1Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
2Cooperative Institute for Climate and Satellites, North Carolina State University and NOAA's National Climatic Data Center, Patton Avenue, Asheville, NC 28801, USA
4NOAA's National Climatic Data Center, Patton Avenue, Asheville, NC 28801, USA
5National Center for Atmospheric Research (NCAR), P.O. 3000, Boulder, CO 80307, USA
*formerly at: Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Abstract. This paper describes the creation of HadISD; an automatically quality-controlled synoptic resolution dataset of temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud cover from global weather stations for 1973–2010. The full dataset consists of over 6000 stations, with 3375 long-term stations deemed to have sufficient sampling and quality for climate applications requiring sub-daily resolution. As with other surface datasets, coverage is heavily skewed towards Northern Hemisphere mid-latitudes.
The dataset is constructed from a large pre-existing ASCII flatfile data bank that represents over a decade of substantial effort at data retrieval, reformatting and provision. The work proceeded in several steps: merging stations with multiple reporting identifiers; reformatting to netcdf; quality control; and then filtering to form a final dataset. Particular attention has been paid to maintaining true extreme values where possible within an automated objective process. Detailed validation has been performed on a subset of global stations and also on UK data using known extreme events to help finalise the QC tests. Further validation was performed on a selection of extreme events world-wide (Hurricane Katrina in 2005, the cold snap in Alaska in 1989 and heat waves in SE Australia in 2009). Some very initial analyses are performed to illustrate some of the types of problems to which the final data could be applied. Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far, due to the complexity of retaining the true distribution of high-resolution data when applying adjustments. Hence non-climatic, time-varying errors may still exist in many of the individual station records and care is needed in inferring long-term trends from these data.
This dataset will allow the study of high frequency variations of temperature, pressure and humidity on a global basis over the last four decades. Both individual extremes and the overall population of extreme events could be investigated in detail to allow for comparison with past and projected climate.