Assimilation of ground-based GNSS data using a local ensemble Kalman filter.

Area of Science:

• Atmospheric Science
• Meteorology
• Geodesy

Background:

• High-resolution models reveal tropical cyclones exhibit nonlinear and dynamically unstable behavior.
• Sub-optimal initial conditions limit forecast accuracy, highlighting the need for improved data assimilation.
• Lack of oceanic ground-based Global Navigation Satellite System (GNSS) observations hinders evaluation of assimilation in these regions.

Purpose of the Study:

• To assess the impact of data assimilation on tropical cyclone forecast accuracy over oceanic regions.
• To evaluate the effectiveness of assimilating various synthetic observations, including GNSS data.
• To investigate the potential of enhancing weather forecasting through GNSS data assimilation.

Main Methods:

• An Observation System Simulation Experiment (OSSE) was conducted using a tropical cyclone case.
• Data assimilation experiments were performed using the WRF-PDAF framework with conventional and GNSS observation operators.
• Synthetic observations (temperature, wind components, precipitable water, zenith total delay) were assimilated using the Local Error-Subspace Transform Kalman filter (LESTKF).

Main Results:

• Data assimilation significantly improved tropical cyclone forecast accuracy over the ocean.
• The assimilation of multiple observation types, particularly GNSS data, further enhanced forecast precision.
• The study demonstrated the critical role and potential of GNSS data assimilation techniques.

Conclusions:

• GNSS data assimilation is a promising technique for advancing tropical cyclone weather forecasting capabilities.
• Establishing ground-based GNSS observation stations over oceans is a crucial step for future improvements.
• Enhanced data assimilation strategies are vital for overcoming limitations in current tropical cyclone modeling.