Gregory R. Herman

Using Reforecasts to Improve Forecasting of Fog and Visibility for Aviation

AbstractFifteen years of forecasts from the National Oceanic and Atmospheric Administration’s Second-Generation Global Medium-Range Ensemble Reforecast (GEFS/R) dataset were used to develop a statistical model that generates probabilistic predictions of cloud ceiling and visibility. Four major airports—Seattle–Tacoma International Airport (KSEA), San Francisco International Airport (KSFO), Denver International Airport (KDEN), and George Bush Intercontinental Airport (KIAH) in Houston, Texas—were selected for model training and analysis. Numerous statistical model configurations, including the use of several different machine learning algorithms, input predictors, and internal parameters, were explored and verified through cross validation to develop skillful forecasts at each station. The final model was then compared with both probabilistic climatology-based forecasts and deterministic operational guidance. Results indicated significantly enhanced skill within both deterministic and probabilistic framewo...

Double Impact: When Both Tornadoes and Flash Floods Threaten the Same Place at the Same Time

AbstractWhile both tornadoes and flash floods individually present public hazards, when the two threats are both concurrent and collocated (referred to here as TORFF events), unique concerns arise. This study aims to evaluate the climatological and meteorological characteristics associated with TORFF events over the continental United States. Two separate datasets, one based on overlapping tornado and flash flood warnings and the other based on observations, were used to arrive at estimations of the instances when a TORFF event was deemed imminent and verified to have occurred, respectively. These datasets were then used to discern the geographical and meteorological characteristics of recent TORFF events. During 2008–14, TORFF events were found to be publicly communicated via overlapping warnings an average of 400 times per year, with a maximum frequency occurring in the lower Mississippi River valley. Additionally, 68 verified TORFF events between 2008 and 2013 were identified and subsequently classifie...

Extreme Precipitation in Models: An Evaluation

AbstractA continental United States (CONUS)-wide framework for analyzing quantitative precipitation forecasts (QPFs) from NWP models from the perspective of precipitation return period (RP) exceedances is introduced using threshold estimates derived from a combination of NOAA Atlas 14 and older sources. Forecasts between 2009 and 2015 from several different NWP models of varying configurations and spatial resolutions are analyzed to assess bias characteristics and forecast skill for predicting RP exceedances. Specifically, NOAA’s Global Ensemble Forecast System Reforecast (GEFS/R) and the National Severe Storms Laboratory WRF (NSSL-WRF) model are evaluated for 24-h precipitation accumulations. The climatology of extreme precipitation events for 6-h accumulations is also explored in three convection-allowing models: 1) NSSL-WRF, 2) the North American Mesoscale 4-km nest (NAM-NEST), and 3) the experimental High Resolution Rapid Refresh (HRRR). The GEFS/R and NSSL-WRF are both found to exhibit similar 24-h a...

“Dendrology” in Numerical Weather Prediction: What Random Forests and Logistic Regression Tell Us about Forecasting Extreme Precipitation

AbstractThree different statistical algorithms are applied to forecast locally extreme precipitation across the contiguous United States (CONUS) as quantified by 1- and 10-yr average recurrence int...

Money Doesn’t Grow on Trees, But Forecasts Do: Forecasting Extreme Precipitation with Random Forests

AbstractApproximately 11 years of reforecasts from NOAA’s Second-Generation Global Ensemble Forecast System Reforecast (GEFS/R) model are used to train a contiguous United States (CONUS)-wide gridd...

Probabilistic Verification of Storm Prediction Center Convective Outlooks

AbstractEight years’ worth of day 1 and 4.5 years’ worth of day 2–3 probabilistic convective outlooks from the Storm Prediction Center (SPC) are converted to probability grids spanning the continental United States (CONUS). These results are then evaluated using standard probabilistic forecast metrics including the Brier skill score and reliability diagrams. Forecasts are gridded in two different ways: one with a high-resolution grid and interpolation between probability contours and another on an 80-km-spaced grid without interpolation. Overall, the highest skill is found for severe wind forecasts and the lowest skill is observed for tornadoes; for significant severe criteria, the opposite discrepancy is observed, with highest forecast skill for significant tornadoes and approximately no overall forecast skill for significant severe winds. Highest climatology-relative skill is generally observed over the central and northern Great Plains and Midwest, with the lowest—and often negative—skill seen in the W...