Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900
d570004
<p>An improved land-ocean global monthly precipitation anomaly reconstruction is developed for the period beginning in 1900. Reconstructions use the available historical data and statistics developed from the modern satellite-sampled period to analyze variations over the historical pre-satellite period. This paper documents the latest in a series of precipitation reconstructions developed by the authors. Although the reconstruction principle is still the minimization of mean-squared error, this latest reconstruction includes the following three major improvements over previous reconstructions: (i) an improved method that first produces an annual first guess, which is then adjusted using a monthly increment analysis; (ii) improved use of oceanic observations in the annual first guess using a canonical correlation analysis; and (iii) reinjection of gauge data where those data are available. These improvements allow more confident analyses and evaluations of global precipitation variations over the reconstruction period.</p> <p><i>(Abstract excerpted from Smith et al. 2012.)</i></p>
dataset
https://gdex.ucar.edu/datasets/d570004/
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climatologyMeteorologyAtmosphere
dataset
revision
2021-03-30
SATELLITES > SATELLITES
GROUND-BASED OBSERVATIONS > GROUND-BASED OBSERVATIONS
revision
2025-10-03
EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION ANOMALIES
revision
2025-10-03
1900
2008
publication
2011-04-25
notPlanned
Creative Commons Attribution 4.0 International License
None
pointOfContact
NSF NCAR Geoscience Data Exchange
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description: The Geoscience Data Exchange (GDEX), managed by the Computational and Information Systems Laboratory (CISL) at NSF NCAR, contains a large collection of meteorological, atmospheric composition, and oceanographic observations, and operational and reanalysis model outputs, integrated with NSF NCAR High Performance Compute services to support atmospheric and geosciences research.
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2025-10-09T01:27:02Z