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Jason E. Nachamkin

Abstract

Mesoscale models are often used to explicitly predict discrete, highly structured phenomena. Information regarding the ability of the model to predict events as coherent entities is thus a useful statement of performance. Observational constraints are a significant problem, though, as the shape, size, and intensity of any given event are often only partially known. Composite techniques offer an attractive approach because the full deterministic information about any one event need not be known. If enough quasi-random observations of a distribution of events exist, bulk properties of the distributions of forecasts and observations can be estimated. Composites are also useful in that the verification measures are based on conditional samples of events. Sample distributions contingent on event existence in either the forecasts or the observations can be compared to one another.

A verification technique in which meteorological events are located and composited on a relative grid centered on each event is described herein. This technique is described and demonstrated by comparing the 27-km Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) mistral wind forecasts to the Special Sensor Microwave Imager (SSM/I) observations for a 1-yr period. Diagnostic information regarding the forecast reliability, error type, and error spatial characteristics are derived. Also, statistics from the conditional distributions of both the observed and predicted events are compared. The difference between the two conditional biases (CBD) is found to reveal valuable information regarding the contribution of false alarms and missed forecasts to the forecast errors. The results indicate the mistral is remarkably predictable with high pattern correlations out to 66 h.

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Jason E. Nachamkin and Jerome Schmidt

Abstract

The fractions skill score (FSS) belongs to a class of spatial neighborhood techniques that measures forecast skill from samples of gridded forecasts and observations at increasing spatial scales. Each sample contains the fraction of the predicted and observed quantities that exist above a threshold value. Skill is gauged by the rate that the observed and predicted fractions converge with increasing scale. In this study, neighborhood sampling is applied to diagnose the performance of high-resolution (1.67 km) precipitation forecasts over central Florida. Reliability diagrams derived from the spatial fractions indicate that the FSS can be influenced by small, low-predictability events. Further tests indicate the FSS is subtly affected by samples from points on and near the grid boundaries. Inclusion of these points tends to reduce the magnitude and sensitivity of the FSS, especially at large scales. An attempt to mine data from the set of neighborhood fractions was moderately successful at obtaining descriptive information about the precipitation fields. The width of the distribution of the fractions at each scale provided information concerning forecast resolution and sharpness. The rate at which the distribution of the fractions converged toward the domain mean with increasing scale was found to be sensitive to the uniformity of coverage of precipitation through the domain. Generally, the 6-h forecasts possessed greater spatial skill than those at 12 h. High-FSS values at 12 h were mostly associated with evenly distributed precipitation patterns, while the 6-h forecasts also performed well for several nonuniform cases.

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Jason E. Nachamkin, Jerome Schmidt, and Cristian Mitrescu

Abstract

Operational cloud forecasts generated by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) were verified over the eastern Pacific Ocean. The study focused on the accuracy of cloud forecasts associated with extratropical cyclone and convective activity during the late winter and spring of 2007. The condensed total water (liquid and solid) path was used as a proxy for cloud cover to compare the forecasts with retrievals from the Geostationary Operational Environmental Satellites (GOES). Analyses of the GOES retrievals indicate that deep cloud systems were generally well represented during daylight hours. Thus, the bulk of the verification focused on the general aspects of quality and timing of these deep systems. Multiple statistics were collected, ranging from simple correlations and histograms to more sophisticated fuzzy and composite statistics. The results show that synoptic-scale systems were generally well predicted to at least two days, with the primary error being an overestimation of deep cloud occurrence. Smaller subsynoptic-scale systems were subject to spatial and timing biases in that a number of the forecasts were lagged by 3–6 h. Despite the bias, 60%–70% of the forecasts of the mesoscale phenomena displayed useful skill.

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Jason E. Nachamkin and William R. Cotton

Abstract

The 19 July 1993 mesoscale convective system (MCS), discussed in Part I, was simulated using the Regional Atmospheric Modeling System (RAMS). The model was initialized with variable physiographic and atmospheric data with the goal of reproducing the convective system and its four-dimensional environment. Four telescopically nested, moving grids allowed for horizontal grid spacings down to 1.67 km on the cloud resolving grid. Comparisons with the analysis show that the propagation, evolution, and structure of this MCS were well simulated.

The simulation is used to further investigate the interactions between this MCS and its surrounding environment. In Part I, the Doppler-derived winds indicated that upshear (westward) propagating gravity waves left upper-tropospheric front-to-rear and midtropospheric rear-to-front flow perturbations in their wake. A similar flow structure developed in the simulated MCS, and unlike the Doppler results, the low-frequency waves that produced it were resolved in the data. In the simulation, much of the convectively generated temperature and momentum perturbations propagated westward with the waves, leaving a warm wake in the clear air trailing the system. Although the gravity waves traveled rearward, the perturbation flow in their wake was not strong enough to reverse the upper-tropospheric storm-relative winds. Thus, most of the anvil condensate advected ahead of the convective line.

As the MCS encountered the low-level jet, the midtropospheric upward mass flux increased, but gravity wave motions became less detectable. The upper-tropospheric anvil pushed westward into the strong flow as the system expanded into a characteristically oval shape. Temperature and momentum perturbations propagated rearward along with the anvil in a disturbance that resembled an advective outflow. Unlike the gravity waves, this disturbance became almost stationary with respect to the ground, and it retained its continuity through the rest of the simulation. Vertical cross sections indicate that a large slab of convectively processed air had detrained into the upper troposphere. Prior to this event, much of the warm temperature anomalies generated within the convective towers either remained in the updrafts, or propagated outward with the gravity waves. Early on, individual updrafts were relatively erect and although condensate did detrain eastward in the forward anvil, the temperature anomalies did not propagate with it. In contrast, convective updrafts associated with the expanding oval anvil disturbance were more continuous, and they tilted strongly westward with height.

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Jason E. Nachamkin, Sue Chen, and Jerome Schmidt

Abstract

Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures.

The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection.

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Jason E. Nachamkin, Ray L. McAnelly, and William R. Cotton

Abstract

Dual-Doppler radar, surface mesonet, satellite, and upper-air sounding data from the 1985 Preliminary Regional Experiment for STORM-Central field experiment are used to analyze the early growth stages of a mesoscale convective complex (MCC) that developed in the network on 3 June 1985. This MCC was characterized by a complex distribution of convective clusters and intervening stratiform echo as it grew from its initial stage to the typical meso-α-scale cloud shield structure at its mature stage. The MCC exhibited two very different states of organization as it grew. The early state was characterized by a relatively weak and disorganized surface pressure pattern and a highly variable three-dimensional mesoscale flow structure. The later state was characterized by a well-developed mesohigh-wake-low surface pressure pattern and more organized mososcale flow fields. The evolution between these two regimes occurred about 1 h after the upper-level cloud shield reached MCC proportions and manifested itself as a rapid, almost discrete transition that took place over a period of about 30 win.

The flow structure in this system was highly complex compared to the two-dimensional squall-line conceptual model. Five separate flow branches coexisted and interacted with one another throughout the observed development of the MCC, and the structure of some of them changed considerably as the system evolved. Notably, the rear inflow evolved from a highly variable westerly flow that ascended in its northern half and descended in the south, to a more uniformly descending rear-inflow jet. This transition was dynamically linked to the development of an upper-tropospheric mesohigh, which we hypothesize blocked the upper-trapospheric flow and partially forced the descent of the rear inflow.

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Jason E. Nachamkin, Ray L. McAnelly, and William R. Cotton

Abstract

This paper is the first in a two part series in which the interactions between a growing mesoscale convective system (MCS) and its surrounding environment are investigated. The system studied here developed in northeastern Colorado on 19 July 1993 and propagated into Kansas as a long-lived nocturnal MCS. High-resolution dual-Doppler and surface mesonet data collected from this system are discussed in Part I, while the results of a numerical simulation are discussed in Part II.

The observations show that organized mesoscale surface pressure and flow features appeared very early in the lifetime of this system, long before the development of any trailing stratiform precipitation. Most of the stratiform anvil advected ahead of the convective line in the strong upper-tropospheric westerlies. In accordance with this, most of the mid- and upper-tropospheric storm-relative flow behind the line remained westerly, or rear-to-front.

Despite the westerlies, the strongest flow perturbations with respect to the ambient winds developed to the rear of the line. The structure of these perturbations was similar to the upper-tropospheric front-to-rear and midtropospheric rear-to-front flows typically found in more mature leading-line/trailing-stratiform systems. The presence of these perturbations on the upwind side of the convective line indicates that gravity wave propagation was primarily responsible for their development.

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Ray L. McAnelly, Jason E. Nachamkin, William R. Cotton, and Melville E. Nicholls

Abstract

The development of two small mesoscale convective systems (MCSs) in northeastern Colorado is investigated via dual-Doppler radar analysis. The first system developed from several initially isolated cumulonimbi, which gradually coalesced into a minimal MCS with relatively little stratiform precipitation. The second system developed more rapidly along an axis of convection and generated a more extensive and persistent stratiform echo and MCS cloud shield. In both systems, the volumetric precipitation rate exhibited an early meso-β-scale convective cycle (a maximum and subsequent minimum), followed by reintensification into a modest mature stage. This sequence is similar to that noted previously in the developing stage of larger MCSs by . They speculated that the early meso-β convective cycle is a characteristic feature of development in many MCSs that is dynamically linked to a rather abrupt transition toward mature stage structure. This study presents kinematic evidence in support of this hypothesis for these cases, as derived from dual-Doppler radar analyses over several-hour periods. Mature stage MCS characteristics such as deepened low- to midlevel convergence and mesoscale descent developed fairly rapidly, about 1 h after the early meso-β convective maximum.

The dynamic linkage between the meso-β convective cycle and evolution toward mature structure is examined with a simple analytical model of the linearized atmospheric response to prescribed heating. Heating functions that approximate the temporal and spatial characteristics of the meso-β convective cycle are prescribed. The solutions show that the cycle forces a response within and near the thermally forced region that is consistent with the observed kinematic evolution in the MCSs. The initial response to an intensifying convective ensemble is a self-suppressing mechanism that partially explains the weakening after a meso-β convective maximum. A lagged response then favors reintensification and areal growth of the weakened ensemble. A conceptual model of MCS development is proposed whereby the early meso-β convective cycle and the response to it are hypothesized to act as a generalized forcing–feedback mechanism that helps explain the upscale growth of a convective ensemble into an organized MCS.

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Craig H. Bishop, Teddy R. Holt, Jason Nachamkin, Sue Chen, Justin G. McLay, James D. Doyle, and William T. Thompson

Abstract

A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

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Jason E. Nachamkin, Adam Bienkowski, Rich Bankert, Krishna Pattipati, David Sidoti, Melinda Surratt, Jacob Gull, and Chuyen Nguyen

Abstract

A physics-based cloud identification scheme, originally developed for a machine-learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-h forecasts. The routine identifies stable and unstable environments by assessing the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed that stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative, and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less-severe short-term cloud cover errors. This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud-base retrievals for their effectiveness at identifying regions of lower-tropospheric cloud cover. Retrieved cloud-base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate that the most accurate cloud-base estimates existed in regions with cloud tops at or below 8 km.

Significance Statement

Cloud forecasts are difficult to verify because the height, depth, and type of the clouds are just as important as the spatial location. Satellite imagery and retrievals are good for verifying location, but these measurements are sometimes uncertain because of obscuration from above. Despite these uncertainties, we can learn a lot about specific forecast errors by tracking general areas of clouds based on their physical forcing mechanisms. We chose to sort by atmospheric stability because buoyant and stable processes are physically very distinct. Studies of this nature exist, but they typically assess mean cloud frequencies without considering spatial and temporal displacements. Here, we address displacement error by assessing the direct overlap between the observed and predicted clouds.

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