scholarly journals Synoptic Analysis and Hindcast of an Intense Bow Echo in Western Europe: The 9 June 2014 Storm

2017 ◽  
Vol 32 (3) ◽  
pp. 1121-1141 ◽  
Author(s):  
Luca Mathias ◽  
Volker Ermert ◽  
Fanni D. Kelemen ◽  
Patrick Ludwig ◽  
Joaquim G. Pinto
Keyword(s):  
Prediction Models ◽  
Climate Model ◽  
Weather Prediction ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Mesoscale Convective System ◽  
The United States ◽  
Convective System ◽  
Severe Thunderstorms ◽  
Bow Echo

Abstract On Pentecost Monday, 9 June 2014, a severe linearly organized mesoscale convective system (MCS) hit Belgium and western Germany. This storm was one of the most severe thunderstorms in Germany in decades. The synoptic-scale and mesoscale characteristics of this storm are analyzed based on remote sensing data and in situ measurements. Moreover, the forecast potential of the storm is evaluated using sensitivity experiments with a regional climate model. The key ingredients for the development of the Pentecost storm were the concurrent presence of low-level moisture, atmospheric conditional instability, and wind shear. The synoptic and mesoscale analysis shows that the outflow of a decaying MCS above northern France triggered the storm, which exhibited the typical features of a bow echo like a bookend vortex and a rear-inflow jet. This resulted in hurricane-force wind gusts (reaching 40 m s−1) along a narrow swath in the Rhine–Ruhr region leading to substantial damage. Operational numerical weather prediction models mostly failed to forecast the storm, but high-resolution regional model hindcasts enable a realistic simulation of the storm. The model experiments reveal that the development of the bow echo is particularly sensitive to the initial wind field and the lower-tropospheric moisture content. Adequate initial and boundary conditions are therefore essential for realistic numerical forecasts of such a bow echo event. It is concluded that the Pentecost storm exhibited a comparable structure and a similar intensity to observed bow echo systems in the United States.

scholarly journals Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
John R. Lawson ◽  
William A. Gallus ◽  
Corey K. Potvin
Keyword(s):  
Mesoscale Convective System ◽  
Real Data ◽  
The United States ◽  
Convective System ◽  
Grid Spacing ◽  
Structure Amplitude ◽  
Cold Pools ◽  
Bow Echo ◽  
Mesoscale Convective ◽  
Horizontal Grid

The bow echo, a mesoscale convective system (MCS) responsible for much hail and wind damage across the United States, is associated with poor skill in convection-allowing numerical model forecasts. Given the decrease in convection-allowing grid spacings within many operational forecasting systems, we investigate the effect of finer resolution on the character of bowing-MCS development in a real-data numerical simulation. Two ensembles were generated: one with a single domain of 3-km horizontal grid spacing, and another nesting a 1-km domain with two-way feedback. Ensemble members were generated from their control member with a stochastic kinetic-energy backscatter scheme, with identical initial and lateral-boundary conditions. Results suggest that resolution reduces hindcast skill of this MCS, as measured with an adaptation of the object-based Structure–Amplitude–Location method. The nested 1-km ensemble produces a faster system than in both the 3-km ensemble and observations. The nested 1-km simulation also produced stronger cold pools, which could be enhanced by the increased (fractal) cloud surface area with higher resolution, allowing more entrainment of dry air and hence increased evaporative cooling.

PREDICTABILITY OF CHAOS INHERENT IN THE OCCURRENCE OF SEVERE THUNDERSTORMS

2006 ◽  
Vol 09 (01n02) ◽  
pp. 77-85 ◽  
Author(s):  
SUTAPA CHAUDHURI
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Prediction Models ◽  
Monsoon Season ◽  
Weather Prediction ◽  
Deterministic Chaos ◽  
Convective System ◽  
Severe Thunderstorms ◽  
Numerical Weather Prediction Models ◽  
Meso Scale

The purpose of the present study is to investigate the existence of deterministic chaos in the time series of occurrence or non-occurrence of severe thunderstorms of the pre-monsoon season over the Northeastern part of India. Results from the current study reveal the existence of chaos in the relevant time series. The corresponding predictabilities are also computed quantitatively. The study recommends that the formulation of numerical weather prediction models for forecasting the occurrence of this high frequency meso-scale convective system must take into account the intrinsic chaos.

scholarly journals An Evaluation of the Impact of Assimilating AERI Retrievals, Kinematic Profilers, Rawinsondes, and Surface Observations on a Forecast of a Nocturnal Convection Initiation Event during the PECAN Field Campaign

2019 ◽  
Vol 147 (8) ◽  
pp. 2739-2764 ◽  
Author(s):  
Samuel K. Degelia ◽  
Xuguang Wang ◽  
David J. Stensrud
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Mesoscale Convective System ◽  
Convective System ◽  
Operational Forecasts ◽  
Outflow Boundary ◽  
Convection Initiation ◽  
The Impact ◽  
Surface Observations ◽  
Wind Profilers

Abstract Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.

scholarly journals On the Relationship of Cold Pool and Bulk Shear Magnitudes on Upscale Convective Growth in the Great Plains of the United States

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1019
Author(s):  
Zachary A. Hiris ◽  
William A. Gallus
Keyword(s):  
United States ◽  
Great Plains ◽  
Prediction Models ◽  
Weather Prediction ◽  
Vertical Wind Shear ◽  
The United States ◽  
Growth Potential ◽  
Cold Pool ◽  
Convective System ◽  
The Impact

Upscale convective growth remains a poorly understood aspect of convective evolution, and numerical weather prediction models struggle to accurately depict convective morphology. To better understand some physical mechanisms encouraging upscale growth, 30 warm-season convective events from 2016 over the United States Great Plains were simulated using the Weather Research and Forecasting (WRF) model to identify differences in upscale growth and non-upscale growth environments. Also, Bryan Cloud Model (CM1) sensitivity tests were completed using different thermodynamic environments and wind profiles to examine the impact on upscale growth. The WRF simulations indicated that cold pools are significantly stronger in cases that produce upscale convective growth within the first few hours following convective initiation compared to those without upscale growth. Conversely, vertical wind shear magnitude has no statistically significant relationship with either MCS or non-MCS events. This is further supported by the CM1 simulations, in which tests using the WRF MCS sounding developed a large convective system in all tests performed, including one which used the non-MCS kinematic profile. Likewise, the CM1 simulations of the non-upscale growth event did not produce an MCS, even when using the MCS kinematic profile. Overall, these results suggest that the near-storm and pre-convective thermodynamic environment may play a larger role than kinematics in determining upscale growth potential in the Great Plains.

Investigation of the Summer Climate of the Contiguous United States and Mexico Using the Regional Atmospheric Modeling System (RAMS). Part II: Model Climate Variability

2007 ◽  
Vol 20 (15) ◽  
pp. 3866-3887 ◽  
Author(s):  
Christopher L. Castro ◽  
Roger A. Pielke ◽  
Jimmy O. Adegoke ◽  
Siegfried D. Schubert ◽  
Phillip J. Pegion
Keyword(s):  
United States ◽  
Climate Model ◽  
Weather Prediction ◽  
Regional Climate ◽  
Summer Rainfall ◽  
Atmospheric Modeling ◽  
Regional Atmospheric Modeling System ◽  
Prediction Mode ◽  
Regional Atmospheric Modeling ◽  
Seasonal Weather

Abstract Summer simulations over the contiguous United States and Mexico with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the NCEP–NCAR Reanalysis I for the period 1950–2002 (described in Part I of the study) are evaluated with respect to the three dominant modes of global SST. Two of these modes are associated with the statistically significant, naturally occurring interannual and interdecadal variability in the Pacific. The remaining mode corresponds to the recent warming of tropical sea surface temperatures. Time-evolving teleconnections associated with Pacific SSTs delay or accelerate the evolution of the North American monsoon. At the period of maximum teleconnectivity in late June and early July, there is an opposite relationship between precipitation in the core monsoon region and the central United States. Use of a regional climate model (RCM) is essential to capture this variability because of its representation of the diurnal cycle of convective rainfall. The RCM also captures the observed long-term changes in Mexican summer rainfall and suggests that these changes are due in part to the recent increase in eastern Pacific SST off the Mexican coast. To establish the physical linkage to remote SST forcing, additional RAMS seasonal weather prediction mode simulations were performed and these results are briefly discussed. In order for RCMs to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.

Spurious Grid-Scale Precipitation in the North American Regional Reanalysis

2007 ◽  
Vol 135 (6) ◽  
pp. 2168-2184 ◽  
Author(s):  
Gregory L. West ◽  
W. James Steenburgh ◽  
William Y. Y. Cheng
Keyword(s):  
North American ◽  
Prediction Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Potential Temperature ◽  
Climate Data ◽  
The North ◽  
North American Regional Reanalysis ◽  
Upper Level ◽  
Regional Reanalysis

Abstract Spurious grid-scale precipitation (SGSP) occurs in many mesoscale numerical weather prediction models when the simulated atmosphere becomes convectively unstable and the convective parameterization fails to relieve the instability. Case studies presented in this paper illustrate that SGSP events are also found in the North American Regional Reanalysis (NARR) and are accompanied by excessive maxima in grid-scale precipitation, vertical velocity, moisture variables (e.g., relative humidity and precipitable water), mid- and upper-level equivalent potential temperature, and mid- and upper-level absolute vorticity. SGSP events in environments favorable for high-based convection can also feature low-level cold pools and sea level pressure maxima. Prior to 2003, retrospectively generated NARR analyses feature an average of approximately 370 SGSP events annually. Beginning in 2003, however, NARR analyses are generated in near–real time by the Regional Climate Data Assimilation System (R-CDAS), which is identical to the retrospective NARR analysis system except for the input precipitation and ice cover datasets. Analyses produced by the R-CDAS feature a substantially larger number of SGSP events with more than 4000 occurring in the original 2003 analyses. An oceanic precipitation data processing error, which resulted in a reprocessing of NARR analyses from 2003 to 2005, only partially explains this increase since the reprocessed analyses still produce approximately 2000 SGSP events annually. These results suggest that many NARR SGSP events are not produced by shortcomings in the underlying Eta Model, but by the specification of anomalous latent heating when there is a strong mismatch between modeled and assimilated precipitation. NARR users should ensure that they are using the reprocessed NARR analyses from 2003 to 2005 and consider the possible influence of SGSP on their findings, particularly after the transition to the R-CDAS.

scholarly journals Impacts of aerosols on weather and regional climate over the Pearl River Delta megacity area in China

2011 ◽  
Vol 11 (8) ◽  
pp. 23275-23316 ◽  
Author(s):  
Y. Wang ◽  
Q. Wan ◽  
W. Meng ◽  
F. Liao ◽  
H. Tan ◽  
...  
Keyword(s):  
Pearl River Delta ◽  
Regional Climate ◽  
Wrf Model ◽  
Mesoscale Convective System ◽  
Heavy Precipitation ◽  
River Delta ◽  
Pearl River ◽  
Convective System ◽  
The Pearl River Delta ◽  
The Pearl River

Abstract. Seven-year measurements of precipitation, lightning flashes, and visibility from 2000 to 2006 have been analyzed in the Pearl River Delta (PRD) region, China, with a focus on the Guangzhou megacity area. Statistical analysis shows that the occurrence of heavy rainfall (>25 mm per day) and frequency of lightning strikes are reversely correlated to visibility during this period. To elucidate the effects of aerosols on cloud processes, precipitation, and lightning activity, a cloud resolving – Weather Research and Forecasting (CR-WRF) model with a two-moment bulk microphysical scheme is employed to simulate a mesoscale convective system occurring on 28 Match 2009 in the Guangzhou megacity area. The model predicted evolutions of composite radar reflectivity and accumulated precipitation are in agreement with measurements from S-band weather radars and automatic gauge stations. The calculated lightning potential index (LPI) exhibits temporal and spatial consistence with lightning flashes recorded by a local lightning detection network. Sensitivity experiments have been performed to reflect aerosol conditions representative of polluted and clean cases. The simulations suggest that precipitation and LPI are enhanced by about 16 % and 50 %, respectively, under the polluted aerosol condition. Our results suggest that elevated aerosol loading suppresses light and moderate precipitation (less than 25 mm per day), but enhances heavy precipitation. The responses of hydrometeors and latent heat release to different aerosol loadings reveal the physical mechanism for the precipitation and lightning enhancement in the Guangzhou megacity area, showing more efficient mixed phase processes and intensified convection under the polluted aerosol condition.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Assessing spatio-temporal variability and biases of climate models

2020 ◽  
Author(s):  
Nedjeljka Žagar
Keyword(s):  
Temporal Variability ◽  
Prediction Models ◽  
Climate Model ◽  
Weather Prediction ◽  
Ocean Model ◽  
Spatial And Temporal Variability ◽  
Dynamical Regime ◽  
Temporal Variance ◽  
Slab Ocean ◽  
Spatio Temporal

<div>Atmospheric spatial and temporal variability are closely related with the former being relatively well observed compared to the latter. The former is also regularly assessed in the validation of numerical weather prediction models while the latter is more difficult to estimate. Likewise, thermodynamical fields and circulation are closely coupled calling for an approach that considers them simultaneously.  </div> <div>In this contribution, spatio-temporal variability spectra of the four major reanalysis datasets are discussed and applied for the validation of a climate model prototype.  A relationship between deficiencies in simulated variability and model biases is derived. The underlying method includes dynamical regime decomposition thereby providing a better understanding of the role of tropical variability in global circulation. </div> <div>Results of numerical simulations are validated by a 20th century reanalysis. A climate model was forced either with the prescribed SST or with a slab ocean model that updates SST in each forecast step.  Scale-dependent validation shows that missing temporal variance in the model relative to verifying reanalysis increases as the spatial scale reduces that appears associated with an increasing lack of spatial variance at smaller scales. Similar to variability, bias is strongly scale dependent; the larger the scale, the greater the bias. Biases present in the SST-forced simulation increase in the simulation using the slab ocean. The comparison of biases computed as a systematic difference between the model and reanalysis and between the SST-forced model and slab-ocean model (a perfect-model scenario) suggests that improving the atmospheric model increases the variance in the model on synoptic and subsynoptic scales but large biases associated with a poor SST remain at planetary scales.</div> <p> </p>

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