Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in the GSI-Based EnVar System: Methodology and Experiment with the 8 May 2003 Oklahoma City Tornadic Supercell

Abstract

A GSI-based EnVar data assimilation system is extended to directly assimilate radar reflectivity to initialize convective-scale forecasts. When hydrometeor mixing ratios are used as state variables (method mixing ratio), large differences of the cost function gradients with respect to the small hydrometeor mixing ratios and wind prevent efficient convergence. Using logarithmic mixing ratios as state variables (method logarithm) fixes this problem, but generates spuriously large hydrometeor increments partly due to the transform to and from the logarithmic space. The tangent linear of the reflectivity operators further contributes to spuriously small and large hydrometeor increments in method mixing ratio and method logarithm, respectively. A new method is proposed by directly adding the reflectivity as a state variable (method dBZ). Without the tangent linear and adjoint of the nonlinear operator, the new method therefore avoids the aforementioned problems. The newly proposed method is examined on the analysis and prediction of the 8 May 2003 Oklahoma City tornadic supercell storm. Both the probabilistic forecast of strong low-level vorticity and maintenance of strong updraft and vorticity in method dBZ are more consistent with reality than in method logarithm and method mixing ratio. Detailed diagnostics suggest that a more realistic cold pool due to the better analyzed hydrometeors in method dBZ than in other methods leads to constructive interaction between the surface gust front and the updraft aloft associated with the midlevel mesocyclone. Similar low-level vorticity forecast and maintenance of the storm are produced by the WSM6 and Thompson microphysics schemes in method dBZ. The Thompson scheme matches the reflectivity distribution with the observations better for all lead times, but shows more southeastward track bias compared to the WSM6 scheme.

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

“…wind variables. That said, there have been recent successes of EnVar at convective-scale with radar data, including Wang and Wang (2017).…”

Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events

“…This indicates To provide a complete picture of forecast performance, the 0-3 h accumulated precipitation forecast at each cycle is also evaluated (Figure 8). To get rid of the influence of lateral boundary on precipitation simulation, we only quantitatively and qualitatively evaluate the accumulated precipitation within subdomains (27)(28)(29)(30)(31)(32)(33)(34)(35) • N, 107-117 • E). The precipitation observations are produced by a method called CMORPH (NOAA CPC Morphing technique) [62].…”

“…Another problem is that too much water vapor and latent heating are added to the cloud analysis, resulting in an increased false alarm rate and over-prediction, especially when many data assimilation cycles are involved [21][22][23]. To solve these problems, more data assimilation approaches have been used, such as latent heat nudging [24], variational techniques [1,[7][8][9][10][11]25,26], the ensemble Kalman filter (EnKF) [5,6,12,[27][28][29], and hybrid variational and ensemble approaches [13,14,30]. These studies have demonstrated that assimilation of reflectivity can improve short-term forecasts.…”

A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

“…Over the past several decades, radar reflectivity observations have been used in many data assimilation (DA) studies (Borderies et al, 2018;Caumont et al, 2010;Gao and Stensrud, 2012;Hu et al, 2006;Jung et al, 2010Jung et al, , 2008aLiu et al, 2019;Putnam et al, 2014;Snook et al, 2012Snook et al, , 2015Sun and Crook, 1997;Sun and Wang, 2013;Tong and Xue, 2005;Wang et al, 2013b;Wang and Wang, 2017;Wattrelot et al, 2014;Xiao et al, 2007;Xue et al, 2006) and they have demonstrated that assimilating this radar reflectivity improves the initial conditions of the convective scale and benefits the subsequent forecasts. To assimilate the reflectivity, it is necessary to transform the model's prognostic variables (e.g., rainwater, snow, and graupel) to the observed radar reflectivity.…”

“…To overcome this deficiency, more comprehensive operators that involve snow and graupel have been developed (Gao and Stensrud, 2012;Tong and Xue, 2005). Several studies (e.g., Gao and Stensrud, 2012;Wang and Wang, 2017) have demonstrated that involving these ice species in the reflectivity operator improves the analysis of hydrometeors in terms of their spatial distribution, especially in the vertical direction.…”