Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts. In particular, using the Tikhonov and Huber regularization in the derivative space, the promise of the proposed framework is demonstrated in static downscaling and fusion of synthetic multi-sensor precipitation data, while a data assimilation numerical experiment is presented using the heat equation in a variational setting

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content yields an analysis comparable in quality with the one obtained using the entire data set

Influence of assimilating rainfall derived from WSR-88D radar on the rainstorm forecasts over the southwestern United States

In this study, the impact of rainfall assimilation on the forecasts of convective rainfall over the mountainous areas in the southwestern United States is investigated. The rainfall is derived from the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network, and the fifth-generation Mesoscale Model (MM5) Four-Dimensional Variational (4DVAR) system is employed in the study. We evaluate the rainfall assimilation skill through two rainstorm events (5-6 August and 11-12 September 2002) that occurred over the southwestern United States in 2002. A series of experiments for the two cases is conducted. The results show that the minimization process in the 4DVAR is sensitive to the length of assimilation window and error variance in the observation data. Assimilation of rainfall can produce a better short-range precipitation forecast. However, the time range of improved forecasts is limited to about 15 hours with the model resolution of 20 km. It is indicated that rainfall assimilation produces more realistic moisture divergence and temperature fields in the initial conditions for the two cases. Therefore the forecast of rainstorms is closer to observations in both quantity and pattern. Copyright 2006 by the American Geophysical Union