Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and Cross-Track Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at top of atmosphere using thousands channels, these observations convey accurate atmospheric information initial condition through data assimilation (DA) schemes. The massive volume has pushed community develop novel approaches reduce number assimilated channels while retaining much content possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations (1) deficiency observational (2) higher cross-channel correlation error (R) matrix. This paper introduces IR observation DA scheme principal component (PC) space. Four-month performance comparison case studies Weather Research Forecasting model (WRF) forecast module between PC-score selected-channel experiment show that can condition’s root-mean-squared for temperature water vapor compared thus improve forecasting precipitation high-impact weather. Case Unified Forecast System Short-Range (UFS-SRW) application also indicate positive impact be retained among different models.