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Nonlinear data assimilation for clouds and precipitation using a gamma inverse‐gamma ensemble filter
Author(s) -
Posselt Derek J.,
Bishop Craig H.
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3374
Subject(s) - ensemble kalman filter , data assimilation , environmental science , meteorology , precipitation , kalman filter , gamma distribution , nonlinear system , cloud computing , mathematics , remote sensing , computer science , statistics , extended kalman filter , physics , geography , quantum mechanics , operating system
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. This study evaluates the effectiveness of the GIGG‐EnKF by using it to estimate the values of warm rain cloud microphysical parameters in a cloud model using observations of precipitation. GIGG‐EnKF estimates of cloud parameters and rainfall are compared with a Bayesian reference solution returned by a Markov chain Monte Carlo algorithm, and with a perturbed‐observations Ensemble transform Kalman filter (ETKF). The ETKF produces an ensemble with a substantial number of negative (non‐physical) precipitation rates and parameter values. In contrast, the GIGG‐EnKF precipitation analysis is positive definite, and is able to adapt to changes in the observation value (and observation uncertainty). Nonlinearity in the parameter–precipitation relationship is handled by implementing an iterative outer loop regression from the observation space to state space. The positive‐definite constraint and natural accommodation of fractional error, along with the iterative outer loop, produces an accurate reproduction of the Bayesian analysis of the precipitation rate and cloud microphysical parameter values.