Enter Zoom Meeting


Forecasting the weather

Forecasting the weather, in particular severe and extreme weather has always been the most important subject in meteorology. This session will focus on recent research and developments on forecasting techniques, in particular those designed for operations and impact oriented. Contributions related to nowcasting, meso-scale and convection permitting modelling, ensemble prediction techniques, and statistical post-processing are very welcome.
Topics may include:
 Nowcasting methods and systems, use of observations and weather analysis
 Mesoscale and convection permitting modelling
 Ensemble prediction techniques
 Ensemble-based products for severe/extreme weather forecasting
 Seamless deterministic and probabilistic forecast prediction
 Post-processing techniques, statistical methods in prediction
 Use of machine learning, data mining and other advanced analytical techniques
 Impact oriented weather forecasting
 Presentation of results from relevant international research projects of EU, WMO, and EUMETNET etc.

Co-organized by NH1/NP5
Convener: Yong Wang | Co-conveners: Aitor Atencia, Chaohui Chen, Lesley De CruzECSECS, Daniele NeriniECSECS
| Mon, 23 May, 15:10–17:42 (CEST)
Room F1

Mon, 23 May, 15:10–16:40

Chairpersons: Aitor Atencia, Lesley De Cruz

Yan Ji et al.

Detecting and predicting heavy precipitation for the next few hours is of great importance in weather related decision-making and early warning systems. Although great progress has been achieved in convective-permitting numerical weather prediction (NWP) over the past decades, video prediction models based on deep neural networks have become increasingly popular over the last years for precipitation nowcasting where NWP models fail to capture the quickly varying precipitation patterns. However, previous video prediction studies for precipitation nowcasting showed that heavy precipitation events are barely captured. This has been attributed to the optimization on pixel-wise losses which fail to properly handle the inherent uncertainty.  Hence, we present a novel video prediction model, named CLGAN, embedding the adversarial loss is proposed in this study which aims to generate improved heavy precipitation nowcasting. The model applies a Generative Adversarial Network (GAN) as the backbone. Its generator is a u-shaped encoder decoder network (U-Net) equipped with recurrent LSTM cells and its discriminator constitutes a fully connected network with 3-D convolutional layers. The Eulerian persistence, an optical flow model DenseRotation and an advanced video prediction model PredRNN-v2 serve as baseline methods for comparison. The models performance are evaluated in terms of application-specific scores including root mean square error (RMSE), critical success index (CSI), fractions skill score (FSS) and the method of object-based diagnostic evaluation (MODE). Our model CLGAN is superior to the baseline models for dichotomous events, i.e. the CSI, with a threshold of heavy precipitation (8mm/h), is significantly higher, thus revealing improvements in accurately capturing heavy precipitation events. Besides, CLGAN outperforms in terms of spatial scores such as FSS and MODE. We conclude that the predictions of our CLGAN architecture match the stochastic properties of ground truth precipitation events better than those of previous video prediction methods. The results encourage the applications of GAN-based video prediction architectures for extreme precipitation forecasting.

How to cite: Ji, Y., Gong, B., Langguth, M., Mozaffari, A., Mache, K., Schultz, M., and Zhi, X.: GAN-based video prediction model for precipitation nowcasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12086, https://doi.org/10.5194/egusphere-egu22-12086, 2022.

Michael Langguth et al.

Inspired by the success of superresolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields. While further increasing the resolution of numerical weather prediction models is computationally very expensive, statistical downscaling models can accomplish this task much cheaper once they have been trained.

In this study, we apply a generative adversarial network (GAN) to downscale the 2m temperature over Central Europe where complex terrain introduces a high degree of spatial variability. GANs are considered superior to purely convolutional networks since the model is encouraged to generate data whose statistical properties are similar to real data. Here, the generator consists of an u-shaped encoder decoder network which is capable of extracting features on various spatial scales. As a quasi-realistic test suite, we map data from the ERA5 reanalysis dataset onto a 0.1°-grid with the help of short-range forecasts from the Integrated Forecasting System (IFS) model. To increase the complexity of the downscaling task, the ERA5 reanalysis data is coarsened beforehand onto a 0.8°-grid, thus increasing the downscaling factor to 8. We evaluate our statistical downscaling model in terms of several evaluation metrics which measure the error on grid point-level as well as the quality of the downscaled product in terms of spatial variability and produced probability function. We also investigate the importance of static and dynamic predictors such as the surface elevation and the temperature on different pressure levels, respectively. Our results motivate further development of deep neural networks for statistical downscaling of meteorological fields. This includes downscaling of other, inherently uncertain variables such as precipitation, operations on spatial resolutions at kilometer-scale and ultimately targets an operational application on output data from global NWP models.

How to cite: Langguth, M., Gong, B., Ji, Y., Amirpasha, M., Mache, K., and Schultz, M. G.: Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12252, https://doi.org/10.5194/egusphere-egu22-12252, 2022.

Faisal Baig et al.

Rainfall plays a significant role in agricultural farming and is considered one of the major natural sources for all living things.  The increase in greenhouse emissions and change in climatic conditions have an adverse effect on the rainfall patterns. Therefore, it becomes crucial to analyze the changing patterns and to forecast rainfall  to mitigate natural disasters that could be caused by the unexpected heavy rainfalls. This paper aims to compare the performance of seven states of the art time series models namely Moving Average(MA), Naïve Forecast(NF), Simple Exponential(SE), Holt’s Linear(HL), Holt’s Linear Additive(HLA), Autoregressive Integrated Moving Average(ARIMA), Seasonal Autoregressive Integrated Moving Average(SARIMA) for the prediction of rainfall. The historical monthly rainfall data from six different stations in United Arab Emirates (UAE) was obtained to assess the performance of seven techniques. Experimental results show that ARIMA outperforms all the prediction models with a mean square error (RMSE) of 9.49 followed by Holt’s Linear model with an RMSE value of 9.91. The performance of all the models is comparable and shows promising performance in rainfall prediction. This also shows the ability of these models to predict the rainfall in arid regions like the UAE

How to cite: Baig, F., Sherif, M., Ali, L., Khan, W., and Faiz, M. A.: Predicting Rainfall using Data-Driven Time Series Approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11143, https://doi.org/10.5194/egusphere-egu22-11143, 2022.

Irene Schicker et al.

In this study, we present a deep learning-based method to provide seamless high-frequency wind speed forecasts for up to 30 hours ahead. For each selected site, our method generates an ensemble forecast with an update frequency of 10 to 15 minutes(depending on the observation site’s update-frequency). The main objective in this machine learning based post-processing method is to optimally exploit highly resolved NWP models and particularly utilize their multi-level meteorological parameters to integrate the three-dimensionality of weather processes. Further key objectives of this research are to consider different spatial and temporal resolutions and different topographic characteristics of the selected sites. We evaluate the best praxis for efficiently post-processing both the 10-meter wind speed at selected Austrian meteorological observation sites and wind speed on hub height of wind turbines in wind farms.

The method is based on an artificial neural network (ANN), particularly a long-short-term-memory (LSTM) adopted to process several differently structured inputs simultaneously (i.e., different gridded inputs along with observed time-series) and generate ensemble output. An LSTM layer models recurrent steps in the ANN and is, thus, useful for time-series, such as meteorological observations.

Our ensemble forecast method is evaluated for a case study in 2021 using several years of training, including extreme weather event for the selection of sites. The utilized data includes the meteorological observations, gridded nowcasting data as well as NWP data from ECMWF IFS and AROME at several pressure/altitude levels. Hourly runs for 12 test locations (selected TAWES sites covering different topographic situations in Austria) and two wind turbine sites in different seasons are conducted. The obtained results indicate that the model succeeds in learning from inputs while remaining computationally efficient. In most cases the ANN method yields high forecast-skills and is compared to available methods such as the raw NWP model output, climatology, and persistence.

How to cite: Schicker, I., Papazek, P., and DeWit, R.: High-frequency ensemble wind speed forecasting using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11240, https://doi.org/10.5194/egusphere-egu22-11240, 2022.

Badreddine Alaoui et al.

In front of determinism limitations, ensemble forecasting provides competitive advantage assessing uncertainty and helping weather information users in decision-making. Analog ensemble method (AnEn) is one of the most intuitive and computationally cheap ensemble methods that leverages a single deterministic model integration to produce probabilistic information. This method builds an ensemble forecast from a set of past observations of the target variable, neatly selected from a historical training dataset. For a given location, the most similar past forecasts to the current prediction are identified and the associated  past observations are nominated  as members of the analog ensemble forecast. However, The  AnEn forecasting quality is tightly affected by the process of skillful analogs selection in the training data which depends on predictor’s weighting among other factors. This work presents a new weighting strategy based on machine learning techniques (XGBoost, Random Forest and Linear regression) and assesses the impact of its application on the AnEn performance  for 10m wind speed  and 2m temperature forecasting over 13 Moroccan airports in the short term forecasting framework (24 hours). To achieve this, hourly forecasts from the operational mesoscale AROME model and the verifying observations covering 5 year period (2016-2020) are used.  The predictors include 2m temperature, 2m relative humidity, 10m wind speed and direction, mean sea level pressure and surface pressure,  meridonal and zonal components of 10m wind. The basic configuration of Delle Monache et al. (2013) -DM13- where all the predictor’s weights are equal to one is used here as a benchmark. The best weights are computed independently from one airport to another. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, and hence preserve physical meaning and correlations of the used weather variables, the AnEn performances are improved by up to 50 % for bias and by 30% for RMSE for most airports. This improvement varies as function of lead-times and seasons compared to AROME and DM13’s configuration. Results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.


Keywords : Analog Ensemble,  Machine Learning, Predictors Weighting Strategies, 2m Temperature, 10m Wind Speed, XGBoost, Linear Regression, Random Forest, Ensemble Forecasting.

How to cite: Alaoui, B., Bari, D., and Ghabbar, Y.: New AI based weighting strategy for 2m temperature and 10m wind speed forecasting over Moroccan airports  using the analog ensemble method., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2450, https://doi.org/10.5194/egusphere-egu22-2450, 2022.

Alexander Kann et al.

For hydrological runoff simulations in hydropower applications, accurate analyses and short-term forecasts of precipitation are of utmost importance. Traditionally, radar-based extrapolations are used for very short-term time scales (approx. 0 - 2 hours ahead). However, during recent years, convection-permitting NWP models have become better at very high spatial and temporal resolution forecasts (e.g. through radar assimilation, RUC configurations). Such models have the advantage of capturing the complex and non-linear evolution of precipitation systems like fronts or thunderstorms in a more physically accurate way than extrapolations, but they are also prone to inaccuracies in precipitation distribution. The aim of this paper is to employ machine learning to combine the strengths of the conventional radar extrapolation (localization and movement of existing storms) with the benefit of the model’s ability to predict storm evolution.  Results show that even a relatively simple sequential deep neural network is able to outperform both, the operational nowcasting and NWP model forecasts. However, the results are highly sensitive to variable selection, loss function, and localization features have a large impact on performance, which is also discussed.

How to cite: Kann, A., Atencia, A., Scheffknecht, P., and Giannakos, A.: AI-based blending of conventional nowcasting with a convection-permitting NWP model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12384, https://doi.org/10.5194/egusphere-egu22-12384, 2022.

Esmail Ghaemi et al.

As one of the essential inputs, accurate precipitation forecasts play a significant role in having a reliable flood warning system. Due to the variability of precipitation in time and space, forecast models need to use the latest observational data as input. However, the accumulation of noise and the imbalance in analyses can decrease the performance of forecasts (Seity et al., 2011). The convection-permitting numerical limited area model AROME (Applications of Research to Operations at Mesoscale) is a non-hydrostatic convective-scale limited-area model with 2.5 km resolution. The three-hourly update cycle AROME has been replaced by the hourly update cycle since 2019 in Austria.

The aim of this study is to assess the operational AROME and INCA (Integrated Nowcasting through Comprehensive Analysis) forecasts with the three-hourly and one-hourly update cycles for three events over Austria. We use the object-oriented Structure-Amplitude-Location (SAL) approach to evaluate the performance of precipitation forecasts. The SAL approach is based on a minimum threshold method to detect precipitation objects for the forecast verification (Wernli et al., 2009). Here, we use a multi-threshold approach to define precipitation objects using the thunderstorm detection and tracking algorithm (Feldmann et al., 2021). Three heavy precipitation events with different characteristics are selected to investigate the performance of AROME and INCA using the SAL metric. The result of this study helps to understand the role of the update frequency in forecasting.

How to cite: Ghaemi, E., Foelsche, U., Kann, A., and Fuchsberger, J.: Assessment of the rainfall forecasts from extrapolation-based INCA nowcasting and AROME forecasts in Austria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8131, https://doi.org/10.5194/egusphere-egu22-8131, 2022.

Ruben Imhoff et al.

Radar rainfall nowcasting, an observation-based rainfall forecasting technique that statistically extrapolates current observations into the future, is increasingly used for short-term forecasting (<6 hours ahead). These first hours ahead are a key time scale for e.g. (flash) flood warnings and they are generally not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models.

A recent development in nowcasting is the transition to more community-driven, open-source models. The Python library pySTEPS is an example of this. One of its main features is an efficient Python implementation of the probabilistic nowcasting scheme STEPS. pySTEPS generates an ensemble of rainfall forecasts by perturbing a deterministic extrapolation nowcast with spatially and temporally correlated stochastic noise. It considers the dynamical scaling of the rainfall predictability by decomposing the rainfall fields into a multiplicative cascade and applies different stochastic perturbations for each scale. This results in large-scale features that evolve more slowly than the small-scale features.

Despite pySTEPS' representation of the uncertainty associated with growth and decay of rainfall in the first 1-2 hours of the nowcast, it quickly loses skill after 2 hours, or even less for convective rainfall events or small radar domains. To extend the skillful lead time to the desired time scale of 6 hours or more, a blending with NWP rainfall forecasts is necessary. We have implemented an adaptive scale-dependent blending in pySTEPS based on earlier work in the STEPS scheme. In this blending implementation, the blending of the extrapolation nowcast, NWP and noise components is performed level-by-level, which means that the blending weights vary per cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a 1-month rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.

We present a validation of the blending approach in a hydrometeorological testbed using Belgian radar and NWP data for the Belgian and Dutch catchments Dommel, Geul and Vesdre. We compare the resulting ensemble rainfall and discharge forecasts of the blending implementation with ensemble nowcasts from pySTEPS, ALARO (NWP) forecasts and a linear blending strategy.

How to cite: Imhoff, R., De Cruz, L., Dewettinck, W., Velasco-Forero, C., Nerini, D., Goudenhoofdt, E., Brauer, C., van Heeringen, K.-J., Uijlenhoet, R., and Weerts, A.: Scale-dependent blending of ensemble rainfall nowcasts with NWP in the open-source pySTEPS library, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7026, https://doi.org/10.5194/egusphere-egu22-7026, 2022.

Lesley De Cruz et al.

Thanks to recent advances in multisensory observation systems and high-resolution numerical weather prediction (NWP) models, a wealth of information is available to feed and improve operational weather forecasting systems. At the same time, end users such as the renewable energy sector and hydrological services require increasingly detailed and timely weather forecasts that take into account the latest observations.

However, data assimilation in NWP models cannot yet leverage the full spatial or temporal resolution of today's observation systems. Moreover, the combined assimilation and model run takes significantly more time than an extrapolation-based nowcast, and cannot match its accuracy at short lead times. Therefore, many National Meteorological Services (NMSs) are moving towards seamless prediction systems. Seamless prediction aims to make optimal use of today’s rapidly available, high-resolution multisensory observations, nowcasting algorithms and state-of-the-art convection-permitting NWP models. This approach integrates multiple data and model sources to provide a single, frequently updating deterministic or probabilistic forecast for lead times from minutes to days.

We present the seamless ensemble prediction system of the Royal Meteorological Institute of Belgium, called Project IMA (Japanese for "now" or "soon"). It provides rapidly updating seamless forecasts for the next 5 minutes to 24 hours. The nowcasting component is based on two systems: (1) the open-source probabilistic precipitation nowcasting scheme pySTEPS, which now features a scale-dependent blending with NWP ensemble forecasts (also presented in this session) and (2) an ensemble of INCA-BE nowcasts using two different NWP models, for other meteorological variables. The short-range NWP component consists of a multimodel lagged Mini-EPS of two convection-permitting configurations of the ACCORD system: AROME and ALARO, running at 1.3km resolution. It features a 3-hourly DA cycle and provides high-frequency precipitation output to facilitate the blending of precipitation nowcasts and forecasts. The system runs robustly using our NodeRunner tool based on EcFlow, ECMWF's operational work-flow package. We will give an overview of the development (past and future), some lessons learned, and use cases for Project IMA.

How to cite: De Cruz, L., Deckmyn, A., Degrauwe, D., Dehmous, I., Delobbe, L., Dewettinck, W., Goudenhoofdt, E., Imhoff, R., Reyniers, M., Smet, G., Termonia, P., Van den Bergh, J., Van Ginderachter, M., and Vannitsem, S.: Project IMA: Building the Belgian Seamless Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12529, https://doi.org/10.5194/egusphere-egu22-12529, 2022.

Ahmed Abdelhalim et al.

Early hydrological hazard warning demands precise weather forecasts to accurately predict the timing and the location of intense precipitation events which can cause severe floods/landslides and present risks to urban and natural environments. Extrapolation of precipitation by radar rainfall products at high space and time scales with short lead times outperforms forecasts of numerical weather prediction. Therefore, developing and improving of rainfall nowcasts systems are essential. Rainfall nowcasting is the process of forecasting precipitation field movement and evolution at high spatial and temporal resolutions with short lead times(<6h) in which the advection of the precipitation fields is estimated by extrapolating real-time remotely sensed observations. Radar rainfall nowcasting is increasingly applied because of the high potential of radar products in short-term rainfall forecasting due to their high spatiotemporal resolutions (typically, 1 km and 5 min). It consists of two procedures in tracking precipitation features to calculate the velocity from a series of consecutive radar images and propagating the most recent precipitation observation into the future using the obtained velocity. Optical flow represents one of the most used methods for tracking the motion fields from consecutive images. Deep learning techniques are those machine learning methods that utilise deep artificial neural networks. Deep learning has become one of the most popular and rapidly spreading methods in different scientific disciplines including water-related research. Deep learning applications in radar-based precipitation nowcasting is still in its early stage with many knowledge gaps and their full potential in rainfall nowcasting requires more investigation. This work evaluates the performance of a deep convolutional neural network (called rainnet) and three optical flow algorithms (called Rainymotion Sparse, Rainymotion Dense, Rainymotion DenseRotation) compared with Eulerian Persistence to assess their predictive skills in nowcasting. Synthetic precipitation scenarios have been created with different motion fields (linear and rotational motions), velocities, intensities, sizes, and locations. The models have been evaluated to forecast different precipitation processes that contribute mainly to model errors such as constant and accelerated linear and rotational motions, growth and decay in both size and intensity. Different verification metrics have been used to evaluate the skill of the forecasts.


Keywords: radar rainfall nowcasting; deep learning; optical flow; extrapolation; rainnet; rainymotion

How to cite: Abdelhalim, A., Rico-Ramirez, M., and Han, D.: Evaluation of radar rainfall nowcasting techniques to forecast synthetic storms of different processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10595, https://doi.org/10.5194/egusphere-egu22-10595, 2022.

Meriem Krouma et al.

Skillful forecast of the Madden Julian Oscillation (MJO) has an important scientific interest because the MJO represents one of the most important sources of  sub-seasonal predictability. Proxies of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2). The challenge is to forecast these two indices. This study aims at providing ensemble forecasts MJO indices  from analogs of the atmospheric circulation, mainly the geopotential at 500 hPa (Z500) by using a stochastic weather generator. We generate an ensemble of 100 members for the amplitude and the RMMs for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using respectively probabilistic and deterministic skill scores. We found that a reasonable forecast could reach 40 days for the different seasons. We compared our SWG forecast with other forecasts of the MJO.

How to cite: Krouma, M., Yiou, P., and Silini, R.: Ensemble forecast of the Madden Julian Oscillation using a stochastic weather generator based on analogs of  Z500, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-742, https://doi.org/10.5194/egusphere-egu22-742, 2022.

Kris Boykin

Ensemble forecasts are calculated to give insight into the range of possible future outcomes and potential risks, but it is challenging for operational forecasters to deal with large ensemble data sets and to distil pertinent information from them, especially during high-impact events where forecasts and warnings must be issued and updated quickly with a high degree of accuracy and consistency.  Therefore, it is important to streamline this process by reducing the amount of data an operational forecaster must digest while still maintaining the necessary accuracy.  To do this, a novel clustering technique has been developed for use on ensemble forecasts to extract likely scenarios in real-time.  This technique uses k-medoids clustering and the spatial separation between frontal regions in ensemble members to group similar members together.  Frontal regions are often associated with heavy rain and strong winds, common high-impact events in the UK.  A single representative member is then extracted from each cluster to present to the forecaster as a potential weather scenario.  The method is illustrated with the UK Met Office operation ensemble forecasting system, MOGREPS-G.

How to cite: Boykin, K.: Extracting likely scenarios from high resolution ensemble forecasts in real-time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7391, https://doi.org/10.5194/egusphere-egu22-7391, 2022.

Mon, 23 May, 17:00–18:30

Chairpersons: Lesley De Cruz, Aitor Atencia

Chen Chaohui et al.

Terrain with different shapes and ground surface properties has extremely complex impacts on atmospheric motion, and the forecast uncertainty and complexity caused by terrain brings great challenges to disaster prevention and mitigation. Therefore, it is essential to design a new-style model topography disturbance model for ensemble prediction system specifically to solve the prediction uncertainty caused by complex terrain. In this paper, on the basis of combing the current models and methods for dealing with different terrain uncertainty, and considering the non-uniformity of terrain gradient, the key element of describing terrain complexity, an orthogonal terrain disturbance method based on terrain gradient is designed and proposed, and the obtained high-resolution orthogonal terrain disturbance is superimposed on the static terrain height of the model to generate different ensemble members, so as to describe the uncertainty in the terrain generation process of high-resolution numerical model. At the same time, a comparative study is carried out with the ensemble forecast of model terrain disturbance between using the new-style method and using different terrain interpolation schemes or smoothing schemes. The preliminary test shows that: first of all, the ensemble dispersion of terrain height disturbance based on the new-style method is closely related to the terrain gradient. The area with small terrain gradient has smaller terrain disturbance ensemble dispersion, while the area with large terrain gradient has larger ensemble dispersion, which shows that the new scheme is more reasonable. Furthermore, compared with the model terrain disturbance schemes with different interpolation or smoothing methods, the dispersion of the new-style method is larger, and the skill of the new-style method becomes more and more obvious with the increase of model resolution. Thirdly, from the comparative study of the forecast effect of high-level and low-level weather elements, the new-style method ensemble forecast has obvious improvement on the forecast effect of low-level variables, especially in areas with complex terrain or large terrain gradient. The possible reason is that the new method can more objectively describe the terrain uncertainty. Fourthly, compared with the ensemble forecast results of different interpolation and smoothing methods, the new-style terrain disturbance scheme can improve the precipitation probability forecast skill and reduce the ensemble average root mean square error, and improve the ensemble average forecast of upper-air elements and near-surface elements. Lastly, the test of the number of ensemble members shows that the prediction effect of new-style terrain disturbance scheme with less members is equivalent or better than that of the interpolation or smoothing terrain disturbance scheme with more members. In summary, the new-style terrain perturbation theory based on terrain gradient in this paper provides a technical reference for the development of complex terrain convection-allowing scale ensemble forecast, which has important theoretical value and application prospect.

Key words: complex terrain,ensemble prediction,convection-allowing scale,topographic perturbation,topographic gradient

How to cite: Chaohui, C., Yi, L., Hongrang, H., Kan, L., and Yongqiang, J.: Preliminary study of a new-style terrain disturbance method based on gradient inhomogeneity in convection-allowing scale ensemble prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13244, https://doi.org/10.5194/egusphere-egu22-13244, 2022.

Javier Díaz Fernández et al.

Mountain lee waves are a kind of gravity waves often associated with adverse weather phenomena, such as turbulence that can affect the aviation safety. Not surprisingly, turbulence events have been related with numerous aircraft accidents reports. In this work, several mountain lee wave events in the vicinity of the Adolfo Suarez Madrid-Barajas airport (Spain) are simulated and analyzed using HARMONIE-AROME, the high-resolution numerical model linked to the international research program ACCORD-HIRLAM. Brightness temperature from the Meteosat Second Generation (MSG-SEVIRI) has been selected as observational variable to validate the HARMONIE-AROME simulations of cloudiness associated with mountain lee wave events. Subsequently, a characterization of the atmospheric variables related with the mountain lee wave formation (wind direction and speed, static stability and liquid water content) has been carried out in several grid points placed in the windward, leeward and over the summits of the mountain range close to the airport. The characterization results are used to develop a decision tree to provide a warning method to alert both mountain lee wave events and associated lenticular clouds. Both HARMONIE-AROME brightness temperature simulations and the warnings associated with mountain lee wave events were satisfactory validated using satellite observations, obtaining a probability of detection and percent correct above 60% and 70%, respectively.  

How to cite: Díaz Fernández, J., Bolgiani, P., Santos Muñoz, D., Sastre, M., Valero, F., Farrán, J. I., González Alemán, J. J., and Martín Pérez, M. L.: Characterization and warnings for mountain waves using HARMONIE-AROME, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2471, https://doi.org/10.5194/egusphere-egu22-2471, 2022.

Inés Cecilia Simone et al.

Thunderstorms in southeastern South America (SESA) often reach extreme intensity, duration, and vertical extension. Diverse techniques have been proposed to identify severe storm signatures in satellite images, such as overshooting tops (OTs). Previous studies have shown a large correlation between OTs and the occurrence of severe weather such as large hail, damaging winds, and tornadoes. In particular, in SESA, deep convection systems initiation is sometimes related to elevated topography such as Sierras de Córdoba and the Andes mountain range. These unique meteorological and geographical conditions motivated the RELAMPAGO-CACTI field campaign, which was conducted to study the storms in this region.

This study aims to characterize the occurrence of OTs in SESA through their spatial distribution as well as their diurnal and seasonal cycles.  An OT analysis is presented using an OT detection algorithm (known as OT-DET) applied to GOES16 satellite data from October 2018 to March 2019. OT-DET sensitivity is evaluated considering two alternatives of tropopause temperature determination and different cloud anvil temperature thresholds. OT-DET is validated against an OT occurrence database generated through an expert detection of OTs using GOES16 visible and IR images. The results of this validation as well as the OT characterization will be described at the conference. 

How to cite: Simone, I. C., Salio, P., Ruiz, J., and Vidal, L.: Study of Deep Convection with Presence of Overshooting Tops During RELAMPAGO Campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-147, https://doi.org/10.5194/egusphere-egu22-147, 2022.

Chun Hay Brian Lo et al.

Various studies in the UK, Great Plains and Southeastern USA have highlighted the presence of certain radar signatures prior to the onset of or during severe convection. One type of such radar signature is a differential reflectivity (ZDR) column, which is defined as a vertical columnar region of enhanced ZDR that extends above the freezing level. Several field campaigns synthesising radar and in-situ measurements confirmed that such columns contain large supercooled millimetre-sized droplets lofted into convective storms and are in, or near strong updrafts. Recent work using a single research radar in Oklahoma also exploited the usefulness of detecting ZDR columns for informing nowcasters of severe convection.

The goal of this study is to identify potential severe convective events in the UK mostly for cases over the summer season using polarimetric radar measurements. The UK Met Office has fully upgraded all 18 C-band radars since January 2018 with full dual-polarisation operational capability. From this network, we derive a 3D radar composite, which provides large coverage on the order of 1000 km for monitoring potentially hazardous weather. Environmental conditions are also investigated prior to and during the onset of convection to understand the effectiveness in ZDR columns as precursors of severe convection depending on synoptic regime.

Using past cases, we track storm cells using maximum reflectivity in the column and identify whether the cells contain ZDR columns, where a ZDR column is identified based on a 3D volume thresholded by reflectivity (ZH) and ZDR. For nowcasting of severe storms, with ZH > 50 dBZ, we find optimal ZH and ZDR thresholds of around 30 dBZ and 2.0 dB respectively existing within ZDR columns. This agrees with past literature and physical understanding indicating a low concentration of large super-cooled water droplets within ZDR columns explained by condensation-coalescence processes, especially during early stages of convective development. In contrast, other works may show ZDR columns associated with areas of high ZH, suggesting detection of such columns in more mature stages of a storm. Algorithm performance in identifying ZDR columns for early detection of severe convection and its optimal parameters vary with synoptic regime.

How to cite: Lo, C. H. B., Stein, T. H. M., Westbrook, C. D., Scovell, R. W., Darlington, T., and Lean, H. W.: Identification of ZDR columns for early detection of severe convection in southern England, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-317, https://doi.org/10.5194/egusphere-egu22-317, 2022.

Ru Yang et al.

To quantify the intensity of squall line in mid-latitudes, the author recently proposed a squall line intensity assessment method based on cold pool, which provides a measure of squall line intensity.

The disturbance potential temperature density is calculated by using the potential temperature, water vapor and all kinds of water condensate output from the numerical weather forecast model, and the boundary of the cold pool is judged according to the disturbance potential temperature density less than -2K. Based on the contour surface buoyancy, the high surface buoyancy is calculated according to the disturbance potential temperature density, and then the strength of the cold pool is calculated. In this method, the intensity of squall line is analyzed comprehensively by principal component analysis, combined with the weather phenomena accompanied by squall line occurrence, such as cold pool intensity, surface wind speed, ground pressure variation, surface temperature variation, simulated radar echo and so on. The above analysis is the local intensity on different grid points when the squall line occurs, and the overall squall line intensity is obtained by accumulating the local intensity in the squall line range.

The method is verified by the model output data of a squall line process occurred in northern Jiangsu on May 16, 2013. The results show that the distribution of the local squall line intensity is coupled with the surface wind field and heavy precipitation. The intensity evolution of the overall squall line reaches the peak in a short time and then decreases, which corresponds to the life history of the birth, development, maturity and dissipation of the squall line, and also reflects the characteristics of the short life history of the squall line developing rapidly and then dissipating. This method provides technical support for the forecast of squall line and the emergency plan issued by meteorological department.

Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41975128 and 42075053).

Keywords: squall line, intensity, assessment method, disturbance potential temperature density

How to cite: Yang, R., Jiang, Y., Chen, C., He, H., Li, Y., and Huang, H.: An Assessment Method of Squall Line Intensity Based on Cold Pool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13532, https://doi.org/10.5194/egusphere-egu22-13532, 2022.