Life cycle of midlatitude deep convective systems in a Lagrangian framework

Feng, Zhe; Dong, Xiquan; Xi, Baike; McFarlane, Sally A.; Kennedy, Aaron; Lin, Bing; Minnis, Patrick

doi:10.1029/2012jd018362

Cited by 73 publications

(98 citation statements)

References 68 publications

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“…DCSs can be separated into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions, with the most intense precipitation in CC regions, light to moderate precipitation in the SR regions, and light or no precipitation in AC regions, using a hybrid classification algorithm (Feng et al 2011(Feng et al , 2012section 2d). This hybrid classification product is produced over a grid resolution of 4 km 3 4 km and is available with the same temporal resolution as SCaMPR instantaneous estimates.…”

Section: Introductionmentioning

confidence: 99%

Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations

Stenz

Dong

et al. 2014

Journal of Hydrometeorology

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Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3-4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.

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Section: Introductionmentioning

confidence: 99%

Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations

Stenz

Dong

et al. 2014

Journal of Hydrometeorology

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“…In this method, physical processes are assumed to occur at defined rates as measured by some universal clock. The wall-clock has been used frequently for tracked clouds and updrafts [7,12,39,47] and for compositing [2,48,49]. Both normalizations can…”

Section: Normalizationmentioning

confidence: 99%

“…From consecutive snapshots from geostationary data, individual clouds can be observed to grow (widen) or decay (narrow) [4][5][6]. Such methods have been used successfully to investigate the growth and decay of storms all over the world [7,8], and have the advantage of observing a natural distribution of clouds. Using sequential ground-based radar volumes can add a vertical component to tracked clouds [9,10].…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship

Igel

2018

Atmosphere

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Abstract:The tropical, oceanic mean relationship between column relative humidity and precipitation is highly non-linear. Mean precipitation remains weak until it rapidly picks up and grows at high column humidity. To investigate the origin of this relationship, a Lagrangian cloud tracking code, RAMStracks, is developed, which can follow the evolution of clouds. RAMStracks can record the morphological properties of convective clouds, the meteorological environment of clouds, and their effects. RAMStracks is applied to a large-domain radiative convective equilibrium simulation, which produces a complex population of convective clouds. RAMStracks records the lifecycle of 501 clouds through growth, splits, mergers, and decay. The mean evolution of all these clouds is examined. It is shown that the column humidity evolves non-monotonically, but that lower-level and upper-level contributions to total moisture do evolve monotonically. The precipitation efficiency of tropical storms tends to increase with cloud age. This is confirmed using a prototype testing method. The same method reveals that different tracked clouds with similar initial conditions evolve in very different ways. This makes drawing general conclusions from individual storms difficult. Finally, the causality of the precipitation-column humidity relationship is examined. A Granger Causality test, as well as regressions, suggest that moisture and precipitation are causally linked, but that the direction of causality is ambiguous. Much of this link appears to come from the lower-level moisture's contribution to column humidity.

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“…Other tracking methods have been applied not only to radar data but also to other high-resolution observational data sets such as satellite data [Lakshmanan et al, 2009;Feng et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Probing the precipitation life cycle by iterative rain cell tracking

Moseley

Berg

Haerter³

2013

JGR Atmospheres

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[1] Monitoring the life cycle of convective rain cells requires a Lagrangian viewpoint where the observer moves with the dominant background flow. To adopt such a moving reference frame, we design, validate, and apply a simple rain cell tracking method-which we term iterative rain cell tracking (IRT)-for spatio-temporal precipitation data. IRT iteratively identifies the formation and dissipation of rain cells and determines the large-scale flow. The iteration is repeated until reaching convergence. As validated using reanalysis wind speeds, repeated iterations lead to substantially increased agreement of the background flow field and an increased number of complete tracks. Our method is thereby able to monitor the growth and intensity profiles of rain cells and is applied to a high-resolution (5 min and 1 1 km 2 ) data set of radar-derived rainfall intensities over Germany. We then combine this data set with surface temperature observations and synoptic observations to group tracks according to convective and stratiform conditions. Convective tracks show clear life cycles in intensity, with peaks shifted off-center toward the beginning of the track, whereas stratiform tracks have comparatively featureless intensity profiles. Our results show that the convective life cycle can lead to convection-dominating precipitation extremes at short time scales, while track-mean intensities may vary much less between the two types. The observed features become more pronounced as surface temperature increases, and in the case of convection even exceeded the rates expected from the Clausius-Clapeyron relation.Citation: Moseley, C., P. Berg, and J. O. Haerter (2013), Probing the precipitation life cycle by iterative rain cell tracking,

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Life cycle of midlatitude deep convective systems in a Lagrangian framework

Cited by 73 publications

References 68 publications

Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations

Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations

Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship

Probing the precipitation life cycle by iterative rain cell tracking

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