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Eric Gilleland, Amanda S. Hering, Tressa L. Fowler, and Barbara G. Brown


Which of two competing continuous forecasts is better? This question is often asked in forecast verification, as well as climate model evaluation. Traditional statistical tests seem to be well suited to the task of providing an answer. However, most such tests do not account for some of the special underlying circumstances that are prevalent in this domain. For example, model output is seldom independent in time, and the models being compared are geared to predicting the same state of the atmosphere, and thus they could be contemporaneously correlated with each other. These types of violations of the assumptions of independence required for most statistical tests can greatly impact the accuracy and power of these tests. Here, this effect is examined on simulated series for many common testing procedures, including two-sample and paired t and normal approximation z tests, the z test with a first-order variance inflation factor applied, and the newer Hering–Genton (HG) test, as well as several bootstrap methods. While it is known how most of these tests will behave in the face of temporal dependence, it is less clear how contemporaneous correlation will affect them. Moreover, it is worthwhile knowing just how badly the tests can fail so that if they are applied, reasonable conclusions can be drawn. It is found that the HG test is the most robust to both temporal dependence and contemporaneous correlation, as well as the specific type and strength of temporal dependence. Bootstrap procedures that account for temporal dependence stand up well to contemporaneous correlation and temporal dependence, but require large sample sizes to be accurate.

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James O. Pinto, Dan L. Megenhardt, Tressa Fowler, and Jenny Colavito


Short-range (2 h) predictions of ceiling and visibility obtained from version 4 of the Rapid Refresh (RAPv4) model are evaluated over Alaska using surface meteorological station data. These forecasts tended to overpredict the frequency of aviation-impacting ceilings in coastal areas by as much as 50%. In winter, this overforecasting bias extends into the interior of Alaska as well. Biases in visibility predictions were more complex. In winter, visibility hazards were predicted too often throughout the interior of Alaska (+5%) and not often enough in northern and western coastal areas (−20%). This wintertime underprediction of visibility restrictions in coastal areas has been linked to the fact that the visibility diagnostic does not include a treatment for the effect of blowing snow. This, in part, results in winter IFR visibilities being detected only 37% of the time. An efficient algorithm that uses quantile matching has been implemented to remove mean biases in 2-h predictions of ceiling and visibility. Performance of the algorithm is demonstrated using two 30-day periods (January and June 2019). The calibrated forecasts obtained for the two month-long periods are found to have significantly reduced biases and enhanced skill in capturing flight rules categories for both ceiling and visibility throughout much of Alaska. This technique can be easily extended to other forecast lead times or mesoscale models.

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