Data Assimilation with a Machine Learned Observation Operator and Application to the Assimilation of Satellite data for Sea Ice Models

• Main Author: Jing, Siyang
• Other Authors: College of Arts and Sciences, Department of Mathematics, Jones, Christopher
• Format: Thesis
• Language: English
• Published: University of North Carolina at Chapel Hill 2019
• Subjects: Melt Pond; Machine Learning; Sea Ice; Ensemble Kalman Filter; Data Assimilation; Dynamical System; Neural Network; Arctic; Sea ice
• Online Access: https://doi.org/10.17615/gcgs-aa70; https://cdr.lib.unc.edu/downloads/kd17cz226?file=thumbnail; https://cdr.lib.unc.edu/downloads/kd17cz226

Data assimilation embodies a wide variety of techniques used to combine model output and real-world observations in an optimal way to estimate the true state of a system. Important to all data assimilation schemes are the model $\\cM$ used to evolve physical state variables forward in time, and the observation operator $\\cH$ used to map those state variables to observed quantities. Ideally, the observed quantities are the state variables themselves in which case $\\cH$ is simply a projection. However, in practice, this may not be the case. In many cases, the relationship between the physical state variables and the observed quantities can be very complex and highly nonlinear. An example is the case of passive microwave satellite observations of sea ice. Sea ice plays a vital role in the Earth's climate system and is a focus of both remote sensing and modeling efforts in modern times. Passive microwave radiometry provides a daily picture of the ice, despite persistent Arctic cloud cover, at a low resolution of 25km. While one cannot resolve many ice features in this data set, the concentration of ice in a given 25km pixel may be derived from the intensities of observed microwaves at various frequencies. Sea ice is far more emissive in the microwave spectrum than open water and that contrast can be exploited to estimate sea ice concentration. The emissivity of the ice depends on its temperature, bulk salinity, thickness, and its snow cover. Further, the microwaves must pass through the atmosphere producing noise in the observed signal. The map from sea ice state variables to observable microwave intensities is thus very difficult to model. More empirical methods can obtain concentrations; the NASA TEAM 2 algorithm is an example. Given the frequent observations possible with passive microwave, the long 30-year record sea ice concentration derived from these observations is an enticing data set for assimilation into large-scale sea ice models. In addition, sea ice concentration is a sea ice state variable allowing ...