Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternative approach is to use machine learning to build new parameterizations directly from high-resolution model output. However, parameterizations learned from three-dimensional model output have not yet been successfully used for simulations of climate. Here we use a random forest to learn a parameterization of subgrid processes from output of a three-dimensional high-resolution atmospheric model. Integrating this parameterization into the atmospheric model leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. The parameterization obeys physical constraints and captures important statistics such as precipitation extremes. The ability to learn from a fully three-dimensional simulation presents an opportunity for learning parameterizations from the wide range of global high-resolution simulations that are now emerging.Comment: Main: 27 pages, 5 figures SI: 19 pages, 11 figures, 4 table

Pregnancy-associated breast cancer - Special features in diagnosis and treatment

For obvious psychological reasons it is difficult to associate pregnancy - a life-giving period of our existence with life-threatening malignancies. Symptoms pointing to malignancy are often ignored by both patients and physicians, and this, together with the greater difficulty of diagnostic imaging, probably results in the proven delay in the detection of breast cancers during pregnancy. The diagnosis and treatment of breast cancer are becoming more and more important, as the fulfillment of the desire to have children is increasingly postponed until a later age associated with a higher risk of carcinoma, and improved cure rates of solid tumors no longer exclude subsequent pregnancies. The following article summarizes the special features of the diagnosis and primary therapy of pregnancy-associated breast cancer with particular consideration of cytostatic therapy

Non-local parameterization of atmospheric subgrid processes with neural networks

Subgrid processes in global climate models are represented by parameterizations that are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that new machine-learning parameterizations learned from high-resolution model output data could be superior to traditional parameterizations. Currently, both traditional and machine-learning parameterizations of subgrid processes in the atmosphere are based on a single-column approach. Namely, the information used by these parameterizations is taken from a single atmospheric column. However, a single-column approach might not be ideal for the parameterization problem since certain atmospheric phenomena, such as organized convective systems, can cross multiple grid boxes and involve slantwise circulations that are not purely vertical. Here we train neural networks using non-local inputs spanning over 3$\times$3 columns of inputs. We find that including the non-local inputs substantially improves the prediction of subgrid tendencies of a range of subgrid processes. The improvement is especially notable for cases associated with mid-latitude fronts and convective instability. Using an explainable artificial intelligence technique called layer-wise relevance propagation, we find that non-local inputs from zonal and meridional winds contain information that helps to improve the performance of the neural network parameterization. Our results imply that use of non-local inputs has the potential to substantially improve both traditional and machine-learning parameterizations.Comment: 31 pages, 17 figures (7 figures in the main file

Defining the Newborn Piglet’s Thermal Environment with an Effective Environmental Temperature

An effective environmental temperature (EET) developed for newborn piglets describes the thermal environment by incorporating the mean radiant temperature, dry-bulb temperature, and air velocity near the newborn. The adequacy of the defined EET was analyzed by comparing with three published studies on newborn sensible heat loss. Results from the published studies indicate that the EET predicted between 87% and 98% of the variability in the data