Data for "A neural network model for shortwave radiative feedback quantification"
This dataset contains a neural network model for the shortwave radiation prediction, scripts to generate data for the radiative feedback quantification in the Arctic, RTM simulated radiative feedbacks, and results from the kernel method. I. File list --- Figures/ scripts to generate plots era5_grid1...
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ftsmithonian:oai:figshare.com:article/14539232 2023-05-15T13:11:39+02:00 Data for "A neural network model for shortwave radiative feedback quantification" Aliia Shakirova (10748276) 2021-05-04T11:19:12Z https://doi.org/10.17632/gy24tn26pb.1 unknown https://figshare.com/articles/dataset/Data_for_A_neural_network_model_for_shortwave_radiative_feedback_quantification_/14539232 doi:10.17632/gy24tn26pb.1 CC BY 4.0 CC-BY Natural Sciences Dataset 2021 ftsmithonian https://doi.org/10.17632/gy24tn26pb.1 2021-05-21T15:40:01Z This dataset contains a neural network model for the shortwave radiation prediction, scripts to generate data for the radiative feedback quantification in the Arctic, RTM simulated radiative feedbacks, and results from the kernel method. I. File list --- Figures/ scripts to generate plots era5_grid1_data/ Sept. 1992 and Sept. 2012 ERA5 data NN/nn_tsr.mat NN model for TSR flux prediction NN/nn_ssr.mat NN model for SSR flux prediction NN/nn_toa.m function for the TSR prediction NN/nn_sfc.m function for the SSR prediction NN/calculate_toa_feedbacks.m radiative feedback quantification at the TOA NN/calculate_sfc_feedbacks.m radiative feedback quantification at the surface Note: the folder NN contains scripts for the shortwave radiative feedback quantification in the Arctic: 1. calculate_toa_feedbacks.m 2. calculate_sfc_feedbacks.m The results generated by these scripts are presented in Figure 8 and Figure S8. II. NN design --- NN model for the top net solar radiation (TSR) prediction. Input variables are: 1. TOA incident solar radiation (W*m**-2), downward positive 2. Total column cloud ice water (kg*m**-2) 3. Total column cloud liquid water (kg*m**-2) 4. Total column water vapour (kg*m**-2) 5. High cloud cover (0-1) 6. Medium cloud cover (0-1) 7. Low cloud cover (0-1) 8. Surface pressure (Pa) 9. Total column ozone (kg*m**-2) 10. Forecast albedo (0-1) Output: TSR (W*m**-2), downward positive All variables are monthly averaged values. Radiation variable is for all-sky conditions. Dataset albedo Arctic Unknown Arctic |
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Natural Sciences |
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Natural Sciences Aliia Shakirova (10748276) Data for "A neural network model for shortwave radiative feedback quantification" |
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Natural Sciences |
description |
This dataset contains a neural network model for the shortwave radiation prediction, scripts to generate data for the radiative feedback quantification in the Arctic, RTM simulated radiative feedbacks, and results from the kernel method. I. File list --- Figures/ scripts to generate plots era5_grid1_data/ Sept. 1992 and Sept. 2012 ERA5 data NN/nn_tsr.mat NN model for TSR flux prediction NN/nn_ssr.mat NN model for SSR flux prediction NN/nn_toa.m function for the TSR prediction NN/nn_sfc.m function for the SSR prediction NN/calculate_toa_feedbacks.m radiative feedback quantification at the TOA NN/calculate_sfc_feedbacks.m radiative feedback quantification at the surface Note: the folder NN contains scripts for the shortwave radiative feedback quantification in the Arctic: 1. calculate_toa_feedbacks.m 2. calculate_sfc_feedbacks.m The results generated by these scripts are presented in Figure 8 and Figure S8. II. NN design --- NN model for the top net solar radiation (TSR) prediction. Input variables are: 1. TOA incident solar radiation (W*m**-2), downward positive 2. Total column cloud ice water (kg*m**-2) 3. Total column cloud liquid water (kg*m**-2) 4. Total column water vapour (kg*m**-2) 5. High cloud cover (0-1) 6. Medium cloud cover (0-1) 7. Low cloud cover (0-1) 8. Surface pressure (Pa) 9. Total column ozone (kg*m**-2) 10. Forecast albedo (0-1) Output: TSR (W*m**-2), downward positive All variables are monthly averaged values. Radiation variable is for all-sky conditions. |
format |
Dataset |
author |
Aliia Shakirova (10748276) |
author_facet |
Aliia Shakirova (10748276) |
author_sort |
Aliia Shakirova (10748276) |
title |
Data for "A neural network model for shortwave radiative feedback quantification" |
title_short |
Data for "A neural network model for shortwave radiative feedback quantification" |
title_full |
Data for "A neural network model for shortwave radiative feedback quantification" |
title_fullStr |
Data for "A neural network model for shortwave radiative feedback quantification" |
title_full_unstemmed |
Data for "A neural network model for shortwave radiative feedback quantification" |
title_sort |
data for "a neural network model for shortwave radiative feedback quantification" |
publishDate |
2021 |
url |
https://doi.org/10.17632/gy24tn26pb.1 |
geographic |
Arctic |
geographic_facet |
Arctic |
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albedo Arctic |
genre_facet |
albedo Arctic |
op_relation |
https://figshare.com/articles/dataset/Data_for_A_neural_network_model_for_shortwave_radiative_feedback_quantification_/14539232 doi:10.17632/gy24tn26pb.1 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.17632/gy24tn26pb.1 |
_version_ |
1766248364534398976 |