The Precipitation Anomaly Classification: A Method for Monitoring Regional Precipitation Deficiency and Excess on a Global Scale

Abstract An objective method to identify and track significant global precipitation anomalies on time scales of a month or longer is presented. The technique requires current observations of monthly precipitation amounts for each station and long term (20 or more years) monthly precipitation histories. Tests indicate that the technique compares favorably with the well-known Palmer Drought Severity Index (PDSI) and Crop Moisture Index (CMI) in the United States. Since monthly precipitation data are readily available in a near real-time framework, this method makes an automated, global precipitation anomaly monitoring system possible.