A Simple Data Assimilation Technique For Vegetation Leaf Area Index Using MODIS Data

A simple data assimilation technique for vegetation leaf area index using MODIS data is presented in this presentation. The objective of this study is to generate more reliable MODIS LAI data for environmental prediction. A series of techniques and procedures which include data quality control, time-series data smoothing, and simple data assimilation are used in this work. Demonstration of this method is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the Global Environmental Multiscale (GEM) model. Results demonstrate that the LAI analysis data is more realistic and appropriate for environmental forecasting than either the old operational LAI data used in the MSC's GEM model or the original MODIS LAI observation data. The final LAI analyses are of high quality, have smooth temporal evolution, and are consistent with static and dynamic databases, which is more reliable for environmental prediction. Results also show that significant differences exist between the new LAI analysis and the LAI currently used in MSC's operational atmospheric models. An evaluation of the impact these new LAI analyses have on regional weather forecasting will be presented at the conference.