This page provides a sampling of recent research using observations related to reanalyses.^*

ECA&D dataset: 

• Original paper: Klein Tank, A.M.G. and et al., 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, 1441-1453, doi:10.1002/joc.773
• Paper updated dataset: Klok, E.J. and A.M.G. Klein Tank, 2009. Updated and extended European dataset of daily climate observations. Int. J. Climatol., 29, 1182, doi:10.1002/joc.1779.

E-OBS dataset:

• Temperature and precipitation: Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones, and M. New, 2008, A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.
• Sea level pressure: van den Besselaar, E.J.M., M.R. Haylock, G. van der Schrier, A.M.G. Klein Tank, 2011, A European Daily High-resolution Observational Gridded Data set of Sea Level Pressure, J. Geophys. Res., 116, D11110, doi:10.1029/2010JD015468

Research papers (by topic):

Circulation

 

• Circulation and temperature extremes: van den Besselaar, E.J.M., A.M.G. Klein Tank, G. van der Schrier, 2010. Influence of circulation types on temperature extremes in Europe. Theor. Appl. Climatol., 99, 431-439, doi:10.1007/s00704-009-0153-6
• Walker circulation:  L'Heureux, M.L., S. Lee, and B. Lyon, 2013. Recent multidecadal strengthening of the Walker circulation across the tropical Pacific.  Nature Clim Change, doi:10.1038/nclimate1840. Discussion Page.
• Winds near the surface. Kent, E. C., Fangohr, S. and Berry, D. I., 2012: A comparative assessment of monthly mean wind speed products over the global ocean. Int. J. Climatol., in press, doi:10.1002/joc.3606.

  Datasets/Analysis Methods

• High resolution global hourly near surface air temperature: Wang, A., X. Zeng, 2013: Development of globaly hourly 0.5-degree land surface air temperature datasets. J. Climate, in press, doi:10.1175/JCLI-D-12-00682.1. Discussion page
• E-OBS evaluation paper: Hofstra, N., M. Haylock, M. New, P.D. Jones, 2009. Testing E-OBS European high-resolution gridded dataset of daily precipitation and surface temperature. J. Geophys. Res., 114, D21101, doi:10.1029/2009JD011799
• Effect of GTS data on climate series: van den Besselaar, E.J.M, A.M.G. Klein Tank, G. van der Schrier, P.D. Jones, 2012, Synoptic messages to extend climate data records, J. Geophys. Res., 117, D07101, doi:10.1029/2011JD016687

 Extremes

• Worldwide trends in indices of extremes: Alexander, L.V., X. Zhang, T.C. Peterson, J. Caesar, B. Gleason, A.M.G. Klein Tank, M. Haylock, D. Collins, B. Trewin, F. Rahimzadeh, A. Tagipour, P. Ambenje, K. Rupa Kumar, J. Revadekar and G. Griffiths, 2006. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290
• Global dataset of temperature and precipitation extremes indices 1901-2010: Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, R. J. H. Dunn, K. M. Willett, E. Aguilar, M. Brunet, J. Caesar, B. Hewitson, C. Jack, A. M. G. Klein Tank, A. C. Kruger, J. Marengo, T. C. Peterson, M. Renom, C. Oria Rojas, M. Rusticucci, J. Salinger, A. S. Elrayah, S. S. Sekele, A. K. Srivastava, B. Trewin, C. Villarroel, L. A. Vincent, P. Zhai, X. Zhang and S. Kitching, 2013: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset, J. Geophys. Res. Atmos., 118, 2098–2118, doi:10.1002/jgrd.50150
• Temperature and precipitation extremes indices for climate monitoring 1951-present:  Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, J. Caesar, 2013: Global land-based datasets for monitoring climatic extremes, Bulletin of the American Meteorological Society, 94, 997-1006, doi:10.1175/BAMS-D-12-00109.1
• Wind extremes: Brönnimann, S., O. Martius, H. von Waldow, C. Welker, J. Luterbacher, G.P. Compo, P.D. Sardeshmukh, and T. Usbeck, 2012: Extreme winds at northern mid-latitudes since 1871. Meteorol. Zeit., 21, 13-27, doi: 10.1127/0941-2948/2012/0337

​Synoptic Events:

 

• Wind gusts thresholds for Meteoalarm: Stepek, A., I.L. Wijnant, G. van der Schrier, E.J.M. van den Besselaar, A.M.G. Klein Tank, 2012. Severe wind gust thresholds for Meteoalarm derived from uniform return periods in ECA&D. Nat. Hazards Earth Syst. Sci., 12, 1969-1981, doi:10.5194/nhess-12-1969-2012

 Trends

• European precipitation extremes: van den Besselaar, E.J.M., A.M.G. Klein Tank, T.A. Buishand, 2012. Trends in European precipitation extremes over 1951-2010. Int. J. Climatol., doi:10.1002/joc.3619
• E-OBS scaling paper: Hofstra, N., M. New, C. McSweeney, 2010. The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Clym.Dyn., 35, 841, doi:10.1007/s00382-009-0698-1
• Walker circulation:  L'Heureux, M.L., S. Lee, and B. Lyon, 2013. Recent multidecadal strengthening of the Walker circulation across the tropical Pacific.  Nature Clim Change, doi:10.1038/nclimate1840.

^*This list is not comprehensive, but the interested reader should feel free to request an account and include any relevant published research the reader think may be useful to communicate. Please use the existing categories, only adding new ones as needed. Add a brief description and use reverse chronological order under each topic. Readers are encouraged to link a Discussion Page summarizing their paper, presentation, or other results.

The ICA&D (International Climate Assessment & Dataset) climate services concept successfully combines the work of WMO`s Expert Team on Climate Change Detection and Indices (ETCCDI) and WMO`s Data Rescue (DARE) activities. The concept builds on the sofware developed for the European Climate Assessment & Dataset (ECA&D), a webportal for daily station data and derived indices brought together under regional cooperation. ICA&D combines the climate monitoring and assessment activities developed in ECA&D with DARE activities. ICA&D is already applied in the four regions mentioned below.

ECA&D:

The European Climate Assessment & Dataset (ECA&D) is the result of a collaboration between meteorological institutes and universities throughout Europe and the Meditteranean area, WMO Region VI. One of the partners is the MEDARE initiative which coordinates DARE activities across the Greater Mediterranean Region.

SACA&D:

The Southeast Asian Climate Assessment & Dataset (SACA&D) is a result of the DiDaH (Digitisasi Data Historis) project, an ongoing cooperation in data digitization between the Indonesian national meteorological institute (BMKG) and KNMI. BMKG staff were trained by the ECA&D team to set-up the ICA&D system in Southeast Asia.

LACA&D:

The ICA&D system is currently under development in Latin America as the Climate Assessment & Dataset (LACA&D) in collaboration with CIIFEN. This actvity builds on a regional indices workshop held in Guayaquil (Ecuador) in January 2011.

WACA&D:

First steps are being taken to set-up the West African Climate Assessment & Dataset (WACA&D) in collaboration with ACMAD.

ECA&D:

The European Climate Assessment & Dataset (ECA&D) contains daily land station observations for >7500 stations throughout Europe and the Mediterranean. The data is provided by National Meteorological Services, Universities as well as by other data providers. Everyone with daily station observations can participate.

The available elements are:

• Minimum temperature
• Mean temperature
• Maximum temperature
• Precipitation amount
• Sea level pressure
• Snow depth
• Relative humidity
• Sunshine duration
• Cloud cover
• Wind speed
• Maximum wind gust
• Wind direction     

About half of the daily station series are available for download for non-commercial research projects and education. The ECA&D data policy gives more details about the use of the available data. Often, additional metadata is available, such as the surface coverage of the station, soil type or a picture of the observing site.

Basic quality control is performed on each of the series. The station series are then blended with those from nearby stations and the Global Telecommunication System (GTS) to create series that are as long as possible. These series are used to derive a large variety of indices of extremes, such as the number of wet days, number of frost days, but also more exotic indices like the growing season length, potential evapotranspiration and the universal thermal climate index. Time series plots as well as derived information, such as trends and anomalies, are available and shown on maps. Also homogeneity checks are done and climatologies are calculated.

E-OBS:

The blended station series are used to create the E-OBS gridded dataset which has a daily resolution. A three-step methodology of interpolating the daily data is used: interpolating the monthly mean using thin-plate splines to define the underlying spatial ‘‘trend’’ of the data; kriging the anomalies with regard to the monthly mean; and applying the interpolated anomaly to the interpolated monthly mean to create the final result. Files with daily standard errors and elevation are available too.

E-OBS is available for the period 1950-present. There are 4 different versions: 2 grid resolutions x 2 grid flavours. Data is made available on a 0.25 and 0.5 degree regular lat-lon grid, as well as on a 0.22 and 0.44 degree rotated pole grid, with the north pole at 39.25N, 162W. The dataset is available for 5 elements:

• Minimum temperature
• Mean temperature
• Maximum temperature
• Precipitation amount
• Sea level pressure

References:

• ATBD: Algorithm Theoretical Basis Document.
• Klein Tank, A.M.G. and et al., 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, 1441-1453, doi:10.1002/joc.773.
• Klok, E.J. and A.M.G. Klein Tank, 2009. Updated and extended European dataset of daily climate observations. Int. J. Climatol., 29, 1182, doi:10.1002/joc.1779.
• Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.
• van den Besselaar, E.J.M., M.R. Haylock, G. van der Schrier, A.M.G. Klein Tank, 2011, A European Daily High-resolution Observational Gridded Data set of Sea Level Pressure, J. Geophys. Res., 116, D11110, doi:10.1029/2010JD015468

From: Vose, R. S., S. Applequist, M. J. Menne, C. N. Williams Jr., and P. Thorne. 2012: An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses. Geophys. Res. Lett., 39, L10703, doi:10.1029/2012GL051387.

Least-squares trends (°C dec^-1) in mean annual temperature over the conterminous United States during the period 1979-2008.

RAOBCORE and RICH

online Viewers now available!

RAOBCORE => RAdiosone OBservation COrrection using REanalyses

RICH              => Radiosonde Innovation Composite Homogenization

New visualization utility for RAOBCORE and RICH adjusted global radiosonde dataset now available:

RAOBCORE/RICH

Version 1.5.1

http://srvx7.img.univie.ac.at/~leo/richvis/newindex.html 

New visualization utility for RAOBCORE 2.0 global radiosonde dataset now available.

Adjustments are not yet provided.

http://srvx7.img.univie.ac.at/~lorenzo/DEVL_rrvis_2.0/html/

Notes: Viewer RAOBCORE 2.0

• Station : WMO radiosonde station number (not all the stations ara available for all the archives)
• Varibale;
• Pressure level [hPa];
• Type: observation, biascorrection (if available), FirstGuess departures (if available) and Analysis departures ( if available); 
• Smooth: running mean over the time serie;
• Experiment: only Control is available;
• Data from: in this menu are shown all the possible soure of data we are using:
  • Stations_ERA_INTERIM: data from ERA_INTERIM (observations and departures);
  • Stations_ERA_40: data from ERA_40 (obseravtiona and departures);
  • Stations_CHUAN_montly: monthly data from the CHUAN archive (observations from CHUAN);
  • Stations_CHUAN_daily: daily data from the CHUAN archive (observation from CHUAN, Analysis departures from 20CR);
  • 20CR: recoverd (only in presence of observation) time series from the 20CR interpolated at the station latitude and longitude;
  • ERA_40: recoverd (only in presence of observation) time series from ERA_40 interpolated at the station latitude and longitude;
  • long_20CR: recoverd time series (covers all the 20CR length) from the 20CR interpolated at the station latitude and longitude;
  • MERGED_archive: it contains merged obseravtions data from ERA_INTERIM, ERA_40 and CHUAN; the analysis departures are from 20CR.
• Adjustment_Method: only Unadjusted is now available;
• Version: only v2.0 is available;
• Long_Time: 
  • 00h: data at Midnight;
  • 12h: data at Noon;
  • 00h - 12h: difference between Midnight  and Noon;
• PlotSetting:
  • Jump_Missing: if there is a missind value, nothing is plotted;
  • Linear_Connection: if there is a missing value, a linear connection between the last available and the new available will be plotted;
• Metadata: shows (if available) the metadata for the selected station; 
• Overplot: allows plotting many time series on the same frame;
• Tooltip: gives hints about the different menus;
• Autorefresh: at each selection it tries to plot the current setting;
• Range: scaled for the time axis;
• Delete Last: deletes the last plot form the frame;
• Delete All : deletes all the plots from the frame;

In order to zoom in: select with a mouse and drag the area that you would like to inspect. It will be magnified in the main frame. 

Suggested stations for a easy start :

• Temperature:

Station: 010393, Merged archive, pressure 1000 or 850 hPa -> long time series back to 1905

Station: 004018, Merged archive, pressure 850 hPa -> continue time serie back to 1946;

• U and V Wind component:

Station: 016716, Merged archive, pressure 200 hPa, Type: Observations and Analysis depertures -> big shift (better visible with smooth bigger than 100);

•  Wind Speed:

Station: 016716, Merged archive, pressure 200 hPa, Type: Observations and Analysis depertures -> big shift (better visible with smooth bigger than 100)  

Several issues arise with quality controlling (QC) station data.

In HadISD version 1, the UK Met Office Hadley Centre is using the following procedures to subset and QC the data:

[RJHD] I've added a one sentence description of the QC tests used in HadISD which were mostly outlined in the talk - just to jog memories.[/RJHD]

Prior to QC:

Duplicate Stations: full station repetition (i.e. that the data in one station ID is repeated under another station ID somewhere. For example,  if merging of stations for some reason didn't happen correctly.)

Internal Tests:

Duplicate Months: full month repetition within station

Frequent Value Check: Identify values on total dataset, flag on annual basis if still anomalous.

Gap Check: Flag months who's median is different to average, flag observations which are different to rest of population (both on calendar month basis)

Streak Check: streaks of the same value are flagged

Spike Check: spikes of up to three points are removed

Climatological Check: observations which are different to the climatology for each hour/month as calculated from the station are flagged.

Variance Check: months with differing within-station variance (high/low) to the rest of the record are flagged

Odd Clusters: short isolated clusters of data are flagged

Diurnal Cycle: periods with diurnal cycles apparently significantly offset to the rest of the series are flagged

Humidity Checks: excessively long periods of supersaturation and dew-point depression are removed.

Cloud Cover Logical Checks: logical consistency checks for Low, Mid, High and Total cloud amounts

External Tests:

Neighbour Check: up to 10 neighbours within 300km and 500m height are used to check if station values are reasonable.  Can also remove flags from certain tests.

Clean-Up: flag months with excessive numbers of flags or very few remaining observations.

Issues:

For these hourly and subdaily data, issues with these include:

1. the 300 km radius may mingle stations that have different climates when performing a neighbor check.
   1. weigthing in favor of the nearest stations during the evaluations might be important
2. surface pressure should be included as a processed variable to allow derivation of specific humidity from the dewpoint
3. Coordinated and somewhat formalized feedback to NCDC should be considered
   1. e.g. when and how will NCDC use recommendations for station fixes uncovered by HadISD - users will eventually want to know this.
4. When examining the dual valued curves, as shown in many of the QC examples, on should inspect to see that only one value is at each time interval, i.e. there are not duplicates at 0Z
5. As much source data metadata, from NCDC ISD, should be carried forward in the netCDF metadata files, e.g.
   1. what ISDversion is the starting point for HADISD.
   2. station metadata (lat, lon, elev),
   3. software version,
   4. doi?
6. Possibly detail inventories for flagging, step by step in the QC process, could be useful for examining systematic errors.  This could also be station by station and maintained online for user viewing.  A condensed version of this should be published - as is planned.
7. Test if Gaussian a good fit, and try other distributions when trying to characterise the width of the population distribution
8. Use wind direction and wind speed  together when doing QC
9. If QC removes large sections of data after a clear break in the system, homogenisation checks won't find the now-removed break
10. Provide metrics for flags and unflagging for final products.
11. More than just 2 levels of flagging
12. Use two missing data indicators, one for missing, one for flagged/removed

Work in progress

Digitization of Indian Monsoon data 1893-1899 are funded by the University of Giessen, Germany.

Feedback information in the ISPD comes from the 20th Century Reanalysis Project. The 20th Century Reanalysis Project used resources of the National Energy Research Scientific Computing Center and of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and Contract No. DE-AC05-00OR22725, respectively. Support for the 20th Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. Support for the compilation of the ISPD is provided by the National Oceanic and Atmospheric Administration Climate Program Office and SwissRe.

Guidelines for station data submission to the International Surface Pressure Databank

To submit station observations, please contact Dr. Gil Compo of the University of Colorado Cooperative Institute for Research in Environmental Sciences  and NOAA Earth System Research Laboratory Physical Sciences Division using <compo@colorado.edu>.

Observations of pressure, either surface or sea level pressure from the time that they were taken, are welcome.

Please include essential metadata such as the time that the observation was taken and station latitude, longitude, and elevation.

Depending on the size of the files, observation data files can be emailed to NOAA's National Climatic Data Center to the dataset assembler Dr. Xungang Yin Xungang.Yin@noaa.gov.

Please submit the data to NOAA's National Climatic Data Center Xungang.Yin@noaa.gov using the ASCII exchange format v1.0. Other data formats can also be accepted. Please coordinate your format with Dr. Yin and Dr. Compo

Depending on the instrument, the pressure data should be corrected for gravity and temperature. The data do not need to be reduced to sea level. 

If you have temperature data of subdaily, daily-averaged, or monthly-averaged resolution, please consider submitting that to surfacetemperatures.org.

Back to ISPD homepage

1. All-Russian Research Institute of Hydrometeorological Information World Data Center
2. Atmospheric Circulation Reconstructions over the Earth (ACRE)
3. Australian Bureau of Meteorology
4. Australian Meteorological Association, Citizen Science team
5. British Antarctic Survey
6. China University of Geosciences
7. China Meteorological Administration
8. Cook Islands Meteorological Service
9. Danish Meteorological Institute
10. Deutscher Wetterdienst (DWD; German Weather Service)
11. Environment Canada, Climate Research Division
12. ETH Zurich, Switzerland
13. European and North Atlantic Daily to Multidecadal Climate Variability (EMULATE)
14. European Reanalysis and Observations for Monitoring (EURO4M)/The WMO MEditerranean DAta REscue Initiative (MEDARE)
15. European Reanalysis of Global Climate Observations (ERA-CLIM)
16. GCOS Atmospheric Observation and Ocean Observation Panels for Climate WG on Surface Pressure
17. GCOS/WCRP Working Group on Observational Data Sets for Reanalysis
18. Hong Kong Observatory
19. Icelandic Meteorological Office (IMO)
20. International Best Track Archive for Climate Stewardship (IBTrACS)
21. International Comprehensive Ocean–Atmosphere Data Set (ICOADS)
22. International Environmental Data Rescue Organization (IEDRO)
23. Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
24. Japan Meteorological Agency
25. Jersey Met Department
26. Koninklijk Nederlands Meteorologisch Instituut (KNMI; Royal Netherlands Meteorological Institute)
27. Lamont-Doherty Earth Observatory of Columbia University
28. McGill University, Canada
29. Met Office Hadley Centre, UK
30. MetéoFrance
31. MeteoFrance—Division of Climate
32. Meteorological and Hydrological Service, Croatia
33. National Center for Atmospheric Research (NCAR), USA
34. National Climate Center, Beijing, China
35. National Institute for Water and Atmospheric Research (NIWA), New Zealand
36. Nicolaus Copernicus University—Department of Meteorology and Climatology, Poland
37. Niue Meteorological Service
38. NOAA Climate Database Modernization Program (CDMP), USA
39. NOAA Earth System Research Laboratory (ESRL) Physical Sciences Division, USA
40. NOAA Midwest Regional Climate Center at UIUC, USA
41. NOAA National Centers for Environmental Prediction (NCEP), USA
42. NOAA National Centers for Environmental Information (NCEI), USA
43. NOAA Northeast Regional Climate Center at Cornell Univ., USA
44. NOAA Pacific Marine Environmental Laboratory, USA
45. Norwegian Meteorological Institute
46. Ohio State Univ.—Byrd Polar Research Center, USA
47. Oldweather.org
48. Portguese Institute of Sea and Atmosphere (IPMA), Portugal
49. Proudman Oceanographic Laboratory, UK
50. Signatures of environmental change in the observations of the Geophysical Institutes (SIGN)
51. South African Weather Service
52. South Eastern Australian Recent Climate History (SEARCH) project, The University of Melbourne
53. Tanzania Meteorological Agency
54. Univ. of Aberdeen, Scotland, UK
55. Univ. of Bern, Switzerland
56. Univ. of Colorado—Cooperative Institute for Research in Environmental Sciences (CIRES)
57. Univ. of East Anglia—Climatic Research Unit, UK
58. Univ. of Giessen—Department of Geography, Germany
59. Univ. of Lisbon—Instituto Dom Luiz, Portugal
60. Univ. of Milan—Department of Physics, Italy
61. Univ. of Porto-Instituto Geofisico, Portugal
62. Univ. Rovira i Virgili—Center for Climate Change (C3), Spain
63. Univ. of South Carolina, USA
64. Univ. of Toronto-Department of Physics, Canada
65. Univ. of Washington, USA
66. Weather Detective citizen science project
67. WeatherRescue.org
68. World Meteorological Organization-Mediterranean Climate Data Rescue (MEDARE)
69. ZentralAnstalt für Meteorologie und Geodynamik (ZAMG; Austrian Weather Service)

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The International Surface Pressure Databank