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.

Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA's Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP's Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAE(cond)). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events.

The Winter Weather Experiment was established to support improvements in HPC’s winter weather forecasts. In its first two years the experiment has hosted 25 visiting forecasters, researchers, and model developers with 7 different HPC forecasters serving as weekly facilitators. The focus of the experiment has varied from exploring the role of high resolution convection allowing models in improving forecasts of precipitation type, snow and freezing rain accumulations, and mesoscale snowbands (2011), to exploring methods to better quantify and communicate uncertainty through an exploration of multiple ensemble systems (2012, 2013).

A multiscale analysis is conducted in order to examine the physical processes that resulted in prolonged heavy rainfall and devastating flash flooding across western and central Tennessee and Kentucky on 1–2 May 2010, during which Nashville, Tennessee, received 344.7 mm of rainfall and incurred 11 flood-related fatalities. On the synoptic scale, heavy rainfall was supported by a persistent corridor of strong water vapor transport rooted in the tropics that was manifested as an atmospheric river (AR). This AR developed as water vapor was extracted from the eastern tropical Pacific and the Caribbean Sea and transported into the central Mississippi Valley by a strong southerly low-level jet (LLJ) positioned between a stationary lee trough along the eastern Mexico coast and a broad, stationary subtropical ridge positioned over the southeastern United States and the subtropical Atlantic. The AR, associated with substantial water vapor content and moderate convective available potential energy, supported the successive development of two quasi-stationary mesoscale convective systems (MCSs) on 1 and 2 May, respectively. These MCSs were both linearly organized and exhibited back-building and echo-training, processes that afforded the repeated movement of convective cells over the same area of western and central Tennessee and Kentucky, resulting in a narrow band of rainfall totals of 200–400 mm. Mesoscale analyses reveal that the MCSs developed on the warm side of a slow-moving cold front and that the interaction between the southerly LLJ and convectively generated outflow boundaries was fundamental for generating convection.

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC. A supplement to this article is available online (DOI:10.1175/BAMS-D-11-00040.2)

While the total snowfall produced in lake-effect storms can be considerable, little is known about how clouds and snow evolve within lake-effect boundary layers. Data collected over Lake Michigan on 10 January 1998 during the Lake-Induced Convection Experiment (Lake-ICE) are analyzed to better understand and quantify the evolution of clouds and snow. On this date, relatively cold air flowed from west to east across Lake Michigan, creating a quasi-steady-state boundary layer that increased from approximate to 675 to approximate to 910 m in depth over a distance of 80 km. Once a cloud deck formed 14-18 km from the upwind shoreline, maximum cloud particle concentrations and liquid water content increased from west to east across the lake. Correspondingly, maximum ice water contents, snowfall rates, and maximum snow particle diameters also increased across the lake. Maximum particle concentrations were found below the mean top of the boundary layer and above the cloud base for both cloud and snow particles.Surprisingly, snow particles were observed 3-7 km upwind of the upwind edge of the lake-effect cloud deck. These snow particles were observed to be rather spatially uniform throughout the boundary layer. Based on available observations, it is hypothesized that of the mechanisms that could produce this snow, the majority of it originated from transient clouds located near the upwind shore. In addition, maximum snow particle concentrations peaked near the middle of the lake before decreasing toward the downwind shore, indicating the location after which aggregation became an important snow growth mechanism. These results show that the evolution of clouds and snow within lake-effect boundary layers may not occur in the uniform manner often depicted in conceptual models.

Flooding is a leading cause of weather-related fatalities in the United States (NWS 2010). Many deaths are a result of flash flooding from extreme rainfall. Just over the past two years, extreme rainfall has resulted in several high-impact flash flooding events, including Atlanta (2009), Nashville (2010), and Caddo Gap, Arkansas (2010). As the nation’s source for quantitative precipitation forecast (QPF) guidance, the Hydrometeorological Prediction Center (HPC) employs a variety of techniques to quantify extreme rainfall threats. Extreme rainfall threats are expressed through the deterministic QPF, probabilistic excessive rainfall outlook, and experimental probabilistic quantitative precipitation product suite. Text forecast discussions supplement the graphical and gridded information. Verification of the HPC day 1 deterministic QPF over the past decade is shown in Fig. 1. Although the threat scores for the 1and 2-in 24 h thresholds show steady improvement over the past decade, the 4-in 24 h 1 threshold exhibits no clear improvement. Thus, there is need to improve extreme rainfall prediction. This presentation will review the products HPC forecasters have available to quantify extreme rainfall threats and highlight ongoing activities to further improve anticipation of extreme rainfall. Particular emphasis is placed on the use of convection-allowing deterministic and ensemble model guidance, and associated post-processed fields. Case examples from the 2010 Nashville and Caddo Gap, Arkansas floods, are used to show the strengths and weaknesses of current and experimental techniques.

The 2014 Spring Forecasting Experiment (SFE2014) was conducted from 5 May – 6 June by the Experimental Forecast Program (EFP) of the NOAA/Hazardous Weather Testbed (HWT). SFE2014 was organized by the Storm Prediction Center (SPC) and National Severe Storms Laboratory (NSSL) with participation from numerous forecasters, researchers, and developers from around the world to test emerging concepts and technologies designed to improve the prediction of hazardous convective weather. SFE2014 aimed to address several primary goals: