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This research paper proposes a new five-step protocol to enhance the result of existing cloud removal algorithms using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products (SCPs). The study has been carried out for the upper Bhagirathi basin (up to Maneri Hydropower Project) located in the Western Himalaya. Gafurov and Bárdossy test employed to validate the performance of the proposed method, followed by comparing with the field observed snow cover duration (SCD) data. The result shows that the mean overall accuracy of the proposed method for cloud removal is about ∼95%. However, the cloud removal method by Gafurov and Bardossy also achieved similar mean overall accuracy but with the higher variability within the individual images as compared with the variability within the results obtained by the proposed method. SCD computed from cloud removed SCPs matched significantly with the field observed SCD for a point location, supporting the accuracy achieved by the cloud removal method. This study also examines the spatiotemporal variability of the snow cover in the study area during the past 18 years (2000–2018). During the observation period, no specific trend was observed for annual maximum snow cover, while yearly minimum snow cover in the basin showed an increasing trend since 2010. Seasonally, December and June month witnessed significant changes. December experienced a declining trend in snow cover between 3000–6000 m a.s.l. covering 88% of the basin area, whereas, June showed an increasing trend between 4500 to 6000 m (a.s.l.). This elevation range covers 61% of the basin area, including core 86% of the glacier area within the basin. September and October experienced the highest inter-annual snow cover variability. Maximum snow cover month of February and minimum snow cover month of August experienced the least variability. The present study suggests significant elevation-dependent increasing as well as the decreasing trend in the snow cover with seasonal contrast, which may affect the glaciers as well as the hydrological behavior of the basin. [ABSTRACT FROM AUTHOR]

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence. [ABSTRACT FROM AUTHOR]

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists’ emotions when visiting a city’s tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists’ emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate how knowledge acquired from occupants' manual window operations can be implemented into BMS automated window operation algorithms. Ten single-occupant offices were selected in a university building in the UK. More than 28,000 hourly data points on indoor and outdoor temperature and open window area (OWA) were analysed from 2015 to 2020. The BMS had adopted nine different automated window operation algorithms during the 5 years. The automated window algorithms could be manually overridden by the office occupants. Automated algorithms were compared against manual window operations. The results showed that the slope and gradient of the regression lines for occupants' manual window operations are smaller than automated operations. OWA of automated window operations increased 20% per 1 °C increase in indoor temperature, however, occupants opened windows 6–8% per 1 °C increase. Occupants react slower to temperature changes than assumed by BMS, which could be considered in BMS automated window operations. • BMS automated window algorithms operate based on indoor temperature in this study. • Automated window operations by BMS impact occupants' manual window operations. • Occupants open windows at higher temperatures on the third floor than first floor. • BMSs need to consider building-related differences and seasonal changes. • A BMS system architecture is proposed to reduce the behaviour gap. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as "bad" outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results. • A new condition monitoring method of the structures under EOV is proposed. • VMD is used to denoise the frequency signals while removing the seasonal pattern. • An RNN is trained to learn the rule behind calculation of the Mahalanobis distances. • The method is tested on both numerical and experimental examples. • The prediction error of Mahalanobis distance increases as damage occurs. [ABSTRACT FROM AUTHOR]

This paper presents a computational parametric study on increasing the Seasonal Coefficient of Performance (SCOP) for residential heat pumps. The studied system consists of a heat pump, low-temperature heat storage, and a control unit. The heat pump enables selection of a low-temperature heat source between ambient air and water in a tank. Two variants of low-temperature heat storage are tested, particularly, insulated-water heat storage and water heat storage sunken in soil. The study is further complemented with a test of selected algorithms for heat pump control: equithermal regulation, a binary algorithm for temperature source selection, a predictive algorithm for the heat storage discharging, and an algorithm for deferred heat storage discharging. A computational model of the system is made using Python. The assessment of HP operation is made based on meteorological data from the years 2008–2019 recorded in the city of Brno, Czech Republic, Central Europe. The results obtained show that using the approaches tested has the potential for increasing the SCOP. This increase reaches as much as 5.19% and it requires only a simple software change in the heat pump control algorithm and connection to meteorological data prediction. • Novelty predictive discharge control with real-world data model. • Optimized discharge of acc. in conditions with significant temperature fluctuations. • Mathematical model combining water and ground heat accumulation. • Proposed control strategies provide SCOP increase. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

In this paper, we evaluate several timely, daily air-sea heat flux products (NCEP, NCEP2, ERA-Interim and OAFlux/ISCCP) against observations and present the newly developed TropFlux product. This new product uses bias-corrected ERA-interim and ISCCP data as input parameters to compute air-sea fluxes from the COARE v3.0 algorithm. Wind speed is corrected for mesoscale gustiness. Surface net shortwave radiation is based on corrected ISCCP data. We extend the shortwave radiation time series by using 'near real-time' SWR estimated from outgoing longwave radiation. All products reproduce consistent intraseasonal surface net heat flux variations associated with the Madden-Julian Oscillation in the Indian Ocean, but display more disparate interannual heat flux variations associated with El Niño in the eastern Pacific. They also exhibit marked differences in mean values and seasonal cycle. Comparison with global tropical moored buoy array data, I-COADS and fully independent mooring data sets shows that the two NCEP products display lowest correlation to mooring turbulent fluxes and significant biases. ERA-interim data captures well temporal variability, but with significant biases. OAFlux and TropFlux perform best. All products have issues in reproducing observed longwave radiation. Shortwave flux is much better captured by ISCCP data than by any of the re-analyses. Our 'near real-time' shortwave radiation performs better than most re-analyses, but tends to underestimate variability over the cold tongues of the Atlantic and Pacific. Compared to independent mooring data, NCEP and NCEP2 net heat fluxes display ~0.78 correlation and >65 W m rms-difference, ERA-I performs better (~0.86 correlation and ~48 W m) while OAFlux and TropFlux perform best (~0.9 correlation and ~43 W m). TropFlux hence provides a useful option for studying flux variability associated with ocean-atmosphere interactions, oceanic heat budgets and climate fluctuations in the tropics. [ABSTRACT FROM AUTHOR]

In Energy Performance Contracts (EPC), savings resulting from building and energy system retrofit are estimated as the difference between the actual consumption an prediction from a baseline model. This paper proposes an automated method to select the most relevant baseline model. In addition to commonly used cooling degree days (CDD), climatic variables mixing temperature and humidity are introduced, such as dew point or moist air enthalpy. In terms of prediction algorithm, 11 algorithms, linear and nonlinear, are compared. The novel methodology enables an energy analyst to easily find the most relevant model for a specific building, with a focus on both precision and seasonal bias. By applying the novel methodology on 11 buildings under real EPCs, the result shows a decrease of prediction error by 23.5% with respect to models referenced in the contracts. Lastly, the proposed method is generalized to address the case where contracts involve multiple buildings of different types or consumption ranges, with the ability to identify a common best model by using new dimensionless indicators, and reach an 11% improvement in prediction error. [ABSTRACT FROM AUTHOR]

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts. [ABSTRACT FROM AUTHOR]