This file was generated on 2024-10-08 by Mohamad Hakam Shams Eddin

A GENERAL INFORMATION

1.  Title of the dataset:
	
	Identifying spatio-temporal drivers of extreme events [data set]
	
2.  Brief description of the research project and its aims:
	
	Although there is a general expectation that extreme events in the water cycle are occurring more frequently and become stronger due to climate change, it remains a challenge to predict them from large simulation data sets. 
	The data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.

3.  Author Information
		
	A. Project Supervisor (Principal Investigator) Contact Information
		Name:Jürgen Gall
		Institution: Institute of Computer Science III, Department of Information Systems and Artificial Intelligence
		Address: Friedrich-Hirzebruch-Allee 8, 53115 Bonn
		Email: gall@iai.uni-bonn.de
		
	B. In case of questions related to this dataset, please contact:
		Name: Mohamad Hakam Shams Eddin
		Institution: Institute of Computer Science III, Department of Information Systems and Artificial Intelligence
		Address: Friedrich-Hirzebruch-Allee 8, 53115 Bonn
		Email: shams@iai.uni-bonn.de
	
4.  Date of data collection:

	2023-08-01
	
5.  Information about funding sources that supported the collection of the data:

	This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1502/1–2022 – project no. 450058266 within the Collaborative Research Center (CRC) for the project Regional Climate Change: Disentangling the Role of Land Use and Water Management (DETECT) and under Germany’s Excellence Strategy – EXC 2070 – project no. 390732324.

6.  Language of the dataset:
	
	English

7.  Geographic location of data collection:
	
	Global
	
B DATA & FILE OVERVIEW
1.  File List:

	|
	|__ CERRA: CERRA reanalysis data (Europe - CERRA domain)
	|__ NOAA_CERRA: NOAA remote sensing data (Europe - CERRA domain)
	|__ ERA5-Land: ERA5-Land reanalysis data (global - over CORDEX domains)
	|__ NOAA_CORDEX: NOAA remote sensing data (global - over CORDEX domains)
	|__ Synthetic:
	    |__ synthetic_Artificial: synthetic data generated based on artificial data
	    |__ synthetic_CERRA: synthetic data generated based on CERRA reanalysis
	    |__ synthetic_NOAA: synthetic data generated based on NOAA remote sensing
	    |__ data_base: climatology data to generate synthetic data

2.  Are there multiple versions of the dataset?
	
	No

C SHARING/ACCESS INFORMATION

1.  Was data derived from another source? 

	Yes
	
	- CERRA: CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
	- NOOA_CERRA: NOAA/NESDIS Center for Satellite Applications and Research
	- ERA5-Land: ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
	- NOAA_CORDEX: NOAA/NESDIS Center for Satellite Applications and Research
	- synthetic_CERRA: CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
	- synthetic_NOAA: NOAA/NESDIS Center for Satellite Applications and Research
	
2.  Licenses/restrictions placed on the data:
	
	Creative Commons Attribution CC BY 4.0: https://creativecommons.org/licenses/by/4.0/
	
	The CERRA reanalysis is provided under Copernicus licence and it is available from the CDS at https://cds.climate.copernicus.eu/
	The ERA5-Land reanalysis is provided under Copernicus licence and it is available from the CDS at https://cds.climate.copernicus.eu/
	For NOAA remote sensing see https://www.star.nesdis.noaa.gov/star/productdisclaimer.php
	
3.  Links to publications that cite or use the data:

	see paper: Identifying spatio-temporal drivers of extreme events (To appear in October. 2024)
	
D METHODOLOGICAL INFORMATION

1.  Description of methods used for collection/generation of data:
	
	see paper: Identifying spatio-temporal drivers of extreme events (To appear in October. 2024)
	
2.  Methods for processing the data:
	
	see paper: Identifying spatio-temporal drivers of extreme events (To appear in October. 2024)

3.  Instrument- and/or software-specific information needed to interpret 
the data:

	https://github.com/HakamShams/Synthetic_Multivariate_Anomalies
	https://github.com/HakamShams/IDEE
	
4.  People involved in sample collection, processing, analysis and/or 
submission:
	
	Mohamad Hakam Shamd Eddin (shams@iai.uni-bonn.de)

----------------------------------------------------------------------

E DATA-SPECIFIC INFORMATION FOR NOAA:

1.  Variable list including full names and definitions of column headings for tabular data:
	
	- VCI               | Vegetation Condition Index               | %      | [1-100]
	- TCI               | Thermal Condition Index                  | %      | [1-100]
	- VHI               | Vegetation Health Index                  | %      | [1-100]
	- mask_cold_surface | mask for invalid pixels and cold regions | binary | [0/1]

3.  Missing data codes/symbols:

	NaN.
	

E DATA-SPECIFIC INFORMATION FOR CERRA:

1.  Variable list including full names and definitions of column headings for tabular data:
	
	- al     | albedo                               | %       | surface
	- hcc    | high cloud cover                     | %       | above 5000m
	- lcc    | low cloud cover                      | %       | surface-2500m
	- mcc    | medium cloud cover                   | %       | 2500m-5000m
	- liqvsm | liquid volumetric soil moisture      | m^3/m^3 | top layer of soil
	- msl    | mean sea level pressure              | Pa      | surface
	- r2     | 2 metre relative humidity            | %       | 2m
	- si10   | 10 metre wind speed                  | m/s     | 10m
	- skt    | skin temperature                     | K       | surface
	- sot    | soil temperature                     | K       | top layer of soil
	- sp     | surface pressure                     | Pa      | surface
	- sr     | surface roughness                    | m       | surface
	- t2m    | 2 metre temperature                  | K       | 2m
	- tcc    | total Cloud Cover                    | %       | above ground
	- tciwv  | total column integrated water vapour | kg/m^2  | surface
	- tp     | total Precipitation                  | kg/m^2  | surface
	- vsw    | volumetric soil moisture             | m^3/m^3 | top layer of soil
	- wdir10 | 10 metre wind direction              | o       | 10m

3.  Missing data codes/symbols:

	NaN.


E DATA-SPECIFIC INFORMATION FOR ERA5-Land:

1.  Variable list including full names and definitions of column headings for tabular data:
	
	- d2m   | 2m dewpoint temperature                        | K                    | 2m     
	- t2m   | 2m temperature                                 | K                    |  2m  
	- fal   | forecast albedo                                | %                    | surface
	- skt   | skin temperature                               | K                    | surface
	- stl1  | soil temperature                               | K                    | soil layer (0 - 7 cm)
	- sp    | surface pressure                               | Pa                   | surface
	- e     | total evaporation                              | m of water equvalent | surface
	- tp    | total precipitation                            | m                    | surface
	- swvl1 | volumetric soil water                          | m^3/m^3              | soild layer (0 - 7 cm)

3.  Missing data codes/symbols:

	NaN.