A Multi-Year Forecast Dataset of Arctic Sea Ice Concentration Based on Spectral Analysis and Modeling

1. Data Description This dataset contains both observed and forecasted Arctic sea ice concentration (SIC) data, as well as the code repository for data processing and forecasting. Forecasted Data: Daily SIC predictions from January 1, 2020 – December 31, 2029. Observed Data: Daily SIC from January 1, 2013 – December 31, 2024, provided by the University of Bremen. 2. SIC data Observed Data (in Input_data.zip): Temporal Coverage: January 1, 2013, to December 31, 2024. Data Formats: NetCDF, format. Naming Convention: Files are named according to the date in the format asi-AMSR2-n6250-YYYYMMDD-v5.4.nc. Forecasted Data ( Forecasted_data.zip): Temporal Coverage: January 1, 2020, to December 31, 2029. Data Formats: GeoTIFF format. both based each-pixel and mean-seried forecasts are included Projection System: Northern Polar Stereographic. Spatial Resolution: 6.25 km. 3. code This repository provides a complete pipeline for long-term daily Arctic sea ice concentration (SIC) prediction based on time series modeling. The included code consists of four major functional components: 🔧 1. Data Preprocessing (Input_data.zip) preprocess.py: This script processes raw NetCDF-format Sea Ice Concentration (SIC) data from the Bremen University dataset. The preprocessed data is also provided as input_data for direct use in 2Time Series Forecasting. 📈 2. Time Series Forecasting (Forecasting_process.zip) forecast.py: Main forecasting script that uses a hybrid model (Least Squares + AutoRegressive). ls_each_pixel.py: Performs least squares fitting period singal for each individual pixel. ls_mean_series.py: Performs least squares fitting period singal for mean series. ar.py: Applies an AutoRegressive model to residuals for further prediction refinement. ls_ar.py: Combines LS and AR forecasts. The output is a predicted .npy file containing daily SIC forecasts for a full year. 🗺 3. GeoTIFF Export (Forecasted_data.zip) npy2tif.py: Converts the predicted SIC .npy data into daily GeoTIFF images using georeferencing from a sample NetCDF file and a North Polar Stereographic projection.