Assimilation of wind speed and direction observations: results from real observation experiments

Details

• Journal Title:
  Tellus A: Dynamic Meteorology and Oceanography
• Personal Author:
  Gao, Feng ; Huang, Xiang-Yu ; Jacobs, Neil A. ; Wang, Hongli
• NOAA Program & Office:
  CIRA (Cooperative Institute for Research in the Atmosphere)
• Description:
  The assimilation of wind observations in the form of speed and direction (asm_sd) by the Weather Research and Forecasting Model Data Assimilation System (WRFDA) was performed using real data and employing a series of cycling assimilation experiments for a 2-week period, as a follow-up for an idealised post hoc assimilation experiment. The satellite-derived Atmospheric Motion Vectors (AMV) and surface dataset in Meteorological Assimilation Data Ingest System (MADIS) were assimilated. This new method takes into account the observation errors of both wind speed (spd) and direction (dir), and WRFDA background quality control (BKG-QC) influences the choice of wind observations, due to data conversions between (u,v) and (spd, dir). The impacts of BKG-QC, as well as the new method, on the wind analysis were analysed separately. Because the dir observational errors produced by different platforms are not known or tuned well in WRFDA, a practical method, which uses similar assimilation weights in comparative trials, was employed to estimate the spd and dir observation errors. The asm_sd produces positive impacts on analyses and short-range forecasts of spd and dir with smaller root-mean-square errors than the u,v-based system. The bias of spd analysis decreases by 54.8%. These improvements result partly from BKG-QC screening of spd and dir observations in a direct way, but mainly from the independent impact of spd (dir) data assimilation on spd (dir) analysis, which is the primary distinction from the standard WRFDA method. The potential impacts of asm_sd on precipitation forecasts were evaluated. Results demonstrate that the asm_sd is able to indirectly improve the precipitation forecasts by improving the prediction accuracies of key wind-related factors leading to precipitation (e.g. warm moist advection and frontogenesis).
• Keywords:
  Weather Forecasting Winds—Speed Weather Forecasting
• Source:
  Tellus A: Dynamic Meteorology and Oceanography, 67(1)
• DOI:
  https://doi.org/10.3402/tellusa.v67.27132
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:dee835fe50f1ddaca3df61564079454dde131bc1fa66acb9a6dcf0635156b0cfb40005ddc045d19cf72e3f1b83dfc625a3643322e402991379d44c7113895736
• Download URL:
  https://repository.library.noaa.gov/view/noaa/26981/noaa_26981_DS1.pdf
• File Type:
  [PDF - 3.03 MB ]