A multiscale investigation into three years of anomalous floods in Pakistan provides insight into their formation, unifying meteorological characteristics, mesoscale storm structures and predictability. Striking similarities between all three floods exist, from planetary and large‐scale synoptic conditions down to the mesoscale storm structures, and these patterns were generally well‐captured with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS). Atmospheric blocking events associated with high geopotential heights and surface temperatures over Eastern Europe were present during all three floods. Quasi‐stationary synoptic conditions over the Tibetan plateau allowed for the formation of anomalous easterly midlevel flow across central India into Pakistan that advected deep tropospheric moisture from the Bay of Bengal into Pakistan, enabling flooding in the region. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar observations show that the flood‐producing storms exhibited climatologically unusual structures during all three floods in Pakistan. These departures from the climatology consisted of westward‐propagating precipitating systems with embedded wide convective cores, rarely seen in this region, that likely occurred when convection was organized upscale by the easterly midlevel jet across the subcontinent. Similar mesoscale structures in storms in other regions of the world contribute significantly to climatological precipitation and produce flash floods because of their combination of longevity and intensity. Predictability analysis using the ECMWF EPS system shows the ability to accurately forecast the conditions favouring storms of this type and hence floods in Pakistan over a week in advance with significant confidence.

Two cases of dryline convection initiation (CI) over north Texas have been simulated (3 April 2012 and 15 May 2013) from a 50-member WRF-DART ensemble adjustment Kalman filter (EAKF) ensemble. In this study, ensemble sensitivity analysis (ESA) is applied to a convective forecast metric, maximum composite reflectivity (referred to as the response function), as a simple proxy for CI to analyze dynamic mesoscale sensitivities at the surface and aloft. Analysis reveals positional and magnitude sensitivities related to the strength and placement of important dynamic features. Convection initiation is sensitive to the evolving temperature and dewpoint fields upstream of the forecast response region in the near-CI time frame (0–12 h), prior to initiation. The sensitivity to thermodynamics is also manifest in the magnitude of dewpoint gradients along the dryline that triggers the convection. ESA additionally highlights the importance of antecedent precipitation and cold pool generation that modifies the pre-CI environment. Aloft, sensitivity of CI to a weak short-wave trough and capping inversion-level temperature is coherent, consistent, and traceable through the entire forecast period. Notwithstanding the (often) non-Gaussian distribution of ensemble member forecasts of convection, which violate the underpinnings of ESA theory, ESA is demonstrated to sufficiently identify regions that influence dryline CI. These results indicate an application of ESA for severe storm forecasting at operational centers and forecast offices as well as other mesoscale forecasting applications.

Using nine years of historical forecasts spanning April 2003–April 2012 from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) ensemble, random forest (RF) models are trained to make probabilistic predictions of severe weather across the contiguous United States (CONUS) at Days 1–3, with separate models for tornado, hail, and severe wind prediction at Day 1 in an analogous fashion to the Storm Prediction Center’s (SPC’s) convective outlooks. Separate models are also trained for the western, central, and eastern CONUS. Input predictors include fields associated with severe weather prediction, including CAPE, CIN, wind shear, and numerous other variables. Predictor inputs incorporate the simulated spatiotemporal evolution of these atmospheric fields throughout the forecast period in the vicinity of the forecast point. These trained RF models are applied to unseen inputs from April 2012 to December 2016, and their forecasts are evaluated alongside the equivalent SPC outlooks. The RFs objectively make statistical deductions about the relationships between various simulated atmospheric fields and observations of different severe weather phenomena that accord with the community’s physical understandings about severe weather forecasting. Using these quantified flow-dependent relationships, the RF outlooks are found to produce calibrated probabilistic forecasts that slightly underperform SPC outlooks at Day 1, but significantly outperform their outlooks at Days 2 and 3. In all cases, a blend of the SPC and RF outlooks significantly outperforms the SPC outlooks alone, suggesting that use of RFs can improve operational severe weather forecasting throughout the Day 1–3 period.

Ensemble Sensitivity Analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for ten convection forecasts. To facilitate the analysis, Observing System Simulation Experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation making impact-predictions poor. Additionally, numerical noise introduced by non-linear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations to non-targeted, randomly-chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for non-linear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems is also discussed.

Excessive rainfall is difficult to forecast, and there is a need for tools to aid Weather Prediction Center (WPC) forecasters when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1–3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a “first guess” in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.