The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

A WRF-UCM-SOLWEIG framework of 10m resolution to quantify the intra-day impact of urban features on thermal comfort

City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning

Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science

The purpose of the New York Workshop on Computer, Earth and Space Sciences is to bring together the New York area's finest Astronomers, Statisticians, Computer Scientists, Space and Earth Scientists to explore potential synergies between their respective fields. The 2011 edition (CESS2011) was a great success, and we would like to thank all of the presenters and participants for attending. This year was also special as it included authors from the upcoming book titled "Advances in Machine Learning and Data Mining for Astronomy". Over two days, the latest advanced techniques used to analyze the vast amounts of information now available for the understanding of our universe and our planet were presented. These proceedings attempt to provide a small window into what the current state of research is in this vast interdisciplinary field and we'd like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011 in New York City, Goddard Institute for Space Studie

Sediment and associated radionuclide dynamics within the Ribble Estuary, North West England.

Intertidal environments represent a spatially complex and dynamic system, rendering point sampling geographically and temporally isolated within the context of the entire estuarine system. Airborne remote sensing has the potential to place these spatially isolated sampling points into a quantitative spatial context. Furthermore it provides a valuable data source for quantifying processes within estuarine zones and can supply calibration and validation information for hydrodynamic models. This study focuses on the Ribble Estuary, Lancashire, England which is accumulating elevated radionuclide concentrations derived from authorized industrial discharges from BNFL Sellafield and Westinghouse Springfields. An image mixture modeling approach was used on Airborne Thematic Mapper (ATM) data to derive accurate estimates of intertidal clay, in comparison to concurrent field sampling (r2=0.828) and radionuclide concentrations (r2= 0.822). Data processed for 2003 was compared with similar data from May 1997 (Rainey 1999; Rainey et aL., 2000; 2003) to investigate spatial changes in intertidal clay and 137Cs contamination. These results compared with field sampling data demonstrates considerable reduction (c.52%) in the activity concentrations, which is primarily attributed to processes of sediment dilution. Calibrated Compact Airborne Spectrographic Imager (CASI) imagery combined with concomitant ground reference data, was used to characterize the suspended sediment concentrations and the total suspended load over each flight line. Two sets of time series image data were compared to assess the spatial and temporal changes in suspended sediment and associated radionuclide transportation within the estuarine environment. In conjunction with total volumetric estimates generated from a two-dimensional vertically resolving hydrodynamic model, this data then allowed estimation of the total flux of suspended sediment and radionuclide over the flood and ebb tidal cycle to a reasonable precision (40%). To establish whether these flux estimates are realistic, the results are compared with time series field based observations collected from monthly observations over a two year cycle. The results provide a unique quantitative insight into the understanding of contaminant and sediment transport within this estuarine environment and the environmental processes controlling them. The contribution of field data with the intertidal and flood-ebb tide imagery has provided an enhanced understanding of the interactions of tides and fluvial flow on the spatial distribution of sediments within the Ribble Estuary. It could also be possible to apply the calibrated clay intertidal maps to other heavy metal pollutants that have a high affinity with fine-grained clay particles i.e. Pb, Zn, Cu, Al in estuarine sediments

London’s foundations protecting the geodiversity of the capital

This report describes a geodiversity audit of London commissioned by a partnership led by the Greater London Authority (GLA), which includes the British Geological Survey (BGS), Natural England, Government Office for London, London Biodiversity Partnership, London Borough of Lambeth, Harrow and Hillingdon Geological Society, South London London RIGS Groups, Hanson UK and Queen Mary College, University of London. The project was funded by an Aggregates Levy Sustainability Fund grant from Natural England plus additional support from the GLA, BGS and Natural England London Region. The audit began with a review of the available geodiversity documentation for London including: BGS field maps, databases and publications; Regional Important Geological Sites (RIGS) Group information; Natural England Sites of Special Scientific Interest (SSSI) and Geological Conservation Review (GCR) documentation; and documentation and data from the GLA and London Boroughs. An initial list of around 470 sites with potential for geodiversity value was compiled from this information. This list was then narrowed down to 100 for further assessment by exporting site locations to a GIS and cross-checking against digital aerial photography backed up by BGS staff local geological expertise. Using the procedure set out in this report field auditing was carried out by BGS staff and the South London RIGS Group between November 2007 and April 2008. From the list of 100 sites, 35 sites were found to be suitable for detailed auditing. Harrow and Hillingdon Geological Society audited a further site in November 2008, bringing the total to 36 sites. Using the criteria set out in this report 14 of the 36 sites are recommended for designation as Regionally Important Geological/geomorphological Sites (RIGS) in borough Local Development Documents. Of the 33 London boroughs, RIGS are recommended in eight, with five in Bromley, three in Croydon and one each in Lewisham, Ealing, Greenwich, Harrow, Hillingdon and Bexley. Using the criteria set out in this report 15 of the 36 sites have the potential to be designated as Locally Important Geological Sites (LIGS). These sites are located in nine boroughs, three in Waltham Forest, two in Bromley, two in Islington and one each in Barnet, Lewisham, Redbridge, Wandsworth, Southwark and Sutton. Planning proposals should have regard to geodiversity in order to implement strategic and local policies. Sites should be protected, managed and enhanced and, where ppropriate, new development should provide improvements to the geodiversity value of a site. This can include measures that promote public access, study, interpretation and appreciation of geodiversity. In addition to individual sites of geodiversity interest, Greater London has distinctive natural landscapes shaped by geological processes, such as undulating chalk downlands with dry valleys in south London, and river terraces forming long flat areas separated by steeper areas of terrace front slopes. This natural topographic geodiversity underlying London should be understood, respected and only altered in that knowledge with full knowledge of it origin and form. Planners are encouraged to use authentic contouring in restoration work and new landscaping schemes, maintain the contributions of natural topography, rock outcrops, landscape features, and to maintain soil quality, quantity and function

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined

Salt-gradient Solar Ponds: Summary of US Department of Energy Sponsored Research

The solar pond research program conducted by the United States Department of Energy was discontinued after 1983. This document summarizes the results of the program, reviews the state of the art, and identifies the remaining outstanding issues. Solar ponds is a generic term but, in the context of this report, the term solar pond refers specifically to saltgradient solar pond. Several small research solar ponds have been built and successfully tested. Procedures for filling the pond, maintaining the gradient, adjusting the zone boundaries, and extracting heat were developed. Theories and models were developed and verified. The major remaining unknowns or issues involve the physical behavior of large ponds; i.e., wind mixing of the surface, lateral range or reach of horizontally injected fluids, ground thermal losses, and gradient zone boundary erosion caused by pumping fluid for heat extraction. These issues cannot be scaled and must be studied in a large outdoor solar pond

A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment