Does Increased Predicted Warm-Season Rainfall Indicate Enhanced Likelihood of Rain Occurrence?

10.1175/820.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1127-1135 ◽  
Author(s):  
William A. Gallus ◽  
Moti Segal
Keyword(s):  
Wrf Model ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
The United States ◽  
Precipitation Forecast ◽  
Convective System ◽  
Good Reliability ◽  
Relative Operating Characteristic ◽  
Probabilistic Forecasts ◽  
Test Sets

Abstract The likelihood of simulated rainfall above a specified threshold being observed is evaluated as a function of the amounts predicted by two mesoscale models. Evaluations are performed for 20 warm-season mesoscale convective system events over the upper Midwest of the United States. Simulations were performed using 10-km versions of the National Centers for Environmental Prediction Eta Model and the Weather Research and Forecasting (WRF) model, with two different convective parameterizations tested in both models. It was found that, despite large differences in the biases of these different models and configurations, a robust relationship was present whereby a substantial increase in the likelihood of observed rainfall exceeding a specified threshold occurred in areas where the model runs forecast higher rainfall amounts. Rainfall was found to be less likely to occur in those areas where the models indicated no rainfall than it was elsewhere in the domain; it was more likely to occur in those regions where rainfall was predicted, especially where the predicted rainfall amounts were largest. The probability of rainfall relative-operating-characteristic and relative-operating-level curves showed that probabilistic forecasts determined from quantitative precipitation forecast values had skill comparable to the skill obtained using more traditional methods in which probabilities are based on the fraction of ensemble members experiencing rainfall. When the entire sample of cases was broken into training and test sets, the probability forecasts of the test sets evidenced good reliability. The relationship noted should provide some additional guidelines to operational forecasters. The results imply that the tested models are better able to indicate the regions where atmospheric processes are most favorable for convective rainfall (where the models generate enhanced amounts) than they are able to predict accurately the rainfall amounts that will be observed.

Multiscale Characteristics and Evolution of Perturbations for Warm Season Convection-Allowing Precipitation Forecasts: Dependence on Background Flow and Method of Perturbation

2014 ◽  
Vol 142 (3) ◽  
pp. 1053-1073 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Ming Xue ◽  
Fanyou Kong ◽  
Gang Zhao ◽  
...  
Keyword(s):  
Warm Season ◽  
Mesoscale Convective System ◽  
Haar Wavelet ◽  
Precipitation Forecast ◽  
Initial Time ◽  
Small Scale ◽  
Convective System ◽  
Mesoscale Convective ◽  
Perturbation Energy ◽  
The Impact

Abstract Multiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. Averaged over 34 forecasts, the small-scale IC perturbations had little impact on large forecast scales while LGPH accounted for about half of the error energy on such scales. The impact of small-scale IC perturbations was also less than, but comparable to, the impact of LGPH perturbations on medium scales. On small scales, the impact of small-scale IC perturbations was at least as large as the LGPH perturbations. The spatial structure of small-scale IC perturbations affected the evolution of forecast perturbations, especially at medium scales. There was little systematic impact of the small-scale IC perturbations when added to LGPH. These results motivate further studies on properly sampling multiscale IC errors.

Characteristics of U.S. Extreme Rain Events during 1999–2003

2006 ◽  
Vol 21 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Russ S. Schumacher ◽  
Richard H. Johnson
Keyword(s):  
United States ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Organizational Structures ◽  
The United States ◽  
Convective System ◽  
Precipitation Total ◽  
Extreme Rain ◽  
Rain Events ◽  
Reflectivity Data

Abstract This study examines the characteristics of a large number of extreme rain events over the eastern two-thirds of the United States. Over a 5-yr period, 184 events are identified where the 24-h precipitation total at one or more stations exceeds the 50-yr recurrence amount for that location. Over the entire region of study, these events are most common in July. In the northern United States, extreme rain events are confined almost exclusively to the warm season; in the southern part of the country, these events are distributed more evenly throughout the year. National composite radar reflectivity data are used to classify each event as a mesoscale convective system (MCS), a synoptic system, or a tropical system, and then to classify the MCS and synoptic events into subclassifications based on their organizational structures. This analysis shows that 66% of all the events and 74% of the warm-season events are associated with MCSs; nearly all of the cool-season events are caused by storms with strong synoptic forcing. Similarly, nearly all of the extreme rain events in the northern part of the country are caused by MCSs; synoptic and tropical systems play a larger role in the South and East. MCS-related events are found to most commonly begin at around 1800 local standard time (LST), produce their peak rainfall between 2100 and 2300 LST, and dissipate or move out of the affected area by 0300 LST.

scholarly journals Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

2021 ◽  
pp. 1-52
Author(s):  
Wenjun Cui ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Great Plains ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Propagation Speed ◽  
Extreme Rainfall ◽  
The United States ◽  
Convective System ◽  
Large Variability ◽  
Mesoscale Convective

AbstractThis study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

scholarly journals Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

2015 ◽  
Vol 143 (4) ◽  
pp. 1035-1057 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Surface Wind ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Lateral Boundary Condition ◽  
Convective System ◽  
Near Surface ◽  
Radar Observations ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated. The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.

scholarly journals Evaluation of Cumulus and Microphysics Parameterizations in WRF across the Convective Gray Zone

2019 ◽  
Vol 34 (4) ◽  
pp. 1097-1115 ◽  
Author(s):  
Julia Jeworrek ◽  
Gregory West ◽  
Roland Stull
Keyword(s):  
Great Plains ◽  
Wrf Model ◽  
Mesoscale Convective System ◽  
The United States ◽  
Smooth Transition ◽  
Cumulus Parameterization ◽  
Convective System ◽  
Gray Zone ◽  
Cumulus Scheme ◽  
Mesoscale Convective

Abstract This study evaluates the grid-length dependency of the Weather Research and Forecasting (WRF) Model precipitation performance for two cases in the Southern Great Plains of the United States. The aim is to investigate the ability of different cumulus and microphysics parameterization schemes to represent precipitation processes throughout the transition between parameterized and resolved convective scales (e.g., the gray zone). The cases include the following: 1) a mesoscale convective system causing intense local precipitation, and 2) a frontal passage with light but continuous rainfall. The choice of cumulus parameterization appears to be a crucial differentiator in convective development and resulting precipitation patterns in the WRF simulations. Different microphysics schemes produce very similar outcomes, yet some of the more sophisticated schemes have substantially longer run times. This suggests that this additional computational expense does not necessarily provide meaningful forecast improvements, and those looking to run such schemes should perform their own evaluation to determine if this expense is warranted for their application. The best performing cumulus scheme overall for the two cases studies here was the scale-aware Grell–Freitas cumulus scheme. It was able to reproduce a smooth transition from subgrid- (cumulus) to resolved-scale (microphysics) precipitation with increasing resolution. It also produced the smallest errors for the convective event, outperforming the other cumulus schemes in predicting the timing and intensity of the precipitation.

Influence of Initial Conditions on the WRF–ARW Model QPF Response to Physical Parameterization Changes

2007 ◽  
Vol 22 (3) ◽  
pp. 501-519 ◽  
Author(s):  
Isidora Jankov ◽  
William A. Gallus ◽  
Moti Segal ◽  
Steven E. Koch
Keyword(s):  
Initial Conditions ◽  
Wrf Model ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Convective System ◽  
Rainfall Forecasting ◽  
Mesoscale Convective ◽  
Arw Model ◽  
Physics Ensemble ◽  
The Impact

Abstract To assist in optimizing a mixed-physics ensemble for warm season mesoscale convective system rainfall forecasting, the impact of various physical schemes as well as their interactions on rainfall when different initializations were used has been investigated. For this purpose, high-resolution Weather Research and Forecasting (WRF) model simulations of eight International H2O Project events were performed. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer (PBL) schemes were used. All cases were initialized with both Local Analyses and Prediction System (LAPS) “hot” start analyses and 40-km Eta Model analyses. To evaluate the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall under the two different initial conditions, the factor separation method was used. The sensitivity to the use of various physical schemes and their interactions was found to be dependent on the initialization dataset. Runs initialized with Eta analyses appeared to be influenced by the use of the Betts–Miller–Janjić scheme in that model’s assimilation system, which tended to reduce the WRF’s sensitivity to changes in the microphysical scheme compared with that present when LAPS analyses were used for initialization. In addition, differences in initialized thermodynamics resulted in changes in sensitivity to PBL and convective schemes. With both initialization datasets, the greatest sensitivity to the simulated rain rate was due to changes in the convective scheme. However, for rain volume, substantial sensitivity was present due to changes in both the physical parameterizations and the initial datasets.

scholarly journals The Impact of Different WRF Model Physical Parameterizations and Their Interactions on Warm Season MCS Rainfall

2005 ◽  
Vol 20 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Isidora Jankov ◽  
William A. Gallus ◽  
Moti Segal ◽  
Brent Shaw ◽  
Steven E. Koch
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Wrf Model ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Skill Score ◽  
Convective System ◽  
Mesoscale Convective ◽  
Physics Ensemble ◽  
The Impact

Abstract In recent years, a mixed-physics ensemble approach has been investigated as a method to better predict mesoscale convective system (MCS) rainfall. For both mixed-physics ensemble design and interpretation, knowledge of the general impact of various physical schemes and their interactions on warm season MCS rainfall forecasts would be useful. Adopting the newly emerging Weather Research and Forecasting (WRF) model for this purpose would further emphasize such benefits. To pursue this goal, a matrix of 18 WRF model configurations, created using different physical scheme combinations, was run with 12-km grid spacing for eight International H2O Project (IHOP) MCS cases. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer schemes were used. Sensitivity to physics changes was determined using the correspondence ratio and the squared correlation coefficient. The factor separation method was also used to quantify in detail the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall. Skill score measures averaged over all eight cases for all 18 configurations indicated that no one configuration was obviously best at all times and thresholds. The greatest variability in forecasts was found to come from changes in the choice of convective scheme, although notable impacts also occurred from changes in the microphysics and planetary boundary layer (PBL) schemes. Specifically, changes in convective treatment notably impacted the forecast of system average rain rate, while forecasts of total domain rain volume were influenced by choices of microphysics and convective treatment. The impact of interactions (synergy) of different physical schemes, although occasionally of comparable magnitude to the impacts from changing one scheme alone (compared to a control run), varied greatly among cases and over time, and was typically not statistically significant.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

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.

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