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In any discussion of forecast evaluation, it is tempting to fall back on statements reflecting unverified assumptions: "this tornado warning had lower skill because the underlying meteorology reflected a complicated or atypical scenario," or "that forecast performed worse than we would have expected given the straightforward setup." These statements of what is and is not a reasonable expectation for warning skill are particularly relevant as the meteorological community's focus has begun to emphasize non-classic storm environments (e.g., tornadoes spawned by quasi-linear convective systems). In this paper, we build a proof-of-concept methodology to quantify the effect of the near-storm environment on tornado warning skill, and we then test these methods on a 15-yr dataset composed of tens of thousands of tornado events and warnings over the contiguous United States. Our findings include that significant tornadoes rated (E)F2+ have a higher probability of detection (POD) than expected based on their near-storm environments, that nocturnal tornadoes have both worse POD and false alarm ratio (FAR) than even their marginal near-storm environments would suggest, and that tornadoes occurring during the summer months also show worse POD and FAR than their environment-based expectation. Quantifying these shifts in performance in an environmental skill score framework allows us to target the situations in which the greatest improvements may be possible, in terms of forecaster training and/or conceptual models. This work also highlights the essential question that should always be asked in the context of forecast verification: what, exactly, is the baseline standard to which we are comparing forecast performance? [ABSTRACT FROM AUTHOR]

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space--time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction. [ABSTRACT FROM AUTHOR]

Analyzing a set of ensemble seasonal reforecasts for 1958-2017 using CFSv2, we evaluate the predictive skill of the U.S. seasonal mean precipitation and examine its sources of predictability. Our analysis is for each of the three periods of 1958-78, 1979-99, and 2000-17, corresponding to the positive phase of the Pacific decadal oscillation (PDO) during 1979-99 and negative ones before and after. The ensemble reforecasts at two-month lead reproduce the spatial distribution of winter precipitation trends throughout the 60 years and the continental-scale increase of summer precipitation since 2000. The predicted signalto- noise (S/N) ratio also reveals greater predictability in the post-1979 period than during 1958-78. A maximized S/N ratio EOF analysis is applied to the ensemble seasonal precipitation predictions. In winter and spring, the most predictable patterns feature a north--south dipole throughout United States. The summer and fall patterns are dominated by the anomalies in central and southern United States, respectively. In verification with observations, the winter--spring patterns are more skillful. ENSO influences on these predictable patterns are most dominant in winter and spring, but other oceanic factors also play an active role during summer and fall. The multidecadal change of the U.S. precipitation predictability is attributable to the low-frequency modulation of the ENSO predictability and the influences of other major climate modes. PDO can be a dominant factor associated with enhanced prediction skill in 1979-99 and reduced skill in 1958-78. Since the 2000s, the forcing from the SST anomalies in the tropical North Atlantic with opposite sign to those in the tropical Pacific becomes a significant factor for the U.S. summer precipitation prediction. [ABSTRACT FROM AUTHOR]

Focuses on the employment prospects of a professional meteorologist in the U.S. List of habits and skills developed; Advantage of a private sector meteorologist who understand the requirements of entrepreneurialism; Emphasis on the use of valid scientific methods.