Graupel Mixing Ratio Research Articles

Direct assimilation of radar reflectivity using an ensemble 3DEnVar approach to improve analysis and forecasting of tornadic supercells over eastern China

AbstractAn ensemble three‐dimensional ensemble‐variational (3DEnVar) data assimilation (En3DA) approach that directly assimilates radar reflectivity was developed based on the Weather Research and Forecasting model data assimilation system. This system adopts radar reflectivity as the control variable to avoid the need for a tangent linear and adjoint of the observation operator. Flow‐dependent covariance was introduced via ensemble forecasts updated by a group of 3DEnVar. The performance of the En3DA system was examined for two selected cases of high‐impact severe tornadic supercells over China. Results for both cases indicated that the structure of the storms in terms of intensity, coverage, and associated low‐level mesocyclones were analysed more accurately when using the En3DA approach than when adopting the 3DVar method. Hydrometeor analysis showed that En3DA provided a more physically reasonable increment of hydrometeors compared to 3DVar, especially for the graupel mixing ratio. Furthermore, the En3DA forecast was better than the 3DVar forecast throughout the forecast period for both studied cases. En3DA produced smaller errors in terms of intensity and location for supercell forecasts with respect to reflectivity and reflectivity swaths. Furthermore, the quantitative forecast skill of radar reflectivity was improved using En3DA. Errors in the wind, temperature, and water vapor forecast fields produced by En3DA were also reduced compared to those of 3DVar. Diagnostics revealed that En3DA predicted an enhanced low‐level cold pool and stronger outflows in the forward‐flank downdraft and the rear‐flank downdraft regions, which are important for tornadogenesis.

Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy

We show, for the first time over Italy and over part of the central Mediterranean Basin, the impact of lightning data assimilation (LDA) on the strokes forecast for a long period. We use the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at convection allowing horizontal resolution (3 km). We carried out a two-seasons experiment (summer 2020 and fall 2021) providing the forecast of lightning and precipitation for the next 6 h (nowcasting), considering two sub-periods (0-3 h and 3-6 h) for verification. The LDA is done through a nudging scheme that increases the water vapor mass in the mixed-phase region based on observed flash density rates and simulated graupel mixing ratio. No changes are made to the model run if spurious convection is predicted or no flashes are observed. LDA can trigger convection missed by the control forecast, without LDA, and/or can redistribute the strokes predicted to be more consistent with observations. LDA has a positive impact on strokes forecast, improving correct forecasts and reducing false alarms. This improvement is however confined to the first three-hours of forecast with negligible to negative impact for longer time ranges, in line with other studies. The improvement pattern is different in summer and fall, depending on the convection development.The analysis of the Fraction Skill Score shows the usefulness of the forecast for practical purposes, considering the current areas used by the Civil Protection Department to issue meteorological alerts for intense convective events over Italy. Finally, it is shown that the forecast at the short-range (0−3h) using LDA can improve the strokes forecast issued on the previous day, not using LDA, and the methodology of this paper can be applied to issue warnings and alerts as the storm is approaching.A brief examination of rainfall forecast shows positive impact of LDA at the short-range (0-3 h), with neutral impact for longer time ranges. The different impact of LDA on the strokes and precipitation forecasts is also highlighted.

Graupel mixing ratio forecast from a cloud resolving numerical weather prediction model as a tool for lightning activity prediction

Graupel mixing ratio over Bulgaria for the warm half year of 2021 based on the AROME-BG numerical weather prediction (NWP) model, is evaluated and connected lightning data detected by the ATDnet lightning location network. Lightning data and forecasted graupel mixing ratios were considered on resolutions of 5×5 km and 10×10 km with flash rate for one and three hours, as well on a daily base using upscaling neighborhood method. Two daily model runs are considered – at 06 and 18 UTC. Commonly used skill-scores in meteorological forecasts are used as evaluation metrics – probability of detection (POD), false alarm rate (F), proportion correct index (PC), and frequency bias index (FBI). Lightning probability forecast (based on graupel mixing ratio) is evaluated at diurnal, monthly, and spatial bases. Results show that graupel mixing ratio taken from the cloud resolving NWP model AROME-BG could be used as a tool to forecast lightning probability with a relatively high performance. Decreases of forecast spatial resolution and time frequency lead to improvement of forecast probability of detection (POD) and frequency bias index (FBI) and to a slight deterioration of its false alarm rate (F) and its percent correct (PC), and the impact of forecast time frequency is more pronounced.

Analysis of Pre-Monsoon Convective Systems over a Tropical Coastal Region Using C-Band Polarimetric Radar, Satellite and Numerical Simulation

Analysis of pre-monsoon convective systems over the southern peninsular India has been performed using C-band radar and numerical simulation. Statistics on the radar polarimetric measurements show that the distribution of differential reflectivity (Zdr) and specific differential phase (Kdp) have much higher spread over convective regions. The distribution of Kdp is almost uniform across the vertical over the stratiform regions. The mean profile of Zdr over stratiform regions shows a distinct local maxima near melting level. A comprehensive analysis has been done on an isolated deep convective system on 13 May 2018. Plan position indicator (PPI) diagrams and satellite measured cloud top temperature demonstrate that pre-monsoon deep convective systems can develop very rapidly within a very short span of time over the region. Heavy precipitation near the surface is reflected in the high value of Kdp (>5° km−1). High values of Zdr (>3 dB) were measured at lower levels indicating the oblate shape of bigger raindrops. A fuzzy logic-based hydrometeor identification algorithm has been applied with five variables (Zh, Zdr, ρhv, Kdp, and T) to understand the bulk microphysical properties at different heights within the storm. The presence of bigger graupel particles near the melting layer indicates strong updrafts within the convective core regions. The vertical ice hydrometeor signifies the existence of a strong electric field causing them to align vertically. Numerical simulation with the spectral bin microphysics (SBM) scheme could produce most of the features of the storm reasonably well. In particular, the simulated reflectivity, graupel mixing ratio and rainfall were in good agreement with the observed values.

Improving Forecast of Severe Oceanic Mesoscale Convective Systems Using FY-4A Lightning Data Assimilation with WRF-FDDA

The Fengyun-4A (FY-4A) geostationary satellite carries the Lightning Mapping Imager that measures total lightning rate of convective systems from space at high spatial and temporal resolutions. In this study, the performance of FY-4A lightning data assimilation (LDA) on the forecast of non-typhoon oceanic mesoscale convective systems (MCSs) is investigated by using an LDA method implemented in the Weather Research and Forecasting-Four Dimensional Data Assimilation (WRF-FDDA). With the LDA scheme, three-dimensional graupel mixing ratio fields retrieved from the FY-4A lightning data and the corresponding latent heating rates are assimilated into the Weather Research and Forecasting model via nudging terms. Two oceanic MCS cases over the South China Sea were selected to perform the study. The subjective evaluation results demonstrate that most of the oceanic convective cells missed by the control experiments are recovered in the analysis period by assimilating FY-4A lightning data, due to the promoted updrafts by latent-heat nudging, the more accurate and faster simulations of the cold pools, and the associated gust-fronts at the observed lightning locations. The cold pools and gust-fronts generated during the analysis period helped to maintain the development of the MCSs, and reduced the morphology and displacement errors of the simulations in the short-term forecast periods. The quantitative evaluation indicates that the most effective periods of the LDA for simulation enhancement were at the analysis time and the nowcasting (0–2 h forecast) periods.

Simulations of an extreme rainstorm event (1056.7 mm/day) along the South China coast: The effect of single- and double-moment cloud microphysics schemes on precipitation

An extreme rainstorm event (1056.7 mm/day) along the South China coast in summer 2018 is investigated using WRF model. We focused on the effects of single-moment WSM6/7 and double-moment WDM6/7 microphysics schemes on the extreme precipitation. (1) The precipitation area, rain band movements and changes in hourly rainfall intensity could be robustly simulated in the 4 experiments. The performance of each scheme varied across different grades of precipitation. WDM7 scheme simulated precipitation extremes and hourly rain intensity that were closest to observations and produced relatively higher scores for light rain; WSM6 scheme had relatively higher scores for moderate rain; and WSM7 scheme showed higher scores for heavy rain and rainstorm. (2) Compared with the single-moment scheme, the double-moment scheme showed higher cloud water and hail mixing ratios. There was no significant difference in cloud water and snow mixing ratios. (3) Schemes incorporating the hail particles showed a larger cloud water mixing ratio but a smaller graupel mixing ratio. Incorporating hail particles caused an increase in simulated precipitation intensity and smaller simulation errors. (4) The increase in graupel mixing ratios was correspondent to the three precipitation peaks. The temporal evolutions of graupel, hail and snow mixing ratios are similar to that of precipitation, with the peak of cloud water mixing ratio appearing earlier than that of precipitation and the peak of ice mixing ratio appearing later than that of precipitation.

Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast

Radar data are assimilated into numerical prediction models to improve precipitation forecasts using three-dimensional (3D) variational (VAR) and hybrid data assimilation (DA) methods. The effect of radar DA (RDA) and prediction accuracy according to DA methods are evaluated by conducting experiments of three heavy rainfall events and analyzing the spatial pattern of accumulated precipitation, profiles of hydrometeors, and cloud microphysical processes. Increments of the initial analysis revealed that the hybrid method simulated the convective band, wind convergence in front of the cold front and a higher amount of water vapor mixing ratio compared to 3DVAR. Based on numerical experiments, the root mean square error of the total cumulative precipitation in 3DVAR was 9.68 mm lower than that of the no-RDA experiment (CTRL), whereas that of the Hybrid was 11.12 mm lower than that of CTRL. RDA improved precipitation forecasts, and Hybrid showed better precipitation forecast accuracy than 3DVAR. Although RDA indirectly changed the water vapor, rain, snow, and graupel mixing ratios, the change in the water vapor amount was the major factor affecting microphysical processes. The increase (decrease) in the water vapor mixing ratio had the greatest impact on precipitation formation (dissipation), whereas the effect of snow, rain, and graupel mixing ratios was relatively small. The results indicate that it is critical to create an environment in which water vapor can transform into precipitating hydrometeors and the hydrometeors can grow through cloud microphysical processes. The accuracy of heavy rain forecast can be improved by hybrid DA method, which can consider the model error in real time, making it more adaptable to the day-to-day changing weather conditions.

A Study of Enhancing the Prediction Techniques of Lightning Over Bangladesh Based on the Lightning Potential Index and Electric Field

The thunderstorm is a typical extreme weather event that has become one of the most alarming disasters in Bangladesh because of its more frequent occurrences and devastations in recent years. The purpose of this study is to investigate the physical and dynamical characteristics of thunderstorms using Weather Research and Forecasting (WRF) model. Three domains of 9-km, 3-km, and 1-km grid size have been used for the simulation of two events over Dhaka (25-April-2018) and Khulna (11-May-2018). The simulated results are then compared with the Bangladesh Meteorological Department (BMD) and University of Wyoming observed data. Lightning Potential Index (LPI) has been calculated by using different hydrometeors and vertical wind. For the calculation of electric field of thunder-cloud, we have considered an algorithm where ice and graupel are the main charge carriers. Convective Available Potential Energy (CAPE) and Total Total Index (TTI) are found over 1500 J/kg and 48°C respectively ten hours prior to the event. Vertical wind has been found around 4 m/s before the event. Ice and graupel mixing ratios are found at very high concentrations at cloud center. Maximum value of LPI is found 0.163 J/kg in Dhaka and 0.8 J/kg in Khulna. The electric field is found to be 70 kV/m at a vertical height of 8 km in Dhaka and -125 kV/m at a vertical height of 7 km in Khulna. All the parameters are recognized to be favorable for the occurrence of thunderstorms and lightning which indicates that the techniques used in this study may be useful to predict future thunderstorm events in Bangladesh.

Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method—A Case Study of a Mesoscale Convective System

Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method (PI3DVar_rh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3DVar_rh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting (WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs; thus, wet forecasts are generated in this study. The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3DVar_rh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning, which can provide more accurate lightning warning information.

Numerical study of performance of two lightning prediction methods based on: Lightning Potential Index (LPI) and electric POTential difference (POT) over Tehran area

The electric POTential difference (POT) and the Lightning Potential Index (LPI) performance in predicting the lightning activity is investigated and the probable relationship between them is examined. These two indices have a similar dependency on microphysical variables such as ice, graupel mixing ratios, and also updraft characteristics within the cloud. Regardless of this similarity, the LPI directly calculated from WRF model is a more favorable parameter for predicting the lightning events in comparison with the POT, which requires an extra package (ELEC) model. Ten years' available data over the Tehran area were reviewed and four thundercloud cases with distinct characteristics (CAPE, time-frequency, intensity) were selected. In order to acquire the associated physical properties, four simulations have been done using the WRF-ELEC model, which is initialized with ERA-Interim data.The assessment conducted within both quantitative and qualitative themes to verify the potential probability of predicting lightning events. In qualitative evaluation framework, the horizontal distribution of LPI and POT were compared to the locations of lightning occurrence detected by WWLLN (The World Wide Lightning Location Network) data as well as the total lightning data obtained from LIS (Lightning Imaging Sensor) in the innermost simulation domain. This evaluation shows that the horizontal patterns of LPI and POT are well consistent with the locations of lightning occurrence. Moreover, in accordance with acquired values of correlation coefficients, it could be inferred that LPI has a better performance in the Number Of Lightning flashes (NOL) prediction than POT. Statistical review of our simulated data (LPI and POT) is performed to study the variation of LPI and POT which resulted in a good correlation between them and therefore, any information about lightning event using LPI values with less computational cost is preferable in the considered domain. Another comparison was carried out based on the calculated correlation coefficient between the simulated NOL and the NOL recorded by WWLLN that does not show any significant correlation between them for none of the cases. Since WWLLN has no observatory in the studied area, inconsistency of the simulated NOL with real atmosphere is justifiable.

Effects of Anthropogenic Aerosols on a Heavy Rainstorm in Beijing

A heavy rainstorm occurred in Beijing on 19–20 July 2016. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) was used to investigate the effects of anthropogenic aerosols on precipitation and microphysical processes. Three conditions were simulated by altering the anthropogenic emissions. When the anthropogenic emissions were increased by 10 times, the area-average accumulated rainfall amount and maximum accumulated rainfall amount both decreased. The cloud water mixing ratio increased and the rain mixing ratio decreased. The radii of cloud droplets decreased, and the collision efficiency of cloud water by rain and the autoconversion rate of cloud water into rain were both low. When the anthropogenic emission was 10% of the original emission, the area of accumulated rainfall amounts greater than 25 mm in the Beijing area was 10% larger than those of the other two tests. The collision efficiency and autoconversion rate of cloud water into rain were high for large contact areas and large cloud droplets. The graupel mixing ratio was the largest. Thus, the process of melting of graupel into rain was the largest. In the WRF-Chem model, the aerosols did not participate as ice nuclei (IN) in the ice-phase microphysical processes, and therefore the aerosols could influence only the warm rain processes and mix-phased processes near the freezing level line. For no influence on ice-phase microphysical processes, the snow and ice mixing ratios did not show many differences among the different tests.

Continuous Assimilation of Lightning Data Using Time‐Lagged Ensembles for a Convection‐Allowing Numerical Weather Prediction Model

AbstractIn this study, a lightning data assimilation method based on the time‐lagged ensembles for predicting severe convection is presented. With the lightning data assimilation scheme, the background error covariances are computed using time‐lagged ensembles, which consist of deterministic forecasts from eight forecast cycles initialized every 3 hr. Pseudo‐observations of graupel mixing ratio (qg) are retrieved from total lightning rates by utilizing empirical vertical profiles obtained from the simulation results of the previous forecast cycles, and the corresponding observation errors are estimated according to the uncertainties in the lightning observations and the empirical vertical profiles of qg. The increments of the model state variables are computed with the Kalman gain matrices and are continuously ingested into the Weather Research and Forecasting model via nudging terms acting on the prognostic equations over each time step during model integration. The effect of the lightning data assimilation scheme on convection analysis and forecast was assessed through a case study of a severe convective event, which took place in the Guangdong of China. Assimilating lightning data recovered many of the observed convective cells, suppressed the spurious convection, and corrected the displacement errors of the convective systems. Quantitative verifications indicate that forecast skills were improved mainly in the convective regions with the impact of assimilating lightning data on stratiform regions being overall less effective.

Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF‐RTFDDA

AbstractIn this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting‐Real‐Time Four‐Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio (qg) is retrieved from observed total lightning. To retrieve qg on model grid boxes, column‐integrated graupel mass is first calculated using an observation‐based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical qg vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved qg fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short‐term (0–6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short‐term lightning and precipitation forecasts by improving the model simulation of the qg fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2 h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0–2 h) applications.

Semi-idealized modeling of lightning initiation related to vertical air motion and cloud microphysics

A three-dimensional charge–discharge numerical model is used, in a semi-idealized mode, to simulate a thunder-storm cell. Characteristics of the graupel microphysics and vertical air motion associated with the lightning initiation are revealed, which could be useful in retrieving charge strength during lightning when no charge–discharge model is available. The results show that the vertical air motion at the lightning initiation sites (Wini) has a cubic polynomial correlation with the maximum updraft of the storm cell (Wcell-max), with the adjusted regression coefficient R2 of approximately 0.97. Meanwhile, the graupel mixing ratio at the lightning initiation sites (qg-ini) has a linear correlation with the maximum graupel mixing ratio of the storm cell (qg-cell-max) and the initiation height (zini), with the coefficients being 0.86 and 0.85, respectively. These linear correlations are more significant during the middle and late stages of lightning activity. A zero-charge zone, namely, the area with very low net charge density between the main positive and negative charge layers, appears above the area of qg-cell-max and below the upper edge of the graupel region, and is found to be an important area for lightning initiation. Inside the zero-charge zone, large electric intensity forms, and the ratio of qice (ice crystal mixing ratio) to qg (graupel mixing ratio) illustrates an exponential relationship to qg-ini. These relationships provide valuable clues to more accurately locating the high-risk area of lightning initiation in thunderstorms when only dual-polarization radar data or outputs from numerical models without charging/discharging schemes are available. The results can also help understand the environmental conditions at lightning initiation sites.

Assimilation of total lightning data using the three-dimensional variational method at convection-allowing resolution

A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall, the improvement from lightning data assimilation can be maintained for about 3 h.

A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR

Lightning network data, considered as a useful supplement to radar observations, are a good indicator of severe convection, and has high temporal and spatial resolution. In Numerical Weather Prediction (NWP) models, lightning data are a new source of data to improve the forecasting of convective systems. In this case study, lightning data assimilation is conducted by converting lightning data to water vapor mixing ratio via a simple smooth continuous function, with input variables of total flash rate and simulated graupel mixing ratio at 9 km gridded resolution. Relative humidity converted from the retrieved water vapor mixing ratio is assimilated into the background field utilizing the three-dimensional variational (3DVAR) method in WRFDA (the Weather Research and Forecasting model Data Assimilation system). The benefits of assimilating lightning data are demonstrated in a series of experiments using data from a strong convection event that affected Beijing, Tianjin, Hebei and Shandong Province, on 31 July 2007. A nested domain with resolutions of 9 km and 3 km is implemented. For this case, assimilating lightning data shows some improvements in predictions of both reflectivity and neighboring precipitation, and in the temperature, dew-point temperature and relative humidity profile after seven hours.

Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations.

This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPSs) on Weather Research and Forecasting model (WRF) simulations of seven, intense winter time cyclones impacting the Mid-Atlantic United States. Five-day long WRF simulations were initialized roughly 24 hours prior to the onset of coastal cyclogenesis off the North Carolina coastline. In all, 35 model simulations (5 BMPSs and seven cases) were run and their associated microphysics-related storm properties (hydrometer mixing ratios, precipitation, and radar reflectivity) were evaluated against model analysis and available gridded radar and ground-based precipitation products. Inter-BMPS comparisons of column-integrated mixing ratios and mixing ratio profiles reveal little variability in non-frozen hydrometeor species due to their shared programming heritage, yet their assumptions concerning snow and graupel intercepts, ice supersaturation, snow and graupel density maps, and terminal velocities lead to considerable variability in both simulated frozen hydrometeor species and radar reflectivity. WRF-simulated precipitation fields exhibit minor spatio-temporal variability amongst BMPSs, yet their spatial extent is largely conserved. Compared to ground-based precipitation data, WRF-simulations demonstrate low-to-moderate (0.217-0.414) threat scores and a rainfall distribution shifted toward higher values. Finally, an analysis of WRF and gridded radar reflectivity data via contoured frequency with altitude (CFAD) diagrams reveals notable variability amongst BMPSs, where better performing schemes favored lower graupel mixing ratios and better underlying aggregation assumptions.

Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation

Abstract. This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model grid column. The water vapour is added as a function of the flash rate, local temperature, and graupel mixing ratio. The methodology is set up to improve the short-term (3 h) precipitation forecast and can be used in real-time forecasting applications. However, results are also presented for the daily precipitation for comparison with other studies. The methodology is applied to 20 cases that occurred in fall 2012, which were characterized by widespread convection and lightning activity. For these cases a detailed dataset of hourly precipitation containing thousands of rain gauges over Italy, which is the target area of this study, is available through the HyMeX (HYdrological cycle in the Mediterranean Experiment) initiative. This dataset gives the unique opportunity to verify the precipitation forecast at the short range (3 h) and over a wide area (Italy). Results for the 27 October case study show how the methodology works and its positive impact on the 3 h precipitation forecast. In particular, the model represents better convection over the sea using the lightning data assimilation and, when convection is advected over the land, the precipitation forecast improves over the land. It is also shown that the precise location of convection by lightning data assimilation improves the precipitation forecast at fine scales (meso-β). The application of the methodology to 20 cases gives a statistically robust evaluation of the impact of the total lightning data assimilation on the model performance. Results show an improvement of all statistical scores, with the exception of the bias. The probability of detection (POD) increases by 3–5 % for the 3 h forecast and by more than 5 % for daily precipitation, depending on the precipitation threshold considered. Score differences between simulations with or without data assimilation are significant at 95 % level for most scores and thresholds considered, showing the positive and statistically robust impact of the lightning data assimilation on the precipitation forecast.

Properties of a Mesoscale Convective System in the Context of an Isentropic Analysis

Abstract Application of an isentropic analysis of convective motions to a simulated mesoscale convective system is presented. The approach discriminates the vertical mass transport in terms of equivalent potential temperature. The scheme separates rising air at high entropy from subsiding air at low entropy. This also filters out oscillatory motions associated with gravity waves and isolates the overturning motions associated with convection and mesoscale circulation. The mesoscale convective system is additionally partitioned into stratiform and convective regions based on the radar reflectivity field. For each of the subregions, the mass transport derived in terms of height and an isentropic invariant of the flow is analyzed. The difference between the Eulerian mass flux and the isentropic counterpart is a significant and symmetric contribution of the buoyant oscillations to the upward and downward mass fluxes. Filtering out these oscillations results in substantial reduction of the diagnosed downward-to-upward convective mass flux ratio. The analysis is also applied to graupel and snow mixing ratios and number concentrations, illustrating the relationship of the particle formation process to the updrafts.

Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming

Abstract This study presents a new method for assimilating lightning data into numerical models that is suitable at convection-permitting scales. The authors utilized data from the Earth Networks Total Lightning Network at 9-km grid spacing to mimic the resolution of the Geostationary Lightning Mapper (GLM) that will be on the Geostationary Operational Environmental Satellite-R (GOES-R). The assimilation procedure utilizes the numerical Weather Research and Forecasting (WRF) Model. The method (denoted MU) warms the most unstable low levels of the atmosphere at locations where lightning was observed but deep convection was not simulated based on the absence of graupel. Simulation results are compared with those from a control simulation and a simulation employing the lightning assimilation method developed by Fierro et al. (denoted FO) that increases water vapor according to a nudging function that depends on the observed flash rate and simulated graupel mixing ratio. Results are presented for three severe storm days during 2011 and compared with hourly NCEP stage-IV precipitation observations. Compared to control simulations, both the MU and FO assimilation methods produce improved simulated precipitation fields during the assimilation period and a short time afterward based on subjective comparisons and objective statistical scores (~0.1, or 50%, improvement of equitable threat scores). The MU generally performs better at simulating isolated thunderstorms and other weakly forced deep convection, while FO performs better for the case having strong synoptic forcing. Results show that the newly developed MU method is a viable alternative to the FO method, exhibiting utility in producing thunderstorms where observed, and providing improved analyses at low computational cost.