An Advanced Deep Learning and Computer Vision Framework for Precipitation Retrieval from Multi-spectral Satellite Information

Among all natural phenomena, precipitation is the main driver of the hydrological cycle and the challenging task of precipitation estimation is an essential element for hydrological and meteorological applications. Recent developments in satellite technologies resulting in higher temporal, spatial, and spectral resolutions, along with advancements in Machine Learning (ML) algorithms and computational power, open the great opportunity to develop analytical and data-driven tools to characterize such natural phenomena and their future behavior more efficiently and accurately. In this dissertation, state-of-the-art data-driven frameworks are proposed based on advanced deep learning algorithms and computer vision tools to extract the most useful features from single or multiple spectral bands of satellite information.Specifically, in the first part of the dissertation, a novel gradient-based cloud segmentation algorithm is proposed to effectively identify clouds and monitor their evolution towards more accurate quantitative precipitation estimation and forecast. This algorithm integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries from single or multi-spectral imagery. This method improves rain detection and estimation skills with an accuracy rate of up to 98\% in identifying cloud regions compared to the existing cloud-patch-based segmentation technique implemented in the operational PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) algorithm. Application of this method is extendable to hurricanes simulations and synthetic satellite imageries simulated by high-resolution weather prediction models. In the second part, an end-to-end deep learning precipitation estimation framework is developed from multiple sources of remotely sensed information to provide half-hourly, 4-km by 4-km precipitation estimates over the CONUS. In the first stage, a Rain/No Rain (R/NR) binary mask is generated by classification of the pixels and then a Fully Convolutional Network (FCN), U-net, is used as a regressor to predict precipitation estimates for rainy pixels. Due to the complex structure of precipitation, and inability of traditional objective functions to capture the true rainfall distribution, a novel distribution matching approach is designed and implemented. The network is trained using both the conditional Generative Adversarial Network (cGAN), and the Mean Squared Error (MSE) loss terms to match the distribution of the generated results and observed data, and to relax the strict assumptions of the traditional and conventional loss function in DNNs. The newly developed precipitation estimation algorithm is introduced as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) algorithm for estimating global precipitation and is termed as PERSIANN-cGAN. Statistics and visualizations of the metrics for PERSIANN-cGAN represent an improvement of the precipitation retrieval accuracy compared to the operational PERSIANN-CCS product, and a baseline model trained using the conventional MSE loss term

Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation

Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data.

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data