Near surface temperature inversions are periods in which a low layer of warm air is trapped between cooler air higher up in the atmosphere and dense cooler air below it near the surface level. By causing cooler air to pool near the surface level, inversions can have detrimental effects for crop growers, including frost, increased moisture, and pesticide drift. As a result, predicting the occurrence and magnitude of these inversions yields substantial benefits for growers. We introduce a Long Short-Term Memory (LSTM) model for temperature inversion forecasting that is able to effectively predict localized, near surface temperature inversions in advance such that growers can take actions to mitigate the detrimental effects. We show a substantial performance gain over a deployed temperature inversion forecasting system, and include a series of ablations that show the benefit of using publicly available terrain-specific feature information when modeling inversions at this scale.

Anomaly detection involves finding unusual data instances of interest. This task has often been compared to looking for a needle in a haystack, especially in high dimensions. One strategy for improving anomaly detection is to leverage expert feedback in a human-in-the-loop process to label anomalies and nominal points. For high-dimensional data, the majority of existing anomaly detection approaches that incorporate expert feedback do not scale and often result in algorithms that are too slow to operate in an interactive setting with a human expert. We introduce a new anomaly detection algorithm intended for high dimensional data that can efficiently incorporate expert feedback on each round of querying. Our approach uses ideas from metric learning to perform an efficient incremental update when the user labels a data instance. We demonstrate through extensive experiments that our work provides the best tradeoff between performance and running time among existing approaches.