While there is a rich body of literature reporting on progress related to both, “weather-scale” and “climate-scale” hydrologic predictions, many challenges face the research community attempting to extend the lead-time and accuracy of predictions. More specifically, despite the progress in each of the three pillars of hydrometeorological prediction systems (models, observations and parameterization) over the past several decades, the improvements in the overall forecast quality is yet to reach the users’ expectations.
An important requirement to achieve progress in forecast quality is the availability of observations of sufficient resolution and length for model input, calibration and evaluation. For this reason reliance on satellite observations, particularly for precipitation is increasing.
This presentation introduces the recent development of a NOAA-supported precipitation dataset by the Center for Hydrometeorology and Remote Sensing (CHRS) at UC Irvine. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides quasi-global daily rainfall estimates at 0.25o lat.-long scale for the period of January 1, 1983 to the near present time. The rainfall estimates are consistent with the Global Precipitation Climatology Project (GPCP) dataset at monthly scale.
PERSIANN-CDR has been used for global precipitation variability and trend analysis. Case studies involving extreme event analyses in floods and droughts are provided. In addition, evaluation of climate models’ historical daily precipitation simulations, using a large number of climate indices provided by the Expert Team on Climate Change Detection and Indices (ETCCDI), will be discussed.