Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

Area of Science:

• Meteorology
• Satellite Remote Sensing
• Data Assimilation

Background:

• The FengYun-4A (FY-4A) satellite's Advanced Geostationary Radiation Imager (AGRI) provides continuous local weather data.
• Cloud cover significantly impacts infrared satellite observations, complicating all-sky data assimilation.
• Cloud presence introduces uncertainty, causing observation-minus-background (O-B) differences to deviate from assumed Gaussian distributions.

Purpose of the Study:

• To develop dynamic observation error models for FY-4A/AGRI all-sky data assimilation.
• To quantify the impact of cloud amount using novel cloud-affected (Ca) indices.
• To improve the assimilation of cloud-affected satellite data by addressing O-B distribution biases.

Main Methods:

• Introduction of two cloud-affected (Ca) indices to measure cloud impact.
• Evaluation of two dynamic observation error models: two-segment and three-segment linear models.
• Testing models for assimilating FY-4A/AGRI all-sky data, focusing on O-B statistics.

Main Results:

• The proposed three-segment linear dynamic observation error model better fits FY-4A/AGRI observation characteristics.
• The three-segment model significantly improves the Gaussianity of the O-B probability density function.
• Dynamic models uniformly handle both cloud-free and cloud-affected AGRI observations.

Conclusions:

• Dynamic observation error models effectively address cloud-induced biases in FY-4A/AGRI data assimilation.
• The three-segment linear model offers a superior approach for improving data assimilation accuracy.
• This method allows for uniform assimilation of all-sky AGRI data without requiring cloud detection.