Dataset
HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index data version 1.0.0.2022f
Abstract
This is the HadISDH.extremes 1.0.0.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.extremes is a near-global gridded monthly land surface extremes index climate monitoring product. It is created from in situ sub-daily observations of wet bulb (converted from dew point temperature) and dry bulb temperature from weather stations. The observations have been quality controlled at the hourly level with strict temporal completeness thresholds applied at daily, monthly, annual, climatological and whole period scales to minimise biases. Gridbox months are assessed for inhomogeneity and scores provided (see Homogeneity Score Document in Docs). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022.
The data are monthly gridded (5 degree by 5 degree) fields. Products are available for 27 different heat extremes indices based on the ET-SCI (Expert Team on Sector-Specific Climate Indices; https://public.wmo.int/en/events/meetings/expert-team-sector-specific-climate-indices-et-sci) framework. These indices capture a range of moderate to severe extremes. They utilise the daily maximum and minimum values of sub-daily dry bulb and wet bulb temperature observations. Note that these will most likely underestimate the true extremes even when hourly data are available. The data are designed for assessing large scale features over long time scales, ideally using the anomaly fields as these are less affected by sampling biases. Users are advised to cross-compare with national datasets other supporting evidence when assessing small scale localised features.
This version is the first with annual updates envisaged. An update record will be maintained in the Docs section.
HadISD.3.3.0.2022f is the basis of HadISDH.extremes.
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, in press: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2347-8. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2347-8
Willett, K. in press: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2348-7. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2348-7
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
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| 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(s): 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.extremes is a global land surface (~2 m) humid and dry heat extremes index dataset and is produced by the Met Office Hadley Centre through the Met Office Climate Science for Service Partnership (CSSP) China project. It is based on the quality controlled sub-daily HadISD from the Met Office Hadley Centre which is in turn based on the ISD dataset from NOAA's NCEI. It is passed to the BADC for archiving and distribution. |
| Data Quality: |
The sub-daily observations have been quality controlled and data completeness thresholds are applied at the daily, monthly, annual, climatological and whole record level. Each gridbox month is given a score relating to likely inhomogeneity which can be used to filter the dataset to reduce the influence of large inhomogeneity.
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| File Format: |
Data are NetCDF formatted
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Related Documents
Citations: 2
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
| Willett, K.M. (2023) HadISDH.extremes Part I: A Gridded Wet Bulb Temperature Extremes Index Product for Climate Monitoring. Advances in Atmospheric Sciences 40, 1952–1967. https://doi.org/10.1007/s00376-023-2347-8 https://doi.org/10.1007/s00376-023-2347-8 |
| Willett, K.M. (2023) HadISDH.extremes Part II: Exploring Humid Heat Extremes Using Wet Bulb Temperature Indices. Advances in Atmospheric Sciences 40, 1968–1985. https://doi.org/10.1007/s00376-023-2348-7 https://doi.org/10.1007/s00376-023-2348-7 |
Process overview
| Title | HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index dataset produced by the Met Office Hadley Centre |
| Abstract | HadISDH.extremes utilises simultaneous sub-daily dry bulb and wet bulb temperature (calculated from dry bulb and dew point temperature) data from over 4000 quality controlled HadISD stations that have sufficiently long records. After checking for sufficient completeness at the daily, monthly, annual, climatological and whole record scale, monthly indices are created from the maximum and minimum of the available daily values. Note that these likely underestimate the true extremes. Climatological averages are calculated over 1991-2020 and monthly climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values) 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. Each gridbox month has an associated homogeneity score obtained from the homogenisation information from HadISDH.landT and HadISDH.landTw. Users can filter the data to remove those gridboxes likely affected by large inhomogeneity. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. For greater detail please see: Willett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, 40, 1952–1967, doi: 10.1007/s00376-023-2347-8. https://link.springer.com/article/10.1007/s00376-023-2347-8. Willett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, 40, 1968–1985, doi: 10.1007/s00376-023-2348-7. https://link.springer.com/article/10.1007/s00376-023-2348-7. See the documentation links in the online resources section of this record for links to both these publications. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
- units: 1
- var_id: hqscore
- long_name: Homogeneity Quality Flag: sum of hq1 to hq7
- units: 1
- var_id: hq1
- long_name: Homogeneity Quality Score 1: station count in gridbox
- units: 1
- var_id: hq2
- long_name: Homogeneity Quality Score 2: inhomogeneity density per station in gridbox
- units: 1
- var_id: hq3
- long_name: Homogeneity Quality Score 3: small (0-0.5 deg C) inhomogeneity density per station in gridbox
- units: 1
- var_id: hq4
- long_name: Homogeneity Quality Score 4: moderate (0.5-1 deg C) inhomogeneity density per station in gridbox
- units: 1
- var_id: hq5
- long_name: Homogeneity Quality Score 5: large (1-2 deg C) inhomogeneity density per station in gridbox
- units: 1
- var_id: hq6
- long_name: Homogeneity Quality Score 6: very large (>2 deg C) inhomogeneity density per station in gridbox
- units: 1
- var_id: hq7
- long_name: Homogeneity Quality Score 7: mean adjustment magnitude over stations in gridbox
- units: 1
- var_id: hq8
- long_name: Homogeneity Quality Score 8: mean absolute adjustment magnitude over stations in gridbox
- units: 1
- long_name: actual number of stations within gridbox
- var_id: stncount
- long_name: climatology period boundaries
- var_id: climbounds
- units: 1
- long_name: mean number of stations within gridbox
- var_id: meanstncount
- var_id: month
- long_name: month of year
- standard_name:
- units: $^{o}$C
- var_id: twxD29
- long_name: near surface (~2m) degrees per month of >= 29$^{o}$C maximum wetbulb temperature
- var_id: twxD29a
- long_name: near surface (~2m) degrees per month of >= 29$^{o}$C maximum wetbulb temperature anomaly
- units: $^{o}$C
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 29$^{o}$C maximum wetbulb temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 29$^{o}$C maximum wetbulb temperature standard deviation
- standard_name:
- units: $^{o}$C
- var_id: twxD31
- long_name: near surface (~2m) degrees per month of >= 31$^{o}$C maximum wetbulb temperature
- units: $^{o}$C
- var_id: twxD31a
- long_name: near surface (~2m) degrees per month of >= 31$^{o}$C maximum wetbulb temperature anomaly
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 31$^{o}$C maximum wetbulb temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 31$^{o}$C maximum wetbulb temperature standard deviation
- standard_name:
- units: $^{o}$C
- var_id: twxD33
- long_name: near surface (~2m) degrees per month of >= 33$^{o}$C maximum wetbulb temperature
- units: $^{o}$C
- var_id: twxD33a
- long_name: near surface (~2m) degrees per month of >= 33$^{o}$C maximum wetbulb temperature anomaly
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 33$^{o}$C maximum wetbulb temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 33$^{o}$C maximum wetbulb temperature standard deviation
- standard_name:
- units: $^{o}$C
- var_id: twxD35
- long_name: near surface (~2m) degrees per month of >= 35$^{o}$C maximum wetbulb temperature
- units: $^{o}$C
- var_id: twxD35a
- long_name: near surface (~2m) degrees per month of >= 35$^{o}$C maximum wetbulb temperature anomaly
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 35$^{o}$C maximum wetbulb temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) degrees per month of >= 35$^{o}$C maximum wetbulb temperature standard deviation
- standard_name:
- units: $^{o}$C
- var_id: tx
- long_name: near surface (~2m) maximum temperature
- units: $^{o}$C
- var_id: txa
- long_name: near surface (~2m) maximum temperature anomaly
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) maximum temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) maximum temperature standard deviation
- standard_name:
- units: $^{o}$C
- var_id: twx
- long_name: near surface (~2m) maximum wetbulb temperature
- units: $^{o}$C
- var_id: twxa
- long_name: near surface (~2m) maximum wetbulb temperature anomaly
- var_id: clm
- units: $^{o}$C
- long_name: near surface (~2m) maximum wetbulb temperature climatology
- var_id: std
- units: $^{o}$C
- long_name: near surface (~2m) maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: tm10p
- long_name: near surface (~2m) percentage of days per month of < 10pct mean temperature
- units: %
- var_id: tm10pa
- long_name: near surface (~2m) percentage of days per month of < 10pct mean temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of < 10pct mean temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of < 10pct mean temperature standard deviation
- units: %
- standard_name:
- var_id: twm10p
- long_name: near surface (~2m) percentage of days per month of < 10pct mean wetbulb temperature
- units: %
- var_id: twm10pa
- long_name: near surface (~2m) percentage of days per month of < 10pct mean wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of < 10pct mean wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of < 10pct mean wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: tm90p
- long_name: near surface (~2m) percentage of days per month of > 90pct mean temperature
- units: %
- var_id: tm90pa
- long_name: near surface (~2m) percentage of days per month of > 90pct mean temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of > 90pct mean temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of > 90pct mean temperature standard deviation
- units: %
- standard_name:
- var_id: twm90p
- long_name: near surface (~2m) percentage of days per month of > 90pct mean wetbulb temperature
- units: %
- var_id: twm90pa
- long_name: near surface (~2m) percentage of days per month of > 90pct mean wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of > 90pct mean wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of > 90pct mean wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: tn18
- long_name: near surface (~2m) percentage of days per month of >= 18$^{o}$C minimum temperature
- units: %
- var_id: tn18a
- long_name: near surface (~2m) percentage of days per month of >= 18$^{o}$C minimum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 18$^{o}$C minimum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 18$^{o}$C minimum temperature standard deviation
- units: %
- standard_name:
- var_id: tx25
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum temperature
- units: %
- var_id: tx25a
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum temperature standard deviation
- units: %
- standard_name:
- var_id: twx25
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum wetbulb temperature
- units: %
- var_id: twx25a
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 25$^{o}$C maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: twx27
- long_name: near surface (~2m) percentage of days per month of >= 27$^{o}$C maximum wetbulb temperature
- units: %
- var_id: twx27a
- long_name: near surface (~2m) percentage of days per month of >= 27$^{o}$C maximum wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 27$^{o}$C maximum wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 27$^{o}$C maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: twx29
- long_name: near surface (~2m) percentage of days per month of >= 29$^{o}$C maximum wetbulb temperature
- units: %
- var_id: twx29a
- long_name: near surface (~2m) percentage of days per month of >= 29$^{o}$C maximum wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 29$^{o}$C maximum wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 29$^{o}$C maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: tx30
- long_name: near surface (~2m) percentage of days per month of >= 30$^{o}$C maximum temperature
- units: %
- var_id: tx30a
- long_name: near surface (~2m) percentage of days per month of >= 30$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 30$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 30$^{o}$C maximum temperature standard deviation
- units: %
- standard_name:
- var_id: twx31
- long_name: near surface (~2m) percentage of days per month of >= 31$^{o}$C maximum wetbulb temperature
- units: %
- var_id: twx31a
- long_name: near surface (~2m) percentage of days per month of >= 31$^{o}$C maximum wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 31$^{o}$C maximum wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 31$^{o}$C maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: twx33
- long_name: near surface (~2m) percentage of days per month of >= 33$^{o}$C maximum wetbulb temperature
- units: %
- var_id: twx33a
- long_name: near surface (~2m) percentage of days per month of >= 33$^{o}$C maximum wetbulb temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 33$^{o}$C maximum wetbulb temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 33$^{o}$C maximum wetbulb temperature standard deviation
- units: %
- standard_name:
- var_id: tx35
- long_name: near surface (~2m) percentage of days per month of >= 35$^{o}$C maximum temperature
- units: %
- var_id: tx35a
- long_name: near surface (~2m) percentage of days per month of >= 35$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 35$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 35$^{o}$C maximum temperature standard deviation
- units: %
- standard_name:
- var_id: tx40
- long_name: near surface (~2m) percentage of days per month of >= 40$^{o}$C maximum temperature
- units: %
- var_id: tx40a
- long_name: near surface (~2m) percentage of days per month of >= 40$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 40$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 40$^{o}$C maximum temperature standard deviation
- units: %
- standard_name:
- var_id: tx45
- long_name: near surface (~2m) percentage of days per month of >= 45$^{o}$C maximum temperature
- units: %
- var_id: tx45a
- long_name: near surface (~2m) percentage of days per month of >= 45$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 45$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 45$^{o}$C maximum temperature standard deviation
- units: %
- standard_name:
- var_id: tx50
- long_name: near surface (~2m) percentage of days per month of >= 50$^{o}$C maximum temperature
- units: %
- var_id: tx50a
- long_name: near surface (~2m) percentage of days per month of >= 50$^{o}$C maximum temperature anomaly
- units: %
- var_id: clm
- long_name: near surface (~2m) percentage of days per month of >= 50$^{o}$C maximum temperature climatology
- units: %
- var_id: std
- long_name: near surface (~2m) percentage of days per month of >= 50$^{o}$C maximum temperature standard deviation
Co-ordinate Variables
- units: degrees_north
- standard_name: latitude
- var_id: latitude
- long_name: gridbox centre latitude
- units: degrees_east
- standard_name: longitude
- var_id: longitude
- long_name: gridbox centre longitude
- standard_name: latitude
- var_id: bounds_lat
- long_name: latitude gridbox boundaries
- standard_name: longitude
- var_id: bounds_lon
- long_name: longitude gridbox boundaries
- long_name: time
- standard_name: time
- var_id: time
- units: days
- standard_name: time
- long_name: time period boundaries
- var_id: bounds_time
Temporal Range
1973-01-01T00:00:00
2022-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |