Key points are not available for this paper at this time.
This paper describes the initial work toward the production of monthly global (land and ocean) analyses of precipitation for an extended period from 1948 to the present. Called the precipitation reconstruction (PREC), the global analyses are defined by interpolation of gauge observations over land (PREC/L) and by EOF reconstruction of historical observations over ocean (PREC/O). This paper documents the creation of the land component of the analyses (PREC/L) on a 2.5° latitude/longitude grid for 1948–2000. These analyses are derived from gauge observations from over 17 000 stations collected in the Global Historical Climatology Network (GHCN), version 2, and the Climate Anomaly Monitoring System (CAMS) datasets. To determine the most suitable objective analysis procedure for gridding, the analyses generated by four published objective analysis techniques those of Cressman, Barnes, and Shepard, and the optimal interpolation (OI) method of Gandin were compared. The evaluation demonstrated two crucial points: 1) better results are obtained when interpolating anomalies rather than the precipitation totals, and 2) the OI analysis procedure provided the most accurate and stable analyses among the four algorithms that were tested. Based on these results, the OI technique was used to create monthly gridded analyses of precipitation over the global land areas for the 53-yr period from 1948 to 2000. In addition, some diagnostic investigations of the seasonal and interannual variability of large-scale precipitation over the global land areas are presented. The mean distribution and annual cycle of precipitation observed in the PREC/L showed good agreement with those in several published gauge-based datasets, and the anomaly patterns associated with ENSO resemble those found in previous studies. The gauge-based dataset (PREC/L) will be updated on a quasi-real-time basis and is available online (ftp.ncep.noaa.gov/pub/precip/50-yr).
Building similarity graph...
Analyzing shared references across papers
Loading...
Chen et al. (Sat,) studied this question.
www.synapsesocial.com/papers/6a00f8bbef8139f8ff77b4d5 — DOI: https://doi.org/10.1175/1525-7541(2002)003<0249:glpaym>2.0.co;2
Mingyue Chen
Pingping Xie
John E. Janowiak
Journal of Hydrometeorology
Building similarity graph...
Analyzing shared references across papers
Loading...