Medium Time Scales Research Articles

Co-estimation of state-of-charge and capacity for series-connected battery packs based on multi-method fusion and field data

Accurate state-of-charge (SOC) and capacity estimations are of great importance for the performance management, predictive maintenance, and safe operation of lithium-ion battery packs in electric vehicles (EVs). However, it is quite challenging to estimate real-world large-sized EV battery packs due to the unpredictable operating profiles and large measurement disturbances. This article proposes an adaptive onboard SOC and capacity co-estimation framework, which incorporates a multi-timescale hierarchy and integrates multiple individual methods adaptively to practical driving profiles. First, this framework considers the most evident inconsistency between battery cells and periodically screens the weakest cells in a long timescale (week-level). Subsequently, the SOC of the battery pack is accurately estimated in a short timescale (in real-time) based on multi-method fusion. Finally, the capacity of the battery pack is periodically calibrated in a medium timescale (minute-level) based on an adaptive state filter and reliable SOC estimation. Both the laboratory and field data were used for validation, and the results demonstrated the proposed method achieved accurate SOC and capacity estimations of large-sized EV battery packs, with the maximum root mean squared errors of <0.7 % and <3.2 %, respectively, and it was run five times faster than the multi-cell model-based method.

Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended (> weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Concomitant determination of pesticides in soil and drainage water over a potato cropping season reveal dissipations largely in accordance with respective models

Pesticides are widely used in agriculture where they do not only reach their targets but also distribute to other environmental compartments and negatively affect non-target organisms. To prospectively assess their environmental risk, several tools and models using pesticide persistence (DT50) and leaching potential (groundwater ubiquity score (GUS), EXPOSIT) have been developed. Here, we simultaneously quantified 18 pesticides in soil and drainage water during a conventionally grown potato culture at field scale with high temporal resolution and compared our findings with predictions of the above models. Overall dissipations of all freshly applied compounds in soil were in line with published DT50 field values and their occurrences in drainage water were generally consistent with GUS and EXPOSIT models, respectively. In contrast, soil concentrations of the legacy pesticide atrazine and one of its transformation products (atrazine-2-hydroxy) were constant during the entire sampling campaign. Moreover, during peak discharge atrazine concentrations in drainage water were diluted whereas those of freshly applied pesticides were maximal. This difference demonstrates that the applied risk assessment tools were capable of predicting environmental concentrations and dissipation of pesticides at the short and medium time scale of a few half-lives after application, but fell short of capturing long-term trace residues.

Effects of major storms over a spit-tidal flat interaction system (Punta Rasa-Samborombón, Argentina)

Sandy coastal areas are very dynamic systems in which morphological changes occur over different time scales from hours to decades. However, it has been widely reported that major storms are the main responsible of the most significant changes in short to medium time scales. Major storms have been defined using a variety of environmental variables, but they are normally associated with high values of wave height, duration, return period and direction.Here, we aim to characterize types of major storms and to categorize associated morphological impacts over a complex coastal system. The study site, known as Punta Rasa, is located in the Samborombón bay in the outer part of the Río de La Plata estuary (Argentina) and corresponds to a zone of interaction between a large sandy spit and a backwash tidal flat system. Methods combine statistics of wave climate time-series, analysis of wave energy using nearshore numerical modelling (SWAN) and comparison of pre- and post-storm morphological changes by means of shoreline change detection and satellite images derived indexes (CoastSat Toolkit and NDWI index respectively).Results allowed to characterize four types of major storms impacting the study area: High-Energy Storms (HES), defined by an average storm wave below the 1 % exceedance (>2.6 m), Long-Lived Storms (LLS) represented by an exceedance of the 1 % of Du (>60 h), Storm Groups (SG) in which storm return period is <6 days and Northeastern moderate storms (NMS) defined by their eastern, onshore oriented direction. Under HES and NMS storms erosional areas are dominant over depositional, causing shoreline retreat, a growth of the end-spit and the increase on sand deposition on the back-barrier areas. Under LLS and SG storms, the morphological impact varies alongshore, with multiple erosional hotspots found along the shoreline accompanied by a general flattens of the end-spit system.

Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India

AbstractA comprehensive assessment of the forecast skill of various meteorological indices over the Indian region from different numerical weather prediction (NWP) models is lacking in the literature. In this study the performance of four NWP models, namely Global Ensemble Forecast System (GEFSv12), European Center for Medium Range Forecasting (ECMWF), Climate Forecast System (CFSv2) and Indian Institute of Tropical Meteorology (IITM) towards forecasting of precipitation, temperature and associated meteorological indices, is evaluated at short to medium timescales across the Indian region. Further, the effect of ocean atmospheric (OA) oscillations on the precipitation/temperature forecast skill from the different NWP models is also assessed. Results show that the NWP models are better in predicting the meteorological indices than the quantitative forecasts. The ECMWF model was found to be the best for PCP forecasting with CFSv2 performing poorly. For temperature the GEFSv12 model performance was the lowest, compared to rest of the models. The models show poor skill in forecasting monsoon season precipitation compared to non‐monsoon and the temperature forecasts from the NWP models are particularly poor for the northern basins. Skilful temperature forecasts are observed in the Northwestern, Indo‐Gangetic and Central basins for the CFSv2 and ECMWF models. The forecast skill of precipitation indices are higher in the northwestern, central and Indo‐Gangetic basins compared to the rest. The skill of precipitation indices, namely rainy days, extreme rainy days and consecutive wet days, is higher during the monsoon seasons while the prediction skill of consecutive dry days is higher during the non‐monsoon season. OA analysis revealed that the ENSO phases have dominant effect on the forecast skill of the precipitation only. The temperature and meteorological indices forecasts are not affected significantly by the OA phases. The outcomes of this study have implications towards irrigation scheduling and water resources management decision making in India.

Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram

IntroductionFunctional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states. MethodsResting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness. ResultsThere was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved. ConclusionLoss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

Decoherence and Turbulence Sources in a Long Laser.

We investigate the turn-on process in a laser cavity where the round-trip time is several orders of magnitude greater than the active medium timescales. In this long delay limit, we show that the universal evolution of the photon statistics from thermal to Poissonian distribution involves the emergence of power dropouts. While the largest number of these dropouts vanish after a few round-trips, some of them persist and seed coherent structures similar to dark solitons or Nozaki-Bekki holes described by the complex Ginzburg-Landau equation. These coherent structures connect stationary laser emission domains having different optical frequencies. Moreover, they emit intensity bursts which travel at a different speed, and, depending on the cavity dispersion sign, they may collide with other coherent structures, thus leading to an overall turbulent dynamics. The dynamics is well-modeled by delay differential equations from which we compute the laser coherence time evolution at each round-trip and quantify the decoherence induced by the collisions between coherent structures.

EEG complexity during mind wandering: A multiscale entropy investigation

Our attention often drifts away from the ongoing task to task-unrelated thoughts, a phenomenon commonly referred to as mind wandering. Ample studies dedicated to delineating its electrophysiological correlates have revealed distinct event-related potentials (ERP) and spectral patterns associated with mind wandering. It remains less clear whether the complexity of the electroencephalography (EEG) changes when our minds wander, a metric that captures the predictability of the time series at varying timescales. Accordingly, this study investigated whether mind wandering impacts EEG signal complexity. We further explored whether such effects differ across timescales, and change in a context-dependent manner as indexed by global and local levels of processing. To address this, we recorded participants' EEG while they completed Navon's global and local processing task and occasionally reported whether they were on-task or mind wandering throughout the task. We found that brain signal complexity as indexed by multiscale entropy decreased at medium timescales in centro-parietal regions and increased at coarse timescales in anterior and posterior regions during mind wandering, as compared to the on-task state, for global processing. Moreover, global processing showed increased complexity at fine to medium timescales compared to local processing. Finally, behavioral performance revealed a context-dependent effect in accuracy measures, with mind wandering showing lower accuracy compared to the on-task state only during the local condition. Taken together, these results indicate that changes in brain signal complexity across timescales may be an important feature of mind wandering.

Global Extreme Heat Forecasting Using Neural Weather Models

Abstract Heatwaves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal time scales. To this purpose, we train a set of neural weather models (NWMs) with convolutional architectures to forecast surface temperature anomalies globally, 1 to 28 days ahead, at ∼200-km resolution and on the cubed sphere. The NWMs are trained using the ERA5 reanalysis product and a set of candidate loss functions, including the mean-square error and exponential losses targeting extremes. We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heatwave prediction task, relative to NWMs trained on the mean-square-error loss. This improvement is accomplished with almost no skill reduction in the general temperature prediction task, and it can be efficiently realized through transfer learning, by retraining NWMs with the custom losses for a few epochs. In addition, we find that the use of a symmetric exponential loss reduces the smoothing of NWM forecasts with lead time. Our best NWM is able to outperform persistence in a regressive sense for all lead times and temperature anomaly thresholds considered, and shows positive regressive skill relative to the ECMWF subseasonal-to-seasonal control forecast after 2 weeks. Significance Statement Heatwaves are projected to become stronger and more frequent as a result of global warming. Accurate forecasting of these events would enable the implementation of effective mitigation strategies. Here we analyze the forecast accuracy of artificial intelligence systems trained on historical surface temperature data to predict extreme heat events globally, 1 to 28 days ahead. We find that artificial intelligence systems trained to focus on extreme temperatures are significantly more accurate at predicting heatwaves than systems trained to minimize errors in surface temperatures and remain equally skillful at predicting moderate temperatures. Furthermore, the extreme-focused systems compete with state-of-the-art physics-based forecast systems in the subseasonal range, while incurring a much lower computational cost.

Nonlinear control of a selective catalytic reduction unit for a bio-fueled cogeneration plant

The paper puts forward a method for control of selective catalytic reduction (SCR) units. First, a dynamic model of SCR performance including heat transfer, ammonia adsorption, NOx reduction, and ammonia oxidation is developed. The model is then more simplified to only include medium time scale dynamics which can be shaped using control. The obtained model is utilised in the design of a nonlinear control strategy for the regulation of the NOx emissions in the system. A robust observer of the system state is developed and exploited for control design. Numerical simulations verify the effectiveness of the proposed control system.

Ionospheric Oscillation with Periods of 6–30 Days at Middle Latitudes: A Response to Solar Radiative, Geomagnetic, and Lower Atmospheric Forcing

This research studies the medium timescale (6–30 days) ionospheric response over the Wuhan area to solar radiative, recurrent geomagnetic, and lower atmospheric forcing. The ionospheric response is examined by wavelet analysis of the total electron content (TEC) over the Wuhan area from 2001 to 2020. Ionospheric oscillations with periods centering at the harmonic oscillations of the 27-day solar rotation (e.g., 27 days, 13.5 days, 9 days, and 6.75 days) are focused upon. The results show that the quasi-27-day TEC oscillations at the middle latitude have a better overall correlation with solar radiation than recurrent geomagnetic activity, but the correlation between TEC and recurrent geomagnetic activity has a significant increase at the solar minimum stage. As for ionospheric oscillations with periods shorter than 15 days, these oscillations correlate better with recurrent geomagnetic activity. Moreover, a quasi-27-day TEC oscillation event at the middle latitude caused by convective activity from the lower atmosphere was studied. This suggests that lower atmospheric forcing is also an important factor causing ionospheric oscillations. In addition, the ionospheric oscillations over the Wuhan area also show unique regional characteristics, as the regional ionosphere does not respond well to the Kp oscillation with periods shorter than 20 days, particularly, 13.5 days.

Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Cross-regional air pollutant spillovers aggravate air pollution in China. To mitigate air pollution, identifying and monitoring air pollution spreaders (APS) is a vital strategy that helps locate the source of air pollution and guides the Joint Prevention and Control of Air Pollution. In this paper, we define an APS as a city with a high spillover impact (CHSI) of air pollution and propose a transfer entropy network to investigate the APS from a multi-timescale analysis perspective. Taking the time series of PM2.5 concentration of 358 Chinese cities from 1 January 2015 to 31 December 2020 as the sample, they are decomposed into short, medium, and long timescales, corresponding to an average period of 12, 111, and 530 days, respectively. Then, we use transfer entropy networks to analyze APS’s spatial distribution and temporal variation patterns on each timescale. The results demonstrate that air pollution spillover widely exists in Chinese cities, and the short-term air pollution spillover dominates all spillovers. The CHSIs form large agglomeration areas in Central and East China on short and medium timescales, while the results of the undecomposed data show a more discrete distribution. In addition, the cities’ air pollution spillover impact is usually high in winter and spring and low in summer. Moreover, the spillover impacts of half of the cities have a lead-lag relationship between short and medium timescales. All results suggest that combining short-term controls and longer-term strategies helps China mitigate air pollution and develop sustainably.

Short- and mid-term forecasts of actual evapotranspiration with deep learning

• Deep learning is applied for nowcasting and forecasting of evapotranspiration. • Weather variables and soil moisture are used as input for the deep learning models. • The models were applied to seven distinct climate zones within the continental U.S. • Results reveal great potential for evapotranspiration forecasting. Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-, and long-term forecasts of actual evapotranspiration (ET a ) are crucial not only for quantifying the impacts of climate change on the water and energy balance, but also for real-time estimation of crop water demand and irrigation water allocation in agriculture. Despite considerable advances in satellite remote sensing technology and the availability of long ground-measured and remotely sensed ET a timeseries, real-time ET a forecasts are deficient. Applying a state-of-the-art deep learning (DL) approach, Long Short-Term Memory (LSTM) models were employed to nowcast (real-time) and forecast (ahead of time) ET a based on (1) major meteorological and ground-measured (i.e., soil moisture) input variables and (2) long ET a timeseries from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard of the NASA Aqua satellite. The conventional LSTM and convolutional LSTM (ConvLSTM) DL models were evaluated for seven distinct climatic zones across the contiguous United States. The employed LSTM and ConvLSTM models were trained and evaluated with data from the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) and with MODIS/Aqua Net Evapotranspiration MYD16A2 product data. The obtained results indicate that when major atmospheric and soil moisture input variables are used for the conventional LSTM models, they yield accurate daily ET a forecasts for short (1, 3, and 7 days) and medium (30 days) time scales, with normalized root mean squared errors (NRMSE) and Nash-Sutcliffe efficiencies (NSE) of less than 10% and greater than 0.77, respectively. At the watershed scale, the univariate ConvLSTM models yielded accurate weekly spatiotemporal ET a forecasts (mean NRMSE less than 6.4% and NSE greater than 0.66) with higher computational efficiency for various climatic conditions. The employed models enable precise forecasts of both the current and future states of ET a , which is crucial for understanding the impact of climate change on rapidly depleting water resources.

Entanglement and correlations in fast collective neutrino flavor oscillations

Collective neutrino oscillations play a crucial role in transporting lepton flavor in astrophysical settings like supernovae and neutron star binary merger remnants, which are characterized by large neutrino densities. In these settings, simulations in the mean-field approximation show that neutrino-neutrino interactions can overtake vacuum oscillations and give rise to fast collective flavor evolution on timescales $t\ensuremath{\propto}{\ensuremath{\mu}}^{\ensuremath{-}1}$, with $\ensuremath{\mu}$ proportional to the local neutrino density. In this work, we study the full out-of-equilibrium flavor dynamics in simple multiangle geometries displaying fast oscillations in the mean field linear stability analysis. Focusing on simple initial conditions, we analyze the production of pair correlations and entanglement in the complete many-body-dynamics as a function of the number $N$ of neutrinos in the system, for up to thousands of neutrinos. Similarly to simpler geometries with only two neutrino beams, we identify three regimes: stable configurations with vanishing flavor oscillations, marginally unstable configurations with evolution occurring on long timescales $\ensuremath{\tau}\ensuremath{\approx}{\ensuremath{\mu}}^{\ensuremath{-}1}\sqrt{N}$, and unstable configurations showing flavor evolution on short timescales $\ensuremath{\tau}\ensuremath{\approx}{\ensuremath{\mu}}^{\ensuremath{-}1}\mathrm{log}(N)$. We present evidence that these fast collective modes are generated by the same dynamical phase transition which leads to the slow bipolar oscillations, establishing a connection between these two phenomena and explaining the difference in their timescales. We conclude by discussing a semiclassical approximation which reproduces the entanglement entropy at short to medium timescales and can be potentially useful in situations with more complicated geometries where classical simulation methods starts to become inefficient.

Discerning Spatiotemporal Patterns and Policy Drivers of Rural Settlement Changes from 1962 to 2020

Despite two centuries of urbanisation worldwide, 45% of the world’s people still live in rural areas. Driven by urban development, the form and structure of rural settlements have undergone drastic changes. Reasonable planning according to the scale of the land and spatial layout of rural settlements is particularly important for the development of rural areas. The continuous development of the economy means that the housing needs of farmers and the macro policy background will inevitably change. We create a relationship curve for the “policy-scale of rural settlements” in different periods according to the laws of Maslow’s psychological demand theory and game theory and conduct an empirical study on Dingzhou City, China. The limited availability of remote sensing data means it is difficult to map the evolution patterns of rural settlements on medium and long time scales, and therefore, this paper explores and decrypts military satellite images, reveals the spatial evolution characteristics of rural settlements in Dingzhou, China from 1962 to 2020, and discusses the impact of policy factors on changes to rural settlements in different periods. The study found that from 1962 to 2020, the total area of rural settlements in Dingzhou showed a trend of continual increase, with a total increase of 8354.97 ha (73%). The average annual growth rates in 1962–1972, 1972–1990, 1990–2000, 2000–2010, and 2010–2020 were 0.29%, 1.17%, 1.81%, 1.26%, and 0.05%, respectively. The growth rate of rural settlements was relatively slow from 1962 to 1972. The policy was mainly because rural homesteads (land for building rural residences) were transformed from private ownership to “one homestead, two systems”, and the expansion of rural settlements was inhibited. From 1972 to 1990, with the deepening of reform and opening up, there was a boom in building houses in rural areas, and the growth rate of rural settlements increased. From 1990 to 2000, although the state strengthened the management of rural settlement use, there was still an increasing trend in the area of rural settlements; from 2000 to 2020 the implementation of policies such as “one house for one household" and “connecting increase and decrease" meant that the growth rate of rural settlements slowed.

Acheulean Diversity in Britain (MIS 15-MIS11): From the Standardization to the Regionalization of Technology

The appearance of the Acheulean and the production of new bifacial tools marked a revolution in human behavior. The use of longer and complex operative chains, with centripetal and recurrent knapping, adapted to different raw materials, created long useful edges, converging in a functional distal end. How and why these handaxes vary has been the subject of intense debates. Britain provides a clearly defined region at the edge of the hominin occupied world for discussing variation in Acheulean assemblages. The environmental changes from MIS 15 to MIS 11 are significant in understanding population change, with probable breaks in evidence during MIS 14 and MIS 12, followed by several sites during the long stable climate of MIS11c. In this latter period, different Acheulean technological expressions appear to coexist in Britain. This paper draws together different studies, combining technology and geometric morphometrics to analyze handaxes from six British sites: Brandon Fields, Boxgrove (Q1B), High Lodge, Hitchin, Swanscombe (UMG), and Elveden. Compared to the earlier Acheulean of MIS 15, the assemblages of MIS 13 show increased standardization and the use of soft hammer percussion for thinning mid-sections and butts of tools, or sharpening tips through tranchet removals. Although there is regional population discontinuity through MIS12 there is no evidence of a marked change in technology after this glacial period. Rather, there is a development towards more intense shaping with the same underlying techniques, but with flexibility in imposed handaxe form. From MIS11 there appear to be distinctive localized traditions of manufacture, which suggest that a recognition of place and territories had developed by this time. These are expressed over medium time-scales of several thousand years and have significance for how we view cultural expression and transmission.

Using birefringent elements and imaging Michelsons for the calibration of high-precision planet-finding spectrographs

Context. One of the main methods used for finding extrasolar planets is the radial velocity technique, in which the Doppler shift of a star due to an orbiting planet is measured. These measurements are typically performed using cross-dispersed echelle spectrographs. Unfortunately, such spectrographs are large and expensive, and their accurate calibration continues to be challenging. Aims. The aim is to develop a different way to provide a calibration signal. Methods. A commonly used way to introduce a calibration signal is to insert an iodine cell in the beam. Disadvantages of this include that the lines are narrow, do not cover the entire spectrum, and light is absorbed. Here I show that inserting a birefringent element or an imaging Michelson, combined with Wollaston prisms, eliminates these three shortcomings while maintaining most of the benefits of the iodine approach. Results. The proposed designs can be made very compact, thereby providing a convenient way of calibrating a spectrograph. Similar to the iodine cell approach, the calibration signal travels with the stellar signal, thereby reducing the sensitivity to spectrograph stability. The imposed signal covers the entire visible range, and any temperature drifts will be consistent and describable by a single number. Based on experience with similar devices that were used in a different configuration by the Helioseismic and Magnetic Imager, it is shown that the calibration device can be made stable at the 0.1 m/s level over a significant wavelength range on short to medium timescales. Conclusions. While the design is promising, many details still need to be worked out. In particular, a number of laboratory measurements are required in order to finalize a design and estimate actual performance, and it would be desirable to make a proof of concept.

Profiling User Needs for Weather and Climate Information in Fostering Drought Risk Preparedness in Central-Southern Nigeria

Weather and climate information trigger early action and facilitate better disaster preparedness. Decision-driven and people-centered weather and climate information are pivotal for the effective uptake. The challenge of early responses in preparing for drought hazard is growing in the dry savannah of tropical sub-Saharan African countries. This paper analyzed user needs for weather and climate information in fostering drought risk preparedness in Central-Southern Nigeria. Stratified, snowball, and simple random samplings were used to obtain a sample of 397 respondents across the agro-ecological zones of Edo State. Structured questionnaire was used to collect farm-level household data across communities. Eight focus group discussions and 11 key informants' interviews were conducted, targeting contact farmers and other agricultural stakeholders in selected key economic sectors of Edo State, Central-Southern Nigeria. Results show that non-users of weather and climate information are more than users in the savannah area. Heckman probit results explained that male gender, farmers' experience, income, and persistent incidence of erratic rainfall have more propensity to facilitate use of WCI in taking critical decisions while group membership or associations and distance of meteorological station prevent stakeholders from developing interest in using WCI for drought preparedness and response. Multi-criteria decision-making indicated that rainfall amount, onset and cessation rainfall dates, and rainfall distributions are the most useful WCI needed by end users in their decision response plan in agriculture; rainfall intensity, rainfall cessation date, rainfall distributions, and length of dry season are ranked as the most useful WCI for water resource management while heat intensity, rainstorms, and drought alerts were ranked as most appropriate for users in the disaster risk reduction in fostering resilience toward anticipated future drought hazard. Subseasonal-to-seasonal (S2S) and medium (4–10 days) timescales information are the most highly rated to facilitate resource planning for efficient utilization and management in all the economic sectors. The users' most preferred delivery method of receiving WCI are mobile telephone, radio, agricultural extension officers, farmers' groups, and contact farmers/specialist for efficiency and convenient criteria in enhancing users' decision capacity to uptake WCI. There is a need for a policy drive to build synergy that will make WCI forecasting systems include impact-based forecast estimates and response advisory across a wide range of natural hazards. A seamless collaborative effort in bringing scientific outputs and users' needs together will increase the utility of WCI through systematic efforts. NiMet should improve on its engagement with the stakeholders, the agricultural extension and planning office, water management authorities, and disaster risk reduction and emergency response personnel as partner institutions. These policy actions would provide a robust collaborative framework for co-producing useable WCI based on user needs in managing decision points against extreme events and mainstream preparedness into existing decision-making apparatus of rural communities in Central-Southern Nigeria.

Hyper-CEST NMR of metal organic polyhedral cages reveals hidden diastereomers with diverse guest exchange kinetics

Guest capture and release are important properties of self-assembling nanostructures. Over time, a significant fraction of guests might engage in short-lived states with different symmetry and stereoselectivity and transit frequently between multiple environments, thereby escaping common spectroscopy techniques. Here, we investigate the cavity of an iron-based metal organic polyhedron (Fe-MOP) using spin-hyperpolarized 129Xe Chemical Exchange Saturation Transfer (hyper-CEST) NMR. We report strong signals unknown from previous studies that persist under different perturbations. On-the-fly delivery of hyperpolarized gas yields CEST signatures that reflect different Xe exchange kinetics from multiple environments. Dilute pools with ~ 104-fold lower spin numbers than reported for directly detected hyperpolarized nuclei are readily detected due to efficient guest turnover. The system is further probed by instantaneous and medium timescale perturbations. Computational modeling indicates that these signals originate likely from Xe bound to three Fe-MOP diastereomers (T, C3, S4). The symmetry thus induces steric effects with aperture size changes that tunes selective spin manipulation as it is employed in CEST MRI agents and, potentially, impacts other processes occurring on the millisecond time scale.