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.
Fit quantile regression model to post-process hydrometeorological forecasts.
Installation: N/A;
Execution: after compiling;
Input: weather forecasts;
QR code:
494 2019-10-18 View Details
Firstly apply log-sinh transformation, then fit joint probability model to the forecast and observations. Given new forecasts, the conditional distribution of observations can be obtained in forms of ensemble forecasts.The method can be applied to remove the bias and dispersion errors in raw weather forecasts.
Installation: N/A;
Execution: after compiling;
Input: precipitation;
QR code:
480 2019-10-19 View Details
Fit logistic regression model to post-process hydrometeorological forecasts.
Installation: N/A;
Execution: after compiling;
Input: weather forecasts;
QR code:
599 2019-10-18 View Details
Firstly apply normal quantile transform (NQT), then fit joint probability model to the forecast and observations. Given new forecasts, the conditional distribution of observations can be obtained in forms of ensemble forecasts.The method can be applied to remove the bias and dispersion errors in raw weather forecasts.
Installation: N/A;
Execution: after compiling;
Input: precipitation;
QR code:
551 2019-10-17 View Details
Fit an ensemble model output statistic (EMOS) model to post-process weather forecasts.
Installation: N/A;
Execution: after compiling;
Input: weather forecasts;
QR code:
485 2019-10-17 View Details
Firstly apply normal quantile transform (NQT), then fit a general linear regression model to the multiple-day observed and simulated streamflow. Given new streamflow simulations, the conditional distribution of observations can be obtained in forms of ensemble forecasts.The method can be applied to remove the bias in raw streamflow simulations.
Installation: N/A;
Execution: after compiling;
Input: streamflow;
QR code:
522 2019-10-17 View Details
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