Mathematical Sciences: Integrating Heterogeneous Geo- physcal Data by Combining Error Structures: An Inter- disciplinary Pilot Project

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9418904 Levy Operational weather and climate predictions models of the near future have three fundamental requirements: 1. Data management facilities that allow the assimilation and manipulation of massive amounts of heterogeneous and imprecise data obtained from a variety of sources. 2. A modeling technology, both physical and statistical, that allows the integration of partial and heterogeneous data, accounting for the realistic non-linear physics of the problem and the often complex error structure encountered. 3. Efficient support of these functions within the data assimilation cycle. Responding effectively to these requirements calls for an interdisciplinary approach. A collaboration which will bring together complementary expertise in the computational, statistical, and the geosciences for cross training and joint research is proposed. The goal is to initiate cross training which will enable the collaborators to address, as a team, crucial problems in the atmospheric data assimilation cycle. The feasibility of applying sophisticated theoretical solutions in practice by building software tools will be tested. By implementing an intensive interdisciplinary plan that will include a colloquium series, cross-teaching, workshops, and regular conferences, the collaborators will broaden their understanding of the complementary fields to be able to jointly propose and develop a general methodology and a prototype for a specific demonstration system. In the demonstration system, a physical Boundary Layer (BL) model will be used to integrate and manage massive satellite sensed data. The processing of such data inevitably involves complex gridding, interpolation, data quality control; and averaging, as well as the infusion of arbitrary ancillary data sets and estimation of error covariance structure. Techniques to provide these functions and apply them to the specific case of scatterometer (ERS-1) stress data will be studied, a nd the feasibility of synthesizing them from current disciplinary methods will be explored. The resulting data will be processed through a Planetary Boundary Layer Model (PBLM) to link them to atmospheric numerical prediction and general circulation models. Consequently, the system that will be proposed at the end of the pilot project will make full use of BL physics, heterogeneous error structure statistics, and high quality data management to obtain better models.