data for testing future land surface and regional model schemes. Critical to this effort is the development of a GCIP data base of gridded data that could be used to force the LDAS systems.
Develop a Strategy to Facilitate Future Reanalyses of GCIP Data
Problems arise when using operational analyses to describe climate and climate variations because of successive upgrades to the analysis systems. Operational analysis systems are constantly being modified and improvements being implemented. For example, the optimal interpolation schemes used in EDAS and MAPS will be replaced next year with a three-dimensional variational analysis method, which will allow, in particular, the direct incorporation of satellite water vapor radiance data (McNally and Vesperini, 1996) and eventually other remote sensing variables such as radar reflectivity and global positioning system (GPS) signal data. It is important that these changes in analysis technique be clearly documented and in the archives. We need to know whether significant changes are due to climatic events or model enhancements.
Despite documentation of improvements in the analysis scheme, discontinuities and artificial jumps in the climatic record will impair the usefulness of GCIP analysis products for the study of long-term climate variations. Already, several global reanalysis programs are under way in an effort to produce consistent multiyear records of coarse-scale atmospheric and surface fields. Although the quality of data inputs may vary, these projects will at least use a consistent procedure to reanalyze data from the recent past. The GCIP archives should be organized so as to allow similar reanalyses. Combined global-mesoscale re-analysis systems could be an effective tool to investigate the relative influence of factors such as topography resolution, land surface physics, different observing techniques, precipitation, and temperature anomalies.
Other products would also benefit from a well-conceived reanalysis plan. For example, given that GCIP's 4-km precipitation analysis is produced in an operational (not research) context, it is essential to allow for a posterior improvements of these archived operational rainfall estimates. Merging various precipitation data is also problematic as we infer area-averaged estimates (100 km2) from kilometer-scale NEXRAD observations. Separate archiving of model-generated, gauge, radar (including separate components of the radar as well as the merged radar products), and satellite data sets is a requirement for assessing and eventually improving their quality. This is especially important for wintertime precipitation estimates, which are known to have large observational errors in both gauge and radar products (Groisman and Legates, 1994).