Machine learning applications for weather and climate need greater focus on extremes

Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to higher resolution and emulating and speeding up expensive model parameterisations. Many of these used ML methods with very high numbers of parameters, such as neural networks, which are the focus of the discussion here. Not much attention has been given to the performance of these methods for extreme event severities of relevance for many critical weather and climate prediction applications, with return periods of more than a few years. This leaves a lot of uncertainty about the usefulness of these methods, particularly for general purpose prediction systems that must perform reliably in extreme situations. ML models may be expected to struggle to predict extremes due to there usually being few samples of such events. However, there are some studies that do indicate that ML models can have reasonable skill for extreme weather, and that it is not hopeless to use them in situations requiring extrapolation. This article reviews these studies and argues that this is an area that needs researching more. Ways to get a better understanding of how well ML models perform at predicting extreme weather events are discussed

Mexico City and the biogeochemistry of global urbanization

Mexico City is far advanced in its urban evolution, and cities in currently developing nations may soon follow a similar course. This paper investigates the strengths and weaknesses of infrastructures for the emerging megacities. The major driving force for infrastructure change in Mexico City is concern over air quality. Air chemistry data from recent field campaigns have been used to calculate fluxes in the atmosphere of the Valley of Mexico, for compounds that are important to biogeochemistry including methane (CH4), carbon monoxide (CO), nonmethane hydrocarbons (NMHCs), ammonia (NH3), sulfur dioxide (SO2), nitrogen oxides (NOx and NOy), soot, and dust. Leakage of liquified petroleum gas approached 10% during sampling periods, and automotive pollutant sources in Mexico City were found to match those in developed cities, despite a lower vehicle-to-person ratio of 0.1. Ammonia is released primarily from residential areas, at levels sufficient to titrate pollutant acids into particles across the entire basin. Enhancements of reduced nitrogen and hydrocarbons in the vapor phase skew the distribution of NOy species towards lower average deposition velocities. Partly as a result, downwind nutrient deposition occurs on a similar scale as nitrogen fixation across Central America, and augments marine nitrate upwelling. Dust suspension from unpaved roads and from the bed of Lake Texcoco was found to be comparable to that occurring on the periphery of the Sahara, Arabian, and Gobi deserts. In addition, sodium chloride (NaCl) in the dust may support heterogeneous chlorine oxide (ClOx) chemistry. The insights from our Mexico City analysis have been tentatively applied to the upcoming urbanization of Asia

Optimal search strategies for identifying sound clinical prediction studies in EMBASE

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies

Measurement of Scintillation and Ionization Yield and Scintillation Pulse Shape from Nuclear Recoils in Liquid Argon

We have measured the scintillation and ionization yield of recoiling nuclei in liquid argon as a function of applied electric field by exposing a dual-phase liquid argon time projection chamber (LAr-TPC) to a low energy pulsed narrow band neutron beam produced at the Notre Dame Institute for Structure and Nuclear Astrophysics. Liquid scintillation counters were arranged to detect and identify neutrons scattered in the TPC and to select the energy of the recoiling nuclei. We report measurements of the scintillation yields for nuclear recoils with energies from 10.3 to 57.3 keV and for median applied electric fields from 0 to 970 V/cm. For the ionization yields, we report measurements from 16.9 to 57.3 keV and for electric fields from 96.4 to 486 V/cm. We also report the observation of an anticorrelation between scintillation and ionization from nuclear recoils, which is similar to the anticorrelation between scintillation and ionization from electron recoils. Assuming that the energy loss partitions into excitons and ion pairs from $^{83m}$Kr internal conversion electrons is comparable to that from $^{207}$Bi conversion electrons, we obtained the numbers of excitons ($N_{ex}$) and ion pairs ($N_i$) and their ratio ($N_{ex}/N_i$) produced by nuclear recoils from 16.9 to 57.3 keV. Motivated by arguments suggesting direction sensitivity in LAr-TPC signals due to columnar recombination, a comparison of the light and charge yield of recoils parallel and perpendicular to the applied electric field is presented for the first time.Comment: v2 to reflect published versio

Meeting the global protein supply requirements of a growing and ageing population

\ua9 The Author(s) 2024.Human dietary patterns are a major cause of environmental transformation, with agriculture occupying ~ 50% of global land space, while food production itself is responsible for ~ 30% of all greenhouse gas emissions and 70% of freshwater use. Furthermore, the global population is also growing, such that by 2050, it is estimated to exceed ~ 9 billion. While most of this expansion in population is expected to occur in developing countries, in high-income countries there are also predicted changes in demographics, with major increases in the number of older people. There is a growing consensus that older people have a greater requirement for protein. With a larger and older population, global needs for protein are set to increase. This paper summarises the conclusions from a Rank Prize funded colloquium evaluating novel strategies to meet this increasing global protein need

A Network of SCOP Hidden Markov Models and Its Analysis

<p>Abstract</p> <p>Background</p> <p>The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before.</p> <p>Results</p> <p>In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components.</p> <p>Conclusions</p> <p>The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs.</p