Data for "Addressing the Nonlinear Effects in the Albedo Feedback Using the Neural Network Method"

• Main Author: Aliia Shakirova
• Format: Dataset
• Language: unknown
• Published: Mendeley 2021
• Subjects: Natural Sciences; Arctic; albedo
• Online Access: https://dx.doi.org/10.17632/gy24tn26pb; https://data.mendeley.com/datasets/gy24tn26pb

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 NN/train_nn_toa.m script to train nn model for TSR flux prediction 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 6 and Figure S7. 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.