Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations

During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent models trained on the ERA5 at 0.25{\deg} resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, the Pangu-Weather, is compared to several NWP models with and without probabilistic post-processing for 2-meter temperature and 10-meter wind speed forecasting at 183 Norwegian SYNOP stations up to +60 hours ahead. The NWP models included are the ECMWF HRES, ECMWF ENS and the Harmonie-AROME ensemble model MEPS with 2.5 km spatial resolution. Results show that the performances of the global models are on the same level with Pangu-Weather being slightly better than the ECMWF models for temperature and slightly worse for wind speed. The MEPS model clearly provided the best forecasts for both parameters. The post-processing improved the forecast quality considerably for all models, but to a larger extent for the coarse-resolution global models due to stronger systematic deficiencies in these. Apart from this, the main characteristics in the scores were more or less the same with and without post-processing. Our results thus confirm the conclusions from other studies that global data-driven models are promising for operational weather forecasting.Comment: 9 pages, 5 figure

The Evaluation of Metals and Other Substances Released into Coal Mine Accrual Waters on the Wasatch Plateau Coal Field, Utah

Six sites on the Wasatch Plateau were chosen representing subsurface coal mines which were discharging or collecting accrual water on this coal field. Water samples were collected monthly at these sites for a period of 1 year (May 1981 to April 1982). Samples were taken before and after each mine\u27s treatment system. Water sampels were analyzed for major anions and cations, trace metals, physical properaties, nutrients, total organic carbon, oil and grease, trihalomethanes, and algal assay. Predictions were made as to the possible effects these coal mine accrual waters would have when used for drinking water, irrigation water, stock and wildlife watering, and as discharges into freshwater aquatic ecosystems. Compliance of the mine water discharges with NPDES regulations was also noted. Crushed coal samples were obtained from each of the six mine sites and evaluated with regard to their leaching characteristics in laboratory upflow leaching columns using an aqueous leaching medium characteristic of the area\u27s water supplies. Leachate samples were anlyzed for major anions and cations, trace metals, physical properaties, and total organic carbon. laboratory leaching characteristics were compared to the chemical nature of the actual mine water discharges. Mine water discharges were not found to be acidic in nature, the values for most parameters monitored during the field and laboratory portions of the study fell below the toxicity criteria for uses mentioned above, and were generally in compliance with NPDES regulations. Boron was present in the mine waters, but at levels which would be predicted to cause only minor or no damage to the most sensitive crops. The drinking water limit and the freshwater aquatic life bioaccumulation criterion for mercury were exceeded on several occasions in the coal mine accrual waters sampled. A comprehensive study of fish tissue samples and water samples taken from bodies of water near coal mines is recommended. Total suspended solids (TSS) and oil and grease were among the most frequently violated parameters with regard to NPDES regulations. Further studies are recommended with regard to the effects of these substances on stream biota, their sources and their rate in aquatic ecosystems. Coal leaching trends in the laboratory column experiments pralleled many of the trends observed in the field data collected. Trends for pH, aluminum, arsenic, beryllium, cadmium, chromium, cobalt, copper, iron, lead, molybdenum, nickel, silver, zinc, boron, lithium, strontium, alkalinity, chloride, cluoride, potassium, sodium, and silica were generally consistent when these comparisons were made. Values for water hardness parameters were observed to be specific to the mine site involved and not always comparable to laboratory leachate column data. Generalizations with respect to leaching trends and origins of chemical substances in coal mine accrual waters must be made with caution due to the great potential variability in coal samples and the complexity of leaching phenomena

Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study

<p>Abstract</p> <p>Background</p> <p>In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.</p> <p>Methods</p> <p>We challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker <it>K</it>^trans, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.</p> <p>Results</p> <p>ADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while <it>K</it>^trans, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, <it>K</it>^trans, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).</p> <p>Conclusions</p> <p>We have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.</p

Exploratory analysis of citizen observations of hourly precipitation over Scandinavia

We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called “unresolved spatial scales” in limited area models, based on three years of data collected at several places in Scandinavia.</p

The early evolution of the H-free process

The H-free process, for some fixed graph H, is the random graph process defined by starting with an empty graph on n vertices and then adding edges one at a time, chosen uniformly at random subject to the constraint that no H subgraph is formed. Let G be the random maximal H-free graph obtained at the end of the process. When H is strictly 2-balanced, we show that for some c>0, with high probability as $n \to \infty$, the minimum degree in G is at least $cn^{1-(v_H-2)/(e_H-1)}(\log n)^{1/(e_H-1)}$. This gives new lower bounds for the Tur\'an numbers of certain bipartite graphs, such as the complete bipartite graphs $K_{r,r}$ with $r \ge 5$. When H is a complete graph $K_s$ with $s \ge 5$ we show that for some C>0, with high probability the independence number of G is at most $Cn^{2/(s+1)}(\log n)^{1-1/(e_H-1)}$. This gives new lower bounds for Ramsey numbers R(s,t) for fixed $s \ge 5$ and t large. We also obtain new bounds for the independence number of G for other graphs H, including the case when H is a cycle. Our proofs use the differential equations method for random graph processes to analyse the evolution of the process, and give further information about the structure of the graphs obtained, including asymptotic formulae for a broad class of subgraph extension variables.Comment: 36 page

Impact of sudden Arctic sea-ice loss on stratospheric polar ozone recovery

We investigate the sensitivity of Northern Hemisphere polar ozone recovery to a scenario in which there is rapid loss of Arctic summer sea ice in the first half of the 21st century. The issue is addressed by coupling a chemistry climate model to an ocean general circulation model and performing simulations of ozone recovery with, and without, an external perturbation designed to cause a rapid and complete loss of summertime Arctic sea ice. Under this extreme perturbation, the stratospheric response takes the form of a springtime polar cooling which is dynamical rather than radiative in origin, and is caused by reduced wave forcing from the troposphere. The response lags the onset of the sea-ice perturbation by about one decade and lasts for more than two decades, and is associated with an enhanced weakening of the North Atlantic meridional overturning circulation. The stratospheric dynamical response leads to a 10 DU reduction in polar column ozone, which is statistically robust. While this represents a modest loss, it has the potential to induce a delay of roughly one decade in Arctic ozone recovery estimates made in the 2006 Scientific Assessment of Ozone Depletion

Balancing Detection and Eradication for Control of Epidemics: Sudden Oak Death in Mixed-Species Stands

Culling of infected individuals is a widely used measure for the control of several plant and animal pathogens but culling first requires detection of often cryptically-infected hosts. In this paper, we address the problem of how to allocate resources between detection and culling when the budget for disease management is limited. The results are generic but we motivate the problem for the control of a botanical epidemic in a natural ecosystem: sudden oak death in mixed evergreen forests in coastal California, in which species composition is generally dominated by a spreader species (bay laurel) and a second host species (coast live oak) that is an epidemiological dead-end in that it does not transmit infection but which is frequently a target for preservation. Using a combination of an epidemiological model for two host species with a common pathogen together with optimal control theory we address the problem of how to balance the allocation of resources for detection and epidemic control in order to preserve both host species in the ecosystem. Contrary to simple expectations our results show that an intermediate level of detection is optimal. Low levels of detection, characteristic of low effort expended on searching and detection of diseased trees, and high detection levels, exemplified by the deployment of large amounts of resources to identify diseased trees, fail to bring the epidemic under control. Importantly, we show that a slight change in the balance between the resources allocated to detection and those allocated to control may lead to drastic inefficiencies in control strategies. The results hold when quarantine is introduced to reduce the ingress of infected material into the region of interest