Improved Forecast Skill Through the Assimilation of Dropsonde Observations From the Atmospheric River Reconnaissance Program. Issue 21 (8th November 2021)

• Record Type: Journal Article
• Authors: Zheng, Minghua; Delle Monache, Luca; Cornuelle, Bruce D.; Ralph, F. Martin; Tallapragada, Vijay S.; Subramanian, Aneesh; Haase, Jennifer S.; Zhang, Zhenhai; Wu, Xingren; Murphy, Michael J.; Higgins, Timothy B.; DeHaan, Laurel
• Is Part Of: Journal of geophysical research. Volume 126:Issue 21(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-08
• Subjects: atmospheric river -- atmospheric river reconnaissance -- data assimilation -- dropsondes -- numerical modeling -- observational impact; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD034967
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Landfalling atmospheric rivers (ARs) over the western US are responsible for ∼30%–50% of the annual precipitation, and their accurate forecasts are essential for aiding water management decisions and reducing flood risks. Sparse coverage of conventional observations over the Pacific Ocean, which can cause inadequate upstream initial conditions for numerical weather prediction models, may limit the improvement of forecast skill for these events. A targeted field program called AR Reconnaissance (Recon) was initiated in 2016 to better understand and reduce forecast errors of landfalling ARs at 1–5 days lead times. During the winter seasons of 2016, 2018, and 2019, 15 Intensive Observation Periods (IOPs) sampled the upstream conditions for landfalling ARs. This study evaluates the impact on forecast accuracy of assimilating these dropsonde data. Data denial experiments with (WithDROP) and without (NoDROP) dropsonde data were conducted using the Weather Research and Forecasting model with the Gridpoint Statistical Interpolation four‐dimensional ensemble variational system. Comparisons between the 15 paired NoDROP and WithDROP experiments demonstrate that AR Recon dropsondes reduced the root‐mean‐square error in integrated vapor transport (IVT) and inland precipitation for more than 70% of the IOPs, averaged over all forecast lead times from 1 to 6 days. Dropsondes have improved the spatial pattern of forecasts of IVT and precipitation in all 15 IOPs. Significant improvements in skill are found beyond the short range (1–2 days). IOP sequences (i.e., back‐to‐back IOPs every other day) show the most improvement of inland precipitation forecast skill. Plain Language Summary: Atmospheric rivers (ARs) are long and narrow corridors of water vapor transport in the atmosphere from the tropics to higher latitudes. Up to half of the annual precipitation in the western US states is related to landfalling ARs. Therefore, accurate forecasts of these events are important to reduce socio‐economic damage from the associated high‐impact weather and to better manage water resources over the western US. However, the forecast accuracy can be limited by sparse observations upstream over the Pacific Ocean. To fill observation gaps and improve forecast skills, a weather reconnaissance mission called Atmospheric River Reconnaissance was initiated in 2016 to release dropsondes from aircraft in key upstream sensitive regions. This study assesses the value of these observations in improving the initial conditions and the forecast skill of a regional model operated at the Center for Western Weather and Water Extremes for near real‐time weather forecasts. We found that the use of these additional observations collected during 2016, 2018, and 2019 has a positive impact on reducing the forecast error of water vapor transport within the Northeastern Pacific domain and the inland precipitation skill over the western US. These observations have more impact in poorly forecasted cases. Key Points: Dropsondes have reduced the forecast error of water vapor transport over the Northeast Pacific with continuous improvement out to day 3 Dropsondes overall improved the precipitation forecast over the US West Coast in 11 out of 15 Intensive Observation Periods (IOPs) IOP sequences (i.e., back‐to‐back IOPs every other day) have the most positive impact on improving the precipitation forecast skill