UV station data based on operational
TEMIS satellite ozone data

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UV archive
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UV index main page  |  UV dose main page
 

Time series of UV data

Time series UV index and UV dose data derived from the operational TEMIS assimilates ozone data -- so-called overpass files -- are generated from the UV data archive for selected places.

For all stations the overpass file contains the cloud-free UV index and UV dose data (starting 1 July 2002). For stations inside the MSG data area, see the 4th column, there is in principle also cloud-modified UV dose data in the overpass file (MSG data is available since 24 Jan. 2004).

Information on the structure of the data files is given below the table.
Information on availability of cloud-modified UV dose data is given on this page.

 

station/place name
(click to download ascii file)
longitude latitude MSG area
AcadiaNatForest, USA -68.30 44.40 no
Adana, Turkey 35.35 36.98 yes
Adelaide, Australia 138.62 -34.92 no
Ahmedabad, India 72.67 23.05 no
Alert, Canada -62.35 82.47 no
AliceSprings, Australia 133.90 -23.80 no
Amman, Jordan 35.93 31.95 yes
Amsterdam, Netherlands 4.90 52.37 yes
Anchorage, USA -149.90 61.22 no
Andorra_la_Vella, Andorra 1.52 42.51 yes
Andoya, Norway 16.00 69.30 no
Angra_do_Heroismo, Azores_Portugal -27.22 38.66 yes
Ankara, Turkey 32.88 39.95 yes
Antalya, Turkey 30.73 36.87 yes
Antartica, Chile -58.98 -62.19 no
Antofagasta, Chile -70.44 -23.45 no
Arica, Chile -70.31 -18.47 no
Arosa, Switzerland 9.67451 46.77916 yes
Athens, Greece 23.7278 37.9840 yes
Atlanta, USA -84.40 33.70 no
Aulnay_Paris, France -0.35 46.02 yes
Baghdad, Iraq 44.43 33.30 yes
Bangalore, India 77.59 12.97 no
Bangkok, Thailand 100.612 13.667 no
Baoding, China 115.46 38.87 no
Barrow, USA -156.60 71.32 no
Basra, Iraq 47.65 30.4167 yes
Beer-Sheva, Israel 34.783 31.233 yes
Belfast, GreatBritain -5.83 54.60 yes
Belgrade, Serbia 20.45 44.79 yes
Belsk, Poland 20.78 51.83 yes
Berlin, Germany 13.41 52.52 yes
Bern, Switzerland 7.45 46.95 yes
Bhopal, India 77.47 23.28 no
Bihar, India 85.375 25.125 no
Bilthoven, Netherlands 5.20 52.12 yes
Bordeaux, France -0.53 44.84 yes
Boston, USA -71.05 42.36 no
Boulder, USA -105.30 40.00 no
Bratislava, Slovakia 17.11 48.15 yes
Briancon, France 6.65 44.90 yes
Brisbane, Australia 153.03 -27.45 no
Brno, CzechRepublic 16.60 49.20 yes
Bucharest, Romania 26.10 44.43 yes
Budapest, Hungary 19.04 47.4979 yes
Buenos_Aires, Argentina -58.48 -34.58 yes
Caldera, Chile -70.77 -27.26 no
Calgary, Canada -114.084 51.084 no
Camborne, GreatBritain -5.30 50.20 yes
Canyonlands, USA -109.80 38.50 no
Carrollton, USA -96.89 32.95 no
Casey, Australia 110.53 -66.28 no
Chengkung, Taiwan 121.34 23.07 no
Chennai, India 80.30 13.08 no
Chiang_Mai, Thailand 98.969 18.771 no
Chilton, GreatBritain -1.32 51.58 yes
Chisinau, Moldova 28.86 47.01 yes
Churchill, Canada -94.00 58.75 no
Clark_New_Jersey, USA -74.31 40.64 no
Concepcion, Chile -73.06 -36.78 no
Copenhagen, Denmark 12.57 55.68 yes
Coyhaique, Chile -72.11 -45.59 no
Dalian, China 121.36 38.54 no
Darwin, Australia 130.89 -12.43 no
Davis, Australia 77.97 -68.58 no
Davos, Switzerland 9.8435 46.8130 yes
DeBilt, Netherlands 5.18 52.10 yes
DeadSea, Jordan 35.55 31.55 yes
Dehradun, India 78.029 30.318 no
Denali, USA -149.00 63.70 no
Dortmund, Germany 7.4690 51.5080 yes
Dublin, Ireland -6.2489 53.3331 yes
Durban, SouthAfrica 30.98 -29.87 yes
Edinburgh, GreatBritain -3.1965 55.9521 yes
Edmonton, Canada -114.10 53.55 no
El_Colorado, Chile -70.29 -33.35 no
El_Tololo, Chile -70.80 -30.17 no
Erzurum, Turkey 41.17 39.95 yes
Eureka, Canada -86.43 80.05 no
Everglades, USA -80.70 25.40 no
FortWilliam, GreatBritain -5.1121 56.8165 yes
Funchal, Madeira_Portugal -16.89 32.64 yes
Gaithersburg, USA -77.20 39.10 no
Galway, Ireland -9.0489 53.2719 yes
Garmisch, Germany 11.07 47.48 yes
Ghor_el_Safi, Jordan 35.467 31.033 yes
Gibilmanna, Italy 14.0186 37.9871 yes
GooseBay, Canada -60.30 53.23 no
Graciosa_Island, Azores_Portugal -28.026 39.092 yes
GreatSmokeyMtns, USA -83.80 35.60 no
Gross-Enzersdorf, Austria 16.56 48.20 yes
Halifax, Canada -63.66 44.73 no
Haute_Provence, France 5.7 43.94 yes
Havana, Cuba -82.38 23.12 no
Helsinki, Finland 24.94 60.17 yes
Hilla_Babylon, Iraq 44.41 32.50 yes
Hohenpeissenberg, Germany 11.02 47.80 yes
Houston, USA -95.37 29.76 no
HradecKralove, CzechRepublic 15.83 50.19 yes
Hyderabad, India 78.43 17.37 no
Invercargill, NewZealand 168.33 -46.42 no
Iquique, Chile -70.17861 -20.53972 no
Isla_de_Pascua, Chile -109.43 -27.16 no
Ispra, Italy 8.63 45.81 yes
Istanbul, Turkey 28.82 40.97 yes
Izana, Tenerife_Spain -16.50 28.49 yes
Izmir, Turkey 27.02 38.52 yes
Jokioinen, Finland 23.50 60.81 yes
Jungfraujoch, Switzerland 7.9853 46.5474 yes
Kagoshima, Japan 130.50 31.50 no
Kamphaeng_Phet, Thailand 99.523 16.483 no
Kansas_City, USA -94.58 39.10 no
Kayseri, Turkey 35.42 38.82 yes
Kiev, Ukraine 30.523 50.45 yes
Kingston, Australia 147.29 -42.99 no
Ko_Samui, Thailand 100.014 9.512 no
Kolkata, India 88.33 22.50 no
Kulmbach, Germany 11.4425 50.1031 yes
La_Quiaca, Argentina -65.60 -22.10 yes
La_Serena, Chile -71.20 -29.92 no
LabskaBouda, CzechRepublic 15.55 50.76 yes
Lampedusa, Italy 12.60 35.50 yes
Lauder, NewZealand 169.68 -45.04 no
Leba, Poland 17.53 54.75 yes
Legionowo, Poland 20.97 52.40 yes
Leigh, NewZealand 175.00 -36.50 no
Lerwick, GreatBritain -1.19 60.14 yes
Lindenberg, Germany 14.12 52.21 yes
Lisbon, Portugal -9.15 38.77 yes
Ljubljana, Slovenia 14.51 46.06 yes
LlanoDeChajnantor, Chile -67.7863 -22.9594 no
Locarno, Switzerland 8.7874 46.1726 yes
London, GreatBritain -0.12 51.4994 yes
Lueneburg, Germany 10.4566 53.2470 yes
Luxembourg, Luxembourg 6.13 49.61 yes
Lyon, France 4.834 45.768 yes
Macquerie_Island, Australia 158.94 -54.50 no
Madrid, Spain -3.70 40.42 yes
Maitri, Antarctica 11.75 -70.75 no
MalinHead, Ireland -7.34 55.37 yes
Malta_airport, Malta 14.48 35.85 yes
Manchester, GreatBritain -2.23 53.28 yes
Mar_del_Plata, Argentina -57.524 -38.017 yes
Marambio, Argentina -64.24 -56.62 no
Mardin, Turkey 40.73 37.30 yes
MaunaLoa, USA -155.58 19.53 no
Mawson, Australia 62.87 -67.60 no
McMurdo_Station, Antarctica 166.668 -77.846 no
Mecca, Saudi_Arabia 39.82 21.42 yes
Melbourne, Australia 145.10 -37.73 no
Melpitz, Germany 12.9280 51.5280 yes
Mendel_Ross_Island, Antarctica -57.88 -63.80 no
Mendoza, Argentina -68.50 -32.53 no
Mil.-Airport_Tatoi, Greece 23.78 38.11 yes
Minsk, Belarus 27.56 53.90 yes
Monaco, Monaco 7.42 43.74 yes
Montreal, Canada -73.75 45.47 no
Moscow, Russia 37.50 55.70 yes
Mosul, Iraq 43.15 36.3167 yes
MountWaliguan, China 100.90 36.30 no
Mugla, Turkey 28.37 37.22 yes
Mumbai, India 72.85 18.93 no
Nadi, Fiji 177.45 -17.76 no
Naha, Japan 127.65 26.17 no
Nashville_Airport, USA -86.68 36.12 no
Nea_Mihaniona, Greece 22.85 40.47 yes
Neuherberg, Germany 11.58 48.22 yes
Neve_Zohar, Israel 35.667 31.20 yes
New_Delhi, India 77.22 28.62 no
Newcastle, Australia 151.72 -32.90 no
Nicosia, Cyprus 33.38 35.19 yes
Norrkoping, Sweden 16.15 58.58 yes
Nuuk, Greenland -51.69 64.18 no
Obninsk, Russia 55.09 35.97 yes
Oesteraas, Norway 10.75 59.92 yes
Offenbach, Germany 8.65 50.01 yes
Oslo, Norway 10.717 59.938 yes
Palmer, Antarctica -64.00 -64.70 no
Paramaribo, Suriname -55.20 5.75 yes
ParanalObservatory, Chile -70.404167 -24.627222 no
Paraparaumu, NewZealand 174.98 -40.90 no
Paris, France 2.34 48.85 yes
Payerne, Switzerland 6.9424 46.8116 yes
Penhas_Douradas, Portugal -7.55 40.58 yes
Perth, Australia 115.96 -31.92 no
Pilar, Argentina -63.88 -31.66 yes
Podgorica, Montenegro 19.26 42.43 yes
Pohang, Korea 129.35 36.00 no
Poprad-Ganovce, Slovakia 20.29 49.00 yes
Potsdam, Germany 13.08 52.36 yes
Prague, CzechRepublic 14.44 50.08 yes
Pristina, Kosovo 21.17 42.67 yes
Pucallpa, Peru -74.55 -8.38 no
Puerto_Madryn, Argentina -64.811 -42.595 no
Puerto_Montt, Chile -73.10 -41.44 no
Puerto_Quequa, Argentina -58.637 -38.566 yes
Pune, India 73.80 18.52 no
PuntaArenas, Chile -70.90 -53.00 no
Putre, Chile -69.56 -18.20 no
Rarotonga, CookIslands -159.80 -21.20 no
Reading, GreatBritain -0.93 51.45 yes
Regina_BrattsLake, Canada -104.74 50.18 no
ResearchTrianglePk, USA -78.90 35.90 no
Resolute, Canada -95.01 74.69 no
Reunion, France 55.5 -20.94 yes
Reykjavik, Iceland -21.82 64.13 yes
Riga, Latvia 24.11 56.95 yes
Rio_Gallegos, Argentina -69.32 -51.60 no
Rio_Negro, Argentina -62.890 -41.081 no
Rize, Turkey 40.52 41.03 yes
RockyMountain, USA -105.50 40.00 no
Rome, Italy 12.52 41.90 yes
SaintPetersburg, USA -82.68 27.77 no
Salar_de_Uyuni, Bolivia -67.40 -20.20 yes
Salzgitter, Germany 10.3310 52.1510 yes
Samsun, Turkey 36.3 41.28 yes
SanDiego, USA -117.11 32.45 no
SanFrancisco, USA -122.42 37.78 no
SanMarino, SanMarino 12.46 43.94 yes
SanPedroDeAtacama, Chile -68.20000 -22.91083 no
Santiago, Chile -70.6545 -33.42 no
SaoPaulo, Brazil -46.64 -23.55 yes
Sapporo, Japan 141.30 43.02 no
Sarajevo, BosniaHerzegovina 18.41 43.86 yes
Saskatoon, Canada -106.71 52.11 no
SaturnaIsland, Canada -123.13 48.78 no
Schauinsland, Germany 7.9079 47.9137 yes
Seattle, USA -122.33 47.61 no
Seoul, South_Korea 127.03 37.35 no
Shenandoah, USA -78.40 38.50 no
Skopje, MacedoniaRepublic 21.43 41.9973 yes
Sodankyla, Finland 26.63 67.37 no
Sofia, Bulgaria 23.32 42.70 yes
Songkhla, Thailand 100.600 7.200 no
Sonnblick, Austria 12.95 47.05 yes
South_Pole, Antarctica 0.001 -89.999 no
Srinagar, India 74.83 34.13 no
Stockholm, Sweden 18.08 59.33 yes
Sulaimaniya, Iraq 45.43 35.55 yes
Sydney, Australia 151.10 -34.04 no
Sylt, Germany 8.3250 54.8920 yes
Syowa, Japan 39.55 -69.03 no
Taipei, Taiwan 121.49 24.99 no
Tallinn, Estonia 24.75 59.44 yes
Tarija, Bolivia -64.721 -21.543 yes
Tartu, Estonia 26.50 58.30 yes
Tateno_Tsukuba, Japan 140.07 36.02 no
Tehran, Iran 51.43 35.67 yes
Tel_Aviv, Israel 34.77 32.070 yes
Temuco, Chile -72.55 -38.70 no
Termas_de_Chillan, Chile -71.41 -36.90 no
Thessaloniki, Greece 22.96 40.63 yes
Tianjin, China 117.20 39.08 no
Tirana, Albania 19.82 41.33 yes
Tokyo, Japan 139.67 35.65 no
Toowoomba, Australia 151.55 -27.22 no
Toronto, Canada -79.47 43.78 no
Townsville, Australia 146.76 -19.33 no
Tromso, Norway 18.93 69.66 no
Trondheim, Norway 10.47 63.43 yes
Ubon_Ratchathani, Thailand 104.869 15.246 no
Uccle, Belgium 4.36 50.80 yes
Ushuaia, Argentina -68.31 -54.85 no
Vaduz, Liechtenstein 9.52 47.14 yes
Valdivia, Chile -73.15 -39.48 no
Vallenar, Chile -70.76 -28.59 no
Valletta, Malta 14.51 36.90 yes
Valparaiso, Chile -71.620 -33.040 no
Van, Turkey 43.32 38.45 yes
Venice, Italy 12.33 45.43 yes
Vienna, Austria 16.35 48.23 yes
VilleneuvedAscq, France 3.14 50.61 yes
Vilnius, Lithuania 25.28 54.69 yes
Vindeln, Sweden 19.77 64.23 yes
VirginIslands, USA -64.80 18.30 yes
Warsaw, Poland 21.01 52.23 yes
Winnipeg, Canada -97.24 49.91 no
Zagreb, Croatia 15.98 45.82 yes
Zakopane, Poland 19.97 49.30 yes
Zingst, Germany 8.6509 50.0048 yes
Zugspitze, Germany 10.98 47.42 yes
Dataset reference:
Van Geffen, J., Van Weele, M., Allaart, M. and Van der A, R.: 2017,
TEMIS UV index and UV dose operational data products, version 2.
Dataset. Royal Netherlands Meteorological Institute (KNMI).
doi.org/10.21944/temis-uv-oper-v2

 
The MSG data area is marked by light grey,
the location of the stations by red dots.

 
 

Data description

The header of an overpass file details contents and structure of the file:

# TEMIS v2.0 UV index and UV dose overpass file
# =============================================
# http://www.temis.nl/uvradiation/UVarchive.html
#
# Station name     = Zugspitze
# Station country  = Germany
# Station lon, lat = 10.98, 47.42
#
# Grid cell size              = 0.25 x 0.25 degrees
# Grid cell centre lon, lat   = 10.875, 47.375
# Grid cell average elevation = 1390 (+/- 424) m
# Grid cell within MSG area   = yes
#
# Data columns:
#      1 = YYYYMMDD        : date string
#   2, 3 = UVIEF, UVIEFerr : cloud-free erythemal UV index      [-]
#   4, 5 = UVDEF, UVDEFerr : cloud-free     erythemal  UV dose  [kJ/m2]
#   6, 7 = UVDEC, UVDECerr : cloud-modified erythemal  UV dose  [kJ/m2]
#   8, 9 = UVDVF, UVDVFerr : cloud-free     vitamin-D  UV dose  [kJ/m2]
#  10,11 = UVDVC, UVDVCerr : cloud-modified vitamin-D  UV dose  [kJ/m2]
#  12,13 = UVDDF, UVDDFerr : cloud-free     dna-damage UV dose  [kJ/m2]
#  14,15 = UVDDC, UVDDCerr : cloud-modified dna-damage UV dose  [kJ/m2]
#     16 = CMF             : average cloud modification factor  [-]
#     17 = ozone           : local solar noon ozone column      [DU]
#
# No-data entry = -1.000
#
#
# YYYYMMDD    UVIEF UVIEFerr  UVDEF UVDEFerr  UVDEC UVDECerr  ...   CMF    ozone
  20020701    8.752   0.526   5.180   0.354  -1.000  -1.000   ...  -1.000  313.2
  20020702    8.009   0.520   4.760   0.351  -1.000  -1.000   ...  -1.000  334.0
...

In case the station lies outside the MSG data area, the columns
with cloud-modified UV dose data have -1.000 throughout.   *)

The UV data is corrected for the effect of surface albedo on the surface UV radiation,
with the surface albedo is derived from a climatology; for details, see this page.

 
*) Note: The yes/no flag in the "MSG area" column of the table and comment line in the file header is based on the area covered by an MSG satellite in the default location (lon,lat)=(0,0). On days when a backup MSG satellite, located to the east or west, is used, stations just inside/outside the MSG perimeter may temporarily miss/have cloud-modified UV dose data.

 


last modified: 13 March 2023
Copyright © KNMI / TEMIS