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Tag: Stevens Institute of Technology

  • Stevens researchers tackle weather forecasters’ blindspot

    Stevens researchers tackle weather forecasters’ blindspot

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    Newswise — Anyone who’s been caught in an unexpected downpour knows that weather forecasting is an imperfect science. Now, researchers at Stevens Institute of Technology are taking aim at one of meteorologists’ biggest blind spots: extremely short-term forecasts, or nowcasts, that predict what will happen in a given location over the next few minutes.

    “This isn’t just about whether you should take your umbrella with you when you go on a walk,” said Temimi. “The forecasts that we’re missing – the ones that look 2 to 5 minutes into the future – are precisely what’s needed to respond to storms, floods, and other emergencies effectively.”

    The National Oceanic and Atmospheric Administration (NOAA) publishes round-the-clock rainfall predictions, but its shortest-term forecasts begin a few hours into the future. The lack of more immediate nowcasting hinders community responses to sudden catastrophes such as Hurricane Ida, for example, in which rapid flooding killed multiple people in New York City, explained Marouane Temimi, a hydrometeorologist at Stevens whose work appears in the Aug. 19 online issue of Environmental Modeling & Software.

    Researchers in Temimi’s lab used historical data from the NOAA’s weather radar systems to test the accuracy of seven different nowcasting algorithms. Studying eight years of meteorological data from the New York region, they were able to provide the first robust comparison of the models’ accuracy across a wide range of weather conditions. The work will help determine which models work best in any given location or use case.

    The Stevens team studied both deterministic and probabilistic nowcasting models. While the former assumes that a rain cell will not change over time, the latter accounts for the chaotic, ever-changing nature of a rain cell, which is determined by the dynamics of warm and cold air within a cloud. For predictions over periods of a few minutes, both models proved highly accurate. Over periods of up to 90 minutes, however, probabilistic models were significantly more accurate.

    If probabilistic models are highly accurate in predicting both long- and short-term rainfall events, why have deterministic models? Validating deterministic models is useful because probabilistic models are far more computationally demanding. For instance, LINDA-P, a probabilistic model, proved to be the most accurate model tested, but it takes 15 minutes to generate a nowcast based on current conditions. Therefore, it can’t be used for extremely short-term projections.

    Some models also perform better in certain conditions: LINDA-P is designed to predict sudden torrential rainfall, enabling it to outperform other models during summer months, when sporadic but intense storms are more likely to occur. Other models make granular predictions that are more error prone, but useful when higher-resolution forecasting is needed.

    “The key takeaway is that we need to select nowcasting models based on their intended use-case,” said Achraf Tounsi, the paper’s lead author who recently completed his doctorate in Temimi’s lab. “If you want to know if it will rain in the next five minutes, you need a deterministic model. If you’re running an airport or seaport and want data for the next 20 minutes, or hour, you’ll be better served with a probabilistic model.”

    Temimi and Tounsi will dig into the reasons why certain models perform better than others across a range of conditions. By using those insights to improve algorithms, and sourcing more precise weather data, it should be possible to develop more versatile and accurate nowcasting models.

    “That’s our next assignment,” said Tounsi. “We hope to develop our own nowcasting model — and teach it to outperform the ones we’ve assessed in this paper.”

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  • For leaders, playing favorites can be a smart strategy

    For leaders, playing favorites can be a smart strategy

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    Newswise — As anyone who’s worked in an office, a factory, or any other workplace can attest, sometimes bosses play favorites. Whether it’s assigning the most comfortable cubicles or the best parking spots, or deciding whose opinions take precedence during planning sessions, leaders inevitably wind up treating some employees better than others.

    That might seem unfair, especially if you aren’t your supervisor’s favorite. But now, for the first time, research shows that in some cases, biased bosses get better results — and not just from the workers they treat best.

    “For leaders, playing favorites isn’t always a bad thing,” explained Haoying (Howie) Xu, assistant professor of management at Stevens Institute of Technology. “Favoritism is a double-edged sword — it can be harmful to team dynamics, but in the right circumstances it can also help organizations to succeed.”

    In his work, reported in the February 2023 print issue of Personnel Psychology, Xu and colleagues studied more than 200 different teams, comprising over 1,100 employees, in several Chinese companies representing a cross-section of different industries. By surveying both employees and supervisors about performance and team dynamics, Xu was able to reveal the ways in which workplace favoritism interacts with other factors to elevate or impede overall team performance.

    The results were striking.

    In teams that were already well-structured, either because some employees were placed in positions of authority or because some employees had more advanced skill sets, performance dipped when leaders played favorites. In less clearly structured teams, however, having a biased boss typically led to better outcomes, with improved coordination and performance across the entire team.

    “That’s an important finding, because most previous research has focused solely on the negative impacts of workplace favoritism,” Xu said. “Now, we’re getting a more nuanced view of the way that leadership biases play out in the real world.”

    Drawing on a branch of management science known as leader-member exchange (LMX) theory, which studies the relationships between supervisors and employees, Xu argues that leadership biases operate by sending signals about the relative status of different team-members. That can be a bad thing: in teams where a social hierarchy already exists, favoritism can create dissonance and spark conflict.

    In teams that lack a clear pecking order, however, a leader’s biases impose structure and help everyone to work together more effectively. If team members don’t already have well-differentiated roles based on levels of authority or particular skills, favoritism provides a framework that reduces conflict and increases efficiency by helping employees to establish a stable dynamic instead of simply butting heads with one another.

    “In homogenous groups, playing favorites can be a way for leaders to clarify the roles that different team-members should play,” Xu explained. “When teams lack obvious hierarchies, it helps if the boss sends clear signals about who’s on top and who is expected to take a more subordinate role.”

    “The key point is that playing favorite has clear positive and negative effects, so leaders need to ensure they’re paying attention to how their favoritism is affecting their team.”

    Other factors can also influence the impact of leadership biases: more recently formed teams are more easily destabilized by workplace favoritism, for instance. Further research is needed to fully explore the way that favoritism works at different levels of organizations, and also to zoom in on the ways in which individual team-members’ interactions are influenced by their supervisor’s favoritism.

    For now, Xu’s research offers team supervisors and more senior managers clear guidance on how to optimize team performance. Managers could adjust their relationships with team-members to ensure they’re sending appropriate signals.

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