Increasing Lead Time Research Articles

Impact of Implementing the Unification of The IT Section into One Division Using a Change Management Strategy at PT XYZ

ABSTRACT In the era of Industry 4.0, many companies demand to determine a Change Management Strategy in order to maximize capacities and efficiency. Currently, PT XYZ sees existing problems in the IT division, which are not meeting demands as expected and slowing down the whole organization’s development. In accordance with these problems, Project Monitoring methods are proposed to be applied in PT XYZ’s organizational structure as a result of the Change Management Strategy. A Change Management Strategy will be implemented in the IT division using the proposed methods; Kotter’s Model and 7-S Framework followed by the development of a project monitoring application called Dashboard Monitoring. In accordance, with Kotter’s Model’s approach, the creation of two new positions is highly recommended inside the IT division to support its effectiveness. These positions are Project Management Officers and Business Analysts. These two positions are expected to focus more on the process of developing new applications and/or systems. Thus, the 7-S Framework implementation will be supporting the change of Shared & Value from the change in the organizational structure of PT XYZ with the objective of simplifying the process of analyzing and decision-making at the managerial level, especially in terms of deciding to create an application. Furthermore, a Dashboard Monitoring application has been implemented in PT XYZ, resulting in improvements in lead time and productivity throughout multiple IT projects. Objective – Developments in the era of Industry 4.0 make all companies adapt towards digitalization, to determine Change Management Strategies to maximize capacity and efficiency. PT XYZ sees problems in the IT division, where the IT Division cannot meet the demands as expected. Thus, resulting in delays in the development of the organization. Obstacles in processing so many requests in making applications between divisions in the company, developing a product application system is the biggest obstacle faced by PT XYZ. Methodology – The author uses the following Research Methodology; (1) understand the identification of existing problems based on the author's observations and communication with PT XYZ; (2) study the organizational structure: (3) Literature Review; (4) Interviews and observations at PT XYZ. The first stage is interviews with all Vice Presidents from all divisions at PT. XYZ. The second stage is an interview by asking the Chief Executive Officer of the company regarding information related to changes in the IT Division. ; (5) Kotter Framework and 7-S approaches, all of which start with analyzing with Dynamic Capability Theory; (6) Provide recommendations, propose to create a new IT division, then analyze the new organizational structure using the 7-S Framework and produce proposed new values. Findings – The author got information from PT XYZ Top Management, where from the results of this interview a Change Management Strategy is proposed to be implemented in the organizational structure of PT XYZ. The Change Management Strategy will be implemented in the IT division using the proposed method, namely the Kotter Model and the 7-S Framework, which were previously analyzed using the Dynamic Capability approach. Then followed by the development of a project monitoring application called Dashboard Monitoring. Novelty – The author implementing the 7-S Framework to support Shared & Value changes from changes in PT XYZ's organizational structure. Next implement the Kotter Model Strategy which explains 8 Strategies to produce recommendations for changes to PT XYZ's organizational structure, by adding the positions of PMO (Project Management Officer) and BA (Business Analyst). In addition, the Dashboard Monitoring application was also implemented at PT XYZ, resulting in increased lead time and productivity in various IT projects.

Improving the accuracy of multimodel short-to-medium-range precipitation and streamflow forecasts over the Upper Bhima river basin, India

Accurate precipitation forecasting with sufficient lead time is a prerequisite for developing a robust flood warning system (FWS), which is very challenging, particularly in developing countries like India. This study evaluates the utility of the TIGGE multimodel ensemble meteorological forecasts over the Upper Bhima river basin and investigated the hydrological utility of the TIGGE forecasts through a calibrated hydrological (VIC-RAPID) model followed by the postprocessing of streamflow through Bayesian model average (BMA) approach. Results show that the quality of the meteorological forecasts of precipitation, and simulated streamflow deteriorated with increasing lead time, which can be ameliorated with a suitable bias-correction technique. The BMA-based postprocessing further improved the streamflow simulations, especially in case of extreme events, which highlighted its efficacy in flood forecasting. From the results of the study, it is recommended that a compound system of improved precipitation prediction, calibrated VIC-RAPID model and postprocessing of streamflows in an integrated manner would facilitate a reliable FWS for operational purposes.

Predictive uncertainty assessment in flood forecasting using quantile regression

Abstract Floods and their associated impacts are topics of concern in land development planning and management, which call for efficient flood forecasting and warning systems. The performance of flood warning systems is affected by uncertainty in water level forecasts, which is due to their inability to measure or calculate a modeled value accurately. Predictive uncertainty is an emerging type of uncertainty modeling technique that emphasizes total uncertainty quantified as a probability distribution conditioned on all available knowledge. Predictive uncertainty analysis was done using quantile regression (QR) for machine learning-based flood models – Hybrid Wavelet Artificial Neural Network model (WANN) and Hybrid Wavelet Support Vector Machine model (WSVM) for different lead times. Comparing QR models of WANN and WSVM revealed that the slope, intercept, spread of forecast, and width of confidence band of the WANN model are more for each quantile indicating more uncertainty as compared to the WSVM model. In both models, with an increase in lead time, uncertainty has shown an increasing trend as well. The performance evaluation of inference obtained from QR models was evaluated using uncertainty statistics such as prediction interval coverage probability, average relative interval length (ARIL), and mean prediction interval (MPI).

Spatiotemporal characteristics and forecasting of short-term meteorological drought in China

Intense short-term meteorological drought may lead to rapid declines in soil moisture, triggering flash drought that can cause major agricultural or socioeconomic damage. Machine learning methods have proven effective in forecasting hydrometeorology, but short-term drought forecasting is still inadequate. We developed a pentad-time-scale (5-day) standardized precipitation evapotranspiration index (SPEI-5d) to quantify short-term meteorological drought and analyzed its spatiotemporal distribution characteristics in China during the period 1962–2018. We used historical SPEI-5d as input and employed five machine learning methods for drought hindcasting: an autoregressive integrated moving average (ARIMA), random forest (RF), recurrent neural network (RNN), long short-term memory (LSTM), and convolutional long short-term memory (ConvLSTM). The results show the following: (1) During the period 1962–2018, 61.9% of the study region showed decreasing trends in drought severity, while 69.6% of the region showed increasing trends in drought intensity. (2) Drought duration, severity, and intensity have distinct seasonal characteristics, and different forecasting models perform differently in each season, with generally lower forecasting accuracy in summer and higher forecasting accuracy in winter. (3) The ConvLSTM model can capture spatiotemporal information well compared to traditional time-series forecasting models; it has the best performance (root mean square error = 0.29, and Nash-Sutcliffe efficiency = 0.92) in the test set and has high forecasting accuracy (R2 > 0.8) for lead times of 1–5 days (with accuracy decreasing as lead time increases). Our findings highlight the spatiotemporal variability of short-term meteorological drought and provide valuable scientific insights for short-term meteorological drought forecasting at 1–5 days of lead time.

Impact-based seasonal rainfall forecasting to trigger early action for droughts

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Beyond the starvation of millions of livestock, close to 23 million individuals in the region are currently facing high food insecurity in Kenya, Somalia and Ethiopia alone. The severity of these impacts calls for the urgent upscaling and optimisation of early action for droughts. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action triggered by forecasts is seldom addressed.This study investigates the potential for early action for droughts by using seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system for the March–April-May (MAM) and October–November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEVmax = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits persist (PEVmax = 0.2). Outside of Kenya, MAM forecasts have limited value. The existence of opportunities for early action during the OND season in Kenya and Somalia is demonstrated by high PEV values, with some regions recording PEVmax values close to 0.8. To illustrate the practical value of this research, we point to a dilemma that a pastoralist in the Kenyan drylands faces when deciding whether to adopt early livestock destocking.This study underscores the importance to determine the value of early actions for forecast users with different action characteristics, and to disseminate this value alongside the standard forecasts themselves. This allows users to trigger effective actions before drought impacts develop.

Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors

Study regionThe Yangtze River basin. Study focusMachine learning techniques have been widely applied to streamflow prediction at short lead times, and increasing numbers of predictors are introduced to improve predictive skills. However, the effect different predictors and their combinations at long lead times remains to be explored. In this study, the Long Short-Term Memory (LSTM) model is used to forecast the daily streamflow over the Yangtze River during flood seasons at lead times of 1–30 days, by using different combinations of streamflow, precipitation, soil moisture, and evapotranspiration as predictors. The effects of different predictors on daily streamflow and high/low flows, together with the difference between short and long lead times, are investigated. New hydrological insights for the regionThe experiment using antecedent streamflow as predictor has a high (0.58–0.99) Kling-Gupta efficiency (KGE) at 1–7days lead times, but KGE decreases rapidly as the lead time increases. Adding precipitation predictor increases the KGE of daily streamflow by 0.09–0.25 and corrects negative/positive biases in high/low flows at long lead times (> 20 days). Soil moisture and evapotranspiration predictors have added values to daily streamflow forecasting at long lead times by further improving the KGE by 0.04–0.11, but exert minor influences on streamflow extremes. Thus, how to incorporate multiple hydrometeorological predictors in LSTM model to improve the forecasts of extremes at long leads is still a challenge.

Evaluation of Processes Related to Stratosphere–Troposphere Coupling in GEFSv12 Subseasonal Hindcasts

Abstract The representation of the stratosphere and stratosphere–troposphere coupling processes is evaluated in the subseasonal Global Ensemble Forecast System, version 12 (GEFSv12), hindcasts. The GEFSv12 hindcasts develop systematic stratospheric biases with increasing lead time, including a too strong boreal wintertime stratospheric polar vortex. In the tropical stratosphere, the GEFSv12 winds and temperatures associated with the quasi-biennial oscillation (QBO) tend to decay with lead time such that they underestimate the observed amplitudes; consistently, the QBO-associated mean meridional circulation is too weak. The hindcasts predict extreme polar vortex events (including sudden stratospheric warmings and vortex intensifications) about 13–14 days in advance, and extreme lower-stratospheric eddy heat flux events about 6–10 days in advance. However, GEFSv12’s ability to predict these events is likely affected by its zonal-mean circulation biases, which increases the rates of false alarms and missed detections. Nevertheless, GEFSv12 shows stratosphere–troposphere coupling relationships that agree well with reanalysis and other subseasonal forecast systems. For instance, GEFSv12 reproduces reanalysis relationships between polar vortex strength and the Northern Annular Mode in the troposphere. It also exhibits enhanced weeks 3–5 prediction skill of the North Atlantic Oscillation index when initialized during strong and weak polar vortex states compared to neutral states. Furthermore, GEFSv12 shows significant differences in Madden–Julian oscillation (MJO) amplitudes and enhanced MJO predictive skill in week 4 during easterly versus westerly QBO phases, though these results are sensitive to the level used to define the QBO. Our results provide a baseline from which future GEFS updates may be measured.

Novel evaluation of sub‐seasonal precipitation ensemble forecasts for identifying high‐impact weather events associated with the Indian monsoon

AbstractWe assess the skill of the fully coupled lagged ensemble forecasts from GloSea5‐GC2, for the sub‐seasonal to seasonal (S2S) timescale up to 4 weeks, with the aim of understanding how these forecasts might be used in a Ready‐Set‐Go style decision‐making framework. Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG‐GPM) are used to seamlessly verify these ensemble forecasts up to monthly timescales whereby forecast and observed precipitation fields are summed over a sequence of increasing lead time accumulation windows (LTAWs), from 1d1d up to 2w2w. Results show that model biases grow with increasing LTAW and with ensemble member age. The S2S model exhibits both wet and dry biases across different parts of the Indian domain. The S2S model error grows from around 10 mm for a 24‐h accumulation to 50 mm for the 2‐week LTAWs. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than actual skill everywhere. The sensitivity to the number and age of ensemble members was tested, with potential skill showing more impact from the exclusion of older members at all LTAWs. We conclude that the older lagged members do not necessarily add value by being included in the short to medium range or even for the extended range forecasts. GloSea5‐GC2 shows some skill in detecting large accumulations, which are not always tied to locations where they are climatologically frequent.

The Evolution from Traditional to Digital Supply Chain Management

The world of supply chain management (SCM) has been undergoing a seismic shift, transitioning from the traditional model to a futuristic digital avatar. The digital revolution, characterized by the Internet of Things (IoT), blockchain technology, and digital financial flows, is transforming how goods and services are manufactured, transported, and delivered, influencing various sectors, including the agri-food supply chain. Traditional SCM used to be linear and hierarchical, where each participant operated in silos with limited communication. The supply chain starts from raw material suppliers, followed by manufacturers, distributors, and retailers before reaching consumers. Such a system was characterized by inefficiencies, including information asymmetry, increased lead times, and lack of visibility.

Channel Changes Choice: An Empirical Study About Omnichannel Demand Sensitivity to Fulfillment Lead Time

We examine a large transaction-level data set of an Italian omnichannel furniture retailer to study channel-specific effects of fulfillment lead time on demand. This omnichannel retailer sells the same products and has the same product fulfillment across three channels: showroom, online, and catalog. The showroom channel carries no inventory but allows customers to touch and feel the products. The online channel provides a website for consumers to browse and order the products. The catalog channel sends a product catalog to all the households in Italy, so they can place an order over the phone. We find that the showroom channel makes consumers less sensitive to fulfillment lead time than both online and catalog channels. In particular, a 10% increase in lead time (1.83 days from the sample mean of 18.26 days) causes a 0.29% reduction in the daily sales volume (approximately 29 units from the sample mean of 10,140 units per day) at the showroom, which is less than the reduction of 1.85% and 0.92% in the online and the catalog channels, respectively. This finding contradicts the common practical and theoretical assumption about homogeneous lead time sensitivity across channels. In addition, we find that niche products and experience goods accentuate the difference of lead time sensitivity between showroom and nonphysical channels. Through an online experiment, we show that higher product information precision, such as that provided in a showroom, can alleviate customers’ anxiety around product uncertainty, thus reducing lead time sensitivity and increasing purchase intent. We further build two analytical models to show the implications of our empirical results for showroom network design and inventory rationing decisions. Our study highlights the previously ignored fulfillment time sensitivity aspect of the physical store’s value. This paper was accepted by Vishal Gaur, operations management. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4839 .

Evaluating and Modeling the Reliability of Continuous No-Rain Forecast from TIGGE Based on the First-Passage Problem and Fuzzy Mathematics

Abstract As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation.

Utilization of El Niño–Southern Oscillation projected by climate models in improvement of seasonal precipitation predictability

AbstractImproved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes.

Analyzing Tornado Warning Performance during Individual Storm Life Cycles

Abstract The National Weather Service plays a critical role in alerting the public when dangerous weather occurs. Tornado warnings are one of the most publicly visible products the NWS issues given the large societal impacts tornadoes can have. Understanding the performance of these warnings is crucial for providing adequate warning during tornadic events and improving overall warning performance. This study aims to understand warning performance during the lifetimes of individual storms (specifically in terms of probability of detection and lead time). For example, does probability of detection vary based on if the tornado was the first produced by the storm, or the last? We use tornado outbreak data from 2008 to 2014, archived NEXRAD radar data, and the NWS verification database to associate each tornado report with a storm object. This approach allows for an analysis of warning performance based on the chronological order of tornado occurrence within each storm. Results show that the probability of detection and lead time increase with later tornadoes in the storm; the first tornadoes of each storm are less likely to be warned and on average have less lead time. Probability of detection also decreases overnight, especially for first tornadoes and storms that only produce one tornado. These results are important for understanding how tornado warning performance varies during individual storm life cycles and how upstream forecast products (e.g., Storm Prediction Center tornado watches, mesoscale discussions, etc.) may increase warning confidence for the first tornado produced by each storm. Significance Statement In this study, we focus on better understanding real-time tornado warning performance on a storm-by-storm basis. This approach allows us to examine how warning performance can change based on the order of each tornado within its parent storm. Using tornado reports, warning products, and radar data during tornado outbreaks from 2008 to 2014, we find that probability of detection and lead time increase with later tornadoes produced by the same storm. In other words, for storms that produce multiple tornadoes, the first tornado is generally the least likely to be warned in advance; when it is warned in advance, it generally contains less lead time than subsequent tornadoes. These findings provide important new analyses of tornado warning performance, particularly for the first tornado of each storm, and will help inform strategies for improving warning performance.

Predictors of No-show in Neurology clinics (P1-5.032)

<h3>Objective:</h3> In this study, we aim to identify predictors of a no-show in neurology clinics at our institution. We conducted a retrospective review of neurology clinics from July 2013 through September 2018. <h3>Background:</h3> No-show in ambulatory neurology clinics does not only affect the efficiency and quality of workflow, but it also directly impacts the revenue and healthcare resources available to other patients. Moreover, clinic no show has significant implications on patients including delayed diagnosis and treatment, clinical worsening, and affects the management of medications that require close laboratory follow up such as anticonvulsant medications and immunotherapies. <h3>Design/Methods:</h3> We compared the odds ratio of patients who missed appointments (no-show) to those who were present at appointments (show) in terms of age, lead-time, subspecialty, race, gender, quarter of the year, insurance type, and distance from hospital. <h3>Results:</h3> There were 60,012 (84%) show and 11,166 (16%) no-show patients. With each day increase in lead time, odds of no-show increased by a factor of 1.0019 (p &lt; 0.0001). Odds of no-show were higher in younger (p ≤ 0.0001, OR = 0.49) compared to older (age ≥ 60) patients and in women (p &lt; 0.001, OR = 1.1352) compared to men. They were higher in Black/African American (p &lt; 0.0001, OR = 1.4712) and lower in Asian (p = 0.03, OR = 0.6871) and American Indian/Alaskan Native (p = 0.055, OR = 0.6318) as compared to White/Caucasian. Patients with Medicare (p &lt; 0.0001, OR = 1.5127) and Medicaid (p &lt; 0.0001, OR = 1.3354) had higher odds of no-show compared to other insurance. <h3>Conclusions:</h3> Young age, female, Black/African American, long lead time to clinic appointments, Medicaid/Medicare insurance, and certain subspecialties (resident and stroke clinics) are associated with high odds of no show. Possible suggested interventions include better communication and flexible appointments for the high-risk groups as well as utilizing telemedicine. <b>Disclosure:</b> Dr. Dhasakeerthi has nothing to disclose. Dr. Elkhider has nothing to disclose. Dr. Sharma has nothing to disclose. Dr. Thatikala has nothing to disclose. Dr. Nalleballe has nothing to disclose. Dr. Onteddu has nothing to disclose.

Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran

Seasonal hydrological forecasts play a critical role in water resources management. The Copernicus Climate Change Service (C3S) data store provides open access to monthly hydrological forecasts for up to six-months. This study aims to evaluate, for the first time, 1- to 3-month runoff forecasts using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensembles of precipitation, runoff, and temperature in 1981–2015 period over a total of 30 s-level basins in Iran. We adopted the 5th, 50th and 95th ECMWF ensemble quantiles for each variable that represent low, medium and high probability of occurrence, respectively. Pearson correlation analysis (Pca), Recursive Feature Elimination (RFE) via random forest (RF) model, and Bayesian Networks (BN) feature selection algorithms were used in order to reduce input variable dimension and select potential predictors to be fed to the machine learning models. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) machine learning models were used with Repeated K-Fold cross validation (rK-Fold CV) while model efficiency was evaluated using modified Kling-Gupta efficiency coefficient (KGE’), Nash-Sutcliffe Efficiency coefficient (NSE), and Normalized Root Mean Square Error (NRMSE). Results of this study revealed that C3S runoff ensembles have the highest impact on forecast accuracy of streamflow, followed by precipitation and temperature. Overall, model performance yield a best-to-worst ranking of ANN, XGBoost, RF, MLR, and SVR with KGE’ values of 0.70, 0.68, 0.66, 0.57, and 0.41, respectively. The predictive performance of all models decreased with lead times beyond 1-month, where ANN and XGBoost outperformed other models with KGE’ of 0.65 for 2-month lead time and 0.60 for 3-month lead time. The three superior models of XGBoost, ANN, and RF, were employed with RFE and BN FSAs most frequently across Iran’s 30 s level basins in all lead times. Almost all models in the arid central region of Iran showed the lowest performance while highest skills were achieved in the western regions of Iran. Finally, for all models and over all regions, the model performance reduced by increase in lead-time.

Evaluating Seasonal Climate Forecasts from Dynamical Models over South America

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.

Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons

This study forecasts multi-step-ahead Potential Evapotranspiration (ETO) in India using globally available fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) gridded climate reanalysis products (ERA5). For this purpose, the potential of six machine learning approaches are examined across different agro climatic zones and cropping seasons. Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Multi-Layer Perceptron (MLP), one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are first evaluated at the station level for up to 28-day-ahead prediction of daily ETO using meteorological station data as well as gridded ERA5 products. Using meteorological observations, at all three stations, SVR performs the best for a prediction horizon (PH) up to 2-days whereas LSTM performs the best for PH of 7-days. Using ERA5 datasets as input, among the three stations, the best performance is observed in Nagpur, where the best models for real-time and 28-day ahead prediction are LSTM (R2 = 0.847 and MAE = 0.474 mm/day) and RF (R2 = 0.722 and MAE = 0.635 mm/day) respectively. As the PH increases from 2-day to 28-day-ahead, models using ERA5 datasets performs better than those using station observations for most of the cases. Although the prediction performance drops initially with the increase in lead time, the drop in performance between 7-day and 28-day-ahead prediction is negligible. Evaluation of gridwise ETO prediction across entire India, using Global Land Evaporation Amsterdam Model (GLEAM) dataset as a reference, indicates MLP and CNN as the top performing models. Considering the crop seasons, model performance during Rabi season (October-March) ranged from 0.103 to 0.145 mm/day (MAE) and 0.977 to 0.988 (R2), which is better than the Kharif season (June-September) where MAE ranged from 0.140 to 0.234 mm/day and R2 ranged from 0.906 to 0.962. During the Rabi season, the ETO prediction performance of the arid agro-climatic zone is found to be superior to the other three agro-climatic zones, with the highest range of R2 (0.939 to 0.955) and lowest range of MAE (0.146 to 0.182 mm/day). Even the worst prediction performance, which is observed in the Humid region during the Rabi season, is also reasonably good (R2 = 0.656 to 0.79); thereby establishing the potential of the proposed models in multi-step ahead ETO prediction across various agro-climatic zones and cropping seasons.

Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory

Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management.

Analysis of Flight Disruption Data to Determine Problem Areas Utilizing Database Applications

Flight delays represent a significant issue to airline profits and passenger satisfaction. Many factors can lead to a flight being delayed and/or cancelled. To compound the issue, COVID restrictions have created a shortage of skilled, willing labor and a disturbance in the supply chain. All aspects of air travel have been affected. For example, aircraft maintenance and new plane constructions have increased lead times due to parts unavailability. Mass layoffs or early retirements as a knee-jerk response to reduced travel in 2020 has exacerbated the problem. Pilot strikes protesting mandates led to mass flight cancellations in 2021. The study will evaluate flight delays, cancellations, and incident data with the goal of visualizing which airports, airlines, cities, or states are experiencing the highest number of flight disruptions relative to others. In the era of new technological development, database applications are commonly used for data analysis to allow pattern recognition and large data distribution and organization. This study will mainly serve the purpose of flight data retrieval, the compilation of data and database design and finally output data visualizations. The outcome of the research will be presented in a user-friendly interface where the user can easily generate visualizations which can be used to analyze flight delay information.

How to Manage Relations Facing Accumulated Supply Chain Disturbances: Pandemic, War and Ripple Effects

How to develop a suitable management style to manage external Supply Chain network relations in the light of unprecedented external disturbances like the Covid pandemic, Ukraine war and inevitable ripple effects caused by explosions or strikes? Impacts in terms of increased lead times, uncertainty about the time required by suppliers and buyers for decision making. But, it also should be suitable in dealing with quite different types of managerial attitudes encountered. The mixed-methods analysis of a 3 tier supply network around a small focal printing company shows that (1) total lead time for raw materials, production and delivery went up from 9 days to 60 days or more. It not only meant increased uncertainty for the focal company but also for its B2B buyers; (2) the decision time of buyers responding to requests to accommodate changes enforced by scarcity or increased lead time of raw materials varied between 3 and 41 days, depending of the size of the buying firm where four size categories were used. Small SMEs were fast deciders but being mostly negative in attitude; multinationals were slow but more flexible and cooperative. (3): zooming in on the managerial or human attitudes encountered in 100 contacts with buyers revealed 9 different human attitudes that could be clustered into a scale with two extremes: Total denial (selfish, exploitative, demanding, brutal, ignorant, helpless) versus Total flexibility (cooperative, tolerant, inspiring). (4) To deal with these different attitudes employee training is recommended, using the DiSC portfolio model invented by William Moulton Marston, standing for Dominance, Influence, Steadiness and Compliance (sometimes referred to as Conscientiousness).