Assimilation of Hourly Surface Observations with the Canadian High-Resolution Ensemble Kalman Filter. Issue 4 (20th October 2017)

• Record Type: Journal Article
• Authors: Chang, Weiguang; Jacques, Dominik; Fillion, Luc; Baek, Seung-Jong
• Is Part Of: Atmosphere-ocean. Volume 55:Issue 4/5(2017)
• Page Start: 247
• Page End: 263
• Publication Date: 2017-10-20
• Subjects: ensemble Kalman Filter -- data assimilation -- METAR data -- high resolution; Ocean-atmosphere interaction -- Canada -- Periodicals; Ocean-atmosphere interaction -- Periodicals; Oceanography -- Canada -- Periodicals; Oceanography -- Periodicals; Meteorology -- Canada -- Periodicals; Meteorology -- Periodicals; 551.5246
• DOI: 10.1080/07055900.2017.1384361
• Languages: English
• ISSNs: 0705-5900

Abstract

ABSTRACT: An hourly-cycling ensemble Kalman filter (EnKF) working at 2.5 km horizontal grid spacing is implemented over southern Ontario (Canada) to assimilate Meteorological Terminal Aviation Routine Weather Reports (METARs) in addition to the observations assimilated operationally at the Canadian Meteorological Centre. This high-resolution EnKF (HREnKF) system employs ensemble land analyses and perturbed roughness length to prevent an ensemble spread that is too small near the surface. The HREnKF then performs continuously for a four-day period, from which twelve-hour ensemble forecasts are launched every six hours. The impact on analyses and short-term forecasts of assimilating METAR data is given special attention. It is shown that using ensemble land surface analyses increases near-surface ensemble spreads for temperature and specific humidity. Perturbing roughness length enlarges the spread for surface wind. Given sufficient ensemble spread, the four-day case study shows that the near-surface model state is brought closer to surface observations during the cycling process. The impact of assimilating surface data can also be seen at higher levels by using aircraft reports for verification. The ensemble forecast verification suggests that METAR data assimilation improves ensemble forecasts of air temperature and dewpoint near the surface up to a lead time of six hours or even longer. However, only minor improvement is found in surface wind forecasts.