HR: 16:15h
AN: H44A-02    [Abstracts]
TI: Exploring New Pathways in Precipitation and Latent Heating Assimilation
AU: * Zhang, S Q
EM: szhang@gmao.gsfc.nasa.gov
AF: Sara Q. Zhang, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771
AU: Hou, A y
EM: hou@gmao.gsfc.nasa.gov
AF: Sara Q. Zhang, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771
AB: Assimilation of precipitation-related data types poses a special challenge in that the forward models for rain process in global forecast system are based on parameterized physics. The systematic errors in the parameterization must be rectified in order to make effective use of precipitation information within a standard statistical analysis framework. In this work we report the development of a variational algorithm assimilating rain rate and latent heating observations from TRMM in NASA's global data assimilation system using the forecast model as a weak constraint. The implementation takes a two-step approach. In the first step within a 1D variational continuous assimilation framework, accumulated TMI surface rain is assimilated to estimate and compensate errors in model's moisture tendencies. The convective and stratiform latent heating retrievals at collocation with the rain observations are assimilated by optimizing selected physical parameters in the model moist physics. In the second step these tendency corrections and parameter adjustments are applied to the 4D assimilation in the same analysis time window to obtain dynamically consistent reanalysis of atmospheric states. Data impact studies show that this assimilation approach effectively acts as an online estimation and correction of forecast model biases. The model's prognostic variables are allowed to directly respond to improved rain and latent heating during the analysis cycle. The combined use of TMI surface rain and vertical latent heating structure improves not only the temporal and spatial distribution of tropical latent heating but also the vertical velocity Field and associated horizontally divergent winds.
DE: 3315 Data assimilation
SC: Hydrology [H]
MN: Fall Meeting 2005