Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States - Qi Zhang; Min Shao (Atmosphere, 2023)
As a part of the World Meteorological Organization (WMO) Global Observing System, HIRAS-1 and HIRAS-2’s observations’ impact on improving the accuracy of numerical weather prediction (NWP) can be summarized into two questions: (1) Will HIRAS observation help the NWP system to improve its accuracy? (2) Which instrument has the greater impact on NWP? To answer the questions, four experiments are designed here: (I) the HIRAS-1 experiment, which assimilates the principal component (PC) scores derived from HIRAS-1 radiance observation from the FY-3D satellite; (II) the HIRAS-2 experiment, which assimilates HIRAS-2 (onboard the FY-3E satellite) radiance-observation-derived PC scores; (III) the J-01 experiment, which assimilates JPSS1 CrIS radiance-observation-derived PC scores; (IV) the control experiment. Each experiment generated a series of forecasts with 24 h lead-time from 16 March 2022 to 12 April 2022 using the Unified Forecast System Short-Range Weather application. Forecast evaluation using radiosonde and aircraft observation reveals: (a) for upper-level variables (i.e., temperature and specific humidity), assimilating HIRAS observation can improve the NWP’s performance by decreasing the standard deviation (Stdev) and increasing the anomaly correlation coefficient (ACC); (b) according to the multi-category Heidke skill score, HIRAS assimilation experiments, especially the HIRAS-2 experiment, have a higher agreement with hourly precipitation observations; (c) based on two tornado-outbreak case studies, which occurred on 30 March 2022 and 5 April 2022, HIRAS observation can increase the predicted intensity of 0–1 km storm relative helicity and decrease the height of the lifted condensation level at tornado outbreak locations; and (d) compared to CrIS, HIRAS-2 still has room for improvement.