Global meteorological drought – Part 1: Probabilistic monitoring

Abstract

Abstract. Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. One of the main constrains of assessing the current drought situation is associated with the lack of reliable sources of observed precipitation on a global scale available in near-real time. Furthermore, monitoring systems also need a long record of past observations to provide mean climatological conditions. To address these problems a novel probabilistic drought monitoring methodology based on ECMWF probabilistic forecasts is presented where probabilistic monthly means of precipitation were derived from short-range forecasts and merged with the long term climatology of the Global Precipitation Climatology Centre (GPCC) dataset. From the merged dataset, the Standardized Precipitation Index (SPI) was estimated. This methodology was compared with the GPCC first guess precipitation product and also SPI calculations using the ECMWF ERA-Interim reanalysis and Tropical Rainfall Measuring Mission (TRMM) precipitation datasets. ECMWF probabilistic forecasts for near-real time monitoring are similar to GPCC and TRMM in terms of correlation and root mean square errors, with the added value of including an estimate of the uncertainty given by the ensemble spread. The real time availability of this product and its stability, i.e. that it does not depend directly on local rain-gauges or single satellite products, are also beneficial in light of an operational implementation.