Thunderstorm Clusters Research Articles

U‐Net Segmentation for the Detection of Convective Cold Pools From Cloud and Rainfall Fields

AbstractConvective cold pools (CPs) mediate interactions between convective rain cells and help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has been hampered by the lack of relevant near‐surface data. Unlike numerical studies, where fields, such as virtual temperature or wind, are available at high resolution and frequently used to detect CPs, observational studies mainly identify CPs based on surface time series, for example, from weather stations or research vessels—thus limiting studies to a regional scope. To expand to a global scope, we here develop and evaluate a methodology for CP detection that relies exclusively on data with (a) global availability and (b) high spatiotemporal resolution. We trained convolutional neural networks to segment CPs in high‐resolution cloud‐resolving simulation output by deliberately restricting ourselves to only cloud top temperature and rainfall fields. Apart from simulations, such data are readily available from geostationary satellites that fulfill both (a) and (b). The networks employ a U‐Net architecture, popular with image segmentation, where spatial correlations at various scales must be learned. Despite the restriction imposed, the trained networks systematically identify CP pixels. Looking ahead, our methodology aims to reliably detect CPs over tropical land from space‐borne sensors on a global scale. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may unveil the role of CPs in convective organization.

Is Amazon deforestation decreasing the number of thunderstorms over South America?

AbstractLightning activity has been predicted to increase with global warming, though estimates of lightning sensitivity to a change of temperature vary widely. Since lightning is a small‐scale process, it must be represented by parametrizations in climate models. This article uses large‐scale meteorological parameters tied to thunderstorm generation to improve existing empirical models that simulate regional thunderstorm behaviour. The response of the number of thunderstorms (as presented here) to climate change is rarely analysed in global studies. This study focuses on Tropical America, and uses the ERA5 higher‐resolution reanalysis data (ERA5) to develop our empirical model. Thunderstorm data were taken from the World Wide Lightning Location Network (WWLLN) and processed using the clustering algorithm developed by Mezuman et al. (2014). The two meteorological parameters that correlated best with thunderstorm clusters in Tropical America were specific humidity (SH) and convective available potential energy (CAPE). The resulting empirical model was run from 1979 to 2019 using ERA5 reanalysis data as input. This approach approximates the long‐term trends in the behaviour of thunderstorms in the regions, in the absence of a complete historical lightning record. To our surprise, Tropical American thunderstorms exhibited a negative trend over this period, with an 8% decrease in thunderstorm clusters since the 1980s even with a rise of 1 K in temperature over the same period. The regions of largest decreases in thunderstorm activity align well with estimates of deforestation. We estimate that for every 1 Tg C lost due to deforestation, there is a 10% decrease in thunderstorm number.

Springtime Southwest Cloud Bands in the Central Caribbean

Abstract Southwest cloud bands during spring (April and May) bring rains to central Caribbean islands at the end of the dry season. A cluster analysis of daily 500-hPa geopotential height fields for 1980–2021 identifies a low-west–high-east dipole pattern related to Pacific–North America response to El Niño–Southern Oscillation (ENSO) and springtime wet spells over the Dominican Republic and Puerto Rico. The regional dipole and local rain time series are ranked to identify the top 10 cases for analysis of meteorological conditions. Hovmöller plots of midlevel meridional wind and specific humidity during April 1983 and May 1986 wet spells reveal a standing Rossby wave train pattern that converges moisture onto the leading edge of a subtropical trough. Composite vertical sections during Caribbean Sea wet spells reveal lower easterly–upper westerly wind anomalies over South America associated with the equatorial Madden–Julian oscillation. Thunderstorm clusters within the southwesterly airflow induce multiday wet spells and flash floods. A second statistical method demonstrated how ENSO underpins Caribbean spring climate anomalies via tropical ocean–atmosphere Rossby wave coupling. Historical trends and long-range projections indicate that springtime tropical–midlatitude interactions may diminish due to an accelerating Hadley cell and retreating jet stream, leading to a delayed onset of the wet season across the Antilles Islands.

Short‐stature maize reduced wind damage during the 2020 midwestern derecho, improving yields and greenhouse gas outcomes

AbstractFuture agricultural systems will need to become more resilient as climate change impacts increase. One expected outcome of climate change is an increase in severe thunderstorms with high velocity straight line winds. In August 2020, a derecho, a cluster of thunderstorms generating high‐speed prolonged‐duration straight line winds, swept through Iowa and Illinois in the north central U.S. Corn Belt. Damage estimates from the derecho range up to US$11.2 billion, with significant impacts to crops, stored grain, and property throughout its path. Fourteen field locations of short‐statured maize research hybrids were growing within the path of the derecho, where data was collected on their lodging damage resistance compared with isogenic and near isogenic tall counterparts. For short‐stature maize, an average of 25% of plants were damaged, compared with 50% for isogenic tall hybrids (across all locations; p = .0009). This decreased lodging damage was associated with a mean estimated yield advantage of 1.57 t ha–1 for short‐stature maize across the breadth of the storm (p = .0011). Economic and environmental models indicated that planting short‐stature maize hybrid across the storm area could have prevented the loss of approximately $938 million in revenue and 1.48 million metric tons of CO2 equivalents.

Examination of the Forecast Process, Mesoscale Environment, and Evolution of the Northern Indiana/Northwest Ohio Derecho of 29 June 2012

A multicell cluster of thunderstorms moved into northern Indiana during the early afternoon hours of 29 June 2012, later evolving into a mature bowing mesoscale convective system (MCS) by the time it exited the County Warning Area of the Northern Indiana National Weather Service. This was the beginning of a derecho that would continue across the Appalachian Mountains and off the Atlantic coast, traveling 1000 km in 10 h and resulting in at least 18 fatalities. This derecho produced a measured wind gust of 41 m s-1 (79 kt) at Fort Wayne International Airport, the highest measured gust along the derecho’s path. The mesoscale environment was characterized by a strong cold pool, extreme instability (including near-record steep midlevel lapse rates), and weak to moderate vertical shear. This paper examines the source of this extreme environment as well as the catalyst for the sustainability of the MCS.

A dynamic and thermodynamic analysis of the 11 December 2017 tornadic supercell in the Highveld of South Africa

Abstract. On 11 December 2017, a tornadic supercell initiated and moved through the northern Highveld region of South Africa for 7 h. A tornado from this supercell led to extensive damage to infrastructure and caused injury to and displacement of over 1000 people in Vaal Marina, a town located in the extreme south of the Gauteng Province. In this study we conducted an analysis in order to understand the conditions that led to the severity of this supercell, including the formation of a tornado. The dynamics and thermodynamics of two configurations of the Unified Model (UM) were also analysed to assess their performance in predicting this tornadic supercell. It was found that this supercell initiated as part of a cluster of multicellular thunderstorms over a dry line, with three ingredients being important in strengthening and maintaining it for 7 h: significant surface to mid-level vertical shear, an abundance of low-level warm moisture influx from the tropics and Mozambique Channel, and steep mid-level lapse rates. It was also found that the 4.4 km grid spacing configuration of the model (SA4.4) performed better than the 1.5 km grid spacing version. SA1.5 underestimated the low-level warm moisture advection and convergence, and missed the storm initiation. SA4.4 captured the supercell; however, the mid-level vorticity was found to be 1 order of magnitude smaller than that of a typical mesocyclone. A grid length of 4.4 km is too coarse to fully capture the details of a mesocyclone, which may also explain why the model underestimated the surface to mid-level wind shear and low-level horizontal mass and moisture flux convergence. Future investigations will involve experimental research over the Highveld region of South Africa to understand mesoscale and local dynamics processes responsible for tornadogenesis in some severe storms. Such a study, to the best of our knowledge, has never been conducted.

PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset.

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15-60 min). It is intended to supersede the PERSIANN-Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm's fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017-18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Towards understanding the impact of convective weather on aircraft arrival traffic at the Hong Kong International Airport

Convective weather (thunderstorm clusters with horizontal extents from tens to hundreds of kilometers, and vertically up to 10 - 15 kilometers in the troposphere) along aircraft flight trajectory is a key factor influencing air traffic management decisions and aviation safety. For an airport, aircraft arrival performance is typically more sensitive to weather conditions than departures. The occurrence of thunderstorms along or around flight routes may force the arriving aircraft to hold/vector in the terminal maneuvering area (TMA) following air traffic control (ATC) instructions, leading to additional fuel burn and delays. Such effects could cascade and bring further negative impacts to air traffic inside TMA. In this work, we perform a joint analysis of high-resolution radar rainfall distribution with 2-D aircraft flight trajectory data surrounding the Hong Kong International Airport (HKIA) to uncover the potential spatio-temporal impact of thunderstorms on air traffic. Results indicate that aircraft arrival traffic is sensitive to the location of convective weather in TMA, which also introduces a time lag effect on subsequent air traffic on the order of hours.

A verification of the lightning detection data from FY-4A LMI as compared with ADTD-2

In this paper the match percentage (MP) of lightning frequency in Jiangsu, China from June to August in 2019 is analyzed, by comparing the lightning mapping imager (LMI) data from FengYun (FY)-4A geostationary meteorological satellite with the Advanced Direction and Time-of-arrival Detecting (ADTD)-2 total lightning location data and radar composite reflectivity. The results show that the MP increases with the spatial matching window, yet when the matching window is larger than 0.8° (~80 km), the MP tends to be stable. Furthermore, the spatial distribution of lightning and the characteristics of the match lightning (ML) current are analyzed. The horizontal detection error of LMI for isolated thunderstorm clusters is about 0.15 ± 0.003° (~15 km). The amplitude of the lightning current is an important but not the only factor affecting the satellite's detection efficiency. Through the 10 min analysis of a large-scale and long-term thunderstorm process in Jiangsu province on July 6, 2019, it is found that the lightning detected by LMI and ADTD-2 occurred mostly in the region with strong echoes above 40 dBZ or in its vicinity. With the development of the thunderstorm, the lightning detected by ADTD-2 gradually increased and reached a maximum at 14:40. Subsequently, with the weakening of the thunderstorm system the lightning detected by ADTD-2 gradually decreased. However, the lightning detected by LMI was less all the time before 18:30 and then increased gradually. In the fourth stage (19:40 on July 6–00:20 on July 7), LMI has the highest relative detection efficiency of 12.78%–14.66% using ADTD-2 as a reference, indicating that the satellite detection is relatively less efficient during the daytime.

Thunderstorm Trends over Africa

AbstractThunderstorms inflict death and damage worldwide due to lightning, heavy rains, hail, and strong winds. While the effect of global warming on future thunderstorm activity is still debatable, this work investigates how thunderstorm activity over Africa may have changed over the last 70 years. Thunderstorm data were obtained from the World Wide Lightning Location Network (WWLLN) and processed to produce thunderstorm clusters. The number and area of clusters in one year (2013) were compared with several climate parameters tied to thunderstorm development, taken from the NCEP–NCAR Reanalysis-1 product (NCEP). The two parameters that correlated best with thunderstorm number were lifted index and specific humidity, with correlations of −0.795 and 0.779, respectively. These parameters were used to construct an empirical model that predicts the number and area of thunderstorm clusters over Africa on a particular day, month, or year. The empirical model was run from 1948 to 2016, providing a reconstruction of long-term thunderstorm activity over Africa. The time series was compared to temperature data from NCEP, and showed that the number of clusters increased with rising surface temperature on annual and decadal time scales, particularly since the mid-1990s. On an annual time scale, the number and area of thunderstorm clusters exhibited a highly sensitive relationship with surface temperature, with a ~40% increase in the number of thunderstorm clusters for every 1-K rise in temperature over Africa. The correlation coefficients with surface temperature were 0.745 and 0.743 for cluster number and area, respectively, indicating that surface temperature explains ~55% of the variability in interannual thunderstorm clusters over the past 70 years.

Uganda rainfall variability and prediction

This study analyzes large-scale controls on Uganda’s rainfall. Unlike past work, here, a May–October season is used because of the year-round nature of agricultural production, vegetation sensitivity to rainfall, and disease transmission. The Uganda rainfall record exhibits steady oscillations of ∼3 and 6 years over 1950–2013. Correlation maps at two-season lead time resolve the subtropical ridge over global oceans as an important feature. Multi-variate environmental predictors include Dec–May south Indian Ocean sea surface temperature, east African upper zonal wind, and South Atlantic wind streamfunction, providing a 33% fit to May–Oct rainfall time series. Composite analysis indicates that cool-phase El Nino Southern Oscillation supports increased May–Oct Uganda rainfall via a zonal overturning lower westerly/upper easterly atmospheric circulation. Sea temperature anomalies are positive in the east Atlantic and negative in the west Indian Ocean in respect of wet seasons. The northern Hadley Cell plays a role in limiting the northward march of the equatorial trough from May to October. An analysis of early season floods found that moist inflow from the west Indian Ocean converges over Uganda, generating diurnal thunderstorm clusters that drift southwestward producing high runoff.

Convective Outbreak over the Red Sea and Downstream Easterly Waves

Abstract This study analyzes a convective outbreak over the Red Sea on 25 August 2009 that generated easterly waves over the Sahel, floods in Ouagadougou, and a hurricane in the east Atlantic. The convective outbreak occurred on the equatorward flank of the African easterly jet 18°–22°N and associated meridional heating gradients over the Arabian Peninsula. The Rift Valley mountains induced a vertical orographic undulation and cyclonic perturbation. Two thunderstorm clusters over the southern Red Sea received moist inflow from the Ethiopian highlands and northern Red Sea. This group of three easterly waves intensified downstream over the Sahel. One of the convective triggers was enhancement of the Arabian Ridge by the northern subtropical jet. Statistical analyses indicate that African easterly waves and subsequent tropical storms are more influenced by upstream kinematic shear than thermodynamic energy. The work offers new insights on the formation of easterly waves over the northern Rift Valley.

Local and global impacts on the fair-weather electric field in Israel

Ground-based measurements of the vertical electric field (Ez or potential gradient) during fair weather days in the Negev desert, southern Israel are presented for the period June 2013–July 2015. We show results of the diurnal variation of Ez on seasonal and annual time scales, and make comparisons with the well-known Carnegie curve. We show a positive correlation between the diurnal Ez values and the number of global thunderstorm clusters on the same days. However, the diurnal Ez curves observed in the Negev desert show a local morning peak (8–10 UT) that is missing from the Carnegie Curve, but observed in other land-based Ez data from around the world. The morning peak is assumed to be a local effect and shown to correlate with a peak in the local aerosol loading in the lower atmosphere due to the increase in turbulence and mixing caused by solar heating in the morning hours.

Do West African thunderstorms predict the intensity of Atlantic hurricanes?

AbstractSince 85% of all major Atlantic hurricanes originate as thunderstorm clusters in equatorial Africa, we have investigated the connection between these African thunderstorms and the consequent development of these disturbances into tropical storms. We have analyzed Meteosat infrared cloud top temperature data to determine the areal coverage of cold cloud tops over a 6 year period from 2005 to 2010. In addition, hurricane statistics from the same period (intensity, date of generation, location, and maximum winds) were obtained from the National Hurricane Center database. We first show that the areal coverage of cold clouds (with brightness temperatures Tb < −50°C) in tropical Africa is a good indicator of the monthly number of African Easterly Waves (AEWs) leaving the west coast of tropical Africa. Furthermore, the AEWs that develop into tropical storms have a significantly larger area covered by cold cloud tops compared with nondeveloping waves. Finally, we show that on a storm‐by‐storm basis, the cold cloud coverage in West Africa is positively correlated (r = 0.57) with the accumulated cyclone energy of the future tropical cyclones that develop out of these waves.

A Preliminary Assessment of Using Spatiotemporal Lightning Patterns for a Binary Classification of Thunderstorm Mode

Abstract This study provides a preliminary, regional assessment of the viability of using spatiotemporal lightning patterns to classify storms into single- versus multi- and supercell storm modes. Total lightning flashes (intracloud and cloud-to-ground flashes) occurring during the afternoon and evening of the period May–August 2012 within an area of the central Appalachian Mountains region were grouped based on their spatial and temporal characteristics using single-linkage clustering. The resulting discrete thunderstorm clusters were characterized in terms of duration, motion, areal extent, and shape. These values were used to formulate four individual attribute scores representing the similarity to the expected values for a typical single-cell thunderstorm. The four scores were then combined into a storm index (SI) using relative weights determined through the analytic hierarchy process (AHP) performed on input from operational forecasters. Of the study days, 89 (72.4%) possessed appreciable lightning, of which 36 (40%) possessed a defined minimum amount of lightning activity required for further analysis. These 36 storm days were divided into two tiers according to the distribution of median daily SI values. The tier containing the 24 storm days (66.7%) with the largest median SI values possessed statistically significant smaller values of 0–6-km wind shear [13.8 knots (kt; 1 kt = 0.51 m s−1)] versus the 12 days in the lower tier of SI values (26.5 kt). This consistency between the total lightning-based classification scheme and increased vertical wind shear associated with lightning-defined multi- and supercells, also evident in synoptic atmospheric composites, lends credibility to the procedure.

Mesoscale Convective Systems and Their Synoptic-Scale Environment in Finland

Abstract The environments within which high-latitude intense and nonintense mesoscale convective systems (iMCSs and niMCSs) and smaller thunderstorm clusters (sub-MCSs) develop were studied using proximity soundings. MCS statistics covering 8 years were created by analyzing composite radar imagery. One-third of all systems were intense in Finland and the frequency of MCSs was highest in July. On average, MCSs had a duration of 10.8 h and traveled toward the northeast. Many of the linear MCSs had a southwest–northeast line orientation. Interestingly, a few MCSs were observed to travel toward the west, which is a geographically specific feature of the MCS characteristics. The midlevel lapse rate failed to distinguish the environments of the different event types from each other. However, in MCSs, CAPE and the low-level mixing ratio were higher, the deep-layer-mean wind was stronger, and the lifting condensation level (LCL) was lower than in sub-MCSs. CAPE, low-level mixing ratio, and LCL height were the best discriminators between iMCSs and niMCSs. The mean wind over deep layers distinguished the severe wind–producing events from the nonsevere events better than did the vertical equivalent potential temperature difference or the wind shear in shallow layers. No evidence was found to support the hypothesis that dry air at low- and midlevels would increase the likelihood of severe convective winds. Instead, abundant low- and midlevel moisture was present during both iMCS cases and significant wind events. These results emphasize the pronounced role of low- and midlevel moisture on the longevity and intensity of deep moist convection in low-CAPE environments.

On the spatial and temporal distribution of global thunderstorm cells

Estimates of global thunderstorm activity have been made predominately by direct measurements of lightning discharges around the globe, either by optical measurements from satellites, or using ground-based radio antennas. In this paper we propose a new methodology in which thunderstorm clusters are constructed based on the lightning strokes detected by the World Wide Lightning Location Network (WWLLN) in the very low frequency range. We find that even with low lightning detection efficiency on a global scale, the spatial and temporal distribution of global thunderstorm cells is well reproduced. This is validated by comparing the global diurnal variations of the thunderstorm cells, and the currents produced by these storms, with the well-known Carnegie Curve, which represents the mean diurnal variability of the global atmospheric electric circuit, driven by thunderstorm activity. While the Carnegie Curve agrees well with our diurnal thunderstorm cluster variations, there is little agreement between the Carnegie Curve and the diurnal variation in the number of lightning strokes detected by the WWLLN. When multiplying the number of clusters we detect by the mean thunderstorm conduction current for land and ocean thunderstorms (Mach et al 2011 J. Geophys. Res. 116 D05201) we get a total average current of about 760 A. Our results show that thunderstorms alone explain more than 90% in the variability of the global electric circuit. However, while it has been previously shown that 90% of the global lightning occurs over continental landmasses, we show that around 50% of the thunderstorms are over the oceans, and from 00-09UTC there are more thunderstorm cells globally over the oceans than over the continents. Since the detection efficiency of the WWLLN system has increased over time, we estimate that the lower bound of the mean number of global thunderstorm cells in 2012 was around 1050 per hour, varying from around 840 at 03UTC to 1150 storms at 19UTC.

Diurnal variation of the global electric circuit from clustered thunderstorms

The diurnal variation of the global electric circuit is investigated using the World Wide Lightning Location Network (WWLLN), which has been shown to identify nearly all thunderstorms (using WWLLN data from 2005). To create an estimate of global electric circuit activity, a clustering algorithm is applied to the WWLLN data set to identify global thunderstorms from 2010 to 2013. Annual, seasonal, and regional thunderstorm activity is investigated in this new WWLLN thunderstorm data set in order to estimate the source behavior of the global electric circuit. Through the clustering algorithm, the total number of active thunderstorms are counted every 30 min creating a measure of the global electric circuit source function. The thunderstorm clusters are compared to precipitation radar data from the Tropical Rainfall Measurement Mission satellite and with case studies of thunderstorm evolution. The clustering algorithm reveals an average of 660±70 thunderstorms active at any given time with a peak‐to‐peak variation of 36%. The highest number of thunderstorms occurs in November (720±90), and the lowest number occurs in January (610±80). Thunderstorm cluster and electrified storm cloud activity are combined with thunderstorm overflight current measurements to estimate the global electric circuit thunderstorm contribution current to be 1090±70 A with a variation of 24%. By utilizing the global coverage and high time resolution of WWLLN, the total active thunderstorm count and current is shown to be less than previous estimates based on compiled climatologies.

Synoptic and Mesoscale Analysis of a High-Latitude Derecho–Severe Thunderstorm Outbreak in Finland on 5 July 2002

Abstract On 5 July 2002, a rapidly propagating bow echo formed over eastern Finland causing severe wind damage in an exceptionally large area. The Ministry of the Interior’s Emergency Response Centers received nearly 400 thunderstorm-related wind damage reports. The 5 July 2002 case is the highest-latitude derecho that has ever been documented. The bow echo developed ahead of a northeastward-moving 500-hPa trough inside of the warm sector of a secondary low and moved north-northwestward on the eastern (warm) side of the quasi-stationary front. The leading edge of the bow echo was oriented perpendicular to the low-level southerly wind shear and the convective system propagated along the 850-hPa equivalent potential temperature ridge with a speed that was close to the maximum wind throughout the troposphere. It is particularly noteworthy that the synoptic pattern was oriented about 90° counterclockwise when compared with the typical synoptic pattern associated with warm season derechos in the United States. This kind of synoptic situation associated along with the derecho mesoscale convective system’s (MCS’s) motion toward the north-northwest has not been mentioned in literature before. The MCS started as a cluster of thunderstorms and became a bow echo a few hours later. The leading edge of the bow echo had a strong reflectivity gradient and the region of stratiform precipitation was behind the strongest echoes. At the most intense stage, a rear-inflow notch was visible both in radar and satellite pictures. It was in good accordance with the location of an area of the most severe damage. Moreover, the storm-relative winds derived from the proximity sounding in the wake of the system showed the existence of rear-to-front flow above 850 hPa. The downdraft air appeared to originate from 4 km ASL, where the relative humidity was less than 50%. This probably led to enhanced evaporative cooling and the intense cold pool, which propagated faster than the mean wind. In the mesoscale, the 5 July 2002 derecho had many similarities to other derecho MCSs that have been described in the literature.

3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR

Abstract In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied. The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes. When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.