339 research outputs found
Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks
Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed
Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs
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
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PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Convolutional Neural Networks
Abstract
Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. 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 datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model
Deep-PHURIE : deep learning based hurricane intensity estimation from infrared satellite imagery
Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensities is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root mean squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A webserver implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE
Earth Science Deep Learning: Applications and Lessons Learned
Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. At the NASA Marshall Space Flight Center (MSFC), the Data Science and Informatics Group (DSIG) has been using deep learning for a variety of Earth science applications. This paper provides examples of the applications and also addresses some of the challenges that were encountered
Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance
Because geostationary satellite (Geo) imagery provides a high temporal
resolution window into tropical cyclone (TC) behavior, we investigate the
viability of its application to short-term probabilistic forecasts of TC
convective structure to subsequently predict TC intensity. Here, we present a
prototype model which is trained solely on two inputs: Geo infrared imagery
leading up to the synoptic time of interest and intensity estimates up to 6
hours prior to that time. To estimate future TC structure, we compute cloud-top
temperature radial profiles from infrared imagery and then simulate the
evolution of an ensemble of those profiles over the subsequent 12 hours by
applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC
intensities at hours 6 and 12, we input operational intensity estimates up to
the current time (0 h) and simulated future radial profiles up to +12 h into a
``nowcasting'' convolutional neural network. We limit our inputs to demonstrate
the viability of our approach and to enable quantification of value added by
the observed and simulated future radial profiles beyond operational intensity
estimates alone. Our prototype model achieves a marginally higher error than
the National Hurricane Center's official forecasts despite excluding
environmental factors, such as vertical wind shear and sea surface temperature.
We also demonstrate that it is possible to reasonably predict short-term
evolution of TC convective structure via radial profiles from Geo infrared
imagery, resulting in interpretable structural forecasts that may be valuable
for TC operational guidance
Forecasting formation of a Tropical Cyclone Using Reanalysis Data
The tropical cyclone formation process is one of the most complex natural
phenomena which is governed by various atmospheric, oceanographic, and
geographic factors that varies with time and space. Despite several years of
research, accurately predicting tropical cyclone formation remains a
challenging task. While the existing numerical models have inherent
limitations, the machine learning models fail to capture the spatial and
temporal dimensions of the causal factors behind TC formation. In this study, a
deep learning model has been proposed that can forecast the formation of a
tropical cyclone with a lead time of up to 60 hours with high accuracy. The
model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th
generation), and best track data IBTrACS (International Best Track Archive for
Climate Stewardship) to forecast tropical cyclone formation in six ocean basins
of the world. For 60 hours lead time the models achieve an accuracy in the
range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15
minutes of training time depending on the ocean basin, and the amount of data
used and can predict within seconds, thereby making it suitable for real-life
usage
CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis
Convolutional neural networks (CNN) have achieved great success in analyzing
tropical cyclones (TC) with satellite images in several tasks, such as TC
intensity estimation. In contrast, TC structure, which is conventionally
described by a few parameters estimated subjectively by meteorology
specialists, is still hard to be profiled objectively and routinely. This study
applies CNN on satellite images to create the entire TC structure profiles,
covering all the structural parameters. By utilizing the meteorological domain
knowledge to construct TC wind profiles based on historical structure
parameters, we provide valuable labels for training in our newly released
benchmark dataset. With such a dataset, we hope to attract more attention to
this crucial issue among data scientists. Meanwhile, a baseline is established
with a specialized convolutional model operating on polar-coordinates. We
discovered that it is more feasible and physically reasonable to extract
structural information on polar-coordinates, instead of Cartesian coordinates,
according to a TC's rotational and spiral natures. Experimental results on the
released benchmark dataset verified the robustness of the proposed model and
demonstrated the potential for applying deep learning techniques for this
barely developed yet important topic.Comment: Submitted to AAAI202
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