Investigating the relationship between eye movements and situation awareness in weather forecasting.

Physiological indicators, including eye tracking measures, may provide insight into human decision making and cognition in many domains, including weather forecasting. Situation awareness (SA), a critical component of forecast decision making, is commonly conceptualized as the degree to which information is perceived, understood, and projected into a future context. Drawing upon recent applications of eye tracking in the study of forecaster decision making, we investigate the relationship among eye movement measures, automation, and SA assessed through a freeze probe assessment method. In addition, we explore the relationship between an automated forecasting decision aid use and information seeking behavior. In this study, a sample of professional weather forecasters completed a series of tasks, informed by a set of forecasting decision aids, and with variable access to an experimental automated tool, while an eye tracking system captured data related to eye movements and information usage. At the end of each forecasting task, participants responded to a set of questions related to the environmental situation in the framework of a survey-based assessment technique in order to assess their level of situation awareness. Regression analysis revealed a moderate relationship between the SA measure and eye tracking metrics, supporting the hypothesis that eye tracking may have utility in assessing SA. The results support the use of eye tracking in the assessment of specific and measurable attributes of the decision-making process in weather forecasting. The findings are discussed in light of potential benefits that eye tracking could bring to human performance assessment as well as decision-making research in the forecasting domain.

On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates.

The launch of NOAA's latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

Toward Probabilistic Prediction of Flash Flood Human Impacts.

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance-specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle-related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time- and space-dependent human vulnerability and risk assessment for short-fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact-based predictive models for flash flood casualties using machine-learning approaches in the future.

Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration.

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth's climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982-2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes. The ET record shows a significant upward global trend of 0.88 mm yr(-2) (P < 0.001) over the 32-year period, mainly driven by vegetation greening (0.018% per year; P < 0.001) and rising atmosphere moisture demand (0.75 mm yr(-2); P = 0.016). Our results indicate that reduced ET growth between 1998 and 2008 was an episodic phenomenon, with subsequent recovery of the ET growth rate after 2008. Terrestrial precipitation also shows a positive trend of 0.66 mm yr(-2) (P = 0.08) over the same period consistent with expected water cycle intensification, but this trend is lower than coincident increases in evaporative demand and ET, implying a possibility of cumulative water supply constraint to ET. Continuation of these trends will likely exacerbate regional drought-induced disturbances, especially during regional dry climate phases associated with strong El Niño events.