A new way of representing microphysical uncertainty in convective-scale data assimilation reduces biases in model states and improves the accuracy of short-term precipitation forecasts.
A study of the impacts of radiative interactions with different cloud types on aggregation of rainstorms finds that interactions with high-clouds and water vapor are key.
Satellite data has been used to correct the aerosol loading and land surface albedo in several AeroCom models, which has improved shortwave flux biases between models and observations.
New metrics used to quantify errors in precipitation show that convection permitting simulations outperform coarser resolution simulations.
Porting and optimizing CESM1.3 to run on the TaihuLight computer enabled an astounding 750 years of simulation with 0.25° grid spacing for land & atmosphere and 0.1° grid spacing for ocean & sea ice.
WeatherBench is a data set compiled to serve as a standard for evaluating new approaches to artificial intelligence–driven weather forecasting.
A comparison of climate models finds that much of the variation in their predictions of global warming arises from differences in how they simulate the response of convective processes to warming.
Simulations of tropical ocean convection help distinguish climate effects resulting from large-scale changes in atmospheric circulation from those resulting from higher temperatures.
New methods that help researchers understand the decision-making processes of neural networks could make the machine learning tool more applicable for the geosciences.
Researchers apply a superparameterization technique to boost the accuracy and efficiency of climate predictions generated by the Energy Exascale Earth System Model.