UV station data based on MSR-2 ozone data

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UV archive
overview

UV index main page  |  UV dose main page
 

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 ozone data period. 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:
In June 2023 the earlier released record over 1970-1979 has been
reprocessed with an improved MSR-2 ozone data record; at the
same time the record was extended backwards to 1960.

 

station/place name
(click to download ascii file)
longitude latitude
Abu_Dhabi, UAE 54.37734 24.45388
AcadiaNatForest, USA -68.30 44.40
Ad_Dammam, Saudi_Arabia 49.97771 26.39267
Adana, Turkey 35.35 36.98
Adelaide, Australia 138.62 -34.92
Ahmedabad, India 72.67 23.05
Ahvaz, Iran 48.67062 31.31833
Al-Khodh, Oman 58.454 23.594
Alert, Canada -62.35 82.47
Alexandria, Egypt 29.91874 31.20009
AliceSprings, Australia 133.90 -23.80
Amman, Jordan 35.93 31.95
Amsterdam, Netherlands 4.90 52.37
Anchorage, USA -149.90 61.22
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
Antartica, Chile -58.98 -62.19
Antofagasta, Chile -70.44 -23.45
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
Balzan, Malta 14.46111 35.895
Bangalore, India 77.59 12.97
Bangkok, Thailand 100.612 13.667
Baoding, China 115.46 38.87
Barrow, USA -156.60 71.32
Basra, Iraq 47.65 30.4167
Batu_Pahat, Malaysia 102.93135 1.85477
Beer-Sheva, Israel 34.783 31.233
Beirut, Lebanon 35.50178 33.89379
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
Bogota, Colombia -74.07209 4.71099
Bordeaux, France -0.53 44.84
Boston, USA -71.05 42.36
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, CzechRepublic 16.60 49.20
Bucharest, Romania 26.10 44.43
Budapest, Hungary 19.04 47.4979
Buenos_Aires, Argentina -58.48 -34.58
Cairo, Egypt 31.23571 30.04442
Caldera, Chile -70.77 -27.26
Calgary, Canada -114.084 51.084
Camborne, GreatBritain -5.30 50.20
Canyonlands, USA -109.80 38.50
Capital_Federal, Argentina -58.38159 -34.60372
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
Concepcion, Chile -73.06 -36.78
Copenhagen, Denmark 12.57 55.68
Cordoba, Argentina -64.18878 -31.42008
Coyhaique, Chile -72.11 -45.59
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
DeadSea, Jordan 35.55 31.55
Dehradun, India 78.029 30.318
Denali, USA -149.00 63.70
Dortmund, Germany 7.4690 51.5080
Dubai, UAE 55.29625 25.27699
Dublin, Ireland -6.2489 53.3331
Durban, SouthAfrica 30.98 -29.87
Edinburgh, GreatBritain -3.1965 55.9521
Edmonton, Canada -114.10 53.55
El_Colorado, Chile -70.29 -33.35
El_Tololo, Chile -70.80 -30.17
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
Ghor_el_Safi, Jordan 35.467 31.033
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
Guadalajara, Mexico -103.34961 20.65970
Guatemala, Guatemala -90.50688 14.63492
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
Hobart, Australia 147.32719 -42.88214
Hohenpeissenberg, Germany 11.02 47.80
Houston, USA -95.37 29.76
HradecKralove, CzechRepublic 15.83 50.19
Huixquilucan, Mexico -99.35090 19.35987
Hyderabad, India 78.43 17.37
Invercargill, NewZealand 168.33 -46.42
Iquique, Chile -70.17861 -20.53972
Isfahan, Iran 51.66798 32.65463
Isla_de_Pascua, Chile -109.43 -27.16
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
Kansas_City, USA -94.58 39.10
Kayseri, Turkey 35.42 38.82
Kermanshah, Iran 47.065 34.31417
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
Kuala_Lumpur, Malaysia 101.68686 3.13900
Kulmbach, Germany 11.4425 50.1031
Kuwait, Kuwait 47.97741 29.37586
La_Plata, Argentina -57.95357 -34.92050
La_Quiaca, Argentina -65.60 -22.10
La_Serena, Chile -71.20 -29.92
LabskaBouda, CzechRepublic 15.55 50.76
Lampedusa, Italy 12.60 35.50
Lanus, Argentina -58.4 -34.7
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
LlanoDeChajnantor, Chile -67.7863 -22.9594
Locarno, Switzerland 8.7874 46.1726
London, GreatBritain -0.12 51.4994
Lueneburg, Germany 10.4566 53.2470
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
Maiduguri, Nigeria 13.15712 11.84692
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
Mannouba, Tunisia 10.12166 36.80814
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
McMurdo_Station, Antarctica 166.668 -77.846
Mecca, Saudi_Arabia 39.82 21.42
Melbourne, Australia 145.10 -37.73
Melpitz, Germany 12.9280 51.5280
Mendel_Ross_Island, Antarctica -57.88 -63.80
Mendoza, Argentina -68.50 -32.53
Mexico_City, Mexico -99.13321 19.43261
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
Mosul, Iraq 43.15 36.3167
MountWaliguan, China 100.90 36.30
Mugla, Turkey 28.37 37.22
Mumbai, India 72.85 18.93
Muscat, Oman 58.54 23.61
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
Neve_Zohar, Israel 35.667 31.20
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, Suriname -55.20 5.75
ParanalObservatory, Chile -70.404167 -24.627222
Paraparaumu, NewZealand 174.98 -40.90
Paris, France 2.34 48.85
Payerne, Switzerland 6.9424 46.8116
Penang, Malaysia 100.33268 5.41639
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
Porto_Alegre, Brazil -51.21766 -30.03465
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_Montt, Chile -73.10 -41.44
Puerto_Quequa, Argentina -58.637 -38.566
Pune, India 73.80 18.52
PuntaArenas, Chile -70.90 -53.00
Putre, Chile -69.56 -18.20
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
Riyadh, Saudi_Arabia 46.67530 24.71355
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
Salta, Argentina -65.42320 -24.78213
Salzgitter, Germany 10.3310 52.1510
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
Schauinsland, Germany 7.9079 47.9137
Seattle, USA -122.33 47.61
Seoul, South_Korea 127.03 37.35
Seremban, Malaysia 101.93812 2.72555
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
South_Pole, Antarctica 0.001 -89.999
Srinagar, India 74.83 34.13
Stockholm, Sweden 18.08 59.33
Sulaimaniya, Iraq 45.43 35.55
Sydney, Australia 151.10 -34.04
Sylt, Germany 8.3250 54.8920
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
Temuco, Chile -72.55 -38.70
Termas_de_Chillan, Chile -71.41 -36.90
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
Tunis, Tunisia 10.18153 36.80650
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
Vallenar, Chile -70.76 -28.59
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
Zingst, Germany 8.6509 50.0048
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: 08 February 2024
Copyright © KNMI / TEMIS