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Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.

  • Blend of Models

    Use ensemble dressing to post-process weather forecasts, then combine multiple model forecasts by regression.

    Installation: N/A;

    Execution: after compiling;

    Input: weather forecasts;

    QR code:

    2019-10-18 111 View Details

  • Ensemble Bayesian Model Averaging

    Fit Bayesian model average (BMA) model to correct the bias in each forecast and combine multiple forecasts.

    Installation: N/A;

    Execution: after compiling;

    Input: weather forecasts;

    QR code:



    2019-10-19 151 View Details

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