Yonghan Choi, Joo-Hong Kim, Joo-Hong Kim, Dong-Hyun Cha
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Data assimilation impact of GNSS RO measurements from cube satellites on Arctic weather forecasts
In this study, the effects of assimilating global navigation satellite system (GNSS) radio occultation (RO) observations from existing and recently added commercial cube satellites on analyses and forecasts over the Arctic region were investigated by conducting observing system experiments (OSEs). Profiles of refractivity were assimilated with a local observation operator using the three-dimensional variational method. The analyses and forecasts from the OSEs were verified against ERA5 reanalysis, radiosonde observations, and buoy observations. In addition to the averaged impact on forecast skill, the impact of GNSS RO observations was further examined for an individual Arctic cyclone case, focusing on the added value of the cube satellite data.
The effects of GNSS RO observations from existing satellites on analyses and forecasts over the Arctic region are positive, and the assimilation of GNSS RO observations from cube satellites leads to additional improvements, particularly for temperature in the upper troposphere and lower stratosphere (UTLS). Temperature biases in the UTLS are significantly reduced in the analyses, and the improved analyses result in better forecasts of upper-level potential vorticity and cyclone development when GNSS RO observations from cube satellites are assimilated. This result demonstrates the potential of GNSS RO data from cube satellites to enhance forecasts over the Arctic region.
期刊介绍:
Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.