Advances in Hurricane Research Online Course https://giladjames.com Section: Initialization of Tropical Cyclones in Numerical Prediction Systems Lesson: 3DVAR systems Advances in Hurricane Research. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Tropical cyclones (here after TCs) are intense atmospheric vortices that form over warm ocean waters. Strong TCs (called hurricanes in the North Atlantic basin, or typhoons in the western north Pacific basin) can cause significant loss of lives and property when making landfall due to destructive winds, torrential rainfall, and powerful storm surges. In order to warn people of hazards from incoming TCs, forecasters must make predictions of the future position and intensity of the TC. In order to make these forecasts, a forecaster uses a wide suite of tools ranging from his or her subjective assessment of the situation based on experience, the climatology and persistence characteristics of the storm, and most importantly, models, which make a prediction of the future state of the atmosphere given the current state. In this lecture, the focus is on dynamical models. A dynamical model is based on the governing laws of the system, which for the atmosphere are the conservation of momentum, mass, and energy. Since the system of partial differential equations that govern the atmosphere is highly nonlinear, a numerical approximation must be made in order to obtain a solution to these equations. Short term (less than 7 days) numerical weather prediction is largely an initial value problem. Therefore it is critical to accurately specify the initial condition. The accuracy of the initial condition depends on the forecast model itself, the quality and density of observations, and how to distribute the information from the observations to the model grid points (data assimilation). Since most TCs exist in the open oceans, most observations come from satellites, and often intensity and structure characteristics are inferred from the remotely sensed data. Therefore a key problem that remains for TC initialization is the lack of observations, especially in the inner-core (less than 150 km from the TC center). TCs are predicted using both global and regional numerical prediction models. Global models simulate the atmospheric state variables on the sphere, while regional model simulate the variables in a specific region, and thus have lateral boundaries. Due to smaller domains of interest, regional models can generally be run at much higher horizontal resolution than global models, and thus they are more useful for predicting tropical cyclone intensity and structure. As an example of how well TC track and intensity has historically been predicted, Fig. 1 shows the average track and intensity errors from official forecasts from the National Hurricane Center from 1990-2009. While there has been a steady improvement in the ability to predict track (left panel), there has been little to no improvement in this time period in the prediction of TC intensity (right panel). Currently there is a large effort to improve intensity forecasts: the National Oceanic and Atmospheric Administration (NOAA) Hurricane Forecast Improvement Project (HFIP). Average mean absolute errors for official TC track (left panel) and intensity (right panel) predictions at various lead times in the North Atlantic basin from 1990-2009. Data is courtesy of the National Hurricane Center in Miami, FL, and plot is courtesy of Jon Moskaitis, Naval Research Laboratory, Monterey, CA. Errors in the future prediction of TC track, intensity and structure in numerical prediction systems arise from imperfect initial conditions, the numerical discretization and approximation to the continuous equations, model physical parameterizations (radiation, cumulus, microphysics, boundary layer, and mixing), and limits of predictability. While improvements in numerical models should be directed at all of these aspects, in this lecture we are focused on the initial condition. The purpose of TC initialization is to give the numerical prediction system the best estima #advances #in #hurricane #research ... https://www.youtube.com/watch?v=Urd8A0Udj-Y

Accuracy of GNSS Methods Online Course https://giladjames.com Section: Evaluation Methods of Satellite Navigation System Performance Lesson: Evaluation algorithm of GNSS signal in space availability - PART 1 Accuracy of GNSS Methods. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Satellite geodesy was developed to overcome the tasks which cannot be accomplished using traditional geodetic techniques, for instance, to measure the motion of continents more precisely, to eliminate the difficulty in line of site problems, to gain performance in all weather conditions, and to be able to monitor global deformations of the earth leading to the contribution in the emerge of a terrestrial reference frame (TRF) which is compatible with the geodynamic events of the earth. Today, global navigation satellite system (GNSS) data are used for various purposes, not only in geodesy but also in all earth science disciplines such as geophysics, geology, meteorology, oceanography, and others. In geodesy, applications range from establishing geodetic control networks to detail surveys. The GNSS instrumentation is designed according to various purposes. Cost-effective instrumentation, hardware, and software are preferred suitable to application and accuracy expectations. The accuracy expectation of users according to their aims is multifold. The instrumentation and the GNSS products are always improved with the developments in technology and analysis methodologies, coding, etc. Hence the user, the GNSS community, needs to be updated in terms of accuracy of the positioning results, orbital products, TRF quality, etc. with continuous research. Positioning accuracy of GNSS The accuracy of GNSS was studied by many researchers; however, the first organized study in which the accuracy prediction formulas for coordinate components were developed for the user was produced by based on an experiment using only GPS data. There the authors emphasized that the accuracy of GNSS positioning was mainly dependent on observation session duration. The dependency on baseline length, which for instance was the essence as emphasized in , was mainly changed with the release of the IGS precise orbit (i.e., with the improvement in orbit quality). Eckl et al. were targeting GPS studies at the national level with Continuously Operating Reference Station (CORS) inter-station distances of up to 300 km. Sanli and Engin changed the angle to tectonic studies in which GPS processing was handled using baseline lengths of up to thousands of kilometers. Again, the dependency on baseline length came back to the agenda. From these studies the authors produced prediction formulas for north, east, and up positioning components. The formulas were useful for the GPS community with various accuracy expectations for various applications. Before field works, they were able to obtain a priory accuracy levels and hence taking necessary precautions in the field to guarantee the desired position accuracy. Then, the research continued to present details in the accuracy assessment of point positions. For instance, Ozturk and Sanli made an attempt to unify accuracy models for baselines of 1–3000 km resulting in a complete accuracy model. Sanli and Kurumahmut took into account the effect of large height difference on positioning and extended the traditional formulation. Ghoddousi-Fard and Dare were among those who studied the accuracy of GPS positioning for web-based GPS software. Schwarz et al. and Hastaoglu and Sanli assessed the accuracy of GPS rapid static positioning. At the end of the day, GPS positioning accuracy prediction formulas from static GPS surveys were produced for three major GNSS software, Bernese, GIPSY/OASIS II, and GAMIT by , respectively. #accuracy #of #gnss #methods ... https://www.youtube.com/watch?v=OMPVU3LDanE

Applied Geochemistry with Case Studies on Geological Formations, Exploration Techniques and Environmental Issues Online Course https://giladjames.com Section: Geochemistry and Tectonic Setting of Neoproterozoic Rocks from the Arabian-Nubian Shield: Emphasis on the Eastern Desert of Egypt Lesson: Geologic overview of the Eastern Desert of Egypt Applied Geochemistry with Case Studies on Geological Formations, Exploration Techniques and Environmental Issues. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction The Arabian-Nubian Shield (ANS) forms one of the largest exposures of juvenile continental crust (1000–525 Ma) on Earth. It consists of mainly juvenile Neoproterozoic crust, now widely exposed in parts of Egypt, Saudi Arabia, Sudan, Eritrea, Ethiopia, Yemen, and Somalia. The ANS was formed during the Neoproterozoic between 900 and 550 Ma through the accretion of intra-oceanic arcs during the closure of the Mozambique Ocean and the amalgamation of Gondwana. These accretion processes led to the formation of well-defined arc-arc and continent-arc suture zones. The ANS was essentially stable continental crust by Early Cambrian time at 530 Ma. The ANS and its surroundings has been the object of geologic investigations for a wide range of geological economic and scientific reasons. The Precambrian basement of the Eastern Desert of Egypt (ED) is a part of the Arabian-Nubian Shield (ANS) and are exposed mainly in the Eastern Desert and Sinai ( ). The Eastern Desert of Egypt comprises variably deformed and metamorphosed volcanic, plutonic, and sedimentary rocks of Precambrian age, unconformably overlain by Cretaceous sediments. The Eastern Desert of Egypt is classified into three domains: north, central, and south , all revealing different aspects of the region’s protracted and intense Neoproterozoic episode of deformation and igneous activity. The Central Eastern Desert (CED) preserves the oldest (Tonian-Cryogenian) history and also best preserves Ediacaran deformation as well as associated (Hammamat) basins. The Southern Eastern Desert (SED) lacks BIF, Ediacaran sedimentary or volcanic successions such as the Hammamat Group and Dokhan Volcanics, whereas the CED does not. The Northern Eastern Desert (NED) is very different than either the CED or the SED. Dokhan volcanics and Hammamat molasses sediments are of widespread occurrence, whereas ophiolites are absent and gneisses are rare. (a) Inset geological sketch map of NE Africa showing the Arabian-Nubian Shield, the Saharan Metacraton, and Archaean and Palaeoproterozoic crust that was remobilized during the Neoproterozoic and (b) geological map of the Eastern Desert of Egypt showing study areas . The reconstructions of this lecture are based on a compiled data of geochemistry and obtained ages on the rock units constituting the Eastern Desert of Egypt. Geochemical data are based on combination of major elements, trace elements, rare earth element (REE) distributions as well as isotope data. In this contribution, I build on previous geological and geochemical studies on the different rock units forming the ED for many years of research to summarize the most important geochemical characteristics of the different rock units and to provide some important information regarding the geochemical dynamic and evolution of Neoproterozoic crust of the ED. This lecture reviews the scope of current geochemical and isotopic datasets for the ANS, with particular emphasis on the Eastern Desert of Egypt. #applied #geochemistry #with #case #studies #on #geological #formations, #exploration #techniques #and #environmental #issues ... https://www.youtube.com/watch?v=bVx4198SBZY

Satellite Information Classification and Interpretation Online Course https://giladjames.com Section: Pan-sharpening Using Spatial-frequency Method Lesson: Multiscale transform-based pan-sharpening techniques - PART 2 Satellite Information Classification and Interpretation. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Classification of the aerospace image is the process of creating an environment systemizing the image pixel values into meaningful segments of the Earth features. There are a number of methods and facilities for classification of aerospace images. Aerospace image classification methods can be formed into three categories: Automatic Manual Hybrid Automatic Manual Hybrid The methods indicated above can be used independently depending on task and available technological maintenance. There is no doubt that each of the methods has advantages and disadvantages. In general, automatic image classification method is the most preferable one used in spatial data classification . It is obvious that aerospace image classification takes a vital place from the first stage of line of image processing up to producing final electronic or hard production. demonstrates a flowchart of the processes as the required actions in the aerospace image classification. Aerospace image processing classification flowchart. Based on the above approach, we can describe standard approach as: Spatial data Extract information for an application Visual and digital image interpretation Field survey Integration spatial data into field survey Thematic map creation Decision-making Spatial data Extract information for an application Visual and digital image interpretation Field survey Integration spatial data into field survey Thematic map creation Decision-making During the work with aerospace images, first of all, the spectral wavelength of shooting is important for researchers which defines: Biogeophysical characteristics of objects transferred by images and technology of achieving the imagewhich depend on Visualization, radiometric, and geometrical properties of images Biogeophysical characteristics of objects transferred by images and technology of achieving the image Visualization, radiometric, and geometrical properties of images These two characteristics represent a basis of the classification of aerospace images considering possibilities of their processing. Spectral wavelength of shooting defines the first, fundamental, level of this classification considering the reflective and radiating characteristics of the objects reproduced in the image. From this point of view, three main types of aerospace images are defined: In visible, near and middle to the infrared wavelength In a thermal infrared wavelength In radio frequency wavelength In visible, near and middle to the infrared wavelength In a thermal infrared wavelength In radio frequency wavelength As the general understanding of the classification is the procedure of related to the object belonging to one of Q-classes. The relation to the object comes and defines on the basis of presence on an object of some features. Classifications are result the objects divided into the classes . Classification process of aerospace images in general provides the following stages: Identification of the main distinctive features (characteristics) reflecting from different objects with different classes Creation of spatial features Definition (calculation) at the studied object of feature(s) on which bases expected to classified of feature(s) Decision-making about object belonging to one of the classes with application of the decisive rule Identification of the main distinctive features (characteristics) reflecting from different objects with different classes Creation of spatial features Definition (calculation) at the studied object of feature(s) on which bases expected to classified of feature(s) Decision-making about object belonging to one of the classes with application of the decisive rule Classification of remote sensing data is the process which is used for receiving aerospace images of the maps of the land surface and any other infor #satellite #information #classification #and #interpretation ... https://www.youtube.com/watch?v=dXYPCPqZtw4