Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

A Machine Learning Approach to Sentinel-3 Feature Extraction In The Context Of Harmful Algal Blooms

Harmful Algal Blooms (HAB) are typically described as blooms of phytoplankton species that can not only cause harm to the environment but also humans. Some species that form these blooms can release biotoxins, which accumulate in shellfish [1]. When humans consume contaminated shellfish, it can cause adverse health problems [2]–[4]. Due to the associated risk of contamination, shellfisheries are forced to close, sometimes for months, leading to significant economic losses. Although microscopes enable toxic species identification, and bioassays enable biotoxin identification and quantification, these methods are impractical for continuous monitoring since they require recurrent in situ data sampling, followed by laboratory analysis. Chlorophyll a is a pigment common to almost all marine phytoplankton groups. It has a spectral signature that enables it to be detectable by remote satellites that capture water-leaving radiance [5]. Remote sensing can be very useful since it allows us to take synoptic measurements of large sea areas [6]. Several machine learning algorithms have been researched to detect or forecast algal biomass or HAB presence [7]–[10]. However, the application of remotely sensed images to detect and forecast biotoxin concentration seems relatively unexplored. Given this problem, two datasets with Sentinel-3 imagery patches were created, from along the west coastal region of Portugal, which differ in size and the preprocessing applied. We assessed the application of Machine Learning (ML) models to extract informative features from the datasets. The models were evaluated quantitatively and qualitatively. The qualitative analysis demonstrated how the features extracted by the models seem to be consistent with features extracted for downstream tasks in the literature, suggesting the features retain helpful information. However, at this time, further work Is required to determine whether the feature can be helpful in the task of biotoxin concentration forecasting.Um Harmful Algal Bloom (HAB) é tipicamente descrito como sendo a proliferação de espécies de fitoplâncton que podem causar danos não só ao ambiente, mas também aos humanos. Algumas espécies que formam HABs podem libertar biotoxinas, que se acumulam nos moluscos [1]. Quando o ser humano consome moluscos contaminados, pode causar problemas de saúde adversos [2]–[4]. Devido ao risco associado de contaminação, as áreas de exploração de bivalves são forçadas a fechar, por vezes durante meses, levando a perdas económicas significantes. A clorofila a é um pigmento comum a quase todos os grupos de fitoplâncton marinho e tem uma assinatura espectral que lhe permite ser detectável por satélites remotos que captam a radiância que sai da água do mar [5]. A detecção remota pode ser muito útil, uma vez que nos permite fazer medições sinópticas de grandes áreas marítimas [6]. Foram pesquisados vários modelos de aprendizagem automática para detectar ou prever a presença de biomassa algal ou HAB [7]–[10]. No entanto, a utilização de imagens de detecção remota para detectar e prever a concentração de biotoxinas parece relativamente inexplorada. Dado este problema, foram criados dois conjuntos de dados com patches de imagens do satélite Sentinel-3 ao longo da região costeira ocidental de Portugal, que diferem em tamanho e no pré-processamento aplicado. Avaliámos diferentes modelos de aprendizagem automática para extrair características informativas dos conjuntos de dados. Os modelos foram avaliados quantitativa e qualitativamente. A análise qualitativa demonstrou como a informação extraída pelos modelos parecem ser consistentes com a extraída na literatura para informar outros modelos, sugerindo que as características retêm informação útil. Contudo, neste momento, é necessário trabalho futuro para determinar se a informação pode ser útil na tarefa de previsão da concentração de biotoxinas

Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction

Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the ever-increasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines.Comment: IEEE TGRS 202

MACHINE LEARNING APPLICATIONS TO DATA RECONSTRUCTION IN MARINE BIOGEOCHEMISTRY.

Driven by the increase of greenhouse gas emissions, climate change is causing significant shifts in the Earth's climatic patterns, profoundly affecting our oceans. In recent years, our capacity to monitor and understand the state and variability of the ocean has been significantly enhanced, thanks to improved observational capacity, new data-driven approaches, and advanced computational capabilities. Contemporary marine analyses typically integrate multiple data sources: numerical models, satellite data, autonomous instruments, and ship-based measurements. Temperature, salinity, and several other ocean essential variables, such as oxygen, chlorophyll, and nutrients, are among the most frequently monitored variables. Each of these sources and variables, while providing valuable insights, has distinct limitations in terms of uncertainty, spatial and temporal coverage, and resolution. The application of deep learning offers a promising avenue for addressing challenges in data prediction, notably in data reconstruction and interpolation, thus enhancing our ability to monitor and understand the ocean. This thesis proposes and evaluates the performances of a variety of neural network architectures, examining the intricate relationship between methods, ocean data sources, and challenges. A special focus is given to the biogeochemistry of the Mediterranean Sea. A primary objective is predicting low-sampled biogeochemical variables from high-sampled ones. For this purpose, two distinct deep learning models have been developed, each specifically tailored to the dataset used for training. Addressing this challenge not only boosts our capability to predict biogeochemical variables in the highly heterogeneous Mediterranean Sea region but also allows the increase in the usefulness of observational systems such as the BGC-Argo floats. Additionally, a method is introduced to integrate BGC-Argo float observations with outputs from an existing deterministic marine ecosystem model, refining our ability to interpolate and reconstruct biogeochemical variables in the Mediterranean Sea. As the development of novel neural network methods progresses rapidly, the task of establishing benchmarks for data-driven ocean modeling is far from complete. This work offers insights into various applications, highlighting their strengths and limitations, besides highlighting the importance relationship between methods and datasets.Driven by the increase of greenhouse gas emissions, climate change is causing significant shifts in the Earth's climatic patterns, profoundly affecting our oceans. In recent years, our capacity to monitor and understand the state and variability of the ocean has been significantly enhanced, thanks to improved observational capacity, new data-driven approaches, and advanced computational capabilities. Contemporary marine analyses typically integrate multiple data sources: numerical models, satellite data, autonomous instruments, and ship-based measurements. Temperature, salinity, and several other ocean essential variables, such as oxygen, chlorophyll, and nutrients, are among the most frequently monitored variables. Each of these sources and variables, while providing valuable insights, has distinct limitations in terms of uncertainty, spatial and temporal coverage, and resolution. The application of deep learning offers a promising avenue for addressing challenges in data prediction, notably in data reconstruction and interpolation, thus enhancing our ability to monitor and understand the ocean. This thesis proposes and evaluates the performances of a variety of neural network architectures, examining the intricate relationship between methods, ocean data sources, and challenges. A special focus is given to the biogeochemistry of the Mediterranean Sea. A primary objective is predicting low-sampled biogeochemical variables from high-sampled ones. For this purpose, two distinct deep learning models have been developed, each specifically tailored to the dataset used for training. Addressing this challenge not only boosts our capability to predict biogeochemical variables in the highly heterogeneous Mediterranean Sea region but also allows the increase in the usefulness of observational systems such as the BGC-Argo floats. Additionally, a method is introduced to integrate BGC-Argo float observations with outputs from an existing deterministic marine ecosystem model, refining our ability to interpolate and reconstruct biogeochemical variables in the Mediterranean Sea. As the development of novel neural network methods progresses rapidly, the task of establishing benchmarks for data-driven ocean modeling is far from complete. This work offers insights into various applications, highlighting their strengths and limitations, besides highlighting the importance relationship between methods and datasets

Machine Learning for Earth Systems Modeling, Analysis and Predictability

Artificial intelligence (AI) and machine learning (ML) methods and applications have been continuously explored in many areas of scientific research. While these methods have lead to many advances in climate science, there remains room for growth especially in Earth System Modeling, analysis and predictability. Due to their high computational expense and large volumes of complex data they produce, earth system models (ESMs) provide an abundance of potential for enhancing both our understanding of the climate system as well as improving performance of ESMs themselves using ML techniques. Here I demonstrate 3 specific areas of development using ML: statistical downscaling, predictability using non-linear latent spaces and emulation of complex parametrization. These three areas of research illustrate the ability of innovative ML methods to advance our understanding of climate systems through ESMs. In Aim 1, I present a first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes and precipitation. In Aim 2, I construct a novel Multi-Input Multi-Output Autoencoder-decoder (MIMO-AE) in an application of multi-task learning to capture the non-linear relationship of Southern California precipitation (SC-PRECIP) and tropical Pacific Ocean sea surface temperature (TP-SST) on monthly time-scales. I find that the MIMO-AE index provides enhanced predictability of SC-PRECIP for a lead-time of up-to four months as compared to Ni{\~n}o 3.4 index and the El Ni{\~n}o Southern Oscillation Longitudinal Index. I also use a MTL method to expand on a convolutional long short term memory (conv-LSTM) to predict Nino 3.4 index by including multiple input variables known to be associated with ENSO, namely sea level pressure (SLP), outgoing longwave radiation (ORL) and surface level zonal winds (U). In Aim 3, I demonstrate the capability of DNNs for learning computationally expensive parameterizations in ESMs. This study develops a DNN to replace the full radiation model in the E3SM

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events