Design Strategies of an Hourly Update 3DVAR Data Assimilation System for Improved Convective Forecasting

Abstract

This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.

Constraining the Large‐Scale Analysis of a Regional Rapid‐Update‐Cycle System for Short‐Term Convective Precipitation Forecasting

“…The LSC is only applied to the 3‐hourly cycle at each update cycle on the inner domain. The use of two RUCs allows large‐scale and small‐scale information being treated separately and optimally, which is consistent with the principle of multistep DA in recent studies (Ha & Lee, ; Tong et al, ).…”

“…Several studies (Li et al, ; Peng et al, ; Xie et al, ) introduced methods to retain the large‐scale information during DA of high‐resolution observations based on the principle of scale separation and optimization. Although the application of these methods (J. Gao et al, ; Ha & Lee, ; Tong et al, ) showed improved analysis fields and precipitation forecasts in real cases, their effect in improving large‐scale analysis is still limited due to the sparseness of large‐scale observations and the inherent shortcomings associated with lateral boundary conditions in LAMs.…”

Assimilating Conventional and Doppler Radar Data with a Hybrid Approach to Improve Forecasting of a Convective System

“…The indirect radar DA approach of Wang et al (2013) was used for radar reflectivity, in which retrieved hydrometer mixing ratios were assimilated. The two-step procedure of Tong et al (2016) was also used to reduce analysis noise in radar DA, via the use of a shorter length scale and analysis cycle. The system was examined by comparing experimental forecast results, i.e., using 3DVar and hybrid experiments without (Exp3DVC and ExpHYBC) and with radar DA (Exp3DVCR and ExpHYBCR).…”

“…In addition, verification against radiosondes and METAR observations (see Figure 4) were also performed for all experiments. The radar DA experiments followed the two-step procedure of Tong et al (2016), as shown in Figure 5. In Step 1, the first assimilation cycle was initiated at 12:00 UTC on 6 August 2016 with GTS, with radiosonde, surface network and aircraft data assimilated.…”

“…Then, three-hourly analysis/forecast cycles were executed through 09:00 UTC on 7 August 2016. The analysis following GTS assimilation The radar DA experiments followed the two-step procedure of Tong et al (2016), as shown in Figure 5. In Step 1, the first assimilation cycle was initiated at 12:00 UTC on 6 August 2016 with GTS, with radiosonde, surface network and aircraft data assimilated.…”