This dataset provides a  record of fuel characteristics at high spatiotemporal resolution: ~9km, daily.
The two main variable groups are fuel load and fuel moisture, both of which are further divided by live/dead and wood/foliage fractions.
The dataset combines state-of-the-art model data (ERA5-Land) with observations from multiple satellites and in-situ variables into a globally complete and consistent dataset. 
The data provides high spatiotemporal resolution data of fuel load, which is essential for modelling wildfire activity, which contributes directly to the derivation of multiple Essential Climate Variables (ECVs).
The data is relevant not only for the wildfire community but also for studying various biogeochemical processes related to land-atmosphere interactions.
The fuel load is initially informed using the static European Space Agency Climate Change Initiative biomass product (ESA-CCI) , which provides high-resolution estimates of above ground biomass. Crucially our dataset adds a time evolution of the dataset to provide a daily product with accurate seasonality based on modelled Carbon Dixoide (CO₂) exchange. 
Furthermore, by collecting estimates on biomass allocation and modelling techniques the dataset allocates the biomass into 4 categories of fuel load, which is relevant for wildfires but not currently estimated from the ESA-CCI product. 
Dead fuel moisture content is based on existing modelling principles which have not been scaled up to the global scale nor have they used the high accuracy mapping and output data available within the Integrated Forecast System (IFS)  or the  the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5). 
The live fuel moisture model was trained on a global insitu dataset which is based on sampling and does not provide a global product, this is the first attempt to achieve such a product using modelling. All variables were validated using independent observations when available.
This is a first version of the dataset which will be updated to provide improved accuracy and expanded through time.

Observation and ERA5-Land derived 9 km global daily vegetation characteristics since 2003

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

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) 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.

Hours	Number of requests	Number of users	Downloaded data
Last 1H	0	1	0 GB
Last 2H	2	1	<1 GB
Last 3H	2	1	<1 GB
Last 6H	2	1	<1 GB
Last 12H	2	1	<1 GB
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	Catalogue entry	Number of users	Downloaded data
1	s2s-reforecasts	1	<1 GB
2	s2s-forecasts	1	<1 GB

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

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