UV station data based on MSR-2 ozone data

go to TEMIS Home Page

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 period: April 1970 - December 2015 (two days are missing: 31 Dec. 2012 and 31 Dec. 2015). Cloud-modified UV dose data is not computed.

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
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
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
Baoding, China 115.46 38.87
Barrow, USA -156.60 71.32
Belsk, Poland 20.78 51.83
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
Briancon, France 6.65 44.90
Brisbane, Australia 153.03 -27.45
Brno, Czechia 16.60 49.20
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
Churchill, Canada -94.00 58.75
Clark_New_Jersey, USA -74.31 40.64
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
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
GooseBay, Canada -60.30 53.23
GreatSmokeyMtns, USA -83.80 35.60
Halifax, Canada -63.66 44.73
Haute_Provence, France 5.7 43.94
Havana, Cuba -82.38 23.12
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
Kayseri, Turkey 35.42 38.82
Kiev, Ukraine 30.523 50.45
Kingston, Australia 147.29 -42.99
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
Lindenberg, Germany 14.12 52.21
Lisbon, Portugal -9.15 38.77
Locarno, Switzerland 8.7874 46.1726
Lyon, France 4.834 45.768
Macquerie_Island, Australia 158.94 -54.50
Maitri, Antarctica 11.75 -70.75
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
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
Norrkoping, Sweden 16.15 58.58
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
Pohang, Korea 129.35 36.00
Poprad-Ganovce, Slovakia 20.29 49.00
Potsdam, Germany 13.08 52.36
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
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
Samsun, Turkey 36.3 41.28
SanDiego, USA -117.11 32.45
SanFrancisco, USA -122.42 37.78
SanPedroDeAtacama, Chile -68.20000 -22.91083
Santiago, Chile -70.6545 -33.42
SaoPaulo, Brazil -46.64 -23.55
Sapporo, Japan 141.30 43.02
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
Sodankyla, Finland 26.63 67.37
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
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
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
Uccle, Belgium 4.36 50.80
Ushuaia, Argentina -68.31 -54.85
Valdivia, Chile -73.15 -39.48
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
Vindeln, Sweden 19.77 64.23
VirginIslands, USA -64.80 18.30
Winnipeg, Canada -97.24 49.91
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: 14 August 2017
Copyright © KNMI / TEMIS