The 24 September 2001 College Park, Maryland, tornado was a long-track and strong tornado that passed within a close range of two Doppler radars. It was the third in a series of three tornadoes associated with a supercell storm that developed in Stafford County, Virginia, and initiated 3 - 4 km southwest of College Park and dissipated near Columbia, Howard County. The supercell tracked approximately 120 km and lasted for about 126 minutes. This study presents a synoptic and mesoscale overview of favorable conditions and forcing mechanisms that resulted in the severe convective outbreak associated with the College Park tornado. Results show many critical elements of the tornadic event, including a negative-tilted upper-level trough over the Ohio Valley, a jet stream with moderate vertical shear, a low-level warm, moist tongue of the air associated with strong southerly flow over south-central Maryland and Virginia, and significantly increased convective available potential energy (CAPE) during the late afternoon hours. A possible role of the urban heat island effects from Washington, DC in increasing CAPE for the development of the supercell is discussed. Satellite imagery reveals banded convective morphology with high cloud tops associated with the supercell that produced the College Park tornado. Operational WSR-88D data exhibits a high reflectivity “debris ball” or tornadic debris signature (TDS) within the hook echo, the evolution of the parent storm from a supercell structure to a bow echo, and a tornado cyclone signature (TCS). Many of the mesoscale features could be captured by contemporary numerical model analyses. This study concludes with a discussion of the effectiveness of the coordinated use of satellite and radar observations in the operational environment of nowcasting severe convection.

Thunderstorms endager life and infrastructure. Accurate and precise prediction of thunderstorms are therefore helpful to enable protections measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description and discussion of a new Julia-based method (JuliaTSnos) for the temporal extrapolation of thunderstorms and the blending of this method with the numerical weather prediction model (NWP) ICON. The combination of ICON and JuliaTSnow attempts to overcome the limitations associated with the pure extrapolation of observations wit Atmospheric Motion Vectors (AMVs) and thus increase the prediction horizon. For the blending the operational ICON-D2 is used, but also the experimental ICON-RUC, which is performed with a higher data assimilation update cycle. The blended products are evaluated against lightning data. The Critical Success Index (CSI) for the blended RUC product is higher for all forecast time steps. This is mainly due to higher resolution of the AMVs (prediction hours 0-2) and the rapid update cycle of ICON-RUC (prediction hours 2-6). The results demonstrate the potential of the rapid update cycle to improve the short term forecasts of thunderstorms. Also the transition between AMV driven nowcasting to NWP is much smoother in the blended RUC product, which points to the advantages of fast data assimilation for seamless predictions. The CSI is well above the critical value of 0.5 for the 0-2 hour forecasts. However, also with RUC the CSI drops below 0.5 as soon as the last forecast is more than 3 hours away from the last data assimilation, indicating the lack of the model physics to accurately predict thunderstorms. This lack is simply a result of chaos theory. Within this scope the role of NWP in comparison with Artificial Intelligence (AI) is discussed and it is concluded that AI could replace physical short term forecasts in the near future.

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight savety. Monitoring and forecast of thunderstorms are therefore important topics. In this work a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWAARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼ 10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 hours, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is with 0.64 still quite high for the 60 minute forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD) and used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

The study investigates the role of NWP filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of 14 different experiments, covering Central Europe. These experiments compiles different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures. As stability filter parameters from Numerical Weather Prediction (NWP) are used. The brightness temperature information results from the IR SEVIRI instrument on-board of Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60 %. The false alarms results from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70 % . A brief review and reflection of the literature clarifies that the this function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) works well as stability filter.

Severe windstorms resulting from mesoscale convective systems (MCS) cause significant disruption to society, including widespread power outages, tree and structural damage, and transportation accidents that affect multi-state regions and metropolitan areas along their track. A derecho, defined as a long-lived, widespread severe convective windstorm, is composed of numerous downbursts (intense localized storm downdrafts) organized into clusters or families of clusters. Derechos can produce winds above hurricane force along a track that may exceed several hundred kilometers. Convective windstorm potential has been expressed as a grouping of stability parameters that are relevant for downburst generation. These include lower-to-mid-tropospheric and equivalent potential temperature (θe) lapse rates, vertical relative humidity differences, and the amount of convective available potential energy (CAPE) in the troposphere. One major environmental factor addressed in the generation of widespread severe convective winds is an elevated mixed layer and its associated instability that promotes powerful storm updrafts and downdrafts. Another critical factor is developing a rear-inflow jet into an MCS that channels unsaturated mid-tropospheric air into the leading convective storm line. Establishing an elevated, ascending front-to-rear flow originating from deep, moist convection, overlying a strong and deep outflow-induced cold pool has been found to generate and sustain a robust rear inflow jet. The North American Derecho of 29-30 June 2012 exhibits many classic progressive and serial derecho features. It remains as one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS) and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. In summary, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS.

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms usually rely on accurate atmospheric motion vectors (AMVs) for the estimation of cooling rate of convective clouds, which corresponds to the updraft strength of the cloud objects. In this study we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose the absolute value of the vector product of two vectors derived from the observed radiances emitted by the water vapor channels in 2 subsequent satellite images is calculated. The x and y components of the vectors consists of the temporal change of the spatial brightness temperature gradients in x- and y-direction, the z component consists of the temporal variation of the brightness temperature at pixel level. The absolute value of the vector product is referred to as normalized updraft strength (NUS). The performance of the method has been investigated for 2 summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66 % for the 2016 period and 62 % for the 2017 period demonstrate the good performance of the method. The POD values for the experiments with the highest CSI values are 90.3 for 2016 period and 87.6 for the 2017 period, respectively The corresponding FAR values are 28.2% (2016) and 32.5 % (2017), respectively. In summary, the method has the potential to reduce the forecast lead time significantly, and can be quite useful in regions without a well maintained radar network.

Strong convective storms can be a serious threat to modern society, influencing both the economy and human life. Thunderstorms can bring much-needed rain after a dry spell, but the nature of thunderstorm mostly causes damage due to high wind associated with it and heavy intensity rain that mostly causes quick overlandflow and flashfloods. The paper focused on the 2011 Pukkelpop event in Belgium where due to heavy thunderstorm a lot of financial damage occurred, and five casualties. In this paper we look into the basic elements that enforces the creation of a thunderstorm like CAPE and windshear. A representative grid has been taken from the Alaro-0 model of 12.5 km resolution for the analysis of the thunderstorm over Belgium which is detailed out in the methodology. The model runs of Alaro-0 from Royal Meteorological Institute of Belgium (RMI) is used for historical and future years for CAPE and Shear. We look into STEnv which is a composite number that can provide some insight on occurrence of thunderstorm, and STEnv is when CAPE*Shear exceeds certain threshold. In this study the threshold has been chosen as 97%. Only summer months have been taken into account into this study as summer(s) has the most thunderstorm in Belgium. The analysis of CAPE and STEnv over historical period (1977-2005), near future (2041-2069) and far future (2071-2099) shows significant increase in occurrence of thunderstorm in the 2071-2099s, specially in extreme cases such as 97% and 99% quantile. CAPE (J/Kg) increases in all future scenarios compared to the historical period. In 2071-2099s, CAPE increases from 2100 J/Kg to 2300 J/Kg (90% quantile) and from 3500 J/Kg to 3900 J/Kg (99% quantile) compared to the historical period. The number of STEnv increases from 300 to 378 in top 10% storm case (90% quantile), 158 to 218 in top 5% storm (95% quantile), 97 to 153 in top 3% storm (97% quantile) and 33 to 66 in top 1% storm (99% quantile) with a 90% confidence interval overall. Shear mostly remains unchanged. The study also shows spatial variation of CAPE, shear and STEnv over Belgium for present and future scenarios in different extremities (90%, 95%, 97% and 99% quantiles). There is a good indication that increase in CAPE translates to increase in STEnv, which is an accepted measure to predict occurrence of thunderstorm. Future climate change also amplifies the occurrence of thunderstorm, which is coherent with relevant studies. The uncertainty related to the study can be reduced with more detailed dataset and involvement of numerical weather prediction models beyond data statistics.

Flash floods repeatedly threaten Barcelona, damaging the city infrastructure and endangering the safety of the population. The city’s urban planning and socioeconomic distribution, associated with the topography and other geographic factors, means that these flood events do not affect the entire city in the same way. This is a key point for surveillance and emergency tasks, which need some patterns and models to improve response capacity. This work aims to gain a better understanding of such events, to add valuable information on how to predict and manage these situations. For this purpose, both radar and ground observational data have been combined to identify the most important precipitation episodes in Barcelona between 2013 and 2018. To make the analysis easier, a new algorithm has been developed to determine the thunderstorm hotspots. Episodes with a higher impact have been analysed in depth. The final objective is to improve the actions taken by the organisation responsible for managing urban floods, which have seen Barcelona recognised as a model city for flood resilience by the United Nations.