Abstract Title
Evaluation of PERSIANN-CDR Product in Reproducing Observed Seasonal Mean and Extreme Precipitation Trends
Abstract
The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks for Climate Data Record (PERSIANN-CDR) reanalysis product provides near-global daily precipitation data record for the last 34 years at 0.25° spatial resolution. We aim to evaluate the performance of the new PERSIANN-CDR product in reproducing the seasonal means and extreme precipitation trends observed in gauge-based data products. For this, we estimate seasonal means and extreme precipitation trends in the PERSIANN-CDR product and compare these estimated trends with those of ground-based rain gauge data, such as the Climate Prediction Center (CPC) U.S. Unified gridded observation data and the Australia precipitation data. Time series analysis and extreme value methods are applied in this study. Our finding will help the scientific community to better understand seasonal changes of global mean and extreme precipitation events, as well as provide valuable information for future climate research.
Evaluation of PERSIANN-CDR Product in Reproducing Observed Seasonal Mean and Extreme Precipitation Trends
The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks for Climate Data Record (PERSIANN-CDR) reanalysis product provides near-global daily precipitation data record for the last 34 years at 0.25° spatial resolution. We aim to evaluate the performance of the new PERSIANN-CDR product in reproducing the seasonal means and extreme precipitation trends observed in gauge-based data products. For this, we estimate seasonal means and extreme precipitation trends in the PERSIANN-CDR product and compare these estimated trends with those of ground-based rain gauge data, such as the Climate Prediction Center (CPC) U.S. Unified gridded observation data and the Australia precipitation data. Time series analysis and extreme value methods are applied in this study. Our finding will help the scientific community to better understand seasonal changes of global mean and extreme precipitation events, as well as provide valuable information for future climate research.