The International Grand Global Ensemble (TIGGE) dataset consists of global medium-range ensemble forecasts from thirteen numerical weather prediction (NWP) centres, starting since October 2006. TIGGE was established as a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity. 
Although the 10-year THORPEX program ended at the end of 2014, the archive has continued and is currently undergoing its next five-year phase (2024-2028).
Please consult the Documentation section provided with this entry since it is a crucial resource for understanding the dataset, as it provides all the necessary information, including data sources, model configurations, and limitations.

TIGGE global medium-range ensemble forecasts from multiple NWP centres since 2006

The Sub-seasonal to Seasonal (S2S) prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community.
The Documentation section provided with this entry  is a crucial resource for understanding this dataset, it provides all the necessary information, including data sources, model configurations, and limitations. 
Such  information is vital in avoiding misuses of the data, which can result in lost time and efficiency, and prevent the production of harmful content. 
By fully understanding the dataset, users can utilize the data to its full potential and make informed decisions.

S2S sub-seasonal to seasonal multi-model and multi-member reforecasts of a wide range of climate variable

Test tigge

The Sub-seasonal to Seasonal(S2S) dataset consists of global ensemble forecasts from thirteen numerical weather prediction (NWP) centres, starting since January 2015. 
The S2S prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community.
Although the 10-year (2013-2023) S2S program has already ended, the archive has continued and is currently undergoing its next five-year phase (2024-2028).
Please consult the Documentation section provided with this entry since it is a crucial resource for understanding the dataset, as it provides all the necessary information, including data sources, model configurations, and limitations.

S2S global sub-seasonal to seasonal ensemble forecasts and reforecasts from multiple NWP centres since 2015

The Sub-seasonal to Seasonal (S2S) prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community.
Please consult the Documentation section provided with this entry since it is a crucial resource for understanding the dataset, as it provides all the necessary information, including data sources, model configurations, and limitations. Such information is vital in avoiding misuses of the data, which can result in lost time and efficiency, and prevent the production of harmful content. By fully understanding the dataset, users can utilize the data to its full potential and make informed decisions.

S2S sub-seasonal to seasonal multi-model and multi-member forecasts of a wide range of meteorological variables

The Sub-seasonal to Seasonal (S2S) prediction project is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The goal of S2S is to improve the forecast skill and understanding on the sub-seasonal to seasonal timescale, and to promote its uptake by operational centers and exploitation by the applications community.
Please consult the Documentation section provided with this entry since it is a crucial resource for understanding the dataset, as it provides all the necessary information, including data sources, model configurations, and limitations. 
Such  information is vital in avoiding misuses of the data, which can result in lost time and efficiency, and prevent the production of harmful content. 
By fully understanding the dataset, users can utilize the data to its full potential and make informed decisions.

	Country	Number of requests	Number of users	Downloaded data
1	AD	0	0	0 GB
2	AL	0	0	0 GB
3	AM	0	0	0 GB
4	AT	0	0	0 GB
5	BA	0	0	0 GB
6	BE	0	0	0 GB

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