Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.

Possible mechanisms related to internal dynamics for anomalously high rainfall during August 2020 of Indian Summer Monsoon

In the present study, an investigation is made to understand the physical mechanism behind the anomalous high rainfall during August 2020 over the Indian subcontinent using both observation and GFS T1534 weather forecast model. According to India Meteorological Department (IMD), the country receives 27% excess rainfall in the month of August 2020. The excess rainfall is mainly contributed by the 5 well marked low pressure systems which formed over Bay of Bengal and moved west-northwestwards across central India up to Western Madhya Pradesh and Rajasthan. The analysis reveals that the observed anomalous rainfall is distributed over central India region extending from coastal Orissa to central part of Chhattisgarh, Madhya Pradesh and western coast of Gujarat region. It is also found that the August-2020 heavy rainfall is mainly contributed by the synoptic (2-10 days) component of the total rainfall whereas the contribution of the large-scale intraseasonal oscillation (ISO) component (10-90 days) is quite less. Although the present operational Global Forecast System (GFS) T1534 (GFS T1534) is able to predict the anomalous high rainfall with day-1 lead time, it underestimates the magnitude of the synoptic variance. Further, the large-scale dynamical and thermodynamical parameters show anomalous behaviour in terms of strong low level (850 hPa) jet, vertical velocity and associated moisture convergence in the lower level. The GFS T1534 is able to forecast the above large-scale features reasonably well even with day-5 lead time. From energetics analysis, it is found that the mean kinetic energy (MKE) is stronger for August 2020 as compared to climatological value and the strong MKE efficiently transfers the energy to the synoptic scale, and hence the synoptic eddy kinetic energy is higher. Along with that, the ISO scale kinetic energy for August 2020 is less compared to the August climatological value. GFS T1534 model has some fidelity in capturing the energy conversion processes, but it has some difficulty in capturing the magnitude with increased lead time.

A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

The Efficacy of the WRF-ARW Model in the Genesis and Intensity Forecast of Tropical Cyclone Fani over the Bay of Bengal

An attempt has been carried out to assess the efficacy of the Weather Research and Forecasting (WRF) model in predicting the genesis and intensification events of Very Severe Cyclonic Storm (VSCS) Fani (26 April – 04 May 2019) over the Bay of Bengal (BoB). WRF model has been conducted on a single domain of 10 km horizontal resolution using the Global Data Assimilation System (GDAS) FNL (final) data (0.250 × 0.250). According to the model simulated outcome analysis, the model is capable of predicting the Minimum Sea Level Pressure (MSLP) and Maximum Sustainable Wind Speed (MSWS) pattern reasonably well, despite some deviations. The model has forecasted the Lowest Central Pressure (LCP) of 919 hPa and the MSWS of 70 ms-1 based on 0000 UTC of 26 April. Except for the model run based on 0000 UTC of 26 April, the simulated values of LCP are relatively higher than the observations. According to the statistical analysis, MSLP and MSWS at 850 hPa level demonstrate a significantly greater influence on Tropical Cyclone (TC) formation and intensification process than any other parameters. The model can predict the intensity features well enough, despite some uncertainty regarding the proper lead time of the model run. Reduced lead time model run, particularly 24 to 48 hr, can be chosen to forecast the genesis and intensification events of TC with minimum uncertainty. Journal of Engineering Science 12(3), 2021, 85-100

Branding in libraries: Identifying key requirements and dimensions to provide a conceptual model

The purpose of this review is to explore factors affecting brand building in libraries. Based on the nine steps of the National Health Service (NHS) center for reviews and dissemination, articles on the subject of library branding were searched in nine Iranian databases and seven international databases. The search period includes all date range of databases until 7–22 January 2021. The results were assessed for quality and 44 English articles and 3 Persian articles were selected for further analysis. Factors in promoting libraries brand building fall five categories. They include library architecture, library information resources and services, librarians’ personal branding, marketing, and library management. Inhibiting factors in libraries brand building have two final categories including internal and external inhibiting factors to brand building. Internal inhibitors covered branding costs, lead-time for branding, effort for branding and its management, the difficulty of strategic brand planning, and library staff unpreparedness. External inhibitors covered the difficulties of branding in the digital age and the economic situation of the country. If libraries manage their brand and move toward rebranding in line with the new information environment, they will be able to survive in today’s competitive world and build their true value in relationship with users.

A Novel Rollover Warning Approach for Commercial Vehicles Using Unscented Kalman Filter

Roll responses of the semitrailer and the tractor provide higher lead time and characterise the roll instability of the commercial vehicles subjected to directional manoeuvres at highway speeds. This paper proposes a novel rollover index based on the synthesized roll angles of the tractor and trailer. Owing to the poor measurability, the unscented Kalman filter (UKF) algorithm is used to estimate the roll angle of the track and trailer, respectively. Meanwhile, different weight coefficients are considered in the rollover index to eliminate the influence of mutual coupling between the tractor and the trailer and improve the accuracy of the warning. For the practical implementation of the algorithm, a two-stage rollover warning method triggered by the video and audio is finally proposed to reduce the possibilities of false warnings. Co-simulation is presented to prove the validity of the proposed rollover warning approach.

Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model

Storm surges are one of the most devastating coastal disasters. Numerous efforts have continuously been made to achieve better prediction of storm surge variation. In this paper, we propose a parametric cyclone and neural network hybrid model for accurate, long lead-time storm surge prediction. The model was applied to the northeastern coastal region of Taiwan, i.e., Longdong station. A total of 14 historical typhoon events were used for model training and validation, and the results and questions associated with this hybrid approach carefully discussed. Overall, the proposed method reduced the complexity of network structure while retaining the important typhoon indicators. In particular, local pressure and winds estimated from the storm parameters through physically-based parametric cyclone models allow for inferring the possible future influence of a typhoon, unlike the simple collection and direct usage of observation data from local stations in earlier works. Meanwhile, the error-tolerance capability of the neural network alleviated some discrepancy in the model inputs and enabled good surge prediction. Further, the proposed method showed better and faster convergence thanks to the retention of storm information and the reduced dimensions of the search space. The hybrid model presented excellent performance or maintained reasonable capability for short lead-time and long lead-time storm surge prediction. Compared with the pure neural network model under the same network dimensions, the present model demonstrated great improvement in accuracy as the prediction lead time increased to 8 h, e.g., 33–40% (13–21%) and 32–37% (18–29%) RMSE and CE, respectively, in the training/validation phase.

Analysis Of Stock Control Of Raw Materials Of Dzohir Noodles In Bengkulu

This study aims to analyze the stock inventory control of Mie Dzohir's raw materials in Bengkulu. The analytical method used is Economic Order Quantity (EOQ), Reorder Point (ROP), Total Inventory Cost (TIC) and Safety Stock (SS). The results showed that to meet the raw material needs of 91,375 kg during the research period, from January 2020 to December 2020 (12 months), the number of economical purchases/EOQ of wheat flour raw materials that had to be made by the Mie Dzohir factory in Bengkulu was as much as 5,372 kg for each order with a purchase frequency of 17 times for a period from January 2020 to December 2020. Reorders or reorder points (ROP) can also be determined, namely reordering should be done when 345 kg of inventory is in warehouse, with a lead time of 1 days so as not to hamper the company's production process. The total inventory cost (TIC) can also be determined, namely the total cost of raw material inventory of Rp. 3.572.188, - therefore, it can save on inventory costs of Rp. 1.051.697,- .Safety stock (SS) of 95 kg which must be in the warehouse, this is intended therefore, there is no shortage of wheat flour raw materials if there is a delay in the delivery of raw materials.