{"id":5805,"date":"2022-05-11T15:30:42","date_gmt":"2022-05-11T19:30:42","guid":{"rendered":"https:\/\/tempoquest.com\/?p=5805"},"modified":"2022-05-13T14:40:05","modified_gmt":"2022-05-13T18:40:05","slug":"hybrid-weather-twin","status":"publish","type":"post","link":"https:\/\/tempoquest.com\/hybrid-weather-twin\/","title":{"rendered":"Hybrid Weather Twin\u2122"},"content":{"rendered":"

Hybrid Weather Twin<\/b>\u2122<\/h1>\n
Blending Accelerated Physics-based with Machine Learning\/Artificial Intelligence Weather Forecast Models for Better\/Faster\/Cheaper Forecasts<\/h6>\n

By: Christian Tanasescu, COO & CTO, TempoQuest<\/em><\/p>\n

Numerical weather prediction is increasingly proving its value in saving lives and protecting property in societies particularly vulnerable to extreme weather events. These extremes become more intense under the influence of climate change, making it ever more important for better numerical weather prediction.\u00a0 One way to enhance the skill of numerical weather prediction is to augment the physics-based algorithms and models with the power of Machine Learning (ML) and Artificial Intelligence (AI) models nurtured by the myriad connected sensor measurements (satellites, radar, lidar, radiometers, etc.) driven by in-situ, remote sensing, and Internet of Things (IoT).<\/span><\/p>\n

The term Digital Twin is being used to refer to the digital replica of a system or physical assets, derived from real-life data collected using various types of sensors and monitoring technologies of the asset while in operation.\u00a0 It often means that an analytical data-driven model (i.e. the twin) is built to analyze, update, predict, and\/or manage the operation at performance of its physical counterpart.<\/span><\/p>\n

ML and AI give predictions based on the past and automate reasoning trained on historical data.\u00a0 It works nicely for nowcasting, however with comprehensive, incremental, and multifaceted retraining, it becomes possible to predict the future with quantifiable certainty.<\/span><\/p>\n

Weather systems are highly nonlinear meaning that a small change now can lead to incredibly large changes in the future. A nonlinear system isn’t random, but the behavior of the system can be difficult to predict.\u00a0Forecasts for medium \u2013 and long-term, in particular, climate change, rely on the first principles of physics laws.\u00a0 Only physics-based modeling and algorithms can predict new cause-effect relationships when there is little data available to initialize the forecast model and when new initial conditions can appear.<\/span><\/p>\n

TQI\u2019s aim is to expand from accelerated physics-based forecasting to augmented forecasting bypassing the required time-consuming calculations using physics-based algorithms. This means adding predictive models generated by ML and deep learning (DL) to replicate physics laws encoded in AceCAST.\u00a0 TQI has adopted a novel approach called <\/span>Hybrid Weather Twin<\/span><\/i> (HWT) for all its products and services that is the combination of physics-based and data-driven modeling for decision-making. The former builds cause and effect relationships to explore the possibility of future events, based on theoretical descriptions and variations of the initial conditions while the latter updates the hybrid model with the real observations. TQI\u2019s HWT harnesses the benefits of both speed and performance with the blending of a digital twin counterpart with the accelerated physics-based predictive model and the modern-day ML\/DL-based off-loaded training model that fully leverages the available contemporary weather data analytic technology.\u00a0<\/span><\/p>\n

Figure 1 below describes the conceptual architecture of HWT.\u00a0 One distinctive feature is that \u201c<\/span>hybrid<\/span><\/i>\u201d applies at different levels:<\/span><\/p>\n