Mesoscale Convective System Events Research Articles

Impact of Urbanization on Mesoscale Convective Systems: Insights From a Radar Wind Profiler Mesonet, Theoretical Analyses, and Model Simulations

AbstractThe role of planetary boundary layer (PBL) in the urban effect on mesoscale convective systems (MCSs) remains highly uncertain. The high‐density Mesonet of radar wind profilers (RWPs) in Beijing, along with geostationary satellite data from Fengyun‐2, were used to investigate the MCS evolution and potential impact of urbanization. An MCS as observed by satellite tends to weaken over urban and downstream areas under strong precipitating conditions as opposed to an invigorated MCS over urban and downstream areas under weak precipitating conditions. The pattern of low‐level convergence and high‐level divergence from RWP Mesonet dominates the PBL of all MCS events under strong precipitating conditions, as compared with fluctuating vertical structures of convergence under weak precipitating conditions. Under strong precipitating conditions, turbulent friction weakens MCS activity over the Beijing urban area. Under weak precipitating conditions, thermal effect dominated over urban areas, favoring the formation of surface‐level convergence and invigorating MCSs. Using the large‐eddy simulation, we further investigated the influence of turbulent structural characteristics on the development of MCS. For the strong (weak) precipitating case, turbulence dissipation (buoyancy) dominated the turbulent kinetic energy over urban areas, explaining well the characteristics of MCSs propagating over these areas. Overall, the theoretical analysis and model simulation results confirm the observed impact of urbanization on MCSs. Besides the above scientific findings, our study highlights the importance of the RWP Mesonet in studying the urban effect on MCSs.

Moisture Recycling through Pumping by Mesoscale Convective Systems

Abstract Moisture recycling, the contribution of local evapotranspiration (ET) to precipitation, has been studied using bulk models assuming a well-mixed atmosphere. The latter is inconsistent with a climatologically stratified atmosphere that slants across latitudes. Reconciling the two views requires an understanding of overturning associated with different weather systems. In this study, we aim to better understand moisture recycling associated with mesoscale convective systems (MCSs). Using a convection-permitting WRF simulation equipped with water vapor tracers (WRF-WVT), we tag moisture from terrestrial ET in the U.S. Southern Great Plains during May 2015, when more than 20 MCS events occurred and produced significant precipitation and flooding. Water budget analysis reveals that approximately 76% of terrestrial ET is advected away from the region while the remaining 24% of terrestrial ET is “pumped” upward within the region, accounting for 12% of precipitation. Moisture recycling peaks during early night hours (1800–2400 LT) due to the mixing of the daytime accumulated ET by active convection. By focusing on five “diurnally driven” MCSs with less large-scale circulation influence than other MCSs during the same period, we find an upright pumping of terrestrial ET at the MCS initiation and development stages, which diverges into two branches during the MCS mature and decaying stages. One branch in the upper level advects the ET-sourced moisture downstream, while the other branch in the mid-to-upper level contributes to the trailing precipitation upstream. Overall, our analysis depicts a pumping mechanism associated with MCSs that mixes local ET vertically, highlighting its specific contributions to enhancing convective precipitation processes.

How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?

AbstractThe historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

Classification of large-scale environments that drive the formation of mesoscale convective systems over southern West Africa

Abstract. Mesoscale convective systems (MCSs) are frequently observed over southern West Africa (SWA) throughout most of the year. These MCS events are the dominant rain-bearing systems, contributing over 50 % of annual rainfall over SWA. However, it has not yet been identified what variations in typical large-scale environments of the seasonal cycle of the West African monsoon may favour MCS occurrence in this region. Here, nine distinct synoptic states are identified and are further associated with being a synoptic-circulation type of either a dry, transition, or monsoon season using self-organizing maps (SOMs) with inputs from reanalysis data. We identified a pronounced annual cycle of MCS numbers with frequency peaks in April and October that can be associated with the start of rainfall during the major rainy season and the maximum rainfall for the minor rainy season across SWA, respectively. Comparing daily MCS frequencies, MCSs are most likely to develop during transition conditions featuring a northward-displaced moisture anomaly (2.8 MCSs per day), which can be linked to strengthened low-level westerlies. Considering that these transition conditions occur predominantly during the pre- and post-monsoon period, these patterns may in some cases be representative of monsoon onset conditions or a delayed monsoon retreat. On the other hand, under monsoon conditions, we observe weakened low-level south-westerlies during MCS days, which reduce moisture content over the Sahel but introduce more moisture over the coast. Finally, we find a majority of MCS-day synoptic states exhibiting positive zonal wind shear anomalies. Seasons with the strongest zonal wind shear anomalies are associated with the strongest low-level temperature anomalies to the north of SWA, highlighting that a warmer Sahel can promote MCS-favourable conditions in SWA. Overall, the SOM-identified synoptic states converge towards high-moisture and high-shear conditions on MCS days in SWA, where the frequency at which these conditions occur depends on the synoptic state.

On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Abstract Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance. Significance Statement Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

Impact of Ensemble-Variational Data Assimilation in Heavy Rain Forecast over Brazilian Northeast

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are responsible for the rain episodes, and at the west side the northeastern hinterland and dry air masses are observed. This work is a case study that aims to evaluate the impact of updating the model initial condition using the 3DEnVar (Three-Dimensional Ensemble Variational) system in heavy rain episodes associated with Mesoscale Convective Systems (MCS). The results were compared to 3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square Root Filter) systems and with no data assimilation. The study enclosed two MCS cases occurring on 14 and 24 January 2017. For that purpose, the RMS (Regional Modeling System) version 3.0.0, maintained by the Center for Weather Forecasting and Climate Studies (CPTEC), used two components: the Weather Research and Forecasting (WRF) mesoscale model and the GSI (Gridpoint Statistical Interpolation) data assimilation system. Currently, the RMS provides the WRF initial conditions by using 3DVar data assimilation methodology. The 3DVar uses a climatological covariance matrix to minimize model errors. In this work, the 3DEnVar updates the RMS climatological covariance matrix through the forecast members based on the errors of the day. This work evaluated the improvements in the detection and estimation of 24 h accumulated precipitation in MCS events. The statistic index RMSE (Root Mean Square Error) showed that the hybrid data assimilation system (3DEnVar) performed better in reproducing the precipitation in the MCS occurred on 14 January 2017. On 24 January 2017, the EnSRF was the best system for improving the WRF forecast. In general, the BIAS showed that the WRF initialized with different initial conditions overestimated the 24 h accumulated precipitation. Therefore, the viability of using a hybrid system may depend on the hybrid algorithm that can modify the weights attributed to the EnSRF and 3DVar matrix in the GSI over the assimilation cycles.

The atmospheric controls of extreme convective events over the southern Arabian Peninsula during the spring season

Mesoscale Convective Systems (MCSs) over the southern Arabian Peninsula are more common during the spring months of March and April. In this paper, the processes behind such severe convective events and their local-scale impacts are investigated using reanalysis data, satellite-derived and observational products. The analysis of 95 MCS events from 2000 to 2020 revealed that they are triggered by low-level wind convergence and moisture advection from the Arabian Sea, Arabian Gulf and/or Red Sea. An equatorward displacement and strengthening of the subtropical jet also precondition the environment, as does the presence of a mid-level trough. The latter is generally part of a large-scale pattern of anomalies that are equivalent barotropic in nature, and therefore likely a response to tropical or subtropical forcing. At more local-scale, a drying of the mid-troposphere between 850 and 250 hPa typically by 50%, a reduction of the upper-level winds by about 5 m s−1, and an increase in the upper-tropospheric temperature on average by 2–3 K, are typically observed during MCS events. Over the 21-year period, a statistically significant increase in the MCSs' duration over southeast Arabian Peninsula has been found, suggesting that such extreme events may be even more impactful in a warming world.

Sensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa

Abstract. This study analyzes the characteristics of GPS tropospheric estimates (zenith wet delays – ZWDs, gradients, and post-fit phase residuals) during the passage of mesoscale convective systems (MCSs) and evaluates their sensitivity to the research-level GPS data processing strategy implemented. Here, we focus on MCS events observed during the monsoon season of West Africa. This region is particularly well suited for the study of these events due to the high frequency of MCS occurrences in the contrasting climatic environments between the Guinean coast and the Sahel. This contrast is well sampled with data generated by six African Monsoon Multidisciplinary Analysis (AMMA) GPS stations. Tropospheric estimates for a 3-year period (2006–2008), processed with both the GAMIT and GIPSY-OASIS software packages, were analyzed and intercompared. First, the case of a MCS that passed over Niamey, Niger, on 11 August 2006 demonstrates a strong impact of the MCS on GPS estimates and post-fit residuals when the GPS signals propagate through the convective cells as detected on reflectivity maps from the MIT C-band Doppler radar. The estimates are also capable of detecting changes in the structure and dynamics of the MCS. However, the sensitivity is different depending on the tropospheric modeling approach adopted in the software. With GIPSY-OASIS, the high temporal sampling (5 min) of ZWDs and gradients is well suited for detecting the small-scale, short-lived, convective cells, while the post-fit residuals remain quite small. With GAMIT, the lower temporal sampling of the estimated parameters (hourly for ZWDs and daily for gradients) is not sufficient to capture the rapid delay variations associated with the passage of the MCS, but the post-fit phase residuals clearly reflect the presence of a strong refractivity anomaly. The results are generalized with a composite analysis of 414 MCS events observed over the 3-year period at the six GPS stations with the GIPSY-OASIS estimates. A systematic peak is found in the ZWDs coincident with the cold pool crossing time associated with the MCSs. The tropospheric gradients reflect the path of the MCS propagation (generally from east to west). This study concludes that ZWDs, gradients, and post-fit phase residuals provide relevant and complementary information on MCSs passing over or in the vicinity of a GPS station.

Predictive Capability of a High-Resolution Hydrometeorological Forecasting Framework Coupling WRF Cycling 3DVAR and Continuum

Abstract The typical complex orography of the Mediterranean coastal areas support the formation of the so-called back-building mesoscale convective systems (MCS) producing torrential rainfall often resulting in flash floods. As these events are usually very small-scaled and localized, they are hardly predictable from a hydrometeorological standpoint, frequently causing a significant amount of fatalities and socioeconomic damage. Liguria, a northwestern Italian region, is characterized by small catchments with very short hydrological response time and is thus extremely prone to the impacts of back-building MCSs. Indeed, Liguria has been hit by three intense back-building MCSs between 2011 and 2014, causing a total death toll of 20 people and several hundred millions of euros of damages. Consequently, it is necessary to use hydrometeorological forecasting frameworks coupling the finescale numerical weather prediction (NWP) outputs with rainfall–runoff models to provide timely and accurate streamflow forecasts. Concerning the aforementioned back-building MCS episodes that recently occurred in Liguria, this work assesses the predictive capability of a hydrometeorological forecasting framework composed by a kilometer-scale cloud-resolving NWP model (WRF), including a 6-h cycling 3DVAR assimilation of radar reflectivity and conventional weather stations data, a rainfall downscaling model [Rainfall Filtered Autoregressive Model (RainFARM)], and a fully distributed hydrological model (Continuum). A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators has been explored to drive the meteorological component of the proposed forecasting framework. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving the quantitative precipitation forecast, and, subsequently, the flash flood prediction in cases of back-building MCS events.

The Characteristics of Tropical and Midlatitude Mesoscale Convective Systems as Revealed by Radar Wind Profilers

AbstractThis study contrasts characteristics of mature squall‐line mesoscale convective systems (MCSs) observed by extended ground‐based radar wind profiler (RWP) deployments from the U.S. Department of Energy Atmospheric Radiation Measurement program. This analysis compares the dynamical structure, precipitation, and cold pool properties associated with MCS events over RWP sites in Oklahoma, USA, (midlatitude) to those observed during a 2‐year RWP deployment to Manaus, Brazil, during GoAmazon2014/5 campaign (tropical). The MCSs indicate similar convective line rainfall rates and total rainfall accumulations. However, midlatitude events suggest a larger fractional stratiform contribution to total precipitation. For both regions, convective line cold pools are associated with sharp decreases (approximately 10 K) in the surface equivalent potential temperature (θe) near the time of line passage. Surface θe properties for both regions suggest a modest relationship between rainfall rate and the probability of observing measurable surface rainfall. The probability of observing convective updrafts in both tropical and midlatitude MCS events is found to be similar as a function of low‐level radar reflectivity. However, midlatitude MCSs are associated with more intense convective updrafts, with upward air motions (mean, maximum) peaking at higher altitude. The most pronounced contrast is the propensity for deeper and more intense downdrafts in midlatitude MCSs. An analysis based on observed downdraft properties is performed using simple mixing assumptions. For these events, the vertical gradient of θe in the lower troposphere is relatively consistent between the Amazon and Oklahoma, suggesting similar mixing rates for downdrafts originating below 3 km (0.1 km−1). However, if downdrafts originate nearer to the level of minimum θe at SGP, mixing may be occurring at rates comparable to 0.3 km−1.

A Radar-Based Climatology of Mesoscale Convective Systems in the United States

Abstract This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate.

Climatological representation of mesoscale convective systems in a dynamically downscaled climate simulation

This research assesses the utility and validity of using simulated radar reflectivity to detect potential changes in linear and nonlinear mesoscale convective system (MCS) occurrence in the Midwest United States between the early and late 21st century using convection‐permitting climate simulation output. These data include a control run and a pseudo‐global warming (PGW) run that is based on RCP 8.5. First, using a novel segmentation, classification, and tracking procedure, MCS tracks are extracted from observed and simulated radar reflectivity. Next, a comparison between observed and the control run MCS statistics is performed, which finds a negative summertime bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi‐linear convective systems (QLCSs)—which can include bow echoes, squall lines, and line echo wave patterns—and generally less‐organized, non‐QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a PGW run suggest that all MCS events are less common in the future (including QLCS and non‐QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant. A discussion on the importance and challenges of simulating QLCSs in convection‐permitting climate model runs is provided. Finally, potential avenues of exploration are suggested related to the aforementioned issues.

Precipitation Characteristics in West and East Africa from Satellite and in Situ Observations

Abstract Using in situ data, three precipitation classes are identified for rainy seasons of West and East Africa: weak convective rainfall (WCR), strong convective rainfall (SCR), and mesoscale convective systems (MCSs). Nearly 75% of the total seasonal precipitation is produced by the SCR and MCSs, even though they represent only 8% of the rain events. Rain events in East Africa tend to have a longer duration and lower intensity than in West Africa, reflecting different characteristics of the SCR and MCS events in these two regions. Surface heating seems to be the primary convection trigger for the SCR, particularly in East Africa, whereas the WCR requires a dynamical trigger such as low-level convergence. The data are used to evaluate the performance of the recently launched Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) project. The IMERG-based precipitation shows significant improvement over its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), particularly in capturing the MCSs, due to its improved temporal resolution.

The microphysical contributions to and evolution of latent heating profiles in two MC3E MCSs

AbstractThe shapes and magnitudes of latent heating profiles have been shown to be different within the convective and stratiform regions of mesoscale convective systems (MCSs). Properly representing these distinctions has significant implications for the atmospheric responses to latent heating on various scales. This study details (1) the microphysical process contributions to latent heating profiles within MCS convective, stratiform, and anvil regions and (2) the time evolution of these profiles throughout the MCS lifetime, using cloud‐resolving model simulations. Simulations of two MCS events that occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E) are conducted. Several features of the simulated MCSs are compared to a suite of observations obtained during the MC3E field campaign, and it is concluded that the simulations reasonably reproduce the MCS events. The simulations show that condensation and deposition are the primary contributors to MCS latent warming, as compared to riming and nucleation processes. In terms of MCS latent cooling, sublimation, melting, and evaporation all play significant roles. It is evident that throughout the MCS lifecycle, convective regions demonstrate an approximately linear decrease in the magnitudes of latent heating rates, while latent heating within stratiform regions is associated with transitions between MCS flow regimes. Such information regarding the temporal evolution of latent heating within convective and stratiform MCS regions could be useful in developing parameterizations representing convective organization.

Assessment of the High-Resolution Rapid Refresh Model’s Ability to Predict Mesoscale Convective Systems Using Object-Based Evaluation

Abstract An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model’s skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time–dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model’s tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity.

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $ 70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.

A 10-Year Survey of Extreme Rainfall Events in the Central and Eastern United States Using Gridded Multisensor Precipitation Analyses

AbstractExtreme rainfall events in the central and eastern United States during 2002–11 were identified using NCEP stage-IV precipitation analyses. Precipitation amounts were compared against established 50- and 100-yr recurrence interval thresholds for 1-, 6-, and 24-h durations. The authors identified points where analyzed precipitation exceeded the threshold, and combined points associated with the same weather system into events. At shorter durations, points exceeding the thresholds were most common in the Southeast, whereas points were more uniformly distributed for the 24-h duration. Most 24-h events have more points than the other durations, reflecting the importance of organized precipitation systems on longer temporal scales. Though monthly peaks varied by region, the maximum (minimum) usually occurred during the summer (winter); however, the 24-h point maximum occurred in September owing to tropical cyclones. The maximum (minimum) in hourly extreme rainfall points occurred at 2300 (1100) LST, though there were regional differences in the timing of the diurnal maxima and minima. Over half of 100-yr, 24-h events were a result of mesoscale convective systems (MCS), with synoptic and tropical systems responsible for nearly one-third and one-tenth, respectively. Of the 10 events with the most points exceeding this threshold, 5 were associated with tropical cyclones, 3 were synoptic events, and 2 were MCSs. Among the MCS events, 7 of the top 10 were training line/adjoining stratiform (TL/AS). While the 49 TL/AS events investigated further had similar moisture availability, the more widespread events had stronger low-level winds, stronger warm air advection, and stronger and more expansive frontogenesis in the inflow.

Evaluation of performance of Micro Rain Radar over the tropical coastal station Thumba (8.5°N, 76.9°E)

The vertically pointing Micro Rain Radar (MRR) and disdrometer (RD-80) were installed at a coastal station, Thumba (8.5°N, 76.9°E), to study the characteristics of tropical rains. This paper presents the first results from these observations over Thumba and highlights the impact of Mie-scattering corrections to the MRR data for proper estimation of the rainfall intensity. To evaluate the performance of MRR, a total number of 11 Mesoscale Convective Systems events were considered during the period September 2006 to December 2007. The uncorrected MRR shows an overestimation of rain rate and this is larger during the high rain rates. The Mie scattering corrections lead to a decrease in rain rate of the order 2%–31% and resulted in substantial improvement in the rainfall accumulation (368.69mm (before) to 302.16mm (after)). The accumulated rainfall is in good agreement with disdrometer total rainfall of 299.14mm. For the first time, the impact of Mie scattering for different rain categories has been investigated and study shows the rainfall sum significantly improved (about 22%) during the moderate and heavy rain categories whereas, it shows improvement of 11% during the light rain. These studies may be used to estimate the rain attenuation at Ka band over Indian region for different rain categories.

VPR correction of bright band effects in radar QPEs using polarimetric radar observations

Vertical profile of reflectivity (VPR) correction of bright band (BB) effects has been a challenge for single‐polarization radar quantitative precipitation estimations (QPEs) for mesoscale convective systems and for cool season stratiform precipitation when the freezing level is low. BB is often found in the radar observations of stratiform precipitation, and the inflated reflectivity intensities in the BB often cause positive biases in radar QPEs. A VPR correction is desirable to mitigate the BB contamination and reduce the bias. However, a well‐defined BB bottom, while critical for an effective correction of the bias, is often not found in the VPRs. Fortunately, polarimetric radar variables, especially the copolar correlation coefficient (ρHV), can provide a much better depiction of vertical BB structure than does reflectivity. In the current study, an apparent vertical profile of ρHV (AVPρHV) correction scheme is developed. For each tilt of the radar volume scan data, the precipitation echoes are segregated into convective and stratiform regions. An apparent VPR (AVPR) and AVPρHV are computed for the stratiform region in the given tilt. Then the bright band top, peak, and bottom are identified from the AVPR and AVPρHV, and a linear VPR correction model is fit to the AVPR in the BB layer. VPR corrections are applied to the reflectivity field in the given tilt based on the linear correction model, and radar QPEs are derived from the corrected reflectivity field. The new AVPR and AVPρHV combined scheme was tested on two mesoscale convective system events and one cold season event in the United States and was shown to be more effective in reducing the radar QPE bias associated with the BB than did the AVPR correction alone.

Categorisation of synoptic environments associated with mesoscale convective systems over the UK

Mesoscale convective systems (MCSs) are relatively rare events in the UK but, when they do occur, can be associated with weather that is considered extreme with respect to climatology (as indicated by the number of such events that have been analysed as case studies). These case studies usually associate UK MCSs with a synoptic environment known as the Spanish plume. Here a previously published 17 year climatology of UK MCS events is extended to the present day (from 1998 to 2008) and these events classified according to the synoptic environment in which they form. Three distinct synoptic environments have been identified, here termed the classical Spanish plume, modified Spanish plume, and European easterly plume. Detailed case studies of the two latter, newly defined, environments are presented. Composites produced for each environment further reveal the differences between them. The classical Spanish plume is associated with an eastward propagating baroclinic cyclone that evolves according to idealised life cycle 1. Conditional instability is released from a warm moist plume of air advected northeastwards from Iberia that is capped by warmer, but very dry air, from the Spanish plateau. The modified Spanish plume is associated with a slowly moving mature frontal system associated with a forward tilting trough (and possibly cut-off low) at 500 hPa that evolves according to idealised life cycle 2. As in the classical Spanish plume, conditional instability is released from a warm plume of air advected northwards from Iberia. The less frequent European easterly plume is associated with an omega block centred over Scandinavia at upper levels. Conditional instability is released from a warm plume of air advected westwards across northern continental Europe. Unlike the Spanish plume environments, the European easterly plume is not a warm sector phenomena associated with a baroclinic cyclone. However, in all environments the organisation of convection is associated with the interaction of an upper-level disturbance with a low-level region of warm advection.