Two-dimensional variational analysis of near-surface moisture from simulated radar refractivity-related phase change observations

Abstract

Because they are most sensitive to atmospheric moisture content, radar refractivity observations can provide high-resolution information about the highly variable low-level moisture field. In this study, simulated radar refractivity-related phase-change data were created using a radar simulator from realistic highresolution model simulation data for a dryline case. These data were analyzed using the 2DVAR system developed specifically for the phase-change data.Two sets of experiments with the simulated observations were performed, one assuming a uniform target spacing of 250 m and one assuming nonuniform spacing between 250 m to 4 km. Several sources of observation error were considered, and their impacts were examined. They included errors due to ground target position uncertainty, typical random errors associated with radar measurements, and gross error due to phase wrapping. Without any additional information, the 2DVAR system was incapable of dealing with phase-wrapped data directly. When there was no phase wrapping in the data, the 2DVAR produced excellent analyses, even in the presence of both position uncertainty and random radar measurement errors. When a separate pre-processing step was applied to unwrap the phase-wrapped data, quality moisture analyses were again obtained, although the analyses were smoother due to the reduced effective resolution of the observations by interpolation and smoothing involved in the unwrapping procedure. The unwrapping procedure was effective even when significant differences existed between the analyzed state and the state at a reference time. The results affirm the promise of using radar refractivity phase-change measurements for near-surface moisture analysis.

Sensitivity of Convective Initiation Prediction to Near-Surface Moisture When Assimilating Radar Refractivity: Impact Tests Using OSSEs

“…In practice, L is often chosen to be comparable to the mean spacing of the observation network. Experiments indicated an optimal horizontal decorrelation length scale of 8 km in the horizontal (not shown) for our refractivity data, in general agreement with Shimose et al (2013). Additionally, the optimal vertical decorrelation length was found to be six vertical grid levels, or roughly 1 km, for the model configuration used.…”

“…in terms of the phase change that is typically much smaller than the absolute phase [see also discussion in Shimose et al (2013)]. Refractivity change measurements dN can be obtained by taking a range derivative of these df,…”

“…Prior to processing the PCD measurements into refractivity, a phase unwrapping procedure is applied. The algorithm includes censoring of poor-quality clutter targets 1 As in Shimose et al (2013), we use the term phase change to refer to the difference in phase of a target between two different times and phase change difference (PCD) to refer to the phase difference between two consecutive targets aligned along the same radial. and smoothing to reduce noise, such that the final refractivity measurements have a spatial resolution of approximately 4 km, similar to that presented in Weckwerth et al (2005).…”

“…The current study focuses on the assimilation of refractivity measurements directly in a numerical weather prediction (NWP) framework, building upon the results of a simpler 2D framework by Shimose et al (2013). The focus is to determine the potential impact of assimilating refractivity observations on CI and subsequent storm forecasts in an NWP model.…”