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Spatial Climate Analyses

Spatial climate analyses depict the geographic distribution and temporal evolution of temperature, precipitation and other climate variables over the past decades. They are derived from measurements at weather stations, satellites and radar. The data is represented comprehensively on a grid, covering all of Switzerland or the entire Alps, and this enables complex applications in hydrology, agronomy, the energy sector and other disciplines. MeteoSwiss provides a broad palette of ready-made data products for these numerous applications.

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The observational network of MeteoSwiss monitors weather and climate at a large number of stations. For many applications, however, climate information is required at high spatial density or at locations without direct measurements. MeteoSwiss responds to these needs with an extensive set of analysis datasets, which are calculations of near-surface climate variables on a high-resolution grid over Switzerland or the entire Alpine region.

Spatial climate analyses integrate data from various observation sources (weather stations, radar and satellite) on the basis of expert knowledge about the distribution of climate. For the development of the underlying statistical methods, MeteoSwiss works in partnerships with the research community, other weather services, and with users of the datasets.

Selected climate datasets can be readily viewed as maps on the website of MeteoSwiss. You can take a journey through the climate of the past decades, in maps, month by month, back to 1981:

Overview of datasets

The compilation of datasets encompasses analyses for precipitation, temperature, sunshine duration, radiation and clouds. The analyses are available on a grid of 1, 2, or 5 kilometres, depending on the dataset. For a set of core variables the datasets range back to at least 1971, at daily, monthly and annual time resolution. They are also available as normals (averages) for the period 1991-2020. In addition, there is an hourly precipitation analysis, available back to 2005 that relies on a combination of radar and station data. Datasets for radiation and cloud cover are derived from high-resolution satellite data. They partly range back to 1991. For long-term climate monitoring, there exists a continuous series of monthly temperature and precipitation analyses beginning with the regular meteorological measurements in 1864. With a few exceptions, all datasets are regularly updated.

Datasets in detail

The choice of a suitable dataset for an application crucially depends on the requirements of the application. Apart from technical criteria (required time period and resolution), aspects of data quality are essential. If it is important that the analysis represents fine-scale detail appropriately, a product should be chosen that integrates as many observations as possible. If, however, the application requires a high consistency over time, a data product that was compiled only from homogenous and uninterrupted measurement series should be chosen. For this reason, the palette of available datasets is broad and an appropriate data selection implies that users carefully reflect on the potential and limitations of the different products. For this purpose, detailed documentations have been written for the data products (in English only). The following tables list the available datasets and link to the product documentations available online.

Acronym

Description

RnormY9120

Mean yearly precipitation (norm value, 1991-2020)

RnormM9120

Mean monthly precipitation (norm value, 1991-2020)

R9120m6190Y

Ratio in yearly precipitation norm values (1991-2020 / 1961-1990)

R9120m6190M

Ratio in monthly precipitation norm values (1991-2020 / 1961-1990)

RhiresY

Yearly precipitation (1961 – present)

RrecabsYNNNN

Yearly precipitation (long-term consistent since NNNN=1864, 1901, 1961)

RanomY9120

Yearly precipitation anomaly (relative to 1991-2020, 1961 – present)

Rrecanom9120YNNNN

Yearly precip anomaly (relative to 1991-2020, long-term consistent since NNNN=1864, 1901, 1961)

Rrecanom6190YNNNN

Yearly precip anomaly (relative to 1961-1990, long-term consistent since NNNN=1864, 1901, 1961)

RhiresM

Monthly precipitation (1961 – present)

RrecabsMNNNN

Monthly precipitation (long-term consistent since NNNN=1864, 1901, 1961)

RanomM9120

Monthly precipitation anomaly (relative to 1991-2020, 1961 – present)

Rrecanom9120MNNNN

Monthly precip anomaly (relative to 1991-2020, long-term since NNNN=1864, 1901, 1961)

Rrecanom6190MNNNN

Monthly precip anomaly (relative to 1961-1990, long-term since NNNN=1864, 1901, 1961)

RhydchprobD

Daily precipitation (ensemble analysis for hydrological units, 1961 – present)

RwarnchprobD

Daily precipitation (ensemble analysis for warn regions, 1961 – present)

RhiresD

Daily precipitation (final analysis, 1961 – last month)

RprelimD

Daily precipitation (preliminary analysis, for past two months)

APGD

Daily precipitation over the Alpine Region (1971-2019)

APGDEns

Daily precipitation ensemble dataset over the Alpine Region (1971-2008)

CPC

Hourly precipitation from radar and stations (real-time analysis, 2005-present)

Acronym

Description

TnormY9120

Mean yearly mean temperature (norm, 1991-2020)

T9120m6190Y

Difference in mean yearly temperature norm (1991-2020 – 1961-1990)

TminnormY9120

Mean yearly daily minimum temperature (norm, 1991-2020)

TmaxnormY9120

Mean yearly daily maximum temperature (norm, 1991-2020)

TnormM9120

Mean monthly mean temperature (norm, 1991-2020)

T9120m6190M

Difference in mean monthly temperature norm (1991-2020 – 1961-1990)

TminnormM9120

Mean monthly daily minimum temperature (norm, 1991-2020)

TmaxnormM9120

Mean monthly daily maximum temperature (norm, 1991-2020)

TnormD9120

Mean calendar day temperature (norm, 1991-2020)

TabsY

Yearly mean temperature (1961 – present)

TrecabsYNNNN

Yearly temperature (long-term consistent since NNNN=1864, 1901, 1961)

TmaxrecabsYNNNN

Yearly maximum temperature (long-term consistent since NNNN=1901, 1961)

TminrecabsYNNNN

Yearly minimum temperature (long-term consistent since NNNN=1901, 1961)

TanomY9120

Yearly temperature anomaly (relative to 1991-2020, >1961 – present)

Trecanom9120YNNNN

Yearly temp. anomaly (relative to 1991-2020, long-term consistent since NNNN=1864, 1901, 1961)

Trecanom6190YNNNN

Yearly temp. anomaly (relative to 1961-1990, long-term consistent since NNNN=1864, 1901, 1961)

Tmaxrecanom9120YNNNN

Yearly maximum temp. anomaly (long-term consistent since NNNN=1901,1961)

Tminrecanom9120YNNNN

Yearly minimum temp. anomaly (long-term consistent since NNNN=1901,1961)

TminY

Yearly mean of daily minimum temperature (1961 – present)

TmaxY

Yearly mean of daily maximum temperature (1961 – present)

TabsM

Monthly mean temperature (1961 – present)

TrecabsMNNNN

Monthly temperature (long-term consistent since NNNN=1864, 1901, 1961)

TmaxrecabsMNNNN

Monthly maximum temperature (long-term consistent since NNNN=1901, 1961)

TminrecabsMNNNN

Monthly minimum temperature (long-term consistent since NNNN=1901, 1961)

TanomM9120

Monthly temperature anomaly (relative to 1991-2020, 1961 – present)

Trecanom9120MNNNN

Monthly temp. anomaly (relative to 1991-2020, long-term consistent since NNNN=1864, 1901, 1961)

Trecanom6190MNNNN

Monthly temp. anomaly (relative to 1961-1990, long-term consistent since NNNN=1864, 1901, 1961)

Tmaxrecanom9120MNNNN

Monthly maximum temp. anomaly (long-term consistent since NNNN=1901, 1961)

Tminrecanom9120MNNNN

Monthly minimum temp. anomaly (long-term consistent since NNNN=1901, 1961)

TminM

Monthly mean of daily minimum temperature (1961 – present)

TmaxM

Monthly mean of daily maximum temperature (1961 – present)

TabsD

Daily mean temperature (1961 – present)

TanomD9120

Daily mean temperature anomaly (relative to 1991-2020, 1961 – present)

TminD

Daily minimum temperature (1961 – present)

TmaxD

Daily maximum temperature (1961 – present)

LSTY

Yearly satellite-based land surface (skin) temperature (1991 – present)

LSTM

Monthly satellite-based land surface (skin) temperature (1991 – present)

Acronym

Description

SnormY9120

Mean yearly relative sunshine duration (norm, 1991-2020)

S9120m6190Y

Ratio in mean yearly sunshine duration. (1991-2020 / 1961-1990)

SnormM9120

Mean monthly relative sunshine duration (norm, 1991-2020)

S9120m6190M

Ratio in mean monthly sunshine duration. (1991-2020 / 1961-1990)

SrelY

Yearly relative sunshine duration (1961 – present)
SrelrecYNNNN Yearly relative sunshine duration (long-term consistent since NNNN=1901, 1961)

SanomY9120

Yearly sunshine duration anomaly (relative to 1991-2020, 1961 – present)
Srecanom9120YNNNN Yearly sunshine duration anomaly (relative to 1991-2020, long-term consistent since NNNN=1901, 1961)
Srecanom6190YNNNN Yearly sunshine duration anomaly (relative to 1961-1990, long-term consistent since NNNN=1901, 1961)

SrelM

Monthly relative sunshine duration (1961 – present)
SrelrecMNNNN Monthly relative sunshine duration (long-term consistent since NNNN=1901, 1961)

SanomM9120

Monthly sunshine duration anomaly (relative to 1991-2020, 1961 – present)
Srecanom9120MNNNN Monthly sunshine duration anomaly (relative to 1991-2020, long-term consistent since NNNN=1901, 1961)
Srecanom6190MNNNN Monthly sunshine duration anomaly (relative to 1961-1990, long-term consistent since NNNN=1901, 1961)

SrelD

Daily relative sunshine duration (1961 – present)

SISY

Yearly satellite-based global radiation (2004 – present)

SISM

Monthly satellite-based global radiation (2004 – present)

SISD

Daily satellite-based global radiation (2004 – present)

SISDIRY

Yearly satellite-based direct radiation (2004 – present)

SISDIRM

Monthly satellite-based direct radiation (2004 – present)

SISDIRD

Daily satellite-based direct radiation (2004 – present)

CFCY

Yearly satellite-based cloud fractional cover (1991 – present)

CFCM

Monthly satellite-based cloud fractional cover (1991 – present)

CFCD

Daily satellite-based global cloud fractional cover (1991 – present)

Grid data for climate normals

Spatial analyses of averages of precipitation, temperature (mean, mean minimum and mean maximum) and sunshine duration over the actual norm period 1991-2020 can be downloaded free of charge:

Precipitation datasets for the entire Alpine region

MeteoSwiss also provides spatial analyses of precipitation for the entire Alpine region. This includes a high-resolution grid for daily precipitation (APGD, Alpine Precipitation Grid Dataset) over the period 1971-2019, based on more than 8500 measuring stations. A probabilistic precipitation analysis (APGDEns)) was also developed with the same measurement data. Finally, a reconstruction of monthly precipitation back to 1871 is available (LAPrec, Long-term Alpine Precipitation Reconstruction). It is calculated in cooperation with the Austrian meteorological service ZAMG and is based on the HISTALP dataset.

Climate at a user-defined location

Instead of grid points, MeteoSwiss can also provide climate data at any location in Switzerland. Let us know the coordinates and elevations of your desired location. We then calculate the respective climate time series for the following parameters: Precipitation, temperature, radiation and sunshine duration. For the sunshine duration, both the relative values (sunshine in relation to the maximum possible sunshine duration) and the absolute values (duration in minutes) can be calculated. An individual offer can be obtained from the customer service.

Uncertainties

Estimates of a climate variable at locations with no direct measurement are subject to uncertainties. Their relevance for an application depends on the respective requirements. It is therefore important that users assess the potential consequences prior to formal application. Indications and summary measures on analysis uncertainty are described in the product documentations. The newly established ensemble analyses (available for precipitation only so far) allow users to track uncertainties through applications in a fully quantitative manner. General implications of and recommendations on dealing with uncertainties in spatial climate datasets are summarized in a DACH Workshop Document.

Renovations

The spatial climate datasets of MeteoSwiss are continuously developed further. Recently, the widely-used precipitation analyses have been extended to cover a larger domain, including areas close to the border outside the country. Information about these recent developments and the upcoming change in the delivery format is provided in our newsletter. Some example datasets in the new format illustrate the technical changes.

zip-file containing the following files:

  • New Swiss coordinate system LV95 - CH1903+
  • Extended analysis domain for RhiresD and RprelimD v2.0 (Swiss coordinates)
  • Extended analysis domain for RhiresD and RprelimD v2.0 (lonlat coordinates)
  • Climatological time axis for norm values

Advice and data delivery

Do you have questions on the availability of spatial climate analysis and their utility for a specific application? PLease don’t hesitate to contact an expert at MeteoSwiss for advice.  The costs for the data depend on data volume and intended use. We are happy to make an individual offer. The data can be supplied in various formats (NetCDF, GeoTIFF und ASCII), as one-time delivery or on a regular basis with the most recent data.

About us

MeteoSwiss is the Federal Office for Meteorology and Climatology. We keep our finger on the pulse of the weather and climate, so it’s all clear as day to you.

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