Despite advancements in numerical modeling and the increasing prevalence of convection-allowing guidance, flash flood forecasting remains a substantial challenge. Accurate flash flood forecasts depend not only on accurate quantitative precipitation forecasts (QPFs), but also on an understanding of the corresponding hydrologic response. To advance forecast skill, innovative guidance products that blend meteorology and hydrology are needed, as well as a comprehensive verification dataset to identify areas in need of improvement.To address these challenges, in 2013 the Hydrometeorological Testbed at the Weather Prediction Center (HMT-WPC), partnering with the National Severe Storms Laboratory (NSSL) and the Earth System Research Laboratory (ESRL), developed and hosted the inaugural Flash Flood and Intense Rainfall (FFaIR) Experiment. In its first two years, the experiment has focused on ways to combine meteorological guidance with available hydrologic information. One example of this is the creation of neighborhood flash flood guidance (FFG) exceedance probabilities, which combine QPF information from convection-allowing ensembles with flash flood guidance; these were found to provide valuable information about the flash flood threat across the contiguous United States.Additionally, WPC has begun to address the challenge of flash flood verification by developing a verification database that incorporates observations from a variety of disparate sources in an attempt to build a comprehensive picture of flash flooding across the nation. While the development of this database represents an important step forward in the verification of flash flood forecasts, many of the other challenges identified during the experiment will require a long-term community effort in order to make notable advancements.

The National Oceanic and Atmospheric Administration's second-generation Global Ensemble Forecast System reforecast dataset is utilized to generate probabilistic quantitative precipitation forecasts (PQPFs) for the cool season during 1985-2011 over the West Coast of the United States using a rank analog post-processing technique. It is found that the rank analog technique produced large improvements in forecast skill and reliability over the raw uncalibrated ensemble. Forecasts for three heavy precipitation events associated with atmospheric rivers are examined to demonstrate the utility of the rank analog technique. The rank analog technique yielded increases in forecast skill for two of the three cases but decreases in forecast skill for the other case, indicating that in some instances the technique can perform poorly. Overall, the results of this study are consistent with the findings from the 2012 Atmospheric River Retrospective Forecasting Experiment, in which participants found the reforecast-based rank analog PQPFs to provide useful guidance for forecasting precipitation over the West Coast of the United States.

NOAA's second-generation reforecasts are approximately consistent with the operational version of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecasts allow verification to be performed across a multidecadal time period using a static model, in contrast to verifications performed using an ever-evolving operational modeling system. This contribution examines three commonly used verification metrics for reforecasts of precipitation over the southeastern United States: equitable threat score, bias, and ranked probability skill score. Analysis of the verification metrics highlights the variation in the ability of the GEFS to predict precipitation across amount, season, forecast lead time, and location. Beyond day 5.5, there is little useful skill in quantitative precipitation forecasts (QPFs) or probabilistic QPFs. For lighter precipitation thresholds [e.g., 5 and 10 mm (24 h)(-1), use of the ensemble mean adds about 10% to the forecast skill over the deterministic control. QPFs have increased in accuracy from 1985 to 2013, likely due to improvements in observations. Results of this investigation are a first step toward using the reforecast database to distinguish weather regimes that the GEFS typically predicts well from those regimes that the GEFS typically predicts poorly.

The influence that the overlake boundary layer has on storm intensity and structure is not well understood. To improve scientists' understanding of the evolution of storms crossing Lake Erie, 111 events during 2001-09 were examined using observations from Weather Surveillance Radar-1988 Doppler (WSR-88D), surface, buoy, and rawinsonde sites. It was found that on average, all storm modes tended to weaken over the lake; however, considerable variability in changes of storm intensity existed, with some storms exhibiting steady-state or increasing intensity in specific environments. Noteworthy changes in the storm maximum reflectivity generally occurred within 60 min after storms crossed the upwind shoreline. Isolated and cluster storm modes exhibited much greater weakening than those storms organized into lines or convective complexes. The atmospheric parameters having the greatest influence on storm intensity over Lake Erie varied by mode. Isolated and cluster storms generally weakened more rapidly with increasingly cold overlake surface air temperatures. Linear and complex systems, on the other hand, tended to exhibit constant or increasing maximum reflectivity with cooler overlake surface air temperatures. It is suggested that strongly stable conditions near the lake surface limit the amount of boundary layer air ingested into storms in these cases.