We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Assessing the impact of pre- GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system.
- Authors
Chambon, Philippe; Zhang, Sara Q.; Hou, Arthur Y.; Zupanski, Milija; Cheung, Samson
- Abstract
The forthcoming Global Precipitation Measurement ( GPM) Mission will provide next-generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction ( NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave ( MW) radiances from a pre- GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF- EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast ( WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.
- Subjects
METEOROLOGICAL precipitation measurement; NUMERICAL weather forecasting; CLOUDS; CONVECTION (Meteorology); RAINFALL measurement; OCEAN surface topography; ANALYSIS of covariance
- Publication
Quarterly Journal of the Royal Meteorological Society, 2014, Vol 140, Issue 681, p1219
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.2215