Radar Data Assimilation for Wintertime Atmospheric River Related Precipitation Events

Landfalling atmospheric rivers (ARs) during winter along the US West Coast can have significant societal and economic impacts. Improving the predictability of these events and their associated cascading impacts, such as flash flooding, debris flows, and wind gusts, is crucial for providing accurate and timely warnings to the region. Accurate heavy precipitation forecast in the short range (0–18 h) is critical for supporting water resource management decision-making. However, the skill for precipitation in the short range over states along the US West is still lower than that over the eastern US, which may be due to complex terrain and less reliable observations. Radar data assimilation (DA) is essential to improve short-range precipitation forecasts given the high spatial and temporal resolution of radar observations.

This study aims to investigate the impact of radar data assimilation on short-range quantitative precipitation forecasts through a case study in late January 2021 over California, using a new radar DA modeling framework developed at the Center for Western Weather and Water Extremes (CW3E). The new DA framework has been built upon the Gridpoint Statistical Integration (GSI) to assimilate radar data, including both the radial winds and reflectivity. Direct assimilation of radar reflectivity is achieved through the modification of control variables in GSI. The modified GSI system is applied to generate analysis for the CW3E’ regional model called West-WRF, which is based on the Weather Research and Forecast (WRF) and developed for the US West weather predictions. The radial winds are from the Next Generation Weather Radar (NEXRAD) level II data, while the reflectivity is 3D gridded national reflectivity mosaics from the Multi-Radar Multi-Sensor (MRMS).

Model outputs were validated using both Stage-IV and MRMS hourly precipitation products. The radar DA experiment improved the forecast of light-to-moderate precipitation in the first hour compared to the experiment without radar DA. However, the heavy precipitation forecast was degraded due to further underestimation, and tuning key components (e.g., observation errors, ensemble sizes, localization scales) in the data assimilation process did not fully resolve the issue. Analysis of the simulated and observed reflectivity revealed discrepancy, which may have contributed to the degradation of the heavy precipitation forecast. It's worth noting that significant inconsistency in precipitation amount and location was observed between Stage-IV and MRMS products for this event, highlighting the need for independent, reliable precipitation observations from multiple sources to validate forecasts. To test the general applicability of this hypothesis and optimize the DA system, we plan to investigate additional AR-related precipitation events.