A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case

Abstract. Integrating the hybrid and multiscale analyses and the
parallel computation is necessary for current data assimilation schemes. A
local data assimilation method, Local DA, is designed to fulfill these
needs. This algorithm follows the grid-independent framework of the local
ensemble transform Kalman filter (LETKF) and is more flexible in hybrid
analysis than the LETKF. Local DA employs an explicitly computed background
error correlation matrix of model variables mapped to observed grid
points/columns. This matrix allows Local DA to calculate static covariance
with a preset correlation function. It also allows the conjugate
gradient (CG) method to be used to solve the cost function and allows
localization to be performed in model space, observation space, or both spaces (double-space
localization). The Local DA performance is evaluated with a simulated
multiscale observation network that includes sounding, wind profiler,
precipitable water vapor, and radar observations. In the presence of a
small-size time-lagged ensemble, Local DA can produce a small analysis error
by combining multiscale hybrid covariance and double-space localization. The
multiscale covariance is computed using error samples decomposed into
several scales and independently assigning the localization radius for each
scale. Multiscale covariance is conducive to error reduction, especially at
a small scale. The results further indicate that applying the CG method for
each local analysis does not result in a discontinuity issue. The wall clock
time of Local DA implemented in parallel is halved as the number of cores
doubles, indicating a reasonable parallel computational efficiency of Local
DA.
