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1,801 to 1,810 of 1,841 Results

zmla_2009.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: 12451f869428ad6285f2a069aefaf9e12405ac854eb24edf4e6f8d868659582f
zmla_2010.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: 93c31f95bc2d5a322dae4831fad96672a2376c0001aaefdfedb88b4d797c4fa0
zmla_2011.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: 2b2d340f9ff84cfba7e87036fcc99fa341a65a863b858fcb6ff1194184f1c061
zmla_2012.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: a392467cc8f82253580f323a285a5559d18a960a7b6dd8c3588c22ab35135c83
zmla_2013.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: e9860c13cb2dcf351615350768100decbfb49789f52e545478aefec24abecaa0
zmla_2014.nc Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe' Plain Text - 911.4 MB - SHA-256: a49c80f9eceaf984da0e1ee01b8fd8b3cc27999380831c8609e504256c6b5794
Advancing AI-based pan-European groundwater monitoring Mar 28, 2022 Ma, Yueling; Montzka, Carsten; Naz, Bibi; Kollet, Stefan, 2021, "Advancing AI-based pan-European groundwater monitoring", https://doi.org/10.26165/JUELICH-DATA/ZBLDIR, Jülich DATA, V2 This study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is nam...
Example_script_for_the_implementation_of_LSTM_TL.ipynb Mar 28, 2022 - Advancing AI-based pan-European groundwater monitoring Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b A Jupyter Notebook showing an example about the implementation of LSTM-TL
COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 197.3 MB - SHA-256: 93abe0a605585b816742fe50b77030254c284fd236ea4319b5d7216c1bbce371 Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regio...
ERA5_bias_corrected_monthly_precipitation_anomaly_FZJ-IBG3_WFDE5_0.1_degree_v1..nc Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring Network Common Data Form - 327.9 MB - SHA-256: e5a3559843458d2ed2e2db5a4572539081d9cbf5c8d4b7a4d420bd019a1270ce Monthly precipitation anomalies (pr_a) derived from the ERA5 bias corrected dataset (referred to: Muñoz Sabater, J.: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds....

zmla_2009.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: 12451f869428ad6285f2a069aefaf9e12405ac854eb24edf4e6f8d868659582f

zmla_2010.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: 93c31f95bc2d5a322dae4831fad96672a2376c0001aaefdfedb88b4d797c4fa0

zmla_2011.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: 2b2d340f9ff84cfba7e87036fcc99fa341a65a863b858fcb6ff1194184f1c061

zmla_2012.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: a392467cc8f82253580f323a285a5559d18a960a7b6dd8c3588c22ab35135c83

zmla_2013.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: e9860c13cb2dcf351615350768100decbfb49789f52e545478aefec24abecaa0

zmla_2014.nc

Aug 29, 2022 - Data set of the manuscript 'Opposite signs in local and nonlocal soil moisture-precipitation couplings across Europe'

Plain Text - 911.4 MB - SHA-256: a49c80f9eceaf984da0e1ee01b8fd8b3cc27999380831c8609e504256c6b5794

Advancing AI-based pan-European groundwater monitoring

Mar 28, 2022

Ma, Yueling; Montzka, Carsten; Naz, Bibi; Kollet, Stefan, 2021, "Advancing AI-based pan-European groundwater monitoring", https://doi.org/10.26165/JUELICH-DATA/ZBLDIR, Jülich DATA, V2

This study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is nam...

Example_script_for_the_implementation_of_LSTM_TL.ipynb

Mar 28, 2022 - Advancing AI-based pan-European groundwater monitoring

Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b

A Jupyter Notebook showing an example about the implementation of LSTM-TL

COSMO_REA6_monthly_precipitation_anomaly_FZJ-IBG3_COSMO_0.1_degree_v1.1995_082019.nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 197.3 MB - SHA-256: 93abe0a605585b816742fe50b77030254c284fd236ea4319b5d7216c1bbce371

Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regio...

ERA5_bias_corrected_monthly_precipitation_anomaly_FZJ-IBG3_WFDE5_0.1_degree_v1..nc

Dec 14, 2021 - Advancing AI-based pan-European groundwater monitoring

Network Common Data Form - 327.9 MB - SHA-256: e5a3559843458d2ed2e2db5a4572539081d9cbf5c8d4b7a4d420bd019a1270ce

Monthly precipitation anomalies (pr_a) derived from the ERA5 bias corrected dataset (referred to: Muñoz Sabater, J.: Near surface meteorological variables from 1979 to 2019 derived from bias-corrected reanalysis, Copernicus Clim. Chang. Serv. Clim. Data Store, doi:10.24381/cds....

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