Monday, September 28, 2015

Feels-Like Temperature Range 2014-2015.

Earlier in the summer I posted maps showing the "typical" range of temperatures during a climatological period. This post is a little different as it uses the "feels like" temperatures during the last year to depict the actual range of apparent temperatures. I use the conventional measures of Heat Index for warm temperatures and Wind Chill for cold temperatures. Only those stations that had sufficient observations to measure both a summer Heat Index and a winter Wind Chill were utilized. That required valid temperature, dewpoint (for Heat Index) and wind (for Wind Chill) measurements.

Heat Index:

The first map (Figure 1) shows the highest Heat Index recorded at ASOS station during the summer of 2015. The Heat Index was computed from hourly ASOS observations using the temperature and dew point (Steadman formula). The maximum dew point for 2015 is shown in Figure 2.

Figure 1. Highest Heat Index recorded in 2015 at ASOS stations.

Figure 2. Highest dew point temperature recorded in 2015 at ASOS stations.

Wind Chill:

Wind Chill is the apparent temperature based on the combined effect of temperature and wind. As many readers know, the wind chill formula was updated in 2001 (Osczevski 2001). In this analysis, I looked as hourly ASOS observations and computed wind chills for every hour during the 2014-2015 winter (November through March). The results are shown in Figure 3.
 Figure 3. Lowest Wind Chill recorded in the 2014-2015 winter at ASOS stations.

Range:

Unsurprisingly, continental climates experience the greatest range of annual temperatures (see interior Alaska and the northern Great Plains. While interior Alaska has the greatest annual temperature range in the U.S. based on climatology, several stations in North Dakota won the feels-like temperature lottery. In Alaska, the coldest temperatures are usually accompanied by calm winds and the warmest summer temperatures usually are so dry that the apparent temperature is 5°F-10°F lower than the actual temperature.

The other surprising aspect to the map is the range in Iowa is greater than the range in neighboring Nebraska.
Figure 4. Difference between highest Heat Index and Lowest Wind Chill at ASOS stations during the last year (summer minus winter).

Figure 5. ASOS stations used in the analysis depicted in Figures 1-4.

NWS Office Facebook and Twitter Update

Every six months or so I take a look at the social media presence of the 118 National Weather Service forecast office (not including San Juan, Guam, and American Samoa). The previous analysis of both the Facebook and Twitter followers is located here ( http://us-climate.blogspot.com/2015/03/nws-social.html ). Please note that the three Alaska NWS Office share a single Facebook page, yet have separate Twitter pages. In lieu of a complete replication of the March 28th post, I want to show a snapshot of where things stand as of now.

A note of caution. While rankings are labeled on the maps, and while it is human nature to make comparisons, keep in mind that the needs and demographics of each WFO are different.

Facebook:

The three Facebook maps below show the raw number of Likes (Figure 1), the rate of Likes per 100,000 population (Figure 2), and the change in Likes over the last six months (Figure 3).

Figure 1. Facebook Likes for 118 National Weather Service forecast offices as of September 28, 2015. Rank label (highest to lowest) added to each WFO.


Figure 2. Facebook Likes per 100,000 population for 118 National Weather Service forecast offices as of September 28, 2015. Rank label (highest to lowest) added to each WFO.


Figure 3. Rate of change of Facebook Likes for 118 National Weather Service between March 28, 2015, and September 28, 2015. Percent change labelled for each WFO.


Twitter

Twitter functions quite a bit differently than Facebook. While Facebook is a more casual source of information, Twitter is much more like a news aggregation site. The information is short, sweet, and timely. My experience has been that Twitter is much more of a specialized type of social media where the participants are in the media, celebrities, actors, athletes, and subject matter specialists.

So, here are my two maps for Twitter. The first (Figure 4, shows the number of Twitter followers for each NWS page. The second show the average number of Tweets sent out by those offices since their Twitter account was activated (Figure 5).

Figure 4. Twitter followers for 120 National Weather Service forecast offices as of September 28, 2015. Rank label (highest to lowest) added to each WFO. (Alaska offices separated out).

Figure 5. Twitter followers per 100,000 population for 120 National Weather Service forecast offices as of September 28, 2015. Rank label (highest to lowest) added to each WFO. (Alaska offices separated out).

Figure 6. Number of Tweets per month sent by the 118 National Weather Service forecast offices as of September 28, 2015. Rank label (highest to lowest) added to each WFO. Value computed by dividing number of tweets by number of months since page's inception. (Alaska offices combined).


Table 1. Raw count of Facebook Likes, Twitter Followers, Tweets, and rate of Tweets by National Weather Service Forecast Office (WFO). Separate entries made for the Alaska WFOs so that Twitter statistics are identifiable. They share identical Facebook values.

WFOFB LikesTwitter FollowersTotal TweetsTweets / Month
Aberdeen9,4633,4252,28558.0
Albany28,4815,7895,438141.6
Albuquerque9,1655,27911,200258.3
Amarillo39,2887,7923,984101.2
Anchorage43,699*4,9512,50165.8
Austin/San Antonio16,33610,3008,812218.1
Baltimore/Washington38,99210,7004,705133.0
Billings14,1863,4644,207104.1
Binghamton85,1977,2839,264254.8
Birmingham78,00917,10012,000297.0
Bismarck20,5923,3124,601113.9
Blacksburg22,6075,7858,358223.6
Boise15,1073,9182,75277.8
Boston72,27532,70017,200447.9
Brownsville23,8984,1476,063150.1
Buffalo10,6947,8411,90056.9
Burlington20,5035,8276,228162.2
Caribou28,6682,4218,889274.8
Charleston (SC)8,40110,2009,228176.0
Charleston (WV)29,9445,1181,87147.5
Cheyenne11,7192,9244,455113.1
Chicago112,55620,80011,400296.9
Cleveland15,6795,9542,65567.4
Columbia12,2923,8485,199109.7
Corpus Christi10,1884,5353,76393.1
Dallas/Fort Worth102,44139,00012,800316.8
Davenport/Quad Cities13,3804,8163,28983.5
Denver/Boulder16,61511,4006,047153.5
Des Moines29,7789,9797,981202.6
Detroit/Pontiac12,2596,0263,27383.1
Dodge City9,8204,2916,904170.9
Duluth8,8382,2042,59475.5
El Paso5,9714,3179,264213.6
Elko6,9432,2857,386182.8
Eureka11,5493,0515,909146.2
Fairbanks43,699*2,6001,96751.8
Flagstaff11,6606,2864,175103.3
Gaylord9,8222,8089,675245.6
Glasgow10,5382,5072,09751.9
Goodland7,5093,4466,866174.3
Grand Forks10,0753,1243,33484.6
Grand Junction5,6312,2495,073143.4
Grand Rapids19,7105,5503,77295.8
Gray/Portland13,0484,6796,611172.2
Great Falls7,9133,4589,199227.7
Green Bay8,3923,9162,40959.6
Greenville-Spartanburg15,6024,9955,253133.4
Hastings19,2104,3657,453189.2
Honolulu17,2488,0501,72232.8
Houston/Galveston18,02411,4006,037153.3
Huntsville27,61910,9004,687116.0
Indianapolis20,74714,9008,245209.3
Jackson (KY)12,7603,8557,853199.4
Jackson (MS)75,29513,20014,400356.4
Jacksonville8,4564,0524,787118.5
Juneau43,699*2,1097,627200.7
Kansas City/Pleasant Hill82,30224,1009,481180.8
Key West28,0817,8634,226104.6
La Crosse12,2704,0066,022152.9
Lake Charles11,1153,3385,264130.3
Las Vegas10,5938,8449,026223.4
Lincoln30,9486,6474,746120.5
Los Angeles/Oxnard18,94515,8008,195208.1
Louisville21,3728,8688,821224.0
Lubbock8,1555,6805,272130.5
Marquette16,7434,0033,72292.1
Medford12,4302,7391,42735.3
Melbourne13,3145,0864,148102.7
Memphis22,4139,8209,628238.3
Miami15,06913,3008,395207.8
Midland/Odessa12,8363,4168,470209.6
Milwaukee/Sullivan25,6525,1694,797121.8
Minneapolis/Twin Cities23,45515,40010,300261.5
Missoula8,7573,5869,657239.0
Mobile/Pensacola25,05214,40012,200301.9
Morristown/Knoxville18,6153,8743,19879.1
Mount Holly/Philadelphia67,48217,3002,63767.0
Nashville55,70713,60014,200360.5
New Orleans/Baton Rouge32,0687,5876,010148.7
New York46,26824,6008,252209.5
Newport/Morehead City12,5052,9415,538152.3
Norman/Oklahoma City173,83953,00014,200390.5
North Little Rock64,21519,1002,86170.8
North Platte7,6583,6777,308185.5
Northern Indiana40,18010,10011,200284.4
Omaha/Valley13,2838,7784,701119.4
Paducah44,80710,1008,107205.8
Peachtree City/Atlanta26,78210,3004,377108.3
Pendleton9,2012,4612,05252.1
Phoenix10,56710,5006,978172.7
Pittsburgh48,43313,5005,404144.6
Pocatello4,0142,4659,095225.1
Portland19,6336,8935,465142.3
Pueblo5,9393,8055,377133.1
Raleigh12,5457,8733,47192.9
Rapid City11,5193,4521,30633.2
Reno14,6574,8335,024124.3
Riverton10,8412,0047,613193.3
Sacramento18,59910,20011,900302.1
Salt Lake City8,05210,20010,400257.4
San Angelo16,3364,6365,234129.5
San Diego13,4276,35512,500288.3
San Francisco Bay Area/Monterey18,98917,10016,800426.5
San Joaquin Valley/Hanford14,7394,20411,500284.6
Seattle12,8397,6702,81273.2
Shreveport29,5334,06911,500292.0
Sioux Falls21,46410,3007,619188.6
Spokane12,3184,6185,67370.6
Springfield27,2608,9644,706119.5
St. Louis36,7158,5643,00274.3
State College20,8115,7448,544216.9
Tallahassee8,9646,7826,249154.7
Tampa14,8377,81012,800316.8
Topeka13,2857,8804,356107.8
Tucson6,3785,4936,606163.5
Tulsa41,22511,1007,171139.5
Wakefield8,8642,53163521.6
Wichita33,73114,0007,561192.0
Wilmington (NC)8,7805,2002,11659.8
Wilmington (OH)39,26811,5005,548140.9