UV station data based on MSR-2 ozone data

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UV archive
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Time series of UV data

Time series UV index and UV dose data derived from the Multi-Sensor Reanalysis (MSR-2) 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 for the MSR-2 period: April 1970 - December 2017 (one day is missing: 31 Dec. 2012). Cloud-modified UV dose data is not computed, since cloud cover data is not available of the full period.

Information on the structure of the data files is given below the table.
In order to reduce file size, each file is zip-ed.

Dataset remark:
Note that the input MSR ozone data prior to 1979 has not been
validated thoroughly; please treat that part of the data with care.

 

station/place name
(click to download ascii file)
longitude latitude
AcadiaNatForest, USA -68.30 44.40
Adana, Turkey 35.35 36.98
Adelaide, Australia 138.62 -34.92
Ahmedabad, India 72.67 23.05
Alert, Canada -62.35 82.47
AliceSprings, Australia 133.90 -23.80
Amsterdam, Netherlands 4.90 52.37
Andorra_la_Vella, Andorra 1.52 42.51
Andoya, Norway 16.00 69.30
Angra_do_Heroismo, Azores_Portugal -27.22 38.66
Ankara, Turkey 32.88 39.95
Antalya, Turkey 30.73 36.87
Arica, Chile -70.31 -18.47
Arosa, Switzerland 9.67451 46.77916
Athens, Greece 23.7278 37.9840
Atlanta, USA -84.40 33.70
Aulnay_Paris, France -0.35 46.02
Baghdad, Iraq 44.43 33.30
Bangalore, India 77.59 12.97
Bangkok, Thailand 100.612 13.667
Baoding, China 115.46 38.87
Barrow, USA -156.60 71.32
Belfast, GreatBritain -5.83 54.60
Belgrade, Serbia 20.45 44.79
Belsk, Poland 20.78 51.83
Berlin, Germany 13.41 52.52
Bern, Switzerland 7.45 46.95
Bhopal, India 77.47 23.28
Bihar, India 85.375 25.125
Bilthoven, Netherlands 5.20 52.12
Bordeaux, France -0.53 44.84
Boulder, USA -105.30 40.00
Bratislava, Slovakia 17.11 48.15
Briancon, France 6.65 44.90
Brisbane, Australia 153.03 -27.45
Brno, Czechia 16.60 49.20
Bucharest, Romania 26.10 44.43
Budapest, Hungary 19.04 47.4979
Buenos_Aires, Argentina -58.48 -34.58
Calgary, Canada -114.084 51.084
Camborne, GreatBritain -5.30 50.20
Canyonlands, USA -109.80 38.50
Carrollton, USA -96.89 32.95
Casey, Australia 110.53 -66.28
Chengkung, Taiwan 121.34 23.07
Chennai, India 80.30 13.08
Chiang_Mai, Thailand 98.969 18.771
Chilton, GreatBritain -1.32 51.58
Chisinau, Moldova 28.86 47.01
Churchill, Canada -94.00 58.75
Clark_New_Jersey, USA -74.31 40.64
Copenhagen, Denmark 12.57 55.68
Dalian, China 121.36 38.54
Darwin, Australia 130.89 -12.43
Davis, Australia 77.97 -68.58
Davos, Switzerland 9.8435 46.8130
DeBilt, Netherlands 5.18 52.10
Dehradun, India 78.029 30.318
Denali, USA -149.00 63.70
Dublin, Ireland -6.2489 53.3331
Durban, SouthAfrica 30.98 -29.87
Edinburgh, GreatBritain -3.1965 55.9521
Edmonton, Canada -114.10 53.55
Erzurum, Turkey 41.17 39.95
Eureka, Canada -86.43 80.05
Everglades, USA -80.70 25.40
FortWilliam, GreatBritain -5.1121 56.8165
Funchal, Madeira_Portugal -16.89 32.64
Gaithersburg, USA -77.20 39.10
Galway, Ireland -9.0489 53.2719
Garmisch, Germany 11.07 47.48
Gibilmanna, Italy 14.0186 37.9871
GooseBay, Canada -60.30 53.23
Graciosa_Island, Azores_Portugal -28.026 39.092
GreatSmokeyMtns, USA -83.80 35.60
Gross-Enzersdorf, Austria 16.56 48.20
Halifax, Canada -63.66 44.73
Haute_Provence, France 5.7 43.94
Havana, Cuba -82.38 23.12
Helsinki, Finland 24.94 60.17
Hilla_Babylon, Iraq 44.41 32.50
Hohenpeissenberg, Germany 11.02 47.80
HradecKralove, CzechRepublic 15.83 50.19
Hyderabad, India 78.43 17.37
Invercargill, NewZealand 168.33 -46.42
Iquique, Chile -70.17861 -20.53972
Ispra, Italy 8.63 45.81
Istanbul, Turkey 28.82 40.97
Izana, Tenerife_Spain -16.50 28.49
Izmir, Turkey 27.02 38.52
Jokioinen, Finland 23.50 60.81
Jungfraujoch, Switzerland 7.9853 46.5474
Kagoshima, Japan 130.50 31.50
Kamphaeng_Phet, Thailand 99.523 16.483
Kayseri, Turkey 35.42 38.82
Kiev, Ukraine 30.523 50.45
Kingston, Australia 147.29 -42.99
Ko_Samui, Thailand 100.014 9.512
Kolkata, India 88.33 22.50
La_Quiaca, Argentina -65.60 -22.10
LabskaBouda, CzechRepublic 15.55 50.76
Lampedusa, Italy 12.60 35.50
Lauder, NewZealand 169.68 -45.04
Leba, Poland 17.53 54.75
Legionowo, Poland 20.97 52.40
Leigh, NewZealand 175.00 -36.50
Lerwick, GreatBritain -1.19 60.14
Lindenberg, Germany 14.12 52.21
Lisbon, Portugal -9.15 38.77
Ljubljana, Slovenia 14.51 46.06
Locarno, Switzerland 8.7874 46.1726
London, GreatBritain -0.12 51.4994
Luxembourg, Luxembourg 6.13 49.61
Lyon, France 4.834 45.768
Macquerie_Island, Australia 158.94 -54.50
Madrid, Spain -3.70 40.42
Maitri, Antarctica 11.75 -70.75
MalinHead, Ireland -7.34 55.37
Malta_airport, Malta 14.48 35.85
Manchester, GreatBritain -2.23 53.28
Mar_del_Plata, Argentina -57.524 -38.017
Marambio, Argentina -64.24 -56.62
Mardin, Turkey 40.73 37.30
MaunaLoa, USA -155.58 19.53
Mawson, Australia 62.87 -67.60
Mecca, Saudi_Arabia 39.82 21.42
Melbourne, Australia 145.10 -37.73
Mendel_Ross_Island, Antarctica -57.88 -63.80
Mendoza, Argentina -68.50 -32.53
Mil.-Airport_Tatoi, Greece 23.78 38.11
Minsk, Belarus 27.56 53.90
Monaco, Monaco 7.42 43.74
Montreal, Canada -73.75 45.47
Moscow, Russia 37.50 55.70
MountWaliguan, China 100.90 36.30
Mugla, Turkey 28.37 37.22
Mumbai, India 72.85 18.93
Nadi, Fiji 177.45 -17.76
Naha, Japan 127.65 26.17
Nashville_Airport, USA -86.68 36.12
Nea_Mihaniona, Greece 22.85 40.47
Neuherberg, Germany 11.58 48.22
New_Delhi, India 77.22 28.62
Newcastle, Australia 151.72 -32.90
Nicosia, Cyprus 33.38 35.19
Norrkoping, Sweden 16.15 58.58
Nuuk, Greenland -51.69 64.18
Obninsk, Russia 55.09 35.97
Oesteraas, Norway 10.75 59.92
Offenbach, Germany 8.65 50.01
Oslo, Norway 10.717 59.938
Palmer, Antarctica -64.00 -64.70
Paramaribo, Surinam -55.20 5.75
Paraparaumu, NewZealand 174.98 -40.90
Paris, France 2.34 48.85
Payerne, Switzerland 6.9424 46.8116
Penhas_Douradas, Portugal -7.55 40.58
Perth, Australia 115.96 -31.92
Pilar, Argentina -63.88 -31.66
Podgorica, Montenegro 19.26 42.43
Pohang, Korea 129.35 36.00
Poprad-Ganovce, Slovakia 20.29 49.00
Potsdam, Germany 13.08 52.36
Prague, CzechRepublic 14.44 50.08
Pristina, Kosovo 21.17 42.67
Pucallpa, Peru -74.55 -8.38
Puerto_Madryn, Argentina -64.811 -42.595
Puerto_Quequa, Argentina -58.637 -38.566
Pune, India 73.80 18.52
PuntaArenas, Chile -70.90 -53.00
Rarotonga, CookIslands -159.80 -21.20
Reading, GreatBritain -0.93 51.45
Regina_BrattsLake, Canada -104.74 50.18
ResearchTrianglePk, USA -78.90 35.90
Resolute, Canada -95.01 74.69
Reunion, France 55.5 -20.94
Reykjavik, Iceland -21.82 64.13
Riga, Latvia 24.11 56.95
Rio_Gallegos, Argentina -69.32 -51.60
Rio_Negro, Argentina -62.890 -41.081
Rize, Turkey 40.52 41.03
RockyMountain, USA -105.50 40.00
Rome, Italy 12.52 41.90
SaintPetersburg, USA -82.68 27.77
Salar_de_Uyuni, Bolivia -67.40 -20.20
Samsun, Turkey 36.3 41.28
SanDiego, USA -117.11 32.45
SanFrancisco, USA -122.42 37.78
SanMarino, SanMarino 12.46 43.94
SanPedroDeAtacama, Chile -68.20000 -22.91083
Santiago, Chile -70.6545 -33.42
SaoPaulo, Brazil -46.64 -23.55
Sapporo, Japan 141.30 43.02
Sarajevo, BosniaHerzegovina 18.41 43.86
Saskatoon, Canada -106.71 52.11
SaturnaIsland, Canada -123.13 48.78
Seoul, South_Korea 127.03 37.35
Shenandoah, USA -78.40 38.50
Skopje, MacedoniaRepublic 21.43 41.9973
Sodankyla, Finland 26.63 67.37
Sofia, Bulgaria 23.32 42.70
Songkhla, Thailand 100.600 7.200
Sonnblick, Austria 12.95 47.05
Srinagar, India 74.83 34.13
Stockholm, Sweden 18.08 59.33
Sulaimaniya, Iraq 45.43 35.55
Sydney, Australia 151.10 -34.04
Syowa, Japan 39.55 -69.03
Taipei, Taiwan 121.49 24.99
Tallinn, Estonia 24.75 59.44
Tarija, Bolivia -64.721 -21.543
Tartu, Estonia 26.50 58.30
Tateno_Tsukuba, Japan 140.07 36.02
Tehran, Iran 51.43 35.67
Tel_Aviv, Israel 34.77 32.070
Thessaloniki, Greece 22.96 40.63
Tianjin, China 117.20 39.08
Tirana, Albania 19.82 41.33
Tokyo, Japan 139.67 35.65
Toowoomba, Australia 151.55 -27.22
Toronto, Canada -79.47 43.78
Townsville, Australia 146.76 -19.33
Tromso, Norway 18.93 69.66
Trondheim, Norway 10.47 63.43
Ubon_Ratchathani, Thailand 104.869 15.246
Uccle, Belgium 4.36 50.80
Ushuaia, Argentina -68.31 -54.85
Vaduz, Liechtenstein 9.52 47.14
Valdivia, Chile -73.15 -39.48
Valletta, Malta 14.51 36.90
Valparaiso, Chile -71.620 -33.040
Van, Turkey 43.32 38.45
Venice, Italy 12.33 45.43
Vienna, Austria 16.35 48.23
VilleneuvedAscq, France 3.14 50.61
Vilnius, Lithuania 25.28 54.69
Vindeln, Sweden 19.77 64.23
VirginIslands, USA -64.80 18.30
Warsaw, Poland 21.01 52.23
Winnipeg, Canada -97.24 49.91
Zagreb, Croatia 15.98 45.82
Zakopane, Poland 19.97 49.30
Zugspitze, Germany 10.98 47.42
Dataset reference:
Van Geffen, J., Van Weele, M., Allaart, M. and Van der A, R.: 2017,
TEMIS UV index and UV dose MSR-2 data products, version 2.
Dataset. Royal Netherlands Meteorological Institute (KNMI).
doi.org/10.21944/temis-uv-msr2-v2

 
The location of the stations is marked by red dots.

 
 

Data description

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

# MSR-2 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   = N/A
#
# 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
...

Cloud-modified UV dose data is not computed for the MSR-2 data record
hence those data columns 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.

 


last modified: 24 May 2018
Copyright © KNMI / TEMIS