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Christopher J. Anderson
and
Raymond W. Arritt

Abstract

Large, long-lived convective systems over the United States in 1992 and 1993 have been classified according to physical characteristics observed in satellite imagery as quasi-circular [mesoscale convective complex (MCC)] or elongated [persistent elongated convective system (PECS)] and cataloged. The catalog includes the time of initiation, maximum extent, termination, duration, area of the −52°C cloud shield at the time of maximum extent, significant weather associated with each occurrence, and tracks of the −52°C cloud-shield centroid.

Both MCC and PECS favored nocturnal development and on average lasted about 12 h. In both 1992 and 1993, PECS produced −52°C cloud-shield areas of greater extent and occurred more frequently compared with MCCs. The mean position of initiation for PECS in 1992 and 1993 followed a seasonal shift similar to the climatological seasonal shift for MCC occurrences but was displaced eastward of the mean position of MCC initiation in 1992 and 1993. The spatial distribution of MCC and PECS occurrences contain a period of persistent development near 40°N in July 1992 and July 1993 that contributed to the extreme wetness experienced in the Midwest during these two months.

Both MCC and PECS initiated in environments characterized by deep, synoptic-scale ascent associated with continental-scale baroclinic waves. PECS occurrences initiated more often as vigorous waves exited the intermountain region, whereas MCCs initiated more often within a high-amplitude wave with a trough positioned over the northwestern United States and a ridge positioned over the Great Plains. The low-level jet transported moisture into the region of initiation for both MCC and PECS occurrences. The areal extent of convective initiation was limited by the orientation of low-level features for MCC occurrences.

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Christopher J. Anderson
and
Raymond W. Arritt

Abstract

Large, long-lived mesoscale convective systems (MCSs) over the United States during the 1997–98 El Niño are documented. Two periods of abnormal MCS activity are identified in 1998: from March to mid-April an unusually large number of quasi-linear MCSs were observed in the Midwest; while quasi-circular MCSs in June–August of 1998 were concentrated near 37°N rather than following a seasonal shift similar to that observed in the climatological distribution. Episodic surges of northerly low-level flow were infrequent in March 1998, thereby leading to an unusually high incidence of quasi-linear MCSs and to precipitation anomalies in the central United States.

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James Correia Jr.
,
Raymond W. Arritt
, and
Christopher J. Anderson

Abstract

The development and propagation of mesoscale convective systems (MCSs) was examined within the Weather Research and Forecasting (WRF) model using the Kain–Fritsch (KF) cumulus parameterization scheme and a modified version of this scheme. Mechanisms that led to propagation in the parameterized MCS are evaluated and compared between the versions of the KF scheme. Sensitivity to the convective time step is identified and explored for its role in scheme behavior. The sensitivity of parameterized convection propagation to microphysical feedback and to the shape and magnitude of the convective heating profile is also explored.

Each version of the KF scheme has a favored calling frequency that alters the scheme’s initiation frequency despite using the same convective trigger function. The authors propose that this behavior results in part from interaction with computational damping in WRF. A propagating convective system develops in simulations with both versions, but the typical flow structures are distorted (elevated ascending rear inflow as opposed to a descending rear inflow jet as is typically observed). The shape and magnitude of the heating profile is found to alter the propagation speed appreciably, even more so than the microphysical feedback. Microphysical feedback has a secondary role in producing realistic flow features via the resolvable-scale model microphysics. Deficiencies associated with the schemes are discussed and improvements are proposed.

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Yong Song
,
Christopher K. Wikle
,
Christopher J. Anderson
, and
Steven A. Lack

Abstract

Parameterizations in numerical models account for unresolved processes. These parameterizations are inherently difficult to construct and as such typically have notable imperfections. One approach to account for this uncertainty is through stochastic parameterizations. This paper describes a methodological approach whereby existing parameterizations provide the basis for a simple stochastic approach. More importantly, this paper describes systematically how one can “train” such parameterizations with observations. In particular, a stochastic trigger function has been implemented for convective initiation in the Kain–Fritsch (KF) convective parameterization scheme within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). In this approach, convective initiation within MM5 is modeled by a binary random process. The probability of initiation is then modeled through a transformation in terms of the standard KF trigger variables, but with random parameters. The distribution of these random parameters is obtained through a Bayesian Monte Carlo procedure informed by radar reflectivities. Estimates of these distributions are then incorporated into the KF trigger function, giving a meaningful stochastic (distributional) parameterization. The approach is applied to cases from the International H2O project (IHOP). The results suggest the stochastic parameterization/Bayesian learning approach has potential to improve forecasts of convective precipitation in mesoscale models.

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Ting-Chi Wu
,
Hui Liu
,
Sharanya J. Majumdar
,
Christopher S. Velden
, and
Jeffrey L. Anderson

Abstract

The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

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Ting-Chi Wu
,
Christopher S. Velden
,
Sharanya J. Majumdar
,
Hui Liu
, and
Jeffrey L. Anderson

Abstract

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

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