Current data acquisition is generally either made by automatic means or routinely entered into computers, ready to be used for different applications. But a good wealth of old data remains in original paper support, and Data Rescue (DARE) activities are needed to ensure their preservation and digitization in order to extend backwards our knowledge about climate. NMHS have DARE projects in different degrees of development (see the EUMETNET Data Rescue homepage), and there are several international initiatives promoting DARE activities worldwide (compiled at the I-DARE portal).

Data Rescue activities can be seen as composed of a few distinctive steps:

1. Imaging of the historic data documents, either by photographing or scanning them in standard digital computer formats. Any analog micro-forms must also be converted to digital files. Afterwards, paper documents must be preserved in adequate archives, as digital images should be the primary source for the following steps. Conservation of digital images will include backup copies and a policy of conversion to open emerging formats to avoid obsolescence.
2. Digitizing the images obtained in step 1 to allow computer processing of the data. Scanned typewritten documents can be treated with an OCR (Optical Character Recognition) program; otherwise human mechanization will be needed, either with ad hoc input programs or with simple spread sheets. After some basic Quality Controls on the data, they can be transferred to the preferred Climate Data Management System.
3. Data rescue ends here, but before being used for climate analysis, rescued series must be further quality controlled and homogenized to remove the frequent alterations due to non-climatic factors. Errors originated during the digitization process must be corrected, but otherwise the raw series must be kept untouched, since different homogenization procedures can yield (slightly) different homogenized series. Homogenized series, however, will be preferred source for climate studies.

Additional information can be found at the WCDMP web page, including the publications Guidelines on Climate Data Rescue (WMO No. 1210) and Guidelines for Hydrological Data Rescue (WMO No. 1046). (See also other WCDMP reports).

In order to achieve long series of climatological or hydrological data, observational series can be extended backwards before the first instrumental measurements by means of proxy data (quantitative or even qualitative observations of strongly related variables). Examples are the width of the tree rings, polinic records or isotopic rates in drill cores, historical reports about extreme events, etc.

Documents on the use of proxy data in Hydrology:

• A philippic for use of historical and paleo data in hydrology. Brief document advocating for the use of proxy data, by Jan Daňhelka.
• Historical hydrology for Studying Flood Risk in Europe. This 2006 paper from Journal of Hydrological Sciences provides a great overview of methodological introduction to historical hydrology.
• Quantitative historical Hydrology in Europe provides again methodological basics and overview of known studies of estimation of historical flood flows over the Western and Central Europe (including UK, Spain and Czech Rep.
• Application of Paleohydrology to Corps Flood Frequency Analysis. US Army Corps of Engineers report that holds a useful introduction to paleofloods identification and estimation of its stage, discharge and time of occurrence

Hydrological proxy data web sites:

• Chronology of British Hydrological Events. Public repository for hydrological facts of the type that come from texts rather than tables.

For less developed NMHS, WMO supports the use of several Climate Data Management Systems (CDMS), as those listed in Guidelines on Climate Data Management (WCDMP No. 60 / WMO-Td No. 1376) and, more recently, MCH (Meteorology, Climatology, Hydrology), which has the advantage of being based on free yet reliable open software, hence relieving NMH services of the burden of costly software licences.

• Unfeasible or unlikely thresholds: Check if the value is out of the possible range (negative rainfall; relative humidity under 0 or over 100%), or very abnormal. In the latter case the references should conform to the local climate, as for example a temperature of 0°C may be common in temperate zone winters, but totally atypical in a tropical seaside location.
• Too big temporal variations: check if the variable changes too much between two consecutive observations. Useful in hourly or more frequent observations.
• Lack of temporal variations: check if the variable does not change during a reasonable number of consecutive observations. Also useful in hourly or more frequent observations, although it neither should happen in other time scales.
• Internal consistency between two or more variables. E.g.: minimum temperature higher than the maximum; precipitation amounts without any reported precipitating weather; etc.
• Spatial consistency: The values of a variable are compared with those recorded at neighboring stations. It is advisable to compare the anomalies rather than the raw values, especially in areas of complex topography, as measurement can vary greatly with altitude and other terrain features.

The first three types of controls apply to individual series, while the others require at least two (of related variables at the same station in the forth type, and at different stations in the fifth).

These quality controls are normally part of Climate Data Management Systems, and some of them are generally applied in other processes, as during the homogenization of the series.

See here a proposal for the Guidelines on Quality Control Procedures for data from Automatic Weather Stations.

Changes in the observing conditions or in the environment of the meteorological stations introduce anomalous perturbations in the data series (inhomogeneities). Therefore, homogenization is of paramount importance to obtain reliable analysis of the variability of climatological series, and there exists an extensive literature about the different methodologies applied so far. This allows climatologists to choose their preferred methods for their research, often implementing those methods by themselves. But some of them have made their developments available to other users by preparing and documenting ready to use computer packages and distributing them in the Internet or by other means. This is very interesting for operational climatology units of many National Meteorological and Hydrological Services around the world, that sometimes can lack the needed expertise to make their own developments, or else can devote the required time to other priorities.

Tables with comparative characteristics of publicly available homogenization packages are maintained at the web site of the Task Team on Homogenization, and another page shows some preliminary results of some automatic benchmarking tests.

One key aspect to improve the assessment of inhomogeneities in the series is the ability to check whether the detected alterations are backed by events in the history of the observatories. Therefore, it is of paramount importance to keep records of any change in the conditions of observations (including changes in the land use of the surroundings) in proper meta-data archives, and to make them available to climate researchers. A couple of examples of such good practices can be seen in the meta-data pages of the Australian Bureau of Meteorology and of the USA National Oceanic and Atmospheric Administration.

• ECA&D: Main EU-wide daily data-set. Different station density, according to varying data access policy. (Increasing data availability is expected in the future as NMHS's move towards an open accessibility of climate data).
• E-OBS gridded data-set: Currently with extreme temperatures and sea level pressure.
• HISTALPS: Historical instrumental climatological surface time series of the Greater Alpine Region.
• Millennium climate indices: Monthly values of several variables derived from ECA&D data.
• CARPATCLIM: Climate data for the Greater Carpathian Region. (Daily grids at 0.1° x 0.1°, 1961-2010, 44°N-50°N and 17°E-27°E).

• GRDC: Daily and monthly river discharge data on a global level (>9200 stations, 160 countries, 400000 station years).
• The Undine Information System (in German) presents short characteristics of selected historical hydrological extremes (low flow or flood events) for the rivers Elbe, Oder, Weser and Rhine. They sketch the hydro-meteorological situation, the development of discharges, damages & losses, available water quality & pollution data, and contain numerous references.