Assimilation of GOES-16 Derived Layer Precipitable Water and Radar Data Using a 3D Ensemble-Variational Hybrid Analysis Technique: Impact on Thunderstorm Forecasting
In the past decade, many studies have been demonstrated that using Doppler radar data to initialize convective-scale numerical weather prediction (NWP) models could help improve severe weather forecast. However, the impact of satellite data, especially high resolution GOES-16 data has not received too much attention despite the fact that it has potential to increase the accuracy and lead time of severe weather forecast. With the launch of GOES-16 in late 2016, effective utilization of its data in convective-scale NWP is more attractive since assimilation of its high temporal and spatial resolution information could ameliorate kinetic and thermodynamic structures of the NWP model’s initial conditions. One of the GOES-16 product is layer precipitable water (PW) which represents the amount of liquid water in a vertical column of unit cross-section area in three layers of the troposphere if atmospheric water vapor contained there was condensed. In the idealized case study, Pan et al. (2017) compared the impact of assimilating total precipitable water (TPW) on top of satellite derived cloud water path and radar data. It showed that TPW data is helpful in correcting a dry bias in the storm environment.
In this study, the impact of GOES-16 derived three-layer precipitable water observations on thunderstorm forecast is examined for a real data case. A strong forcing event on 16 May 2017 is explored in this study. In this case, three-layer PW data along with radar data are assimilated into an ARW-WRF 36-member ensemble and an extra control member with 2-km horizontal grid spacing using the hybrid NSSL Experimental Warn-on-Forecast System. One experiment only assimilates radar velocity observations, while another one assimilates GOSE-R derived three-layer PW data in addition to the radar observations. Of particular interest is if PW can help improve storm moisture environment so that the short-term (1-3 hours) forecast of severe weather using WRF model can be improved.