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Age-related declines in episodic memory do not affect all types of mnemonic information equally: when to-be-remembered information is in line with one’s prior knowledge, or schema-congruent, older adults often show no impairments. There are two major accounts of this effect: One proposes that schemas compensate for memory failures in aging, and the other proposes that schemas instead actively impair older adults’ otherwise intact memory for incongruent information. However, the evidence thus far is inconclusive, likely due to methodological constraints in teasing apart these complex underlying dynamics. We developed a paradigm that separately examines the contributions of underlying memory and schema knowledge to a final memory decision, allowing these dynamics to be examined directly. In the present study, healthy older and younger adults first searched for target objects in congruent or incongruent locations within scenes. In a subsequent test, participants indicated where in each scene the target had been located previously, and provided confidence-based recognition memory judgments that indexed underlying memory, in terms of recollection and familiarity, for the background scenes. We found that age-related increases in schema effects on target location spatial recall were predicted and statistically mediated by age-related increases in underlying memory failures, specifically within recollection. We also found that, relative to younger adults, older adults had poorer spatial memory precision within recollected scenes but slightly better precision within familiar scenes—and age increases in schema bias were primarily exhibited within recollected scenes. Interestingly, however, there were also slight age-related increases in schema effects that could not be explained by memory deficits alone, outlining a role for active schema influences as well. Together, these findings support the account that age-related schema effects on memory are compensatory in that they are driven primarily by underlying memory failures, and further suggest that age-related deficits in memory precision may also drive schema effects. 

Abstract Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods. 

Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons.