798 research outputs found
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 33 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
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