Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard

Journal Article · Wed Mar 23 00:00:00 EDT 2022 · Atmospheric Research

DOI:https://doi.org/10.1016/j.atmosres.2022.106155· OSTI ID:1863356

Kim, Dae-Hui ^[1]; Kim, Hyun Mee ^[1]

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1. Yonsei Univ., Seoul (Korea, Republic of)

Although the Polar Weather Research and Forecasting (PWRF) model has been developed for polar environments, simulations of atmospheric states in the polar region still have large uncertainties. Therefore, the effects of data assimilation (DA) to improve forecasts in the polar region were investigated for September 2017 using PWRF and the three-dimensional variational (3DVAR) DA method. The experiments without DA and those with DA assimilating only conventional observations and both conventional observations and satellite radiance data were performed. The forecasts from all experiments both without and with DA underestimated (overestimated) the downward longwave (shortwave) radiation flux due to the underestimation of the amount of Arctic clouds. When satellite radiance data (i.e., the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS)) were assimilated in addition to conventional observations in the PWRF, the distribution and amount of water vapor became closer to observations, which improves cloud liquid water forecasts. Therefore, when both conventional observations and radiance data were assimilated, the 25–30 h forecast errors of the downward longwave and shortwave radiation fluxes and sensible and latent heat fluxes decreased by 12.7%, 8.1%, 3.2%, and 7.8% with the WSM5 scheme and by 17.1%, 4.7%, 2.5%, and 3.1% with the 2-moment Morrison scheme, respectively, compared to those in the experiments without DA. The forecast errors of the 10 m wind and 2 m temperature with DA were smaller than those without DA at most observation stations. Therefore, the uncertainties of the Arctic forecasts in the PWRF decreased when using DA. To further improve Arctic forecasts, the assimilation of various additional satellite data is necessary.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
• Sponsoring Organization: Ministry of Science, ICT and Future Planning (MSIP) of Korea; USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Contributing Organization: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1863356
• Journal Information: Atmospheric Research, Journal Name: Atmospheric Research Vol. 272; ISSN 0169-8095
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
polar weather research and forecasting (WRF) model
prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard
satellite data assimilation
three-dimensional variational (3DVAR) data assimilation
uncertainty of the arctic forecast