This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Global climate and weather forecasting use predictive models that divide Earth's atmosphere into a grid, with each section typically covering tens to hundreds of kilometers. Now a new model, run by a team at the University of Tokyo, has narrowed this grid spacing to just 220 meters (720 feet).
This higher resolution eliminated instances of "popcornlike" rain—bursts of heavy rain that appear in simulations but not in real life—providing more accurate predictions of extreme local weather from a global model. This year has been one of record-breaking weather: Intense storms battered Europe, August was blisteringly hot and tragic flooding devastated parts of Asia and Africa. Strong and unpredictable storms are expected to become more common due to climate change, so precise climate modeling and weather forecasting, along with early warning systems, are essential to help avert future disasters.
Global storm-resolving models (GSRM) are among the most advanced tools we have for such a task. They are used to simulate the global distribution of clouds and storms, and to see how small- and large-scale atmospheric and energy systems interact and influence each other. Looking at Earth as a whole, even when making local predictions, is important because even distant and small-scale events can have ripple effects.
Compared to regular global climate models (GCMs), which divide Earth's atmosphere into a grid with sections spanning tens to hundreds of kilometers, GSRMs use supercomputing power to narrow that resolution to just 1 kilometer to 10 km (0.6 to 6.2 miles) per section. Now, a team at the University of Tokyo has created a new simulation that narrows that resolution even further, to just 220 meters (720 feet) per section, about the length of two football (soccer) fields. The work has been published in Geophysical Research Letters.
"This finer resolution allows us to represent the internal structure of convective clouds, including individual updrafts and downdrafts, much more explicitly than before," explained project researcher Shuhei Matsugishi from the Atmosphere and Ocean Research Institute at the University of Tokyo. "Performing such simulations globally opens up the possibility of studying not only individual convective clouds, but also how they interact with each other and with the larger-scale atmospheric circulation." The team has called this the world's first demonstration of a "global large-eddy simulation" ("GLES"), because it can explicitly represent the finer structures of deep convective clouds, such as storm-bringing cumulonimbus, rather than predicting their behavior based on coarser-resolution formulas and parameters. Thanks to its higher resolution, the model was able to eliminate a long-standing "bias" called popcornlike rain, which occurs in current GSRMs.
In this context, a bias refers to a recurring and consistent error in a model. Popcornlike rain occurs when intense bursts of rain appear in a model but don't occur in real life. With the GLES, precipitation appeared more realistically, without unrealistically intense, localized events.
Extract — continue reading at the source.
