This website uses cookies. By continuing to use this website you are agreeing to our use of cookies. 

Dataset

 

HadISDH blend: gridded global monthly ocean surface humidity data version 1.0.0.2019f

Update Frequency: Not Planned
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2020-06-29
DOI Publication Date: 2020-08-05
Download Stats: last 12 months

This dataset has been superseded. See Latest Version here
Abstract

This is the 1.0.0.2019f version of the HadISDH (Integrated Surface Database Humidity) blend data. It combines HadISDH.land.4.2.0.2019f and HadISDH.marine.1.0.0.2019f. These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2019.

The data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in NetCDF format.

This version is the first available.

To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.

For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/

References:

When using the dataset in a paper please cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference):

Willett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I.: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data, in review, doi:XX.XXXX/essd-XX-XXXX-2020, 2020.

Willett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014.

Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to the historical marine climate record. International Journal of Climatology. doi:10.1002/joc.4775.

Dunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.

Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1

Citable as:  Willett, K.M.; Dunn, R.J.H.; Kennedy, J.J.; Berry, D.I.; Thorne, P.W.; Bell, S.; de Podesta, M.; Parker, D.E.; Jones, P.D.; Williams, J.C.N. (2020): HadISDH blend: gridded global monthly ocean surface humidity data version 1.0.0.2019f. Centre for Environmental Data Analysis, 05 August 2020. doi:10.5285/d38d5949dfb1438185894321095583f4. http://dx.doi.org/10.5285/d38d5949dfb1438185894321095583f4
Abbreviation: Not defined
Keywords: HadISDH, blend, humidity, surface, marine, gridded, station, specific humidity, temperature, dew point temperature, wet bulb temperature, dew point temperature, vapour pressure, in-situ

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

HadISDH.blend is a global land (~2 m) and ocean (~10 m) surface humidity dataset and is produced by the Met Office Hadley Centre in collaboration with CRU, Maynooth University, NPL, NOAA-NCEI and NOC. It is based on the sub-daily station observations from HadISD (originally from ISD) and ship observations from ICOADS. It is passed to the BADC for archiving and distribution. Gridboxes containing both land and marine data are combined using a weighted average with a minimum and maximum weighting of 25% and 75% respectively.

Data Quality:
Uncertainty estimates are provided as part of the dataset both at the station and gridbox level, this includes information covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty. See dataset associated documentation for full details.
File Format:
These data are provided in NetCDF and ASCII formats, descriptors of the file formats are included in Docs.

Process overview

This dataset was generated by the computation detailed below.
Title HadISDH blend: gridded global land (~2 m) and ocean (~10 m) surface humidity dataset produced by the Met Office Hadley Centre
Abstract HadISDH.blend combines HadISDH.marine and HadISDH.land at the 5 degree by 5 degree gridbox monthly mean level. Gridboxes containing both land and marine data are combined using a weighted average with a minimum and maximum weighting of 25% and 75% respectively. HadISDH.marine utilises simultaneous sub-daily temperature and dew point temperature data from ICOADS ship data. All humidity variables are calculated at hourly resolution. Quality control, buddy checking and bias adjustment is applied at hourly resolution to adjust all observations to an observing height of 10 m, accounting for changing ship heights over time, and to adjust all non-ventilated instruments to mitigate the moist bias. Gridded monthly means, monthly mean anomalies and 1981 to 2010 climatologies are created. See Docs 'HadISDH.marine process diagram'. Observation measurement, climatological, whole number presence and bias adjustment uncertainties are estimated for each observation and then gridded. 5° by 5° gridboxes are centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Given the uneven distribution of observations over time and space, sampling uncertainty is estimated for each gridbox month. For greater detail please see: Willett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I.: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data, in review, doi:XX.XXXX/essd-XX-XXXX-2020, 2020. Docs contains links to this publication. HadISDH.land utilises simultaneous subdaily temperature and dew point temperature data from over 3000 quality controlled HadISD stations that have sufficiently long records. All humidity variables are calculated at hourly resolution and monthly means are created. Monthly means are homogenised to detect and adjust for features within the data that do not appear to be of climate origin. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. The NCEI's Pairwise Homogenisation Algorithm has been used directly on DPD and T. An indirect PHA method (ID PHA) is used whereby changepoints detected in DPD and T are used to make adjustments to q, e, Tw and RH. Changepoints from DPD are also applied to T. Td is derived from homogenised T and DPD. See Docs 'HadISDH.land process diagram'. Station measurement, climatological and homogeneity adjustment uncertainties are estimated for each month. Climatological averages are calculated over 1981-2010 and monthly mean climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values and uncertainty components) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Given the uneven distribution of stations over time and space, sampling uncertainty is estimated for each gridbox month. For greater detail please see: Willett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. Willett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013. Docs contains links to both these publications.
Input Description None
Output Description None
Software Reference None
  • long_name: actual number of observations within gridbox
  • var_id: land_stncount
  • units: 1
  • names: actual number of observations within gridbox
  • long_name: climatology period boundaries
  • var_id: climbounds
  • names: climatology period boundaries
  • long_name: mean number of observations within gridbox
  • var_id: land_meanstncount
  • units: 1
  • names: mean number of observations within gridbox
  • var_id: marine_meanpseudostncount
  • long_name: mean number of pseudo stations within gridbox
  • units: 1
  • names: mean number of pseudo stations within gridbox
  • long_name: month of year
  • var_id: month
  • names: month of year
  • standard_name: air_temperature
  • long_name: near surface (~2/10m) air temperature
  • units: deg C
  • var_id: tas
  • names: air_temperature, near surface (~2/10m) air temperature
  • long_name: near surface (~2/10m) air temperature anomaly
  • units: deg C
  • var_id: tasa
  • names: near surface (~2/10m) air temperature anomaly
  • long_name: near surface (~2/10m) air temperature climatological standard deviations
  • var_id: marine_clmstd
  • units: deg C
  • names: near surface (~2/10m) air temperature climatological standard deviations
  • var_id: clm
  • long_name: near surface (~2/10m) air temperature climatology
  • units: deg C
  • names: near surface (~2/10m) air temperature climatology
  • long_name: near surface (~2/10m) dewpoint depression
  • standard_name: dew_point_depression
  • var_id: dpds
  • units: deg C
  • names: dew_point_depression, near surface (~2/10m) dewpoint depression
  • var_id: dpdsa
  • units: deg C
  • long_name: near surface (~2/10m) dewpoint depression anomaly
  • names: near surface (~2/10m) dewpoint depression anomaly
  • var_id: marine_clmstd
  • units: deg C
  • long_name: near surface (~2/10m) dewpoint depression climatological standard deviations
  • names: near surface (~2/10m) dewpoint depression climatological standard deviations
  • long_name: near surface (~2/10m) dewpoint depression climatology
  • var_id: clm
  • units: deg C
  • names: near surface (~2/10m) dewpoint depression climatology
  • standard_name: dew_point_temperature
  • var_id: tds
  • long_name: near surface (~2/10m) dewpoint temperature
  • units: deg C
  • names: dew_point_temperature, near surface (~2/10m) dewpoint temperature
  • long_name: near surface (~2/10m) dewpoint temperature anomaly
  • var_id: tdsa
  • units: deg C
  • names: near surface (~2/10m) dewpoint temperature anomaly
  • long_name: near surface (~2/10m) dewpoint temperature climatological standard deviations
  • var_id: marine_clmstd
  • units: deg C
  • names: near surface (~2/10m) dewpoint temperature climatological standard deviations
  • var_id: clm
  • long_name: near surface (~2/10m) dewpoint temperature climatology
  • units: deg C
  • names: near surface (~2/10m) dewpoint temperature climatology
  • long_name: near surface (~2/10m) relative humidity
  • standard_name: relative_humidity
  • units: %rh
  • var_id: hurs
  • names: relative_humidity, near surface (~2/10m) relative humidity
  • var_id: hursa
  • units: %rh
  • long_name: near surface (~2/10m) relative humidity anomaly
  • names: near surface (~2/10m) relative humidity anomaly
  • var_id: marine_clmstd
  • units: %rh
  • long_name: near surface (~2/10m) relative humidity climatological standard deviations
  • names: near surface (~2/10m) relative humidity climatological standard deviations
  • long_name: near surface (~2/10m) relative humidity climatology
  • var_id: clm
  • units: %rh
  • names: near surface (~2/10m) relative humidity climatology
  • var_id: vps
  • long_name: near surface (~2/10m) vapor pressure
  • units: hPa
  • standard_name: water_vapor_partial_pressure_in_air
  • names: water_vapor_partial_pressure_in_air, near surface (~2/10m) vapor pressure
  • long_name: near surface (~2/10m) vapor pressure anomaly
  • var_id: vpsa
  • units: hPa
  • names: near surface (~2/10m) vapor pressure anomaly
  • long_name: near surface (~2/10m) vapor pressure climatological standard deviations
  • var_id: marine_clmstd
  • units: hPa
  • names: near surface (~2/10m) vapor pressure climatological standard deviations
  • var_id: clm
  • long_name: near surface (~2/10m) vapor pressure climatology
  • units: hPa
  • names: near surface (~2/10m) vapor pressure climatology
  • long_name: near surface (~2/10m) wetbulb temperature
  • units: deg C
  • standard_name: wet_bulb_temperature
  • var_id: tws
  • names: wet_bulb_temperature, near surface (~2/10m) wetbulb temperature
  • units: deg C
  • var_id: twsa
  • long_name: near surface (~2/10m) wetbulb temperature anomaly
  • names: near surface (~2/10m) wetbulb temperature anomaly
  • var_id: marine_clmstd
  • units: deg C
  • long_name: near surface (~2/10m) wetbulb temperature climatological standard deviations
  • names: near surface (~2/10m) wetbulb temperature climatological standard deviations
  • long_name: near surface (~2/10m) wetbulb temperature climatology
  • var_id: clm
  • units: deg C
  • names: near surface (~2/10m) wetbulb temperature climatology
  • var_id: land_std
  • long_name: near surface (~2m) air temperature standard deviation
  • units: deg C
  • names: near surface (~2m) air temperature standard deviation
  • var_id: land_std
  • long_name: near surface (~2m) dewpoint depression standard deviation
  • units: deg C
  • names: near surface (~2m) dewpoint depression standard deviation
  • var_id: land_std
  • units: deg C
  • long_name: near surface (~2m) dewpoint temperature standard deviation
  • names: near surface (~2m) dewpoint temperature standard deviation
  • var_id: land_std
  • long_name: near surface (~2m) relative humidity standard deviation
  • units: %rh
  • names: near surface (~2m) relative humidity standard deviation
  • var_id: land_std
  • long_name: near surface (~2m) vapor pressure standard deviation
  • units: hPa
  • names: near surface (~2m) vapor pressure standard deviation
  • var_id: land_std
  • units: deg C
  • long_name: near surface (~2m) wetbulb temperature standard deviation
  • names: near surface (~2m) wetbulb temperature standard deviation
  • long_name: number of 1by1 daily grids within gridbox
  • var_id: marine_gridcount
  • units: 1
  • names: number of 1by1 daily grids within gridbox
  • var_id: marine_clmstdgridcount
  • units: 1
  • long_name: number of 1by1 daily grids within gridbox climatological standard deviations
  • names: number of 1by1 daily grids within gridbox climatological standard deviations
  • long_name: number of 1by1 daily grids within gridbox climatology
  • var_id: marine_clmgridcount
  • units: 1
  • names: number of 1by1 daily grids within gridbox climatology
  • long_name: number of observations within gridbox
  • var_id: marine_obscount
  • units: 1
  • names: number of observations within gridbox
  • var_id: marine_clmstdobscount
  • long_name: number of observations within gridbox climatological standard deviations
  • units: 1
  • names: number of observations within gridbox climatological standard deviations
  • var_id: marine_clmobscount
  • units: 1
  • long_name: number of observations within gridbox climatology
  • names: number of observations within gridbox climatology
  • long_name: number of pseudo stations within gridbox
  • var_id: marine_pseudostncount
  • units: 1
  • names: number of pseudo stations within gridbox
  • var_id: abs_obsunc
  • long_name: uncorrelated 2 sigma combined observation uncertainty for actual values
  • units: hPa
  • names: uncorrelated 2 sigma combined observation uncertainty for actual values
  • var_id: anoms_obsunc
  • units: hPa
  • long_name: uncorrelated 2 sigma combined observations uncertainty for anomaly values
  • names: uncorrelated 2 sigma combined observations uncertainty for anomaly values
  • long_name: uncorrelated 2 sigma sampling uncertainty for gridbox actual values
  • var_id: abs_sampunc
  • units: hPa
  • names: uncorrelated 2 sigma sampling uncertainty for gridbox actual values
  • long_name: uncorrelated 2 sigma sampling uncertainty for gridbox anomaly values
  • var_id: anoms_sampunc
  • units: hPa
  • names: uncorrelated 2 sigma sampling uncertainty for gridbox anomaly values
  • var_id: abs_stdunc
  • long_name: uncorrelated combined 2 sigma uncertainty for gridbox actual values
  • units: hPa
  • names: uncorrelated combined 2 sigma uncertainty for gridbox actual values
  • var_id: anoms_stdunc
  • units: hPa
  • long_name: uncorrelated combined 2 sigma uncertainty for gridbox anomalu values
  • names: uncorrelated combined 2 sigma uncertainty for gridbox anomalu values

Co-ordinate Variables

  • standard_name: latitude
  • var_id: latitude
  • long_name: gridbox centre latitude
  • units: degrees_north
  • names: latitude, gridbox centre latitude
  • units: degrees_east
  • standard_name: longitude
  • long_name: gridbox centre longitude
  • var_id: longitude
  • names: longitude, gridbox centre longitude
  • long_name: latitude gridbox boundaries
  • standard_name: latitude
  • var_id: bounds_lat
  • names: latitude, latitude gridbox boundaries
  • long_name: longitude gridbox boundaries
  • var_id: bounds_lon
  • standard_name: longitude
  • names: longitude, longitude gridbox boundaries
  • long_name: time
  • standard_name: time
  • var_id: time
  • names: time
  • long_name: time period boundaries
  • var_id: bounds_time
  • standard_name: time
  • names: time, time period boundaries
Coverage
Temporal Range
Start time:
1973-01-01T00:00:00
End time:
2019-12-31T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°