Download or read book On the Use of Image Processing and Pattern Recognition Tools to Enhance High Resolution Satellite Precipitation Estimation Based on Cloud Classification written by Majid Mahrooghy and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Satellite precipitation estimation at high spatial and temporal resolutions is beneficial for research and applications in the areas of weather, flood forecasting, hydrology, and agriculture. In this research, image processing and pattern recognition tools are incorporated into the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) methodology to enhance satellite precipitation and rainfall estimation. The enhanced algorithm incorporates five main steps to derive precipitation estimates: 1) segmenting the satellite infrared cloud images into patches; 2) extracting features from the segmented cloud patches; 3) feature selection or dimensionality reduction; 4) categorizing the cloud patches into separate groups; and 5) obtaining a relationship between the brightness temperature of cloud patches and the rain- rate (T-R) for every cluster. In this study, in addition to the features utilized for cloud patch classification, wavelet and lightning features are also extracted. The lightning feature is defined as the number of flashes occurring within 15 minutes of the nominal IR image scan. Both feature selection and dimensionality reduction techniques are examined to reduce the dimensionality as well as diminish the effects of the redundant and irrelevant features. The feature selection technique includes a Feature Similarity Selection (FSS) method and a Filter-Based Feature Selection using Genetic Algorithm (FFSGA). The Entropy Index (EI) fitness function is used to evaluate the feature subsets. Furthermore, Independent Component Analysis (ICA) was examined and compared to other linear and nonlinear unsupervised dimensionality reduction techniques to reduce the dimensionality and increase the estimation performance. In addition to a Self Organizing Map (SOM) neural network, the link-based cluster ensemble method is also examined in this research. In the final step, the Median Merging (MM) and Selected Curve Fitting (SCF) techniques are incorporated. After applying a Probability Matching Method (PMM) to each single patch and obtaining the T-R for each patch, a Median Merging technique which computes the median rain-rate for a given temperature is applied. A Selected Curve Fitting (SCF) procedure is also used to obtain the T-R for each cluster. The results show that the enhanced algorithm incorporating the above techniques improves precipitation estimation.
Download or read book Improving Infrared based Precipitation Retrieval Algorithms Using Multi spectral Satellite Imagery written by Nasrin Nasrollahi and published by . This book was released on 2013 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Moderate Resolution Imaging Spectro-radiometer (MODIS) instrument aboard the NASA Earth Observing System (EOS) Aqua and Terra platform with 36 spectral bands provides valuable information about cloud microphysical characteristics and therefore precipitation retrievals. Additionally, CloudSat, selected as a NASA Earth Sciences Systems Pathfinder (ESSP) satellite mission, is equipped with a 94 GHz radar that can detect the occurrence of surface rainfall. The CloudSat radar flies in formation with Aqua with only an average of 60 s delay. The availability of surface rain occurrence based on CloudSat observation together with the multi-spectral capabilities of MODIS makes it possible to create a training data set to distinguish false rain areas based on their radiances in satellite precipitation products (e.g. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)). The brightness temperature of 6 MODIS water vapor and infrared channels are used in this study along with surface rain information from CloudSat to train an Artificial Neural Network model for no-rain recognition. The results suggest a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation. The second approach to identifying no-rain regions, developed in this study, is to find the areas covered with non-precipitating clouds. The cloud type data available from CloudSat is used as a target value to train an artificial neural network model to identify non-precipitating clouds such as cirrus and altostratus. Application of the trained model on two case studies investigated in this research, show significant improvements in near real-time PERSIANN rain estimations. In addition, a cloud type classification algorithm was developed to classify clouds into 7 different classes (cumulus (Cu), stratocumulus (Sc), altocumulus (Ac), altostratus (As), nimbostratus (Ns), high cloud and deep convective cloud). The classification model uses a self organizing features map to classify clouds based on multi-spectral MODIS data and CloudSat cloud types. The result of the classification model shows acceptable results for summertime. The winter season cloud classification is challenging due to dominance of low and middle level clouds. A better cloud classification algorithm for wintertime is achievable using active radar data and is beyond the capabilities of currently available remotely sensed multi-spectral information.
Download or read book Improved Global High Resolution Precipitation Estimation Using Multi satellite Multi spectral Information written by Ali Behrangi and published by . This book was released on 2009 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, xxii directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Multi-Spectral Analysis (PERSIANN-MSA) the effectiveness of using multi-spectral data for precipitation estimation are examined. In comparison to the use of a single thermal infrared channel, using multi-spectral data has a potential to significantly improve rain detection and estimation skills; (3) a method proposed to integrate the previously developed cloud classification system (PERSIANN CCS) with PERSIANN-MSA. Through the integration, PERSIANN-MSA benefits from both cloud-patch classification capability as well as multi-spectral information to culminate the GEO-based precipitation estimation techniques; (4) finally, a new combination technique that incorporates multi-sensor information is developed. The technique is called REFAME, short for Rain Estimation using Forward Adjusted advection of Microwave Estimates. REFAME allows more consistent integration of MW VIS/IR information through hybrid advection and adjustment of MW precipitation rate along cloud motion streamlines obtained from a 2D cloud tracking algorithm using successive GEO/IR images. Evaluated over a range of spatial and temporal scales it is demonstrated that REFAME is a robust technique for real-time high resolution precipitation estimation using multi-satellite information.
Download or read book Development of Techniques to Specify Cloudiness and Rainfall Rate Using GOES Imagery Data written by H. Stuart Muench and published by . This book was released on 1979 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report summarizes the work accomplished during the first phase of an investigation concerning methods of introducing digitized satellite imagery into short-range, objective forecasting operations. The data archive being assembled for this study is described, with particular attention given to the steps taken to maximize the accuracy of the satellite imagery. These steps included 'fine tuning' the navigation and selecting procedures for 'normalizing' the data by correcting for the effects of Lambertian and anisotropic scattering. Consistency of the data, spatial and temporal, was tested by analysis of ground reflectance during cloudless days, and a pilot test of the specification of single layers of clouds was conducted. Both of these tests gave encouraging results. An investigation of specifying precipitation rate, using just the visible reflectance and infrared temperature of the cloud top, also produced good results. Nomograms for the average rate during the hour following the satellite observation, as well as for the probability of observing more than 0.01 in. and 0.10 in. of precipitation, are illustrated. Two appendices present the geometrical and optical equations relevant to the investigation. (Author).
Download or read book Improving Warm Rainfall Detection and Rainfall Estimation of a Multiple Satellite based Rainfall Retrieval Algorithm written by Negar Karbalaee and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precipitation as an essential component of the hydrologic cycle has a great importance to be measured accurately due to various applications such as hydrologic modeling, extreme weather analysis, and water resources management. Among different methods, meteorological satellites are one of the instruments that are widely used for precipitation estimation in fine spatial and temporal resolution. Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Network Cloud Classification System (PERSIANN-CCS) uses infrared (IR) data from Geostationary Earth Orbit (GEO) satellites to retrieve precipitation based on relationship between clout top temperature (Tb) and rainfall rate (RR) using a neural network technique. The complexity of Tb-RR relationship for estimating precipitation causes uncertainty in PERSIANN-CCS rainfall product. This research is focused on improving PERSIANN-CCS rainfall retrieval using several approaches:1) Bias adjustment of PERSIANN-CCS rainfall estimates using PMW satellite rainfall data: Using multi satellite data can enhance the quality of rainfall estimation considerably; in this research we have combined the rainfall data from PERSIANN-CCS and PMW rainfall to enhance the bias of PERSIANN-CCS precipitation estimates. The results showed improvement of rainfall estimation during summer and winter time.2) Increasing the rainfall detection by including warm clouds rainfall: PERSIANN-CCS currently cannot detect rainfall from clouds with temperature warmer than 253 K. This study explores the impacts of increasing the temperature threshold on precipitation estimation. The results show that increasing the threshold level can improve the PERSIANN-CCS rainfall detection.3) Generating a probabilistic framework for precipitation retrieval: The current version of PERSIANN-CCS retrieves precipitation based on the exponential function fitted to Tb-RR. The major assumption behind this relationship is that the heavier rainfalls are associated with colder clouds which cause underestimation of warmer clouds and overestimation of colder clouds rainfall. The probabilistic approach uses the corresponding sample relationship between cloud temperature and rainfall rate. The model is evaluated during a full summer season which showed improvement in both detection and estimation of rainfall in compare with the current PERSIANN-CCS algorithm.
Download or read book Clouds Motion Estimation from Ground Based Sky Camera and Satellite Images written by Ali Youssef Zaher and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of cloud motion is a challenging task due to the non-linear phenomena of cloud formation and deformation. Satellite images processing is a popular tool used to study the characteristics of clouds which constitute major factors in forecasting the meteorological parameters. Due to the low resolution of satellite images, researchers have turned towards analyzing the high-resolution images captured by ground-based sky cameras. The first objective of this chapter is to demonstrate the different techniques used to estimate clouds motion and to compare them with respect to the accuracy and the computational time. The second aim is to propose a fast and efficient block matching technique based on combining the two types of images. The first idea of our approach is to analyze the low-resolution satellite images to detect the direction of motion. Then, the direction is used to orient the search process to estimate the optimal motion vectors from the high-resolution ground-based sky images. The second idea of our method is to use the entropy technique to find the optimal block sizes. The third idea is to imply an adaptive cost function to perform the matching process. The comparative study demonstrates the high performance of the proposed method with regards to the robustness, the accuracy and the computation time.
Download or read book A Deep Learning Framework for Precipitation Estimation from Bispectral Satellite Information written by Yumeng Tao and published by . This book was released on 2017 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compared to ground-based precipitation measurements, satellite-based precipitation estimates have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation observations is still insufficient to serve many weather, climate, and hydrologic applications. In the development of a satellite-based precipitation product, the two most important aspects are the provision of sufficient precipitation-related information in the selected satellite data and the use of the proper methodologies to extract such information and link it to precipitation estimates.In this dissertation, a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, Infrared (IR) and water vapor (WV) channels, is developed. I explore the effectiveness of deep learning techniques in extracting useful features from the satellite information and demonstrate the value of incorporating multiple satellite channels.Specifically, I first provide a bias reduction model for satellite-based precipitation products using deep learning approaches to demonstrate their capability of extracting additional useful information from the satellite data. I then design a two-stage framework for precipitation estimation from bispectral information, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the non-zero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to precisely delineate precipitation regions. In the second stage, the model aims to estimate the point-wise precipitation amount accurately while preserving its heavy-tailed distribution. Stacked denoising auto-encoders (SDAEs), a commonly used deep learning method, are applied in both stages.The operational product, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), serves as a baseline model throughout this dissertation. I evaluate performance along a number of common performance measures, including both R/NR and real-valued precipitation accuracy. Case studies focusing on the model's performance for specific events are also included. The experiments show that our proposed two-stage model outperforms original PERSIANN-CCS in different verification periods over the central United States and in large-scale application. Therefore, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation algorithm.
Download or read book Handbook of Pattern Recognition and Computer Vision written by C. H. Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.
Download or read book Measuring Precipitation from Space written by V. Levizzani and published by Springer Science & Business Media. This book was released on 2007-05-11 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: No other book can offer such a powerful tool to understand the basics of remote sensing for precipitation, to make use of existing products and to have a glimpse of the near future missions and instruments. This book features state-of-the-art rainfall estimation algorithms, validation strategies, and precipitation modeling. More than 20 years after the last book on the subject the worldwide precipitation community has produced a comprehensive overview of its activities, achievements, ongoing research and future plans.
Download or read book Satellite Rainfall Applications for Surface Hydrology written by Mekonnen Gebremichael and published by Springer Science & Business Media. This book was released on 2009-12-02 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: With contributions from a panel of researchers from a wide range of fields, the chapters of this book focus on evaluating the potential, utility and application of high resolution satellite precipitation products in relation to surface hydrology.
Download or read book Earth Resources written by and published by . This book was released on 1986 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Improving High resolution IR Satellite based Precipitation Estimation a Procedure for Cloud top Relief Displacement Adjustment PHD written by Shayesteh Esmaelili-Mahani and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Digital Image Processing and Visual Communications Technologies in Meteorology written by Paul Janota and published by . This book was released on 1987 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Study of Precipitation Occurrence Using Visual and Infrared Satellite Data written by Linda Sue Paul and published by . This book was released on 1983 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bi-spectral satellite thresholds for precipitation specification are explored with visual and infrared satellite data collocated with Service-A hourly observations for 137 surface stations in the southeastern United States. The data span the month of August 1979 and total 70,623 observations, including 538 daylight precipitation observations. The distributional and statistical differences of four satellite resolution sizes ranging from 484 to 2025 nmi2 are explored and determined to be significant in the representation of weather conditions. Precipitation and no-precipitation data can be statistically differentiated with the visual and infrared mean and standard deviation values. For overcast ceiling reports, a simple linear bi-spectral threshold based on a 50% probability of precipitation is defined as extending from albedo 1.00 to 0.60 with associated cloud top temperatures 290K and 210K, respectively. For overcast and broken ceiling reports, and albedo greater than 0.80 specifies a 50% probability of precipitation. (Author).
Download or read book Estimation and Mapping of Clouds and Rainfall Areas with an Interactive Computer written by Cynthia Ann Nelson and published by . This book was released on 1982 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: An automated cloud analysis program was developed and established on the SPADS(Satellite Data Processing and Display System) computer system at the Naval Environmental Prediction Research Facility (NEPRF). The program evaluates GOES(Geostationary Operational Environmental Satellite) visual and infrared satellite imagery simultaneously. The analysis method produces information on cloud types, cloud amount, precipitation intensity, and cloud top height and temperature through use of threshold tests of radiance, texture, and temperature. A review of current work on the evaluation of satellite information by computer and by manual analysis is included. A maritime region 460 X 460 nautical miles in size was selected for test analysis. The satellite imagery was manually evaluated and compared to the computer generated output. Reasonably good patterns of cloud types, precipitation and cloud amount were produced by the computer, although further testing and verification is needed.
Download or read book Satellite Precipitation Measurement written by Vincenzo Levizzani and published by Springer Nature. This book was released on 2020-04-10 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a complete overview of the measurement of precipitation from space, which has made considerable advancements during the last two decades. This is mainly due to the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission, CloudSat and a carefully maintained constellation of satellites hosting passive microwave sensors. The book revisits a previous book, Measuring Precipitation from Space, edited by V. Levizzani, P. Bauer and F. J. Turk, published with Springer in 2007. The current content has been completely renewed to incorporate the advancements of science and technology in the field since then. This book provides unique contributions from field experts and from the International Precipitation Working Group (IPWG). The book will be of interest to meteorologists, hydrologists, climatologists, water management authorities, students at various levels and many other parties interested in making use of satellite precipitation data sets. Chapter “TAMSAT” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Download or read book Clouds and Climate written by A. Pier Siebesma and published by Cambridge University Press. This book was released on 2020-08-20 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive overview of research on clouds and their role in our present and future climate, for advanced students and researchers.
