Moving Beyond State Variables to Enabling Underlying Physical Processes for Increased Predictability

Dr. James Stalker's over a decade-long R&D efforts on atmospheric predictability at Regional Earth System Predictability Research, Inc. (RESPR) has led to the recognition of the limitations that the current weather/climate science is overly relying on a relatively small amount of information in the form of state variables (e.g., wind). With this recognition in 2004, Dr. Stalker undertook his efforts into a cutting-edge method to move beyond state variables to enable the underlying physical process variables. This method, also referred to as the Stalker method now by the American Society of Civil Engineers (ASCE)/Environmental & Water Resources Institute (EWRI), is being considered for its use in the commentary for the 44-19 Standards revision on the appropriate advanced modeling efforts. The Stalker method allows for determining weather/climate state variables at locations and time periods (e.g., future conditions in the forecast mode) with higher and more robust accuracy than what is currently possible, where measurements are not available in space or time. The robustness in the newly developed state variables results from the fact that such variables are now correctly influenced by many of the relevant physical processes operating in the background that tend to constantly shape these state variables in the first place. Stalker will provide background on his method and give some examples during the oral presentation. One of the key applications for the Stalker method is to address the dire and daunting climate change issue. It is of note that the Stalker method has wider applicability to the vast computational fluid dynamics (CFD) field so it goes beyond the atmospheric and oceanic fluids to cover, for example, small-scale industrial fluids as well.

Climate change, whether due to natural variability and/or man-made input, falls into three basic scenarios: 1) it is increasing global temperature or 2) it is decreasing global temperature or 3) it has no effect on global temperature. Unfortunately, when currently available observations, models, analysis methods, etc. depend heavily on state variables (e.g. wind), without accounting for the underlying physical process variables (e.g., water phase transition), climate change will be rather difficult to quantify with today's limitations in the weather/climate science.