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1. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

   https://doi.org/10.1175/AIES-D-22-0038.1  

   Weirich-Benet, Elizabeth ; Pyrina, Maria ; Jiménez-Esteve, Bernat ; Fraenkel, Ernest ; Cohen, Judah ; Domeisen, Daniela I. V. ( April 2023 , Artificial Intelligence for the Earth Systems)

   Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times.more »For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

   Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

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2. Experimental Analysis of Chiller Cooling Failure in a Small Size Data Center Environment Using Wireless Instrumentation

   https://doi.org/10.1115/IPACK2018-8334  

   Tradat, Mohammad ; Sammakia, Bahgat ; Alissa, Husam ; Nemati, Kourosh ( January 2018 , ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems)

   Given the vital rule of data center availability and since the inlet temperature of the IT equipment increase rapidly until reaching a certain threshold value after which IT starts throttling or shut down because of overheat during cooling system failure. Hence, it is especially important to understand failures and their effects. This study presented experimental investigation and analysis of a facility-level cooling system failure scenario in which chilled water interruption introduced to the data center. Quantitative instrumentation tools including wireless technology such as wireless temperature and pressure sensors were used to measure the discrete air inlet temperature and pressure differential though cold aisle enclosure, respectively. In addition, Intelligent Platform Management Interface (IPMI) and cooling system data during failure/recovery were reported. Furthermore, the IT equipment performance and response for opened and contained environments were simulated and compared. Finally, an experiment based analysis of the Ride Through Time (RTT) of servers during chilled water interruption of the cooling infrastructure presented as well. The results showed that for all three classes of servers tested during the cooling failure, CAC helped keep the server’s cooler for longer. The containment provided a barrier between the hot and cold air streams and caused slight negative pressuremore »to build up, which allowed the servers to pull cold air from the underfloor plenum. In addition, the results show that the effect of CAC in containment solutions on the IT equipment performance and response could vary and depend on the server’s airflow, generation and hence types of servers deployed in cold aisle enclosure. Moreover, it was shown that when compared to the discrete sensors, the IPMI inlet temperature sensors underestimate the Ride Through Time (RTT) by 42% and 12% for the CAC and opened cases, respectively.« less
3. Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Recovery

   https://doi.org/10.1109/TSP.2017.2691661  

   Gu, Renliang ; Dogandzic, Aleksandar ( July 2017 , IEEE Transactions on Signal Processing)

   We develop a projected Nesterov’s proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov’s momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁-norm penalty on the signal’s linear transform coefficients. The PNPG approach employs a projected Nesterov’s acceleration step with restart and a duality-based inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG converges faster than the methods that do not adjust to the local curvature of the NLL. We present an integrated derivation of the momentum acceleration and proofs of O(k⁻²) objective function convergence rate and convergence of the iterates, which account for adaptive step size, inexactness of the iterative proximal mapping, and the convex-set constraint. The tuning ofmore »PNPG is largely application independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach.« less
4. Multiple Dimensions of Resilience: How NEON Supports Ecology and the Research Community in the Face of Compounding Disasters

   https://doi.org/10.3389/fenvs.2022.653666  

   Robinson, Natalie ; Barnett, David T. ; Jones, Katherine D. ; Stanish, Lee F. ; Parker, Stephanie M. ( March 2022 , Frontiers in Environmental Science)

   Quantifying the resilience of ecological communities to increasingly frequent and severe environmental disturbance, such as natural disasters, requires long-term and continuous observations and a research community that is itself resilient. Investigators must have reliable access to data, a variety of resources to facilitate response to perturbation, and mechanisms for rapid and efficient return to function and/or adaptation to post-disaster conditions. There are always challenges to meeting these requirements, which may be compounded by multiple, co-occurring incidents. For example, travel restrictions resulting from the COVID-19 pandemic hindered preparations for, and responses to, environmental disasters that are the hallmarks of resilient research communities. During its initial years of data collection, a diversity of disturbances—earthquakes, wildfires, droughts, hurricanes and floods—have impacted sites at which the National Ecological Observatory Network (NEON) intends to measure organisms and environment for at least 30 years. These events strain both the natural and human communities associated with the Observatory, and additional stressors like public health crises only add to the burden. Here, we provide a case-study of how NEON has demonstrated not only internal resilience in the face of the public health crisis of COVID-19, but has also enhanced the resilience of ecological research communities associated with the networkmore »and provided crucial information for quantifying the impacts of and responses to disturbance events on natural systems—their ecological resilience. The key components discussed are: 1) NEON’s infrastructure and resources to support its core internal community, to adapt to rapidly changing situations, and to quickly resume operations following disruption, thus enabling the recovery of information flow crucial for data continuity; 2) how NEON data, tools, and materials are foundational in supporting the continuation of research programs in the face of challenges like those of COVID-19, thus enhancing the resilience of the greater ecological research community; and 3) the importance of diverse and consistent data for defining baseline and post-disaster conditions that are required to quantify the effects of natural disasters on ecosystem patterns and processes.« less
5. Long-time non-contact water level measurement with a 5.8-GHz DC-coupled interferometry radar

   https://doi.org/10.1109/I2MTC.2018.8409735  

   Peng, Zhengyu ; Mishra, Ashish ; Davis, Justin R. ; Bridge, Jennifer A. ; Li, Changzhi ( May 2018 , 2018 IEEE International Instrumentation and Measurement Technology Conference)

   Flooding caused by tropical cyclones, tsunami, and many other phenomena is one type of natural disaster that occurs all around the world. While these disasters cannot be prevented, the communities can be made more resilient and damages caused by them to lives and infrastructure can be minimized by developing early warning systems. Microwave-based systems provide a non-contact measurement setup to monitor water level, thus requiring low maintenance and operation costs. In this paper, a DC-coupled 5.8-GHz interferometry radar was designed and tested by observing water level in a barrel, which had water poured in and drained out over a long-time period. By adding more gains to the RF chain and removing the gain in the baseband, the proposed water-level monitoring radar system eliminates the requirement of complex DC tuning structure in the previous works. The experiment demonstrated that the proposed water-level monitoring radar system was able to accurately measure the relative position of water with mm-accuracy.