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In the past, we have shown you not only that geoengineering is very real, but that it is being used in real world situations on a regular basis, with zero oversight. We have shown beyond a shadow of a doubt that much of the current governmental body in the U.S. has very little concern for the well-being and quality of life of the average individual, despite that being their very charge.
Today, Americans are witnessing an unprecedented surge of weather just at a moment when the political pyres are burning brightest and those in power have the most to lose, or the most to gain.
It is no question that the American psyche has never been more weighed down by the current onslaught of manipulations and heavy-handed deceptions that are now being all but openly perpetrated by those who are beginning to make their presence known: The Deep State. Never before have actual real-world problems been more overlooked and outright ignored in lieu of emotional and wholly orchestrated tangential problems that are, in and of themselves, back-handedly counterintuitive. Which is why such a blatant manipulation can be carried out right in front of our eyes while most are too distracted, vehemently arguing about Donald Trump or the true meaning of race or gender, and viciously defending those opinions as indisputable fact (thanks to the likes of CNN). Absurdly, we are being led like marionettes to argue about some of the least important issues facing Americans today, and those most rooted in feeling, over fact.
In the video below you will see indisputable evidence that, at the very least, Hurricane Harvey was fed from different land points in order to intensify the storm. We can speculate as to why, but the how is made very clear. One obvious possibility is to distract from the growing awareness around the US’ current path of destruction in the Middle East, and its overwhelming civilian body count. Another is to create the justification and context to support the narrative that these storms are caused by climate change or global warming, or the broad and general actions of the oil industry at large.
This isn’t to suggest that humanity’s actions are not taking their toll on the planet, anyone with eyes can see those effects, but the topic of “climate change” stems from a disingenuous place. The idea, as it stands, is deeply rooted in long-term political agendas designed to bring about massive shifts in both economic and societal norms, think “carbon tax,” and is a highly politicized discussion that is next to impossible to rationally debate; much like many of the hot-button topics of today, and that is no coincidence.
Make no mistake, there is a reason for all of it. There is a reason that this country is more divided now than at most any other time in history.
Thanks to the brave work of WeatherWars101, as well as many others, the reality of the current destruction, and those truly responsible, becomes painfully clear. Even to the laymen, what you will see in the video below is very hard to deny and will make you question everything.
Thank you so much fellas! About face made! Doh! It’s human nature to feel empathy for victims of “natural disasters”…. It is absolutely foreign to account for who and what caused the disasters besides what we perceive as the “only causes ” ..
It is always deeper and always darker than we could possibly imagine.
In the realm of advanced atmospheric science, the concept of using 5G beamforming technology to catalyze atmospheric ionization and selectively heat parts of the upper atmosphere has emerged as a cutting-edge approach. The idea is not to directly supply the energy needed for significant heating but to act as a catalyst, lowering the activation energy required for natural processes to take place. This approach leverages the distributed network of mobile towers, which can work in concert to amplify the natural ionization and thermal effects in the atmosphere, particularly by enhancing the fair weather current—the flow of charged particles that naturally exists between the ground and the ionosphere.
The scientific principles behind this concept are rooted in atmospheric physics. Electromagnetic radiation, particularly in the radio frequency (RF) range used by 5G, can ionize the atmosphere. This ionization process creates charged particles, which, when catalyzed, can enhance the fair weather current, leading to localized heating in the upper atmosphere. By influencing the distribution of these charged particles, it is hypothesized that the natural flow of energy can be modulated, potentially affecting weather patterns.
The precision and focus offered by 5G beamforming technology are crucial. Unlike traditional broadcast methods, 5G’s advanced beamforming capabilities allow for the precise direction of radio waves to specific areas of the atmosphere. This precision is key to catalyzing the ionization process without dispersing energy inefficiently. Moreover, the dynamic adjustment of beam intensity and direction can be used to fine-tune the ionization, adapting to real-time atmospheric conditions. To achieve the desired atmospheric effects, multiple 5G towers need to operate in a synchronized manner. Network coordination and management systems play a vital role in optimizing the beamforming patterns, ensuring that emissions from different towers work together to create a controlled and effective ionization field. This networked approach leverages the distributed nature of mobile towers, transforming them into a coordinated system for atmospheric modification.
Agent-based modeling (ABM) is a critical tool in this research. ABMs can simulate the complex interactions between the electromagnetic emissions from 5G towers and the atmosphere, predicting the behavior of charged particles and the resulting thermal effects. These models, when integrated with high-fidelity atmospheric models, provide a comprehensive understanding of the system. The simulations help researchers determine the optimal frequency, power, and duration of the radio emissions needed to achieve the catalytic effect.
In the context of weather and forecasting, ABM principles have been widely applied, offering insights into various phenomena and systems. For instance, in microscale weather phenomena, ABMs can simulate the behavior of individual convective cells and their interactions, providing a more detailed understanding of storm formation and evolution. This is particularly useful for convective storms and thunderstorms, where the microscale processes are often challenging to represent in numerical weather prediction (NWP) models. Additionally, ABMs can simulate turbulence and boundary layer processes, helping to predict local weather phenomena such as wind gusts and temperature variations.
In complex environmental systems, ABMs have been applied to simulate wildfire spread, an area strongly influenced by atmospheric conditions. By modeling the interactions between fire, wind, and vegetation, these models can predict the spread of wildfires and inform firefighting strategies. ABMs have also been used to study urban heat islands, simulating the interactions between urban infrastructure, vegetation, and the atmosphere to model the formation and impacts of these heat islands, which can inform urban planning and climate adaptation strategies.
In socio-environmental systems, ABMs can integrate human behavior into weather forecasting, particularly in scenarios like evacuation planning during severe weather events. By simulating how individuals and communities respond to weather warnings, these models can improve the effectiveness of emergency management. ABMs can also simulate the interactions between farmers, weather conditions, and crop growth, helping to optimize agricultural practices and predict yields under different weather scenarios.
AI and language models play a significant role in enhancing the capabilities of ABMs and weather forecasting. At institutions like the Turing Institute in the UK and the Santa Fe Institute in the US, researchers are exploring how AI can improve the accuracy and efficiency of these models. Machine learning techniques, such as deep learning and reinforcement learning, can be used to predict the parameters that govern agent behaviors, making the models more adaptive and responsive to changing conditions. For example, AI can help in data assimilation by integrating observational data more effectively into weather models, leading to more accurate initial conditions and improved short-term forecasts.
Moreover, language models can assist in the interpretation and communication of complex weather data, generating clear, understandable reports and visualizations that make the insights from ABMs and weather simulations more accessible to a wide range of users, from meteorologists to policymakers and the general public. This enhanced communication is crucial for effective decision-making in emergency management, urban planning, and agricultural practices.
For data assimilation and ensemble forecasting, ABMs can assimilate observational data into weather models, improving the precision of short-term weather forecasts by providing more accurate initial conditions. ABMs can also generate ensemble forecasts by simulating a range of possible scenarios and their interactions, offering probabilistic forecasts that account for uncertainties in initial conditions and model parameters.
Research and development in this field are ongoing, with hybrid models combining the strengths of ABMs and NWP being explored. These hybrid models leverage the detailed, agent-level interactions of ABMs while benefiting from the large-scale, physics-based simulations of NWP. Machine learning techniques are also being integrated to enhance the accuracy of weather predictions, making the models more adaptive and responsive to changing conditions.
Case studies and examples from institutions like the University of Reading and the National Center for Atmospheric Research (NCAR) highlight the practical applications of ABMs. Researchers at the University of Reading have used ABMs to study the formation and evolution of convective storms, providing insights into storm behavior. NCAR has explored the use of ABMs in ensemble forecasting and data assimilation, aiming to improve the accuracy and reliability of weather predictions.
Notably, major technology companies and research institutions are at the forefront of leveraging cloud computing to address the computational challenges of these models. For example:
• IBM and The Weather Company: IBM’s acquisition of The Weather Company has led to the integration of advanced cloud computing resources to run complex weather simulations. IBM Cloud provides the necessary computational power and data management capabilities to handle large-scale ABMs and NWP models, enhancing the accuracy and efficiency of weather forecasting.
• NASA and the Cloud: NASA has utilized cloud computing platforms to run high-fidelity simulations of atmospheric phenomena. By leveraging cloud resources, NASA can handle the computational demands of high-resolution models and large data sets, improving the precision of their predictions.
• Google Cloud and Atmospheric Science: Google Cloud offers robust services for data storage, processing, and machine learning, which are essential for running complex ABMs. Google’s Earth Engine, a cloud-based platform for analyzing and visualizing Earth’s environmental data, is a powerful tool for integrating and processing the vast amounts of data required for atmospheric research and weather forecasting.
Challenges in computational complexity and data availability have been significantly mitigated through the advancements in cloud-based computing and the emerging potential of quantum computing. Cloud platforms offer scalable resources and high-performance computing, enabling the efficient execution of computationally intensive ABMs, even for large-scale systems. They also ensure high-quality data is readily available for model initialization and validation, enhancing the precision and reliability of simulations.
The integration of quantum computing introduces a new mathematical paradigm, revolutionizing the handling of complex simulations. Quantum algorithms, such as Quantum Monte Carlo for stochastic simulations, Quantum Walks for efficient agent movement, Quantum Optimization for parameter tuning, Quantum Machine Learning for predictive modeling, and Quantum Fourier Transform for solving linear equations, provide exponential speedups and more accurate solutions. These algorithms can dramatically reduce simulation times and enable the exploration of more detailed and accurate models.
Interdisciplinary collaboration between meteorologists, computer scientists, and domain experts remains crucial. However, the synergy between cloud-based computing and quantum computing provides powerful tools that significantly enhance the precision, efficiency, and scalability of ABMs. This integration is driving innovation and advancing the field of weather forecasting and atmospheric science, paving the way for new approaches to atmospheric modification and environmental management.
Just as the weather is a complex system influenced by myriad interacting variables, online human interactions can be viewed as a simpler yet still intricate network of dynamic elements. In the digital realm, individual actions, like posts, comments, and shares, are akin to the atmospheric particles that collectively form weather patterns. These interactions create emergent phenomena such as viral trends, online communities, and information cascades, which can be studied using agent-based models (ABMs) much like atmospheric phenomena. The implications of this analogy are significant: just as understanding and predicting weather helps in managing natural disasters and optimizing agricultural practices, understanding online human interactions can enhance social media governance, combat misinformation, and improve public communication strategies. By leveraging the same computational tools and models developed for atmospheric science, researchers can gain deeper insights into the dynamics of online behavior, leading to more informed and effective digital policies and interventions.