Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the Object-Oriented Cluster Analysis Method for Precipitation Fields

Johnson, Aaron; Wang, Xuguang; Kong, Fanyou; Xue, Ming

doi:10.1175/mwr-d-11-00015.1

Cited by 39 publications

(54 citation statements)

References 60 publications

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“…This result is not surprising as the tendency for convection-allowing models to overpredict precipitation is well documented (e.g., Schwartz et al 2010;Clark et al 2009;Skamarock and Weisman 2009), and Johnson and Wang (2013) and Johnson et al (2013) also found substantial overprediction of objects in convection-allowing forecasts. Generally, Thompson overpredicts the most, while Morrison overpredicts the least.…”

Section: B Spatial Distribution Of Time-domain Objectsmentioning

confidence: 62%

“…In the future, we believe that MODE-TD concepts will continue to have particularly useful applications to convection-allowing (i.e., sub-4-km grid spacing) forecast systems, because it will allow for an efficient and objective method for evaluating explicitly depicted convective phenomena such as supercells, MCSs, and flash-flood-producing rainfall systems. This future work will require the adaptation of fuzzy-logic-based algorithms used for matching and/or merging spatial objects (e.g., Davis et al 2009;Johnson et al 2011a;Johnson and Wang 2013) to time-domain objects, which was not attempted in this study.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Other studies applying MODE to convectionallowing forecasts include Johnson et al (2011a,b), in which cluster analyses were performed to evaluate the systematic similarity-dissimilarity of ensemble precipitation forecasts; Johnson and Wang (2012), in which MODE was used to generate and calibrate object-based probabilistic precipitation forecasts; Johnson and Wang (2013), in which MODE was used to compare the skill of convection-allowing ensemble members using the Advanced Research core of the WRF model (ARW) versus that found when using the Nonhydrostatic Mesoscale Model (NMM) dynamics core of the WRF model; and Johnson et al (2013), which systematically compared the performance of 1-and 4-km grid-spacing forecasts. Finally, Duda and Gallus (2013) used MODE to diagnose the skill of 3-km grid-spacing WRF model simulations at predicting the convective initiation and upscale evolution of convection into MCSs.…”

Section: Introductionmentioning

confidence: 99%

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Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Clark

Bullock²,

Jensen³

et al. 2014

Weather and Forecasting

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Meaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach-a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]-was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold used to define objects [8 km and 0.10 in. (1 in. 5 2.54 cm), respectively], the most common object duration was 3 h in both models and observations. With an increased smoothing radius and rainfall threshold, the most common duration became shorter. The simulations depicted the diurnal cycle of object frequencies well, but overpredicted object frequencies uniformly across all forecast hours. The simulations had spurious maxima in initiating objects at the beginning of the forecast and a corresponding spurious maximum in dissipating objects slightly later. Examining average object velocities, a slow bias was found in the simulations, which was most pronounced in the Thompson member. These findings should aid users and developers of convection-allowing models and motivate future work utilizing time-domain methods for verifying high-resolution forecasts.

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Section: B Spatial Distribution Of Time-domain Objectsmentioning

confidence: 62%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Clark

Bullock²,

Jensen³

et al. 2014

Weather and Forecasting

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“…Since 2007, the Center for the Analysis and Prediction of Storms (CAPS) has run a storm-scale ensemble during the HWT Spring Forecast Experiment, with an increased number of members at 4-km horizontal grid spacing each year (Xue et al 2007(Xue et al , 2011. Results from the SSEF for the prediction of severe weather and heavy rainfall have been very promising, as have the new methods for displaying and analyzing vast amounts of model output (e.g., Schwartz et al 2010;Coniglio et al 2010;Johnson et al 2011;Clark et al 2011Clark et al , 2012a. The details of the 2010 SSEF will be presented in section 3.…”

Section: B Storm-scale Ensemble Predictionmentioning

confidence: 99%

Factors Influencing the Development and Maintenance of Nocturnal Heavy-Rain-Producing Convective Systems in a Storm-Scale Ensemble

Schumacher

Clark

Xue

et al. 2013

Monthly Weather Review

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From 9 to 11 June 2010, a mesoscale convective vortex (MCV) was associated with several periods of heavy rainfall that led to flash flooding. During the overnight hours, mesoscale convective systems (MCSs) developed that moved slowly and produced heavy rainfall over small areas in south-central Texas on 9 June, north Texas on 10 June, and western Arkansas on 11 June. In this study, forecasts of this event from the Center for the Analysis and Prediction of Storms' Storm-Scale Ensemble Forecast system are examined. This ensemble, with 26 members at 4-km horizontal grid spacing, included a few members that very accurately predicted the development, maintenance, and evolution of the heavy-rain-producing MCSs, along with a majority of members that had substantial errors in their precipitation forecasts. The processes favorable for the initiation, organization, and maintenance of these heavy-rain-producing MCSs are diagnosed by comparing ensemble members with accurate and inaccurate forecasts. Even within a synoptic environment known to be conducive to extreme local rainfall, there was considerable spread in the ensemble's rainfall predictions. Because all ensemble members included an anomalously moist environment, the precipitation predictions were insensitive to the atmospheric moisture. However, the development of heavy precipitation overnight was very sensitive to the intensity and evolution of convection the previous day. Convective influences on the strength of the MCV and its associated dome of cold air at low levels determined whether subsequent deep convection was initiated and maintained. In all, this ensemble provides quantitative and qualitative information about the mesoscale processes that are most favorable (or unfavorable) for localized extreme rainfall.

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“…Scale separation methods have been used by De Sales andLiu et al (2011), and field deformation approaches by Nan et al (2010). Feature-based techniques have been used by Demaria et al (2011), Hartung et al (2011), Johnson et al (2011, Gorgas and Dorninger (2012b), Wapler et al (2012), Crocker and Mittermaier (2013), Mittermaier and Bullock (2013), Weniger and Friederichs (2016), and Mittermaier et al (2016). The verification of forecasts at observing locations has been extended since papers by Theis et al (2005), Ben Bouallègue and Theis (2014), Mittermaier (2014), and Mittermaier and Csima (2017), who compared deterministic and ensembles forecasts on the kilometer scale.…”

Section: The Setup Of the Mesovict Projectmentioning

confidence: 99%

The Setup of the MesoVICT Project

Dorninger

Gilleland

Casati

et al. 2018

Bulletin of the American Meteorological Society

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MesoVICT focuses on the application, capability, and enhancement of spatial verification methods as applied to deterministic and ensemble forecasts of precipitation, wind, and temperature over complex terrain and includes observation uncertainty assessment. THE SETUP OF THE MesoVICT PROJECTManfreD Dorninger, eriC gillelanD, BarBara Casati, Marion P. MitterMaier, elizaBeth e. eBert, BarBara g. Brown, anD laurenCe J. wilson A s numerical weather prediction (NWP) models began to increase considerably in resolution, it became clear that traditional gridpoint-bygridpoint verification methods did not provide adequate or sufficient diagnostic information about forecast performance for some users (e.g., Mass et al. 2002). Double penalties arising from spatial displacement (or perhaps timing) errors and more rapid growth of small-scale errors often result in poorer performance scores for higher-resolution forecasts than their coarser counterparts, even when subjective evaluation would support the higher-resolution models as being better. Subsequently, a host of new verification methods were developed very rapidly (Ebert and McBride 2000;Harris et al. 2001;Casati et al. 2004;Nachamkin 2004;Davis et al. 2006;Keil and Craig 2007;Roberts and Lean 2008;Marzban et al. 2009;Gilleland et al. 2010b), which we will refer to as spatial methods. There still are gaps in our understanding when it comes to interpreting what all the new spatial methods tell us; gaining an in-depth understanding of forecast performance depends on grasping the full meaning of the verification results. Furthermore, the investment required to implement a new spatial method is relatively high compared to traditional verification methods, making it important to have some criteria on which to decide which methods would best suit a particular user's need(s). Therefore, a spatial methods meta-verification, or intercomparison, project was created to try to answer some of these questions.The first spatial verification methods intercomparison project (ICP;Gilleland et al. 2009Gilleland et al. , 2010a was initiated in 2007 with the aim of better understanding the rapidly increasing literature concerning new spatial verification methods, and several questions were addressed:• How does each method inform about forecast performance overall?• Which aspects of forecast error does each method best identify (e.g., location error, scale dependence of the skill, etc.)? • Which methods yield identical information to each other and which methods provide complementary information?The aim of the ICP was to analyze the behavior and provide a structured and systematic cataloging of the existing spatial verification methods, with the final goal of providing guidance to the users on which methods are best for which purpose. The project focused on prescribed errors for idealized cases and quantitative precipitation forecasts, where the ability of the verification methods to diagnose the known error was assessed (Ahijevych et al. 2009). For this first intercomparison, complex terrain ...

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Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the Object-Oriented Cluster Analysis Method for Precipitation Fields

Cited by 39 publications

References 60 publications

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Factors Influencing the Development and Maintenance of Nocturnal Heavy-Rain-Producing Convective Systems in a Storm-Scale Ensemble

The Setup of the MesoVICT Project

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