Nonlinear data assimilation for clouds and precipitation using a gamma inverse‐gamma ensemble filter
Abstract
Where clouds occur, their water content is always positive definite, and may be near zero. In addition, it is common for errors in remote‐sensing observations of clouds and rainfall to be represented as a fraction of the measurement. Furthermore, there is nonlinearity in the relationships among cloud environment, cloud microphysical processes, and the amount and distribution of cloud and precipitation. For these reasons, data assimilation algorithms that rely on linearity and assumptions of Gaussian probability distributions may have difficulty in assimilating observations in cloudy regions, as well as producing an analysis that realistically represents the actual distribution of clouds and precipitation. A recently developed ensemble filter algorithm, the Gamma, Inverse‐Gamma, and Gaussian Ensemble Kalman Filter (GIGG‐EnKF), allows for fractional observation errors and positive‐definite quantities. As such, it has promise for producing more effective and accurate data assimilation and retrievals of clouds and precipitation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2018
- Source ID
- 10.1002/qj.3374
Entities
People
- Craig H Bishop
- Derek Posselt
Organizations
- California Institute of Technology
- Jet Propulsion Laboratory
- Office of Naval Research
- United States Naval Research Laboratory