Abstract

The proliferation of ensemble forecast system output in recent years motivates this investigation into how operational forecasters utilize convection-permitting ensemble forecast system guidance in the forecast preparation process. A 16-member, convection-permitting ensemble forecast of the high-impact heavy precipitation resulting from Tropical Storm Fay (2008) is conducted and evaluated. The ensemble provides a skillful, albeit underdispersive and bimodal, forecast at all precipitation thresholds considered. A forecasting exercise is conducted to evaluate how forecasters utilize the ensemble forecast system guidance. Forecasters made two storm-total accumulated precipitation forecasts: one before and one after evaluating the ensemble guidance. Concurrently, forecasters were presented with questionnaires designed to gauge their thought processes in preparing each of their forecasts. Exercise participants felt that the high-resolution ensemble guidance added value and confidence to their forecasts, although it did not meaningfully reduce forecast uncertainty. Incorporation of the ensemble guidance into the forecast preparation process resulted in a modest mean improvement in forecast skill, with each forecast found to be skillful at all accumulated precipitation thresholds. Forecasters primarily utilized the ensemble guidance to identify a “most likely” forecast outcome from disparate deterministic guidance solutions and to help quantify the uncertainty associated with the forecast. Forecasters preferred ensemble guidance that enabled them to quickly understand the range of solutions provided by the ensemble, particularly over the entirety of the domain. Forecasters were generally aware of the diversity of solutions provided by the ensemble guidance; however, only a select few actively interrogated this information when revising their forecasts and each did so in different ways.

Abstract

This study investigates the impact of abnormally moist soil conditions across the southern Great Plains upon the overland reintensification of North Atlantic Tropical Cyclone Erin (2007). This is tested by analyzing the contributions of three soil moisture–related signals—a seasonal signal, an along-track rainfall signal, and an early postlandfall rainfall signal—to the intensity of the vortex. In so doing, a suite of nine convection-permitting numerical simulations using the Advanced Research Weather Research and Forecasting model (WRF-ARW) is used. Of the signals tested, soil moisture contributions from the anomalously wet months preceding Erin are found to have the greatest positive impact upon the intensity of the vortex, though this impact is on the order of that from climatological soil moisture conditions. The greatest impact of the early rainfall signal contributions is found when it is added to the seasonal signal. Along-track rainfall during the simulation period has a minimal impact.

Variations in soil moisture content result in impacts upon the boundary layer thermodynamic environment via boundary layer mixing. Greater soil moisture content results in weaker mixing, a shallower boundary layer, and greater moisture and instability. Differences in the intensity of convection that develops and its accompanying latent heat release aloft result in greater warm-core development and surface vortex intensification within the simulations featuring greater soil moisture content. Implications of these findings to the tropical cyclone development process are discussed. Given that the reintensification is shown to occur in, apart from land, an otherwise favorable environment for tropical cyclone development and results in a vortex with a structure similar to developing tropical cyclones, these findings provide new insight into the conditions under which tropical cyclones develop.

Abstract

This study evaluates the influence of planetary boundary layer parameterization on short-range (0–15 h) convection initiation (CI) forecasts within convection-allowing ensembles that utilize subsynoptic-scale observations collected during the Mesoscale Predictability Experiment. Three cases, 19–20 May, 31 May–1 June, and 8–9 June 2013, are considered, each characterized by a different large-scale flow pattern. An object-based method is used to verify and analyze CI forecasts. Local mixing parameterizations have, relative to nonlocal mixing parameterizations, higher probabilities of detection but also higher false alarm ratios, such that the ensemble mean forecast skill only subtly varied between parameterizations considered. Temporal error distributions associated with matched events are approximately normal around a zero mean, suggesting little systematic timing bias. Spatial error distributions are skewed, with average mean (median) distance errors of approximately 44 km (28 km). Matched event cumulative distribution functions suggest limited forecast skill increases beyond temporal and spatial thresholds of 1 h and 100 km, respectively. Forecast skill variation is greatest between cases with smaller variation between PBL parameterizations or between individual ensemble members for a given case, implying greatest control on CI forecast skill by larger-scale features than PBL parameterization. In agreement with previous studies, local mixing parameterizations tend to produce simulated boundary layers that are too shallow, cool, and moist, while nonlocal mixing parameterizations tend to be deeper, warmer, and drier. Forecasts poorly resolve strong capping inversions across all parameterizations, which is hypothesized to result primarily from implicit numerical diffusion associated with the default finite-differencing formulation for vertical advection used herein.

Abstract

This study investigates the short-range (0–12 h) predictability of convection initiation (CI) using the Advanced Research Weather Research and Forecasting (WRF) Model (ARW) with a horizontal grid spacing of 429 m. A unique object-based method is used to evaluate model performance for 25 cases of CI across the west-central high plains of the United States from the 2010 convective season. In the aggregate, there exists a high probability of detection but, due to the significant overproduction of CI events by the model, high false alarm and bias ratios that lead to modestly skillful forecasts. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 3 min and distance errors of around 38 km. The operational utility and inherent biases of such high-resolution simulations are discussed.

Abstract

In this study, the dynamical processes contributing to warm-core meso-β-scale vortex formation associated with the 8 May 2009 “super derecho” are examined utilizing two complementary quasi-Lagrangian approaches—a circulation budget and backward trajectory analyses—applied to a fortuitous numerical simulation of the event. Warm-core meso-β-scale vortex formation occurs in a deeply moist, potentially stable environment that is conducive to the development of near-surface rotation and is somewhat atypical compared to known derecho-supporting environments.

Air parcels in the vicinity of the developing vortex primarily originate near the surface in the streamwise vorticity-rich environment, associated with the vertical wind shear of the low-level jet, immediately to the east of the eastward-moving system. Cyclonic vertical vorticity is generated along inflowing air parcels primarily by the ascent-induced tilting of streamwise vorticity and amplified primarily by ascent-induced vortex tube stretching. Descent-induced tilting of crosswise vorticity contributes to cyclonic vertical vorticity generation for the small population of air parcels in the vicinity of the developing vortex that originate to its north and west. No consistent source of preexisting vertical vorticity is present within the environment.

Cyclonic circulation on the scale of the warm-core meso-β-scale vortex increases in the lower troposphere in response to the mean vortex-scale convergence of cyclonic absolute vorticity and the local expulsion of eddy anticyclonic vertical vorticity generated within the system’s cold pool. Increased cyclonic circulation is partially offset by the system-scale tilting of horizontal vorticity associated with the low-level jet, rear-inflow jet, environmental vertical wind shear, and rotational flow of the warm-core vortex itself.

Abstract

A statistical–dynamical tropical cyclone (TC) intensity model is developed from a large ensemble of algorithms through evolutionary programming (EP). EP mimics the evolutionary principles of genetic information, reproduction, and mutation to develop a population of algorithms with skillful predictor combinations. From this evolutionary process the 100 most skillful algorithms as determined by root-mean square error on validation data are kept and bias corrected. Bayesian model combination is used to assign weights to a subset of 10 skillful yet diverse algorithms from this list. The resulting algorithm combination produces a forecast superior in skill to that from any individual algorithm. Using these methods, two models are developed to give deterministic and probabilistic forecasts for TC intensity every 12 h out to 120 h: one each for the North Atlantic and eastern and central North Pacific basins. Deterministic performance, as defined by MAE, exceeds that of a “no skill” forecast in the North Atlantic to 96 h and is competitive with the operational Statistical Hurricane Intensity Prediction Scheme and Logistic Growth Equation Model at these times. In the eastern and central North Pacific, deterministic skill is comparable to the blended 5-day climatology and persistence (CLP5) track and decay-SHIFOR (DSHF) intensity forecast (OCD5) only to 24 h, after which time it is generally less skillful than OCD5 and all operational guidance. Probabilistic rapid intensification forecasts at the 25–30 kt (24 h)⁻¹ thresholds, particularly in the Atlantic, are skillful relative to climatology and competitive with operational guidance when subjectively calibrated; however, probabilistic rapid weakening forecasts are not skillful relative to climatology at any threshold in either basin. Case studies are analyzed to give more insight into model behavior and performance.

Abstract

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso-α- to synoptic-scale observations collected in upstream, preconvective environments is insufficient to improve short-range ensemble convection initiation (CI) forecast skill over the set of cases considered by the 2013 Mesoscale Predictability Experiment (MPEX) because of a limited influence upon the lower-tropospheric phenomena that modulate CI occurrence, timing, and location. The ensemble Kalman filter implementation within the Data Assimilation Research Testbed as coupled to the Advanced Research Weather Research and Forecasting (WRF) Model is used to initialize two nearly identical 30-member ensembles of short-range forecasts for each case: one initial condition set that incorporates MPEX dropsonde observations and one that excludes these observations. All forecasts for a given mission begin at 1500 UTC and are integrated for 15 h on a convection-permitting grid encompassing much of the conterminous United States. Forecast verification is conducted probabilistically using fractions skill score and deterministically using a 2 × 2 contingency table approach at multiple neighborhood sizes and spatiotemporal event-matching thresholds to assess forecast skill and support hypothesis testing. The probabilistic verification represents the first of its kind for numerical CI forecasts. Forecasts without MPEX observations have high fractions skill score and probabilities of detection on the meso-α scale but exhibit a considerable high bias for forecast CI event count. Assimilating MPEX observations has a negligible impact upon forecast skill for the cases considered, independent of verification metric, as the MPEX observations result in only subtle differences primarily manifest in the position and intensity of atmospheric features responsible for focusing and/or triggering deep, moist convection.

Abstract

It is known that both Dvorak technique and advanced Dvorak technique–derived intensity estimates for tropical cyclones during extratropical transition are less reliable because the empirical relationships between cloud patterns and cyclone intensity underlying each technique are primarily tropical in nature and thus less robust during extratropical transition. However, as direct observations of cyclone intensity during extratropical transition are rare, the precise extent to which such remotely sensed intensity estimates are in error is uncertain. To address this uncertainty and provide insight into how advanced Dvorak technique–derived intensity estimates during extratropical transition may be improved, the advanced Dvorak technique is applied to synthetic satellite imagery derived from 25 numerical simulations of Atlantic basin tropical cyclones—five cases, five microphysical parameterizations—during extratropical transition. From this, an internally consistent evaluation between model-derived and advanced Dvorak technique–derived cyclone intensity estimates is conducted. Intensity estimate error and bias peak at the beginning of extratropical transition and decline thereafter for maximum sustained surface wind. On average, synthetic advanced Dvorak technique–derived estimates of maximum sustained surface wind asymptote toward or remain near their weakest-possible values after extratropical transition begins. Minimum sea level pressure estimates exhibit minimal bias, although this result is sensitive to microphysical parameterization. Such sensitivity to microphysical parameterization, particularly with respect to cloud radiative properties, suggests that only qualitative insight regarding advanced Dvorak technique–derived intensity estimate error during extratropical transition may be obtained utilizing synthetic satellite imagery. Implications toward developing improved intensity estimates during extratropical transition are discussed.

Abstract

This study evaluates forecast vertical thermodynamic profiles and derived thermodynamic parameters from two regional/convection-allowing model pairs, the North American Mesoscale Forecast System and the North American Mesoscale Nest model pair and the Rapid Refresh and High Resolution Rapid Refresh model pair, in warm-season, thunderstorm-supporting environments. Differences in bias and mean absolute error between the regional and convection-allowing models in each of the two pairs, while often statistically significant, are practically small for the variables, parameters, and vertical levels considered, such that the smaller-scale variability resolved by convection-allowing models does not degrade their forecast skill. Model biases shared by the regional and convection-allowing models in each pair are documented, particularly the substantial cool and moist biases in the planetary boundary layer arising from the Mellor–Yamada–Janjić planetary boundary layer parameterization used by the North American Mesoscale model and the Nest version as well as the middle-tropospheric moist bias shared by the Rapid Refresh and High Resolution Rapid Refresh models. Bias and mean absolute errors typically have larger magnitudes in the evening, when buoyancy is a significant contributor to turbulent vertical mixing, than in the morning. Vertical thermodynamic profile biases extend over a deep vertical layer in the western United States given strong sensible heating of the underlying surface. The results suggest that convection-allowing models can fulfill the use cases typically and historically met by regional models in operations at forecast entities such as the Storm Prediction Center, a fruitful finding given the proposed elimination of regional models with the Next-Generation Global Prediction System initiative.

Abstract

This study introduces a novel method for comparing vertical thermodynamic profiles, focusing on the atmospheric boundary layer, across a wide range of meteorological conditions. This method is developed using observed temperature and dewpoint temperature data from 31 153 soundings taken at 0000 UTC and 32 308 soundings taken at 1200 UTC between May 2019 and March 2020. Temperature and dewpoint temperature vertical profiles are first interpolated onto a height above ground level (AGL) coordinate, after which the temperature of the dry adiabat defined by the surface-based parcel’s temperature is subtracted from each quantity at all altitudes. This allows for common sounding features, such as turbulent mixed layers and inversions, to be similarly depicted regardless of temperature and dewpoint temperature differences resulting from altitude, latitude, or seasonality. The soundings that result from applying this method to the observed sounding collection described above are then clustered to identify distinct boundary layer structures in the data. Specifically, separately at 0000 and 1200 UTC, a k-means clustering analysis is conducted in the phase space of the leading two empirical orthogonal functions of the sounding data. As compared to clustering based on the original vertical profiles, which results in clusters that are dominated by seasonal and latitudinal differences, clusters derived from transformed data are less latitudinally and seasonally stratified and better represent boundary layer features such as turbulent mixed layers and pseudoadiabatic profiles. The sounding-comparison method thus provides an objective means of categorizing vertical thermodynamic profiles with wide-ranging applications, as demonstrated by using the method to verify short-range Global Forecast System model forecasts.