persistence across a particular location will correlate directly with the amount of snow that accumulates at that location.
One of the more useful features of the pattern recognition application shown in this paper is the ability to display several different historical events corresponding to a current event. The returned historical events usually appear reasonably similar to each other. However there are times when a variety of different looking events are returned. Generally speaking, there are two possible reasons why the returned events may occasionally not appear to be very similar to each other. The first reason would be that the current flow pattern is such that very minor differences in the associated soundings could result in large differences in lake effect snow morphology. In these cases, dissimilar returned events would indicate that the outcome of the current event is highly uncertain, even if the low-resolution model has a reasonably good handle on the large-scale flow pattern. The other possibility is that the current event is unusual, and as a result the database does not contain enough truly similar cases for each returned case to be a good match to the current case. This possibility will become increasingly less likely as additional cases continue to be added to the database.
Finally, the concept of developing a pattern recognition enhancement application based on comparison of parameters associated with current conditions to parameters from analogue events, may have applications in areas other than lake-effect snow forecasting. The key to identifying forecast challenges that would be well suited to this type of approach would be to identify scenarios where models forecast the background environment reasonably well, but do not necessarily make good, explicit forecasts of the phenomena in question. For example, the NWSFO at BGM has been experimenting with developing a similar application to help anticipate warm-season convective mode. Aviation forecasting could be another area where this type of approach may be appropriate.
The key to developing similar applications for different forecast problems is to identify key attributes of the sounding that modulate the evolution of the phenomena in question. In the case of lake effect snow, the large body of previous research plus a great degree of local operational experience made identifying several key parameters relatively easy. Even so, ongoing local research plus experience with the application will result in the inclusion of additional parameters in the future. As such, this application provides an excellent opportunity to input results from research into operations. In contrast to lake effect snow, other phenomena may be less well understood, and a major research effort may be required prior to the development of a similar application. Once the key parameters are identified, the next steps are to create the database of parameters, derive similarity algorithms, and create the database search application.
The authors would like to thank the NWS at Buffalo, NY for the use of their 6 km WRF model in the case studies. In addition, we would like to thank BGM forecasters Michael Jurewicz and Robert Mundschenk for helping to develop and quality control the historical database. Finally, we would like to thank BGM forecaster Justin Arnott, ER SSD meteorologists Jeff Waldstreicher and Dave Novak, and two anonymous reviewers, for reviewing this manuscript.
Mike Evans has been the science operations officer at the National Weather Service Forecast Office in Binghamton, NY since 2002. Before coming to BGM, Mike worked in the National Weather Service as an intern in Charleston, WV, a journeyman forecaster in White Lake, Michigan, and a lead forecaster in State College, Pa. Mike received a B.S. in Meteorology from Penn State in 1985, and an M.S. in Atmospheric Science from SUNY Albany in 1992.
Ron Murphy has been the information technology officer at the National Weather Service Forecast Office in Binghamton, NY, since 2003. Before holding his current position, Ron was a National Weather Service intern in Lake Charles La, a journeyman and lead forecaster at Birmingham, AL, and a lead forecaster at Binghamton. Ron received a B.S. in Meteorology from SUNY Oswego in 1987.
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The algorithm to identify historical events that are "most similar" to the current event is a point system. Each event in the database is compared to the current event, and assigned points based on the similarity of its sounding parameters to the corresponding parameters of the current event. The events with the most points are returned as being "most similar". Before points are assigned, a large number of historical events are eliminated from consideration using the following rules:
If the difference in mean wind direction between the historical case and current case is greater than 10 degrees, remove the historical case from consideration.
If the level of the base of the inversion of the current case is above 750 hPa, and the level of the base of the inversion of the historical case is below 880 hPa remove the historical case from consideration.
If the level of the base of the inversion of the current case is below 880 hPa and the level of the base of the inversion of the historical case is above 750 hPa, remove the historical case from consideration.
If the difference in the depth of the moist layer (defined by all layers where the dew point depression is 3 º C or less) between the historical case and the current case is greater than 200 hPa, remove the historical case from consideration.
Once this subset of historical cases has been removed from consideration, points are assigned to the remaining events by the criteria in table A1:
Table A1. A summary of the point system used to determine the most similar historical events in
the pattern recognition application described in this paper.
Assign 5 points
to the historical
Assign 4 points
to the historical
Assign 3 points
to the historical
Assign 2 points
to the historical
Assign 1 point
to the historical
Mean wind direction
in the mixed layer
< 5 degrees
5-10 º
Mean wind speed
< 10 kts
10-15 kts
16-20 kts
850 hPa temperature
< 5 º
5-8 º
9-15 º
850 hpa / Lake
temperature diff.
< 5 º
5-8 º
9 to 15 º
Inversion base
< 20 hPa
20-40 hPa
41-80 hPa
81-150 hPa
Moisture depth
< 41 hPa
41-80 hPa
81-150 hPa
Dendritic growth
Zone depth
< 41 hPa
41-80 hPa
81-150 hPa
Max lift in the
Dendritic growth
Zone *
< 6 μbs-1
6-10 μbs-1
11-15 μs-1
Max Tdd below
900 hPa
< 3 º
3-4 º
RMS Shear
≤ 0.5
0.51 – 1.00
1.01 – 1.5
Both are persistent
Both are not
Time of day
* The point thresholds for this parameter would have to be adjusted when using data from grids with significantly higher or lower resolution than the 12 km NAM or 13 km RUC.
** A match for time of day is defined as follows: If the current time is 00 UTC, a historical time of 18 UTC or 00 UTC is a match. If the current time is 06 UTC, a historical time of 06 UTC or 12 UTC is a match. If the current time is 12 UTC, a historical time of 06 UTC or 12 UTC is a match. If the current time is 18 UTC, a historical time of 18 UTC or 00 UTC is a match.
Algorithm for objectively determining the base of the subsidence inversion
Inversions are typically defined as layers where the temperature increases with height. However, in the case of lake effect snow forecasting, we have observed that forecasters do not use this strict definition of an inversion when determining the height of the subsidence inversion. Instead, we have observed that forecasters subjectively define the subsidence inversion as a layer where subsidence is indicated by drying and a decreased (but not necessarily negative) lapse rate. In addition, we found that forecasters often ignore very shallow, surface-based inversions. For this application, we decided to define the subsidence inversion height objectively, using an algorithm that produces a result that matches what forecasters would set as the subsidence inversion as closely as possible. After considerable trial and error, the following algorithm was developed:
Define ΔT as the temperature at the current level subtracted from the temperature at the next level above the current level in the BUFR data. For example, if temperature increases with height, ΔT is positive.
Start sampling at the level closest to 950 hPa and repeat the following at every level until an inversion is defined, or until you reach the level closest to 850 hPa.
If ΔT >= 0 set this as your inversion level.
If ΔT < 0 but > - 0.4 then check the value of the dew point depression.
If the dew point depression >= 5C, then set this as your inversion level.
If ΔT <= -0.4, you have no inversion
If no inversion has been defined by the time you have sampled up to 850 hPa, do the following at each layer until you define an inversion, or until you reach the layer closes to 500 hPa.
If the dew point depression >= 5C then set this as your inversion level.
If delta T > -0.8 set this as your inversion level.
If no subsidence inversion level has been defined before reaching 500 hPa, set the inversion level at 500 hPa.
Fig. 1. Domain for the WRF model run at the NWS forecast office at Buffalo, NY. Note that
the domain was chosen to capture the effects of Lakes Erie, Ontario and Huron.
Fig. 2. 12 UTC 16 January 2007 NAM 850 hPa wind (kts) and temperature (ºC) valid at
a) 18 UTC 16 Jan. 2007, b) 00 UTC 17 Jan. 2007, c) 06 UTC 17 Jan. 2007 and
d) 12 UTC, 17 Jan. 2007.
Fig. 3. 12 UTC 16 January 2007 NAM 925 hPa omega (-µbs-1), with upward vertical motion
shaded, valid at a) 18 UTC 16 Jan. 2007, b) 00 UTC 17 Jan. 2007 c) 06 UTC 17 Jan.
2007 and d) 12 UTC 17 Jan. 2007.
Fig. 4. 12 UTC 16 January 2007 NAM forecast 3-hour quantitative precipitation (inches) valid
at a) 18 UTC Jan. 16 2007, b) 00 UTC Jan. 17 2007, c) 06 UTC Jan. 17 2007 and
d) 12 UTC Jan. 17 2007.
Fig. 5. 12 UTC 16 January 2007 6 km Workstation (or local) WRF 6-hour quantitative
precipitation forecasts (inches) valid at a) 18 UTC 16 Jan. 2007, b) 00 UTC 17 Jan.
2007, c) 06 UTC 17 Jan. 2007 and d) 12 UTC 17 Jan. 2007.
Figure 6. 12 UTC 16 January 2007 6-km WRF 24-hour quantitative precipitation (inches)
forecast valid at 12 UTC 17 Jan. 2007.
Fig. 7. 12 UTC 16 January 2007 NAM model forecast soundings valid at SYR as displayed by
BUFKIT software and valid at a) 18 UTC 16 Jan. 2007, b) 00 UTC 17 Jan. 2007,
c) 06 UTC 17 Jan. 2007 and d) 12 UTC 17 Jan. 2007.
Fig. 8. Tabular output from the WFO BGM pattern recognition application for forecasts valid at
a) 18 UTC 16 Jan. 2007, b) 00 UTC 17 Jan. 2007, c) 06 UTC 17 Jan.2007, and
d) 12 UTC 17 Jan. 2007.
Fig. 9. (a) Observed radar 0.5º reflectivity from the case study at 18 UTC 16 January. 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern recognition
Fig. 10. (a) Observed 0.5º radar reflectivity from the case study at 00 UTC 17 January 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern recognition
Fig. 11. (a) Observed 0.5º radar reflectivity from the case study at 06 UTC 17 January 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern recognition
Fig. 12. (a) Observed 0.5º radar reflectivity from the case study at 12 UTC 17 January 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern recognition
Fig. 13. Observed snowfall (inches) from 12 UTC 16 January 2007 through 12 UTC 17 January
Fig. 14. 12 UTC 17 March 2007 NAM 850 hPa wind (kts) and temperature (ºC) valid at
a) 18 UTC 17 Mar. 2007, b) 00 UTC 18 Mar. 2007 c) 06 UTC 18 Mar. 2007, and
d) 12 UTC 18 Mar. 2007.
Fig. 15. 12 UTC 17 March 2007 850 hPa omega (-µbs-1), with upward vertical motion shaded,
valid at a) 18 UTC 17 Mar. 2007 b) 00 UTC 18 Mar. 2007, c) 06 UTC 18 Mar. 2007
and d) 12 UTC 18 Mar. 2007.
Fig. 16. 12 UTC 17 March 2007 NAM forecast 3-hour quantitative precipitation (inches) valid
at a) 00 UTC 18 Mar. 2007, b) 06 UTC 18 Mar. 2007, c) 12 UTC 18 Mar. 2007;
d) 12 UTC 17 March 2007 NAM 24-hour quantitative precipitation (inches) valid at
12 UTC 18 Mar. 2007.
Fig. 17. 12 UTC 17 March 2007 6-km WRF 3 hour quantitative precipitation forecast (inches)
valid at a) 00 UTC 18 Mar. 2007, b) 06 UTC 18 Mar. 2007, c) 12 UTC 18 Mar. 2007;
d) 12 UTC 17 Mar. 2007 6 km WRF 24-hour quantitative precipitation forecast valid
at 12 UTC 18 Mar. 2007.
Fig. 18. 12 UTC 17 March 2007 NAM model forecast soundings valid at SYR as displayed by
BUFKIT software and valid at a) 18 UTC 17 Mar. 2007, b) 00 UTC 18 Mar. 2007,
c) 06 UTC 18 Mar. 2007, and d) 12 UTC 18 Mar. 2007.
Fig. 19. Tabular output from the WFO BGM pattern recognition application for forecasts valid
at a) 18 UTC 17 Mar. 2007 b) 00 UTC 18 Mar. 2007, c) 06 UTC 18 Mar. 2007, and
d) 12 UTC 18 Mar. 2007.
Fig. 20. (a) Observed 0.5º radar reflectivity from the case study at 18 UTC 17 March 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern
recognition application.
Fig. 21. (a) Observed 0.5º radar reflectivity from the case study at 00 UTC 18 March 2007.
(b-d) Imagery from 3 corresponding similar events selected by the pattern
recognition application.
Fig. 22. (a) Observed 0.5º radar reflectivity from the case study at 06 UTC 18 March 18 2007;
(b-d) Imagery from 3 corresponding similar events selected by the pattern recognition
Fig. 23. (a) Observed 0.5º radar reflectivity from the case study at 12 UTC 18 March 2007.
(b-d) Imagery from 3 corresponding similar events selected by the pattern
recognition application.
Fig. 24. Observed snowfall from 12 UTC 17 March 2007 through 12 UTC 18 March 18 2007.
Corresponding author address: Michael Evans, National Weather Service, 32 Dawes Drive, Johnson City, NY 13790. E-mail: email@example.com


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