UC Irvine

Accurate and timely precipitation estimates are critical for many hydrological applications, including monitoring and forecasting natural disasters, developing water resources management and planning strategies, as well as conducting climatological studies. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex data sets, appear well-suited to the task of precipitation estimation. By leveraging the deep learning algorithms with an ample amount of high-resolution satellite datasets, I introduce one historical and one near-real time precipitation estimation dataset: 1) The near-real time dataset called Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Convolutional Neural Networks (PERSIANN-CNN). This developed near- real time dataset provides precipitation estimates at 0.08-degree spatial and an hourly temporal resolution. 2) The historical precipitation estimation dataset named Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR). This dataset offers precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the globe.Chapter 2 and 3 investigate the application of two different deep neural networks algorithms for improving the near-real time precipitation estimation. Most near real-time precipitation retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. Their sole reliance on IR information is problematic. Indeed, it limits their ability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. However, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. Chapters 2 investigates the effectiveness of adding Water Vapor (WV) channels from geostationary satellites to IR information and the application of convolutional neural networks for improving the accuracy of near real-time precipitation algorithms. Chapter 3 further improves the model introduced in Chapter 2 by adding geographical information (i.e., latitude and longitude) to IR information and utilizing a U-Net-based convolutional neural network. The developed dataset called PERSIANN-CNN, which provides near-real time precipitation estimates at the spatial resolution of 0.08-degree and at an hourly time scale over the CONUS. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the current operational near-real time precipitation datasets at various temporal and spatial scales. Chapter 4 and 5 introduce a new historical precipitation estimation dataset through introducing a new framework for bias correcting and downscaling the precipitation estimates. Accurate, long-term, global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Chapters 4 and 5 introducing a new historical dataset that address these limitations by introducing a bias correcting and a downscaling framework, respectively. The developed dataset (called PERSIANN-CCS-CDR) provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. PERSIANN-CCS-CDR shows improved performance compared to PERSIANN-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events. In chapter 6, the applications of the developed datasets as well as PERSIANN family datasets are assessed to investigate the spatiotemporal variations in heavy precipitation events that occurred in early spring (March 21st to April 20th) over Iran. The Results show that PERSIANN family datasets are an attractive dataset for detecting the near-real time and historical precipitation estimates.