HR: 1340h
AN: H33A-0972    [Abstracts]
TI: Improving Estimation of Over-Lake Precipitation - An Application to Lake Erie
AU: * Chatterjee, A
EM: abhishch@umich.edu
AF: The University of Michigan, Department of Civil and Environmental Engineering, Ann Arbor, MI 48109, United States
AU: DeMarchi, C E
EM: demarchi@umich.edu
AF: The University of Michigan, School of Natural Resources and Environment, Ann Arbor, MI 48109, United States
AU: Michalak, A M
EM: amichala@umich.edu
AF: The University of Michigan, Department of Civil and Environmental Engineering, Department of Atmospheric, Oceanic and Space Sciences, Ann Arbor, MI 48109, United States
AB: Over-lake precipitation is a key component of the water balance of the Great Lakes. Its correct estimation is, therefore, vital for planning and operational purposes. Yet, reliable estimates of precipitation are difficult to obtain in the Great Lakes region not only due to the lack of gages over the lakes themselves, but also due to the scarcity of gages in the draining basins. Traditionally, over-lake precipitation has been estimated by distance-weighted or other data-driven interpolation methods. In spite of their wide acceptance, these conventional methods suffer from intrinsic limitations as they fail to take into account the spatial and temporal variability of rainfall. Recently, multisensor products combining radar-based precipitation estimates and rain gage data (MPE) have provided a suitable alternative to estimates based on the sparse gage data. However, the presence of biases in the MPE data has raised serious concerns about their accuracy. A promising approach for overcoming the limitations of either type of data for producing better precipitation estimates is to spatially integrate the MPE data with the gage observations in a geostatistical framework. Using available gage and MPE data for the Lake Erie region, we propose a suite of spatial interpolation techniques based on universal kriging, for estimating monthly-averaged over-lake precipitation. The estimates from these techniques are compared to (i) more traditional methods such as inverse-distance weighted interpolation and ordinary kriging, both of which use only the gage data and (ii) the available MPE data. Results indicate that the universal kriging setup outperforms the estimation methods based only on one of the two data types, by providing estimates with significantly lower root mean square error and lower overall bias. Overall, the results demonstrate the robustness of the proposed approach in assimilating information from two different data types for providing more accurate and reliable estimates of over-lake precipitation.
DE: 1853 Precipitation-radar
DE: 1854 Precipitation (3354)
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting