AS1.6 | From Mesoscale Convection to Convective-Scale Predictions: advances in process modelling, observations, data assimilation and machine learning
EDI
From Mesoscale Convection to Convective-Scale Predictions: advances in process modelling, observations, data assimilation and machine learning
Convener: Julia CurioECSECS | Co-conveners: Cornelia Klein, Kalli Furtado, Jian Li, Tijana Janjic, Tomislava Vukicevic, Tobias NeckerECSECS
Orals
| Mon, 15 Apr, 08:30–12:30 (CEST)
 
Room M2
Posters on site
| Attendance Mon, 15 Apr, 16:15–18:00 (CEST) | Display Mon, 15 Apr, 14:00–18:00
 
Hall X5
Orals |
Mon, 08:30
Posters on site
Mon, 16:15
Understanding severe convection and associated hazardous weather is crucial to mitigate societal impacts now and in a warmer future. However, convective-scale data analysis and severe weather predictions still present significant challenges for atmospheric sciences. Addressing these challenges requires a synergy of high-resolution convection-permitting modelling, observations, and data assimilation advances. For this reason, our session connects recent advancements in convective-scale process modelling, process understanding, data assimilation, prediction, observing systems, and machine learning.

Session objectives:
• To improve the process understanding and modelling of mesoscale and severe convection in current and future climates.
• To improve convective-scale data assimilation, forecasting and observation methodologies.
• To provide a collaborative platform for enhancing the predictability, uncertainty quantification and understanding of severe weather events and their impacts.
• To bridge mesoscale convective studies with novel convective-scale data assimilation and modelling techniques.

Key Topics:
• Dynamics, thermodynamics and microphysics of mesoscale and severe convection on weather and climate timescales.
• Impact of land-convection interactions, considering environmental factors like complex topography, soil moisture feedbacks,or land use (change).
• Advances in convective-scale data assimilation, forecasting and observations.
• Advances in machine learning for improved modelling of convective-scale processes.

Solicited authors:
•    Pieter Groenemeijer (European Severe Storms Laboratory) - “Severe Storms Research at ESSL”
•    Laure Raynaud (MeteoFrance) - “ML for weather prediction at Météo-France: current status and future plans”

Orals: Mon, 15 Apr | Room M2

Chairpersons: Julia Curio, Cornelia Klein
08:30–08:35
5-minute convener introduction
08:35–08:45
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EGU24-12158
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solicited
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On-site presentation
Severe Storms Research at ESSL 
Pieter Groenemeijer, Francesco Battaglioli, Tomáš Púčik, Alois Holzer, and Mateusz Taszarek

Convective storms are an important weather hazard in Europe as shown by the high number of severe wind gusts, large hail, tornadoes, and flash floods recorded each year in the European Severe Weather Database. A recent innovation to the ESWD was the introduction of the new tornado International Fujita scale for rating tornado and wind intensity from damage. In 2023, no fewer than 62182 new reports were entered, and reinsurer Munich Re estimated severe thunderstorms to account for the majority of weather-related losses in Europe in 2023 with a total damage of € 10 billion.

At the core of ESSL’s mission is conducting and facilitating research on severe weather at a European level. Over the years, the organisation has grown with support from its members which include most of Europe’s weather services and commercial sector partners. In addition to research ESSL is active in the area of forecaster training and the evaluation of novel forecasting and nowcasting applications at the ESSL Testbed.

The recorded multi-year trends of severe weather apparent in the ESWD are often dominated by non-meteorological factors, but for large hail indications are strong that its frequency is changing, illustrated by the new hailstone size record of 19 cm diameter in northern Italy in July 2023. ESSL’s recent models of large hail climatology across Europe and the world support these trends. A key challenge for the research community is to develop methods to estimate trends from ever higher-resolution reanalyses and climate models. This is not straightforward as even the highest resolution models do not resolve tornadoes or microbursts, let alone hailstones, and already show biases at coarse scales.

The mentioned work modelling severe weather has given new insights into which environmental characteristics are important to severe weather occurrence. For hail, we additionally studied the conditions under which individual hailstorms in 2021, 2022, and 2023 that were particularly severe. We show the importance of the vertical distribution of buoyancy and wind in a storm-centred reference framework, defined using radar-observed storm motion.

High vertical wind shear above the boundary layer and high CAPE above the -10 °C isotherm for hail, and a combination of vertical vorticity and strong streamwise vorticity for tornadoes. ESSL is collaborating with ECMWF to develop forecast tools based on these concepts. That said, many questions remain, for example regarding the pre-convective environment and mountain ranges, and with the developing storms. For instance, an important concentration of severe weather is evident surrounding the Alps. To address related questions, ESSL has taken the initiative for a multi-year multi-national field campaign in central Europe called TIM (Thunderstorm Intensification from Mountains to plains), in which it will collaborate with a large number of research institutes.

How to cite: Groenemeijer, P., Battaglioli, F., Púčik, T., Holzer, A., and Taszarek, M.: Severe Storms Research at ESSL, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12158, https://doi.org/10.5194/egusphere-egu24-12158, 2024.

Drivers of organised & severe convection
08:45–08:55
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EGU24-328
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ECS
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On-site presentation
Object-Based Analyses of Mesoscale Convective Systems and Embedded Storms over the Indian Monsoon Zone Using Datasets from Satellite, Radar and Model Simulations                
Manisha Tupsoundare, Sachin Deshpande, Zhe Feng, Subrata kumar Das, Medha Deshpande, and Harshad Hanmante

Mesoscale convective systems (MCSs), the largest type of deep convective storms are formed when convection aggregates and grows upscale, forming a distinct mesoscale circulation through the interaction of multiple storms. Thus, storms play an important role in MCS organization. Due to their large size, longer duration, and larger precipitation, MCSs cause high-impact extreme weather events like lightning, damaging hail, gusty winds, and flooding. During the Indian summer monsoon (June-September), synoptic-scale weather systems move across the monsoon zone (MZ), causing MCSs to form frequently. MCSs often produce widespread and heavy rain throughout the MZ. Hence, studies on structure and evolution of MCSs highlighting the organization of convection are needed for an improved understanding of MCS.

In this study, we used an object-based cloud-tracking method (Feng et al., 2018) to identify and track MCSs and embedded storms in remote sensing observations and numerical simulations. The work is divided into three parts. In the first part, we tracked MCSs over the monsoon zone using geostationary satellite infrared brightness temperature (IRTb) and GPM IMERG precipitation from June-September, 2014 to 2019 and examined various aspects of observed MCSs (n=2092) such as spatial coverage, diurnal cycle, rainfall amount, and land-ocean contrast. The majority of MCSs are positioned in the monsoon trough's southeast-northwest stretch and account for more than 60% of total precipitation. For MCSs with short and long lifespans, there was a clear land-ocean divide and varied lifecycle trends. Oceanic MCSs last longer, are deeper, and provide more rainfall over a larger area than land-based MCSs.

In the second part of the study, we explored embedded storm structures for those MCSs that exist within the radar domain (n=65) by applying a storm classification algorithm to the S-band Doppler radar observations during June-September 2015. We observed that an MCS contains many precipitation features, especially during early stages of development when multiple convective clusters begin to amalgamate. Furthermore, we investigated the co-evolution of numerous storm parameters (e.g., areas of convective/stratiform precipitation, convective core length, and top heights) as a function of MCS lifetime. Distinct vertical structures are observed for the convective, stratiform, and anvil components of MCSs.

In the third part of this work, we examine the ability of a convection-permitting Weather Research Forecast (WRF) model in simulating MCSs and their characteristics (initiation, size, intensity, lifetime, propagation) during June-September 2015. A similar cloud-tracking algorithm is applied to WRF-simulated data (reflectivity, IRTb, and precipitation) to identify and track MCS in the simulation. Although the model underestimated the number of observed MCSs, the composite evolution and frequency distribution of convective area, precipitation amount, MCS propagation speed produces reasonable agreement with observations but underestimate stratiform areas. Consistent with observations, the simulated MCS properties showed a gradual increase from convective initiation to around the first half of the MCS lifetime. We observed that an MCS contains multiple precipitation features, particularly during the initial development stage when multiple convective clusters begin to aggregate. More details on observed MCSs and embedded storm structures, as well as their representation in simulation, will be presented.

How to cite: Tupsoundare, M., Deshpande, S., Feng, Z., Das, S. K., Deshpande, M., and Hanmante, H.: Object-Based Analyses of Mesoscale Convective Systems and Embedded Storms over the Indian Monsoon Zone Using Datasets from Satellite, Radar and Model Simulations               , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-328, https://doi.org/10.5194/egusphere-egu24-328, 2024.

08:55–09:05
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EGU24-1936
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ECS
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On-site presentation
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Mesoscale Convective Systems across Australia 
Ewan Short and Todd Lane

A major aspiration of operational and research meteorology is to relate the average behaviour of convective-scale flows to the more predictable, larger-scale flows in which they occur. This goal is difficult, partly because convective flows often self-organize at mesoscales, with the dynamics of such mesoscale convective systems (MCSs) distinct from those at convective and synoptic scales. In this study we use a tracking algorithm to detect MCSs in Australian operational radar data, revealing regional, seasonal and sub-seasonal, i.e. synoptic, differences in organizational characteristics. Restricting to MCS observations with nominally two-dimensional mean system-relative flows, spatio-temporal organizational differences are generally well explained by theoretical ideas regarding the breakdown of two-dimensional overturning flows. Theoretically, breakdown is characterised by a single non-dimensional convective Richardson number R, which provides the ratio of thermodynamic potential energy to inflow kinetic energy. Specifically, 76% of MCS relative trailing-stratiform, up-shear tilted observations, nominally associated with primarily non-overturning system-relative flows, occur when R>5, whereas 72% of relative leading-stratiform, down-shear tilted observations, nominally indicating primarily overturning system-relative flows, occur when R<5. Spatiotemporal variations in observed organizational characteristics are broadly consistent with spatiotemporal variations in median R. These results likely have implications for convective parametrisation, and operational convective permitting model testing and development.

How to cite: Short, E. and Lane, T.: Mesoscale Convective Systems across Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1936, https://doi.org/10.5194/egusphere-egu24-1936, 2024.

09:05–09:15
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EGU24-3210
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On-site presentation
How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future? 
Zhe Feng, Xiaodong Chen, and Ruby Leung

The historic 22-26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record-breaking rainfall and $3 billion of damage in the region. A month-long regional convection-permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high-emission (SSP5-8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4-6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low-level jet. A warmer and moister lower troposphere leads to 36-59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold (Fig. 1). However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak-to-moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

Figure 1. (a) Frequency distribution of MCS grid-point hourly rain rates, and (b) normalized cumulative distribution of rainfall amount by hourly rain rates. The region of the data included is show in the inset.

How to cite: Feng, Z., Chen, X., and Leung, R.: How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3210, https://doi.org/10.5194/egusphere-egu24-3210, 2024.

09:15–09:25
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EGU24-11456
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ECS
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On-site presentation
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Thunderstorm and Hail Frequencies in South America and Australia Based on Overshooting Tops 
Jannick Fischer, Michael Kunz, and Kristopher Bedka

Convective storms over South America and Australia are among the most intense worldwide (e.g., Zipser 2006). However, they are less researched compared to US and Europe. This study analyses the thunderstorm climatology over South America and Australia based on over 20 years of overshooting cloud top (OT) satellite detections (Khlopenkov et al. 2021). These OTs serve as robust, horizontally homogeneous indicators of strong updrafts and hence intense thunderstorms. Furthermore, we focus on the frequency of severe storms and hail by using ERA5 Reanalysis data to exclude OTs in unfavorable environments (e.g., Punge et al. 2023).
The resulting climatologies of intense thunderstorms and hail are largely consistent with existing literature, showing strong thunderstorm activity in tropical regions but more severe (e.g., hail-producing) storms in south-central South America and southeast Australia. Some notable details will also be discussed, such as the discrepancy with observational hotspots near the coast in South America and a surprisingly strong signal over northwest Australia. Furthermore, regarding a climate change signal, preliminary analysis indicates no significant trend for South America. However, the multi-year variations are strongly linked to the El Ninjo-Southern Oscillation (ENSO).

How to cite: Fischer, J., Kunz, M., and Bedka, K.: Thunderstorm and Hail Frequencies in South America and Australia Based on Overshooting Tops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11456, https://doi.org/10.5194/egusphere-egu24-11456, 2024.

09:25–09:35
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EGU24-5848
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On-site presentation
The characteristics of eastward-moving regional rainfall events originating in the northeastern of Tibetan plateau 
Haoming Chen and Mu He

Using the FengYun (FY) satellite products and hourly rain gauge data, the east-moveing regional rainfall events (RREs) with long duration and large areas originated in the northeastern Tibetan Plateau (TP) were identified. Our findings reveal that 70% of heavy and long-duration(≥6h) RREs originating in the northeastern TP have the potential to move a thousand kilometers eastward during the warm-season. We noted distinct differences in the speed and spatial location of rainfall for the two types of eastward-moving RREs under investigation.. For the long-distance eastward-moving RREs, three local enhancements of precipitation centers, corresponding to the center moving out of 105°E, 110°E and 115°E are evident. In contrast, for the short-distance eastward-moving RREs, the precipitation centers mainly reach the second topographical terrace without further eastward moving. The evolution of mid-level trough and upper troposphere warm anomalies are closely related to the eastward-moving RREs. With the eastward movements of middle troposphere trough, coupled with the synergistic effects of the convergence and a change in wind orientation at the lower level, and the divergence at the upper-level, collectively contribute to the long-distance eastward moving RREs. The short-distance eastward moving RREs, influenced by the ridge of western Pacific subtropical high over North China and the low-level anomalous anticyclone, remains west of 110°E. This study offers an in-depth understanding of how upstream precipitation events influences the downstream rainfall.

How to cite: Chen, H. and He, M.: The characteristics of eastward-moving regional rainfall events originating in the northeastern of Tibetan plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5848, https://doi.org/10.5194/egusphere-egu24-5848, 2024.

Land-convection feedbacks
09:35–09:45
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EGU24-5880
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On-site presentation
Multiday mesoscale soil moisture persistence and atmospheric predictability – an illustration from the Sahel 
Christopher Taylor, Cornelia Klein, and Bethan Harris

The hydro-climate of the Sahel is dominated by organised Mesoscale Convective Systems (MCSs), which typically bring intense rain every few days during the West African Monsoon season. MCSs leave a swath of wet soil often hundreds of kilometres across, which in turn create strong spatial patterns of surface fluxes of heat and water back into the atmosphere. Previous studies have shown that soil moisture patterns exert a strong control on the initiation and propagation of MCSs, significantly enhancing the predictability of convection on scales of 10 – 100s km. Here, we use satellite observations to examine how this strong, locally negative, soil moisture-precipitation feedback evolves and impacts rainfall patterns over a series of storms.

We track the response of the surface and atmosphere to over 5,000 MCS events from the period 2004-2020, using a combination of satellite-derived products (Land Surface Temperature; LST, soil moisture, Vegetation Optical Depth, rainfall, cloud-top temperature). Initial anomalies in LST and soil moisture weaken rapidly in the 3-4 days after the MCS, particularly in climatologically wetter regions. However, a statistically significant memory of the original MCS event still remains in surface anomalies out to 20 days. In terms of rainfall, we see a strong suppression of convection in the first 48 hours after the MCS in areas which initially received heavy rain. There is also some evidence of enhanced MCS activity around the edges of the original swath in the first 4 days. The persistence over several days of mesoscale rainfall patterns anti-correlated with the original MCS point to an important role for surface-atmosphere feedbacks. Synoptic forcing cannot explain the finer scale rainfall response, whilst post-MCS cold pool effects are too short-lived. On longer time scales (5-20 days) in climatologically drier areas, we also find a weak but statistically significant enhancement of rainfall around the original initiation zone.

These results have important implications for rainfall forecasting on scales of tens to several hundred kilometres. Pre-existing soil moisture heterogeneity provides strong predictability of where future convection will occur under favourable synoptic conditions. This provides skill out to 2-4 days, but strongly depends on regional rainfall frequencies. Because new MCSs create new soil moisture patterns, the combination of storms every few days and a strong negative land feedback at the mesoscale actively degrades longer term predictability within the rainy season, effectively limiting intra-seasonal to seasonal forecast skill for severe weather.

How to cite: Taylor, C., Klein, C., and Harris, B.: Multiday mesoscale soil moisture persistence and atmospheric predictability – an illustration from the Sahel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5880, https://doi.org/10.5194/egusphere-egu24-5880, 2024.

09:45–09:55
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EGU24-6302
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On-site presentation
Relationships between growing cloudy updrafts, deep convection initiation, and orographic flow 
James Marquis, Adam Varble, Zhe Feng, Enoch Jo, and William Gustafson

Shallow cumulus cloud fields often organize and deepen within regions of mesoscale ascent associated with orographic flows. However, there is significant uncertainty in the relative roles of mesoscale and cloud-scale factors ultimately controlling the location of orographic deep convection initiation (DCI). These factors include spatial heterogeneity of the magnitude of mesoscale vertical mass flux associated with orographic convergence, near-cloud convective ingredients (e.g. CAPE, CIN, LFC, and shear), and entrainment effects. More fundamentally, it is not well understood how these factors influence the initial width and strength of low-level cloudy updrafts, which are increasingly cited as important governors of their ultimate depth potential. Thus, it is important to better understand these relationships for increased predictability of DCI.

 

Numerous DCI events observed along the Sierras de Córdoba range during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) project were modeled by the U.S. Department of Energy’s LES ARM Symbiotic Simulation and Observation (LASSO) team. In this study, we examine the connection between low-level cloudy updrafts, DCI, and the ascent associated with the mesoscale orographic circulation using LES with 100-m and 500-m grid spacing across multiple days. We hypothesize that the width and strength of low-level cloudy updrafts and the probability of DCI events along the ridge are proportional to the width, strength, and depth of the local orographic convergence. To test this, we examine correlations between the width and depth of developing cloudy updrafts and the: i) 3D structure of the evolving orographic ascent, and ii) convective meteorological ingredients (e.g., convective available potential energy, convective inhibition, level of free convection, moisture, etc.).

 

Preliminary results indicate that DCI events do not always occur in regions of the strongest or widest orographic ascent along the mountain range. Further, the strength and width of low-level cloudy updrafts that precede DCI are only weakly correlated with most orographic ascent metrics. Overall, the apparent relative roles of mesoscale ascent and convective sounding parameters governing DCI varied significantly across case days. Near-cloud relative humidity located near and just above the level of free convection steadily increased with time during each afternoon, likely owing to orographic vertical moisture flux and/or cloud detrainment. Thus, in addition to highly varied roles of the background conditions, the fate of individual growing cloudy updrafts may further depend on complex cloud-scale factors, such as entrainment and microphysical processes.

How to cite: Marquis, J., Varble, A., Feng, Z., Jo, E., and Gustafson, W.: Relationships between growing cloudy updrafts, deep convection initiation, and orographic flow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6302, https://doi.org/10.5194/egusphere-egu24-6302, 2024.

09:55–10:05
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EGU24-19982
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ECS
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On-site presentation
Assessing the Influence of the Shillong Plateau Topography on Thunderstorm Activities in North-east India 
Rajesh Kumar Sahu, Hylke E Beck, and Bhishma Tyagi

Northeast India (NEI) experiences frequent thunderstorms during the pre-monsoon season, which can be catastrophic, resulting in loss of life and damage to infrastructure and property. The Shillong Plateau (SP) has been identified as a key factor in triggering these thunderstorms over NEI. Our study focuses on monitoring changes in thermodynamic indicators over NEI to assess the impact of the SP on the initiation and propagation of thunderstorms. The results demonstrate a significant increase in thermodynamic index values across NEI when the SP topography is elevated, indicating an increase in thunderstorm activity. Conversely, when the SP topography is reduced, there is a decrease in these indicators, corresponding with lower thunderstorm intensity. Notably, a lower SP topography is associated with increased precipitation, whereas a higher SP topography is linked to decreased precipitation. These findings underscore the crucial role of SP topography in influencing pre-monsoon thunderstorms over NEI, which has implications for understanding and predicting regional weather patterns.

Keywords: Thunderstorms; Thermodynamic Indices; Topography; Shillong Plateau; WRF

How to cite: Sahu, R. K., Beck, H. E., and Tyagi, B.: Assessing the Influence of the Shillong Plateau Topography on Thunderstorm Activities in North-east India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19982, https://doi.org/10.5194/egusphere-egu24-19982, 2024.

10:05–10:15
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EGU24-3714
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ECS
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On-site presentation
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Influences of urbanization on an afternoon heavy rainfall event over the Yangtze River Delta region in East China 
Xiaoling Jiang, Da-Lin Zhang, and Yali Luo

This study examines the urban impacts associated with a developed city belt on generating an afternoon heavy rainfall event over a coastal developing city that is 70–100 km downwind from the city belt over the Yangtze River Delta region. Observational analyses show pronounced urban heat island (UHI) effects along the upstream city belt prior to convection initiation (CI). A series of cloud-permitting model simulations with the finest grid spacing of 1 km are performed to examine the impacts of urbanization on CI and the subsequent heavy rainfall event. Results reveal the generation of warm anomalies and low-level convergence in the planetary boundary layer along the upstream city belt, thereby inducing upward motion for CI. The southwesterly flows of the monsoonal warm-moist air, enhanced by the UHI effects along the city belt, allow the development of convective cells along the belt. Some of the cells merge during their downstream propagation, promoting to the ultimate generation of the distinct heavy rainfall centers in favor of local convective clusters over the coastal city where atmospheric columns are more moist and potentially unstable under the influences of sea breezes. Sensitivity simulations show small contribution of the downstream city but more influences from the upstream city belt on the heavy rainfall event. The above findings help elucidate how the UHI effects could assist the CI in a weak-gradient environment, and explain why urbanization can contribute to increased downwind mean and extreme precipitation under the influences of favorable regional forcing conditions. These findings have been published in Monthly Weather Review.

How to cite: Jiang, X., Zhang, D.-L., and Luo, Y.: Influences of urbanization on an afternoon heavy rainfall event over the Yangtze River Delta region in East China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3714, https://doi.org/10.5194/egusphere-egu24-3714, 2024.

Coffee break
Chairpersons: Tobias Necker, Tijana Janjic, Tomislava Vukicevic
10:45–10:50
convener introduction
10:50–11:00
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EGU24-1675
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solicited
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Highlight
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On-site presentation
ML for weather prediction at Météo-France : current status and future plans 
Laure Raynaud, Clément Brochet, and Gabriel Moldovan

Applications of Machine Learning (ML) in the different stages of weather forecasting have considerably developed recently. Such progress is likely to change the landscape and offer new perspectives to speed up and improve forecast performances, at different spatio-temporal scales. In this context, Météo-France engaged more actively in this new area of research, with the objective to further explore the capabilities and opportunities of ML for operational forecasting. Major ongoing projects include ML to significantly enhance the size of convective-scale ensemble forecasts, high-resolution statistical downscaling and the development of data-driven kilometre-scale forecasting systems. Early results will be presented and our short-term roadmap will be discussed.

How to cite: Raynaud, L., Brochet, C., and Moldovan, G.: ML for weather prediction at Météo-France : current status and future plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1675, https://doi.org/10.5194/egusphere-egu24-1675, 2024.

Observations, data assimilation, and prediction of convective events
11:00–11:10
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EGU24-9241
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ECS
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On-site presentation
Designing a Global Weather Station Network 
Stavros Keppas, Haris Balis, Ioannis Dravilas, and John Pagonis

Designing a weather station network is a demanding, multi-objective optimisation problem and usually constrained to local geographies. In this study, the authors deviate from typical approaches that focus the design of weather station networks on a small or country-wide area and present a method that is applicable on a global scale.

Prior art suggests that weather networks should exhibit high density, often at 1-3km or finer resolution, especially when deployed over complex topographies and urban landscapes. High station density is usually required to support research on urban micrometeorology, agricultural applications and to capture intricate meteorological mesoscale phenomena such as convective precipitation and sea breeze. High density is also required due to the persistence of temperature inversions at near-surface layers is significantly influenced by topography, leading to prolonged periods of temperature inversion.

In this novel approach, the authors suggest the design of a global weather network distributed over millions of hexagons covering the entire world. The number of weather stations per hexagon is determined by the topology (e.g. maximum elevation difference, aspects, water formations, etc.) and the land use (urban coverage, green areas, etc.) of the covered area.

The method is materialized via an open-source software tool (available on GitHub) which utilizes freely available elevation data (Copernicus DEM) and land use data (OpenStreetMap) and is capable of preparing the global weather station network in reasonable computation time (~24 hours on a 16-core CPU).

Finally, the authors present their findings, discuss the effect of various hexagon sizes and suggest that the design of a global weather station network is viable and computationally feasible.

How to cite: Keppas, S., Balis, H., Dravilas, I., and Pagonis, J.: Designing a Global Weather Station Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9241, https://doi.org/10.5194/egusphere-egu24-9241, 2024.

11:10–11:20
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EGU24-13906
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ECS
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On-site presentation
Multi-scale interaction and predictability of moist convection and tropical cyclones  
Masashi Minamide and Derek Posselt

Predicting tropical cyclone intensity changes, especially the onset of rapid intensification, has been a more challenging topic than tropical cyclone tracking because of its chaotic nature in multi-scale physical process with significant contributions from convective-scale phenomena. Before intensification onset, tropical cyclones experience precession process, in which tilted vortices rotate counterclockwise around the center of circulation, and develop an axisymmetric structure. The forecast uncertainty in precession process limits the predictability of early-stage development and intensification of TCs.

In this study, we have explored the contribution of moist convective activity to the predictability and variability of TC intensification onset through the precession process. Our recent investigation in Minamide and Posselt (2022) proposed a Lagrangian-based approach to identify the potential signals of individual convective occurrence. Using the technique, we conducted sensitivity experiments to control specific convective activities within the inner-core of early-stage TCs with convection-permitting Weather Research and Forecasting model (WRF-ARW). The results indicate that the spatiotemporal variability of convective activity even governs whether early-stage vortex completes precession and initiates RI, indicating the importance of accurately constraining convective activity in the severe weather event predictions. Given the strong nonlinearity of the onset process of RI, the advancement of our understanding of the uncertainty sources will provide an insight about the observation network that may effectively constrain the TC forecasting.

How to cite: Minamide, M. and Posselt, D.: Multi-scale interaction and predictability of moist convection and tropical cyclones , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13906, https://doi.org/10.5194/egusphere-egu24-13906, 2024.

11:20–11:30
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EGU24-13964
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ECS
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On-site presentation
Evaluating historic atmospheric river associated extreme rainfall and its flooding potentials based on a high-resolution climate model 
Marc Prange, Ming Zhao, and Elena Shevliakova

Atmospheric Rivers (ARs) transport vast amounts of water vapor from the tropics to mid-latitudes, resulting in sustained, heavy precipitation that explains about 50 % of mid-latitude annual mean rainfall. AR events over the Western US have shown particularly high societal impact, where orographic and soil conditions make communities vulnerable to floods and mudslides. Climate modelling approaches for capturing extreme precipitation and water runoff on land are both strongly constrained by the horizontal resolution that is currently deployed, typically on the order of 100 km. Such grid spacing neither allows for explicitly resolving key processes associated with extreme precipitation like atmospheric convection, nor complex terrain that controls water runoff. However, recent advances in computational capabilities and model development at the Geophysical Fluid Dynamics Laboratory (GFDL) at a finer horizontal resolution of 50 and 25 km have shown promising perspectives for simulating important characteristics of ARs and their associated mean and extreme precipitation. In addition, advances in GFDL land model hydrology now allow for investigating climate model capabilities in predicting precipitation induced flood hazard precursors like excessive runoff and streamflow in a physically coupled, orography-aware atmosphere-land framework.

Here, we make use of the high resolution GFDL coupled atmosphere-land model by running hindcast experiments for a handful of high impact AR events over the Western US. We evaluate the model’s predictive skill in AR associated precipitation by running ensemble forecasts on weather time scales, which we evaluate against observations and reanalysis. We attribute the found biases in terms of dynamical and thermodynamic drivers, revealing current model constraints. Accounting for the biases found in precipitation, we turn to the land hydrology and evaluate catchment associated hydrological characteristics, which we compare to satellite derived and in-situ observations.

How to cite: Prange, M., Zhao, M., and Shevliakova, E.: Evaluating historic atmospheric river associated extreme rainfall and its flooding potentials based on a high-resolution climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13964, https://doi.org/10.5194/egusphere-egu24-13964, 2024.

11:30–11:40
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EGU24-9266
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ECS
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On-site presentation
Impact of aerosol and microphysical uncertainty on the evolution of a severe hailstorm 
Patrick Kuntze, Annette Miltenberger, Corinna Hoose, Michael Kunz, and Lena Frey

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays an important role but so do uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. Here, we investigate the impact of cloud microphysical parameter uncertainties on the forecast of a selected severe convective storm over South-Eastern Germany in 2019, which is generally referred to as the Munich hailstorm (Wilhelm et al., 2020).
The storm is simulated using the ICON model (2-moment cloud microphysics, 1 km grid-spacing) with perturbed microphysical parameters related to graupel and hail formation. Combinations of parameter perturbations are chosen according to a Latin hyper cube design and one-at-a-time parameter perturbations for the smallest and largest parameter values. Important impacts on surface (hail) precipitation are found for parameters pertaining to (i) CCN and INP activation, (ii) diffusional growth of ice, and (iii) the mass-diameter and mass-fall velocity relations for graupel. The behavior of graupel particles are thereby controlled by their density.
The one-at-a-time parameter perturbation simulations are used to track microphysical process rates. By closing the hydrometeor mass budgets we explore changes in precipitation formation pathways (based on the approach by Barrett and Hoose, 2023) arising from perturbations of the most impactful parameters. Preliminary results show a strong influence of graupel density on the hail particle size distribution as well as total precipitation, but less so on surface hail amount.
The analysis allows us to draw conclusions about the most impactful cloud microphysical parameters for hail forecast uncertainty as well as the underlying mechanisms.

How to cite: Kuntze, P., Miltenberger, A., Hoose, C., Kunz, M., and Frey, L.: Impact of aerosol and microphysical uncertainty on the evolution of a severe hailstorm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9266, https://doi.org/10.5194/egusphere-egu24-9266, 2024.

11:40–11:50
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EGU24-4082
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ECS
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Virtual presentation
Assessing the influence of observations on the analysis in ensemble-based data assimilation systems 
Guannan Hu, Sarah Dance, Alison Fowler, David Simonin, and Joanne Waller

Convection-permitting numerical weather prediction (NWP) is crucial for forecasting high-impact weather events such as heavy precipitation, storms, floods, wind gusts and fog. The assimilation of observations plays a significant role in improving the forecasting skill of these weather events. To make better use of existing observations and guide the design of future observation networks, accurately assessing the influence of assimilated observations is essential. The degrees of freedom for signal (DFS) has long been used to assess the influence of observations on the analysis. While various methods exist for calculating the DFS in variational data assimilation (DA) systems, calculating the DFS in ensemble-based DA systems (e.g., the ensemble transform Kalman filter) is a largely unexplored area. Since ensemble-based DA systems are becoming increasingly dominant for convection-permitting NWP, practical implementation of the DFS in such DA systems is needed. Unlike in variational DA systems, the background error covariance matrix is not static in ensemble-based DA methods. Consequently, the DFS calculated at each assimilation step measures the observation influence for a certain background error covariance matrix. This means that the DFS estimates are flow dependent. In addition, domain localisation of observations is often used in ensemble-based DA systems (e.g., local ensemble transform Kalman filter). This implies that the DFS should be calculated locally. In this work, we propose novel approaches for calculating the DFS in ensemble-based DA systems and investigate existing approaches applicable to such systems. We establish their consistency under idealised conditions and discuss their differences in practical applications. To validate our theoretical findings, we conduct simple numerical experiments using JEDI (Joint Effort for Data assimilation Integration) developed by JCSDA (Joint Center for Satellite Data Assimilation).  Our results provide useful information for assessing the influence of observations in ensemble-based DA systems. This work is financially supported by the Met Office and is fully in line with the Met Office’s strategy and its ongoing development of the next generation data assimilation and observation processing system.

How to cite: Hu, G., Dance, S., Fowler, A., Simonin, D., and Waller, J.: Assessing the influence of observations on the analysis in ensemble-based data assimilation systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4082, https://doi.org/10.5194/egusphere-egu24-4082, 2024.

11:50–12:00
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EGU24-12544
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On-site presentation
Testing ensemble-based estimates of potential observation impact 
Philipp Griewank, Tobias Necker, and Martin Weissmann

While ensemble methods to estimate the impact of assimilated observations on forecast error have been widely used (known as EFSO), similar methods to estimate the benefit of potential observations not assimilated have received less attention. For this presentation we use a toymodel to illustrate these methods and highlight their strengths and weaknesses. We show that these methods work well over a wide range of lead times and for different types of observations, but only when the localization used in ensemble data assimilation to mitigate sampling errors is accounted for. While previous studies struggled to achieve quantitative results because they treated the localization inconsistently, we found three methods to overcome this limitation. 

How to cite: Griewank, P., Necker, T., and Weissmann, M.: Testing ensemble-based estimates of potential observation impact, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12544, https://doi.org/10.5194/egusphere-egu24-12544, 2024.

12:00–12:10
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EGU24-2793
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ECS
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On-site presentation
Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV 
Yongbo Zhou, Yubao Liu, Yuefei Zeng, and Wei Han

The Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite provides high spatiotemporal resolution visible reflectance data since March 12th, 2018. Data assimilation experiments under the framework of observing system simulation experiment have shown great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To effectively assimilate the AGRI data, it is important to address the quality the observations. In this study, the FY-4A/AGRI channel 2 (0.55 μm - 0.75 μm) reflectance was evaluated by the equivalents derived from the short-term model forecasts of the China Meteorological Administration Mesoscale Model (CMA-MESO) using the Radiative Transfer for TOVS (RTTOV, v 12.3). It is shown that the observation minus background (O – B) statistics could be used to reveal the abrupt changes related to the measurement calibration processes. In addition, O - B statistics are negatively biased. Potential causes include measurement errors, the unresolved processes, forward-operator errors, etc. The relative mean biases of O-B computed for cloud-free and cloudy pixels were used to correct the systematic differences for cloudy and clear pixels separately. Results indicate that the bias correction method could effectively reduce the biases and standard deviations of O-B. In addition, an ensemble forecast has advantages over a deterministic forecast in correcting the biases in FY-4A/AGRI visible reflectance data. The finding suggests an effective method to monitor the performance of FY-4A/AGRI visible measurements and to correct the biases in the observations. 

How to cite: Zhou, Y., Liu, Y., Zeng, Y., and Han, W.: Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2793, https://doi.org/10.5194/egusphere-egu24-2793, 2024.

12:10–12:20
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EGU24-4506
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ECS
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Virtual presentation
Integrating Sentinel-5P Satellite Data and Machine Learning Algorithms for Air Quality Index Prediction in Tehran: A Comprehensive Study on Factors Influencing Air Quality 
Amir Mohammad Kafi, Mahdi Hosseinipoor, Maryam Zare Shahne, and Amirmoez Jamaat

Frequent air pollution episodes pose severe health and environmental challenges in Tehran, Iran. Despite recent efforts, pollutant levels often exceed WHO-based national standards. This study addresses the pressing need for accurate air quality prediction by leveraging advanced satellite data and machine learning techniques. Our methodology integrates Sentinel-5P satellite data with optical depth remote sensing information. We systematically evaluated five machine learning algorithms to identify the most effective approach for AQI prediction. This study aims to advance air quality prediction in Tehran by integrating Sentinel-5P satellite data with machine learning algorithms. We examined the efficacy of various algorithms, including Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Logistic Regression, in correlating air pollutant levels with the Air Quality Index (AQI). The selection criteria focused on algorithmic efficiency and accuracy in handling diverse environmental datasets. The Random Forest algorithm, utilizing Sentinel-5P and optical depth data, achieved a remarkable accuracy of 74% in predicting AQI. Further enhancement was observed by incorporating climatic data, COVID-19 status, and environmental parameters; the model achieved a significant predictive accuracy of up to 75.6%. These findings underscore the critical impact of nitrogen dioxide, ozone, and aerosol optical depth on Tehran's AQI, with notable variations observed post-COVID-19 restrictions. The increase in AQI following the lifting of COVID-19 restrictions suggests a significant correlation between human activity and air quality. These insights can inform targeted environmental policies in Tehran. We demonstrate the potential of integrating satellite data with machine learning to predict AQI accurately. Our approach offers a scalable model for urban air quality management with implications for environmental policy and public health initiatives.

How to cite: Kafi, A. M., Hosseinipoor, M., Zare Shahne, M., and Jamaat, A.: Integrating Sentinel-5P Satellite Data and Machine Learning Algorithms for Air Quality Index Prediction in Tehran: A Comprehensive Study on Factors Influencing Air Quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4506, https://doi.org/10.5194/egusphere-egu24-4506, 2024.

12:20–12:30
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EGU24-12000
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On-site presentation
Can a wrf hybrid 4DEnVar iso system be used to improve forecasts for convective scale systems? A roadmap to using isotope enabled assimilation systems for studying convective scale systems. 
(withdrawn)
Jörg Steinwagner and Stephen Tjemkes

Posters on site: Mon, 15 Apr, 16:15–18:00 | Hall X5

Display time: Mon, 15 Apr, 14:00–Mon, 15 Apr, 18:00
X5.30
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EGU24-1300
Ensemble Sensitivity-Based Subsetting for Convection: Progress Toward Operational Use 
Brian Ancell and Austin Coleman

Ensemble sensitivity is a statistical tool applied within an ensemble that reveals the atmospheric flow features (e.g. position of a jet streak, or magnitude of a low-level moisture plume) at early forecast times that are related to a chosen forecast response later in the forecast window.  The response function is chosen to diagnose high-impact forecast features such as maximum updraft helicty over a specified area, or number of grid points of simulated reflectivity exceeding 40 dBZ in a chosen region.  Since ensemble sensitivity highlights the features early in a forecast important to the prediction of high-impact features later in the forecast, a subset of members with the smallest errors in sensitive regions can be chosen that might improve probabilistic forecasts of the response relative to the full ensemble. Similar to ensemble data assimilation, this process incorporates observational information to beneficially update forecast distributions.  The subsetting procedure can be done quickly once an ensemble has been run, and sensitivity-based subsets can typically be generated well before the next extended forecast can be run within a cycling storm-scale data assimilation and forecasting system. In turn, the subsetting procedure, if shown to improve forecasts, could be a unique and useful operational forecasting tool.

 

Ensemble sensitivity-based subsetting has been tested within the Texas Tech University operational ensemble system in both an idealized framework and in more operational settings in real time during several years of the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT).  Response functions that diagnose severe convective hazards, such as updraft helicity, hail size, and simulated reflectivity have been tested to gain an understanding of both the general capability of the technique and the perception of forecasters regarding its value in a real-time forecasting environment.  Here we discuss this effort and its associated results, the technique’s current status, and future plans toward ultimate operational implementation.

How to cite: Ancell, B. and Coleman, A.: Ensemble Sensitivity-Based Subsetting for Convection: Progress Toward Operational Use, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1300, https://doi.org/10.5194/egusphere-egu24-1300, 2024.

X5.31
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EGU24-3735
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ECS
Impacts of Urbanization and Its Parameters on Thermal and Dynamic Fields in Hangzhou: A Sensitivity Study Using the Weather Research and Forecasting Urban Model 
Mengwen Wu, Meiying Dong, Feng Chen, and Xuchao Yang

The impact of urbanization and the sensitivity of urban canopy parameters (UCPs) on a typical summer rainfall event in Hangzhou, China, is investigated using three groups of ensemble experiments. In this case, urbanization leads to higher temperatures, lower mixing ratios, lower wind speeds before precipitation, and more precipitation in and around the urban area. Both the thermal and dynamical effects of urbanization contribute to an increase in temperature and precipitation, with thermal effects contributing 71.2% and 63.8% to the temperature and precipitation increase, respectively, while the thermal and dynamical impacts cause the opposite changes to the mixing ratio and wind speed. Compared to the other three meteorological elements, the model has the largest uncertainty in the simulation of precipitation, which includes the sensitivity of the different parameterization schemes to the simulation of precipitation in urban areas, and the uncertainty brought by the urban effect on precipitation is not confined within the city but extends to the surrounding areas as well. Temperature and mixing ratio are more sensitive to thermal-related UCPs, while the wind speed is mainly affected by the structural parameters. These variations, however, are sometimes contradictory to precipitation changes, which further adds to the complexity of precipitation simulation.

How to cite: Wu, M., Dong, M., Chen, F., and Yang, X.: Impacts of Urbanization and Its Parameters on Thermal and Dynamic Fields in Hangzhou: A Sensitivity Study Using the Weather Research and Forecasting Urban Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3735, https://doi.org/10.5194/egusphere-egu24-3735, 2024.

X5.32
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EGU24-5399
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ECS
Characteristics of Warm‐Season Mesoscale Convective Systems Over the Yangtze–Huaihe River Basin (YHR): Comparison Between Radar and Satellite 
Yutong Lu, Jianping Tang, Xin Xu, Ying Tang, and Juan Fang

Mesoscale convective systems (MCSs) are crucial in modifying the water cycle and frequently induce high-impact weather events over eastern China. Radar and Climate Prediction Center (CPC)-4 km satellite-derived infrared cloud top temperature (Tb) data were used to thoroughly analyze the long-term climatology of MCSs over eastern China, particularly in the Yangtze–Huaihe River Basin (YHR) in the warm season from 2013 to 2018. For the first time, we contrasted the effects of data set selection and threshold setting on research outcomes. The large-scale environments of MCSs initiation were also investigated using the latest global reanalysis data ERA5. It is found that striction of thresholds, including duration, reflectivity/Tb, area, and linearity, would lead to a greater proportion of early-morning MCSs. Satellite-identified MCSs differed from radar-derived ones, exhibiting afternoon diurnal peaks, faster movement speeds, longer travel distances, and expansive impact areas. The center of MCS and related precipitation shifted northward from Pre-Meiyu to Post-Meiyu seasons, contributing to up to 20% of total rainfall, with most MCSs moving along eastward trajectories. MCSs typically had the most substantial impact in the Meiyu season because of the most prolonged duration, largest convective core area, and strongest precipitation intensity. Warm-season MCSs initiated ahead of midlevel troughs and were related to strong anomalous low-level convergence and midlevel upward. The circulation anomalies were the strongest in the Pre-Meiyu season among the three subseasons, with most moisture sourced from the southwest.

How to cite: Lu, Y., Tang, J., Xu, X., Tang, Y., and Fang, J.: Characteristics of Warm‐Season Mesoscale Convective Systems Over the Yangtze–Huaihe River Basin (YHR): Comparison Between Radar and Satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5399, https://doi.org/10.5194/egusphere-egu24-5399, 2024.

X5.33
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EGU24-6900
Analysis of convective storm characteristics to classify the storm severity information using weather radar variables 
Eunbi Kang, Soohyun Kwon, and Seungwoo Lee

  Recently, the increase in convective storms that develop rapidly within a short period of time and on a very small area causes severe damage to property and human life. Thus, it is important to understand the characteristics of convective activities and to provide the information about severity of the developing storms.  In order to address these issues, object-based analysis of convective systems is essential to provide severity information on convective precipitation systems including their life-cycle from initiation to dissipation. 
  In this study, we analyzed the developing stage of convective storms by using the statistics of storms detected by the Fuzzy Logic Algorithm for Storm Tracking (FAST). The Column Maximum (CMAX) was used to provide the information on detection and severity of storms. A convective storm was defined as a CMAX values above 35dBZ and small convective cells with an area less than 20km2 were filtered out. The identified storm was tracked on a fuzzy basis using storm speed and its morphological characteristics. Within the detected storm area, we analyzed the characteristics of the storm by averaging variables such as reflectivity (ZH), echo top height corresponding to ZH, rainfall rate at 1.5km altitude, VIL (Vertical Integrated Liquid) contents, etc.
  This study aims to provide quantitative information on severity of individual storms by using these radar variables and storm characteristics. We calculated and modified the threshold values of each predictor for determining the severity of the convective storms. Furthermore, we plan to analyze the intensity and frequency of severe precipitation storms in associated with the occurrence or absence of lightning event during their life cycle.

Key words : Weather Radar, convective storms, Radar parameter, storm severity

※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather 

How to cite: Kang, E., Kwon, S., and Lee, S.: Analysis of convective storm characteristics to classify the storm severity information using weather radar variables, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6900, https://doi.org/10.5194/egusphere-egu24-6900, 2024.

X5.34
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EGU24-8739
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ECS
Trends in Warm Season Mesoscale Convective Systems OverAsia in 2001–2020 
Yuanjing Guo, Qiang Fu, L. Ruby Leung, Ying Na, and Riyu Lu

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. The trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

How to cite: Guo, Y., Fu, Q., Leung, L. R., Na, Y., and Lu, R.: Trends in Warm Season Mesoscale Convective Systems OverAsia in 2001–2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8739, https://doi.org/10.5194/egusphere-egu24-8739, 2024.

X5.35
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EGU24-13519
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ECS
Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 2021 
Liangliang Li, Wenshou Tian, Jian Li, Jinlong Huang, Rui Wang, and Jiali Luo

From 19 to 21 July 2021, Henan province of China experienced an extreme precipitation event that caused massive flooding and great loss of lives. This event is thus far the second heaviest precipitation event observed by rain gauges in this region. Based on the ERA5 reanalysis data, the ECMWF operational global ensemble forecasts and numerical simulations using the ARW-WRF model, impacts of an upper tropospheric cold low (UTCL) on the extreme precipitation are examined. It is found that due to the influence of the persistent intrusion of stratospheric high potential vorticity (PV) air, a long-lived UTCL was detached from the upper level flow a week prior to the extreme precipitation event. The UTCL then moved westward, reaching the Yellow Sea and the East China Sea and maintaining there until the precipitation event ended. During this event, a broad northeast-southwest oriented area of ascending motion associated with the UTCL could be observed in front of the UTCL and strong ascending motions developed in the upper troposphere above Henan province. Analysis of the ECMWF operational global ensemble forecasts reveals that the amount of precipitation over Henan is positively correlated with the UTCL intensity. The UTCL impact on the extreme precipitation and the underlying mechanisms are further investigated based on results of numerical experiments. The control experiment reasonably reproduces the UTCL location as well as the distribution and evolution of the extreme precipitation. When the UTCL intensity is reduced in the initial condition using the piecewise PV inversion for sensitivity experiment, the upper tropospheric divergence reduces correspondingly and the dynamical ascending motion weakens in the second precipitation stage. As a result, the amount and intensity of precipitation both decrease. When the UTCL is completely removed from the initial condition, the sensitivity experiment indicates that the upper tropospheric divergence and dynamical ascending motion further weaken, resulting in a large decrease in precipitation intensity during the whole precipitation period. These findings highlight that the occurrence of the long-lived UTCL is a crucial factor that affects the intensity of the extreme precipitation event.

How to cite: Li, L., Tian, W., Li, J., Huang, J., Wang, R., and Luo, J.: Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 2021, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13519, https://doi.org/10.5194/egusphere-egu24-13519, 2024.

X5.36
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EGU24-20211
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ECS
Does higher temperature accentuate convective cell clustering within European MCSs? 
Nicolas A. Da Silva and Jan O. Haerter

Mesoscale Convective Systems (MCSs) are clusters of thunderstorms composed of narrow and heavy convective-type precipitation adjacent with wider and lighter stratiform-type precipitation. MCSs are the largest contributor of extreme precipitation events over Europe (Da Silva & Haerter, 2023). 

While convective and stratiform-type precipitation contributions within MCSs are each expected to increase according to the Clausius-Clapeyron law (~7%°C-1), their statistical superimposition is shown to increase at a faster rate due to increased MCS convective fraction with temperature (Da Silva & Haerter, submitted). 

 

For better prediction of floods induced by MCSs, it is also important to characterize the relationship between temperature and the spatio-temporal clustering of convective cells within MCSs. For that purpose, we use the high resolution EUropean Cooperation for LIghtning Detection (EUCLID) lightning dataset and combine it with MCS tracking data (derived from the RADOLAN radar precipitation dataset; Bartels et al., 2004) over Germany. Identifying convective cells through lightning records, we measure the degree of convection clustering using an organization index which we adapt to the MCS geometry. In this process, we use a Monte Carlo method to estimate the reference random distribution of nearest neighbor distances of convective centroids. 

 

We associate our organization index with surface dew-point temperatures from neighboring weather stations from the German Weather Service (Deutscher Wetterdienst, DWD). We select the temperature upstream of the MCS tracks, as a proxy of the moisture source involved in the formation of MCS precipitation. Idealized simulations suggest that both the mean and the spatial variability of surface temperature could be relevant for convective aggregation (Pendergrass, 2020; Shamekh et al., 2020). Our study considers both and also investigates the potential role of other triggers for convective aggregation such as convective cold pools (Haerter, 2019) or the diurnal cycle (Haerter et al., 2020).




References:

 

Bartels, H. et al. Projekt RADOLAN Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer) (2004).

https://www.dwd.de/DE/leistungen/radolan/radolan_info/abschlussbericht_pdf.pdf?__blob=publicationFile&v=2

 

Da Silva, N. A., & Haerter, J. O. (2023). The precipitation characteristics of mesoscale convective systems over Europe. Journal of Geophysical Research: Atmospheres, 128, e2023JD039045. https://doi.org/10.1029/2023JD039045

 

Da Silva, N. A, & Haerter J. O.. Non super-Clausius-Clapeyron scaling of convective precipitation extremes, 08 January 2024, PREPRINT (Version 1) available at Research Square

https://doi.org/10.21203/rs.3.rs-3777860/v1

 

Haerter, J. O. (2019). Convective self-aggregation as a cold pool-driven critical phenomenon. Geophysical Research Letters, 46, 4017–4028. https://doi.org/10.1029/2018GL081817

 

Haerter, J.O., Meyer, B. & Nissen, S.B. Diurnal self-aggregation (2020). npj Clim Atmos Sci 3, 30. https://doi.org/10.1038/s41612-020-00132-z


Pendergrass, A. G. (2020). Changing degree of convective organization as a mechanism for dynamic changes in extreme precipitation. Current climate change reports, 6, 47-54.

 

Shamekh, S., C. Muller, J. Duvel, and F. D’Andrea (2020), How Do Ocean Warm Anomalies Favor the Aggregation of Deep Convective Clouds?. J. Atmos. Sci., 77, 3733–3745, https://doi.org/10.1175/JAS-D-18-0369.1.

How to cite: Da Silva, N. A. and Haerter, J. O.: Does higher temperature accentuate convective cell clustering within European MCSs?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20211, https://doi.org/10.5194/egusphere-egu24-20211, 2024.

X5.37
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EGU24-20250
Probabilistic nowcasting of severe storms in Africa: workflow and online tools for monitoring 
Cornelia Klein, Seonaid Anderson, Steven Cole, Christopher Taylor, Steven Wells, Gemma Nash, and Abdoulahat Diop

Mesoscale convective systems (MCSs) dominate rainfall and its extremes in most parts of West Africa, frequently producing flash floods that result in major damage and loss of life. As West African storms are already intensifying, these effects are expected to become more frequent and severe under climate-change and rapid urban expansion. To help mitigate these impacts, the NFLICS (Nowcasting FLood Impacts of Convective storms in the Sahel) project has co-developed a prototype nowcasting system with West African meteorological services based on conditioned climatologies of organised convection as seen from the Meteosat Second Generation (MSG) satellites since 2004.  Data on historical convective activity, conditioned on the present location and timing of observed convection, are used to produce probabilistic forecasts of convective activity out to six hours ahead. Verification against the convective activity analysis and the 24-hour raingauge accumulations over Dakar suggests that these probabilistic nowcasts provide useful information on the occurrence of convective activity. The highest skill (compared to nowcasts based solely on climatology) is obtained when the probability of convection is estimated over spatial scales between 100 and 200km, depending on the forecast lead-time considered. Furthermore, recent advances have included incorporation of land surface temperature anomalies to modify nowcast probabilities – this recognises that MCS evolution favour drier land. We present the workflow of this nowcasting system and discuss our current understanding of the land surface effects that play a role for storm development and prediction. The developed nowcasting system is crucially computationally inexpensive to run operationally and achieves skill in the absence of rainfall radar, as is the case over most of Africa. Operational trials over the 2020 and 2021 rainy seasons, and during intensive nowcasting testbeds with researchers and forecasters, has shown the utility of these new nowcast products to support Impact-based Forecasting, and are currently being extended for use during a testbed with meteorological services in southern Africa in 2024.

Latest West Africa nowcasts alongside pan-African cloud and surface state imagery are publicy accessible on https://eip.ceh.ac.uk/hydrology/sub-saharan-africa/nowcasting

How to cite: Klein, C., Anderson, S., Cole, S., Taylor, C., Wells, S., Nash, G., and Diop, A.: Probabilistic nowcasting of severe storms in Africa: workflow and online tools for monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20250, https://doi.org/10.5194/egusphere-egu24-20250, 2024.

X5.38
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EGU24-20472
Initiation and Evolution of Long-Lived Eastward-Propagating Mesoscale Convective Systems over the Second-Step Terrain alongYangtze-Huaihe River Valley
(withdrawn after no-show)
Yuanchun Zhang
X5.39
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EGU24-17689
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ECS
Impact of microphysical perturbations on convective precipitation predictability 
Beata Czajka, Christian Barthlott, and Corinna Hoose

The predictability of deep moist convection is subject to large uncertainties, mainly due to inaccurate initial and boundary data, incomplete description of physical processes, or uncertainties in microphysical parameterizations. In this study we present results from a large 108-member ensemble focussing solely on the perturbation of microphysical uncertainties. We perturb the cloud condensation nuclei concentrations, the ice nucleating particle concentrations, the graupel sedimentation velocity as well as the width of the cloud droplet size distribution, all of which are not well constrained by observations. The model simulations are conducted with a convection-permitting configuration of the ICON model using a double-moment microphysics scheme. Results from four convective episodes during the Swabian MOSES field campaigns conducted in the summers of 2021 and 2023 show a large spread in convective precipitation in Germany. Based on convection-related parameters and microphysical process rates, the sensitivities of convection initiation, cloud and precipitation formation to the microphysical uncertainties are discussed. An important finding is e.g. the large sensitivity of hail formation on all analysed days. These results demonstrate the benefits of using an aerosol-aware double-moment microphysics scheme and that the use of microphysical uncertainties for ensemble modelling strategies can produce a sufficiently large ensemble spread for convective-scale predictability.

How to cite: Czajka, B., Barthlott, C., and Hoose, C.: Impact of microphysical perturbations on convective precipitation predictability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17689, https://doi.org/10.5194/egusphere-egu24-17689, 2024.

X5.40
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EGU24-17415
Characteristics of hail cloud in southwestern China based on X-band phased array radars
(withdrawn)
Hui Wang
X5.41
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EGU24-5305
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ECS
Testing Hybrid-3DEnVar in the convective scale NWP model AROME-Austria 
Kaushambi Jyoti, Martin Weissmann, Philipp Griewank, and Florian Meier
We test a Hybrid 3-Dimensional Ensemble Variational (Hybrid-3DEnVar) Data Assimilation (DA) method in the limited-area NWP model AROME over Austria at 2.5km horizontal resolution, with a flow-dependent error covariance matrix sampled from a 50-member ensemble.
Rapidly evolving highly non-linear convective-scale processes and the unique orography of the Austrian Alps intensify the complexities of estimating model error correlations. While the climatological error covariance matrix can not well represent the non-linear error growth of convective-scale weather, these errors can be incorporated into the assimilation using the ensemble-based error covariance matrix. We explore 11 weighted combinations of climatological and sampled covariance matrices, ranging from a purely climatological (weight of 0) to a purely ensemble-based (weight of 1) B-matrix, with incremental weight adjustments to the ensemble by 10 percent increments. The pure climatological configuration (3-dimensional variational data assimilation, 3DVar) is the operational DA scheme of GeoSphere Austria and serves as a comparative benchmark for our experiments. Multiple distinct summertime convective weather scenarios with a special focus on local convection were tested, while cold and warm fronts also influenced some of these cases. Aircraft wind and temperature observations are split into assimilated and non-assimilated parts so that the latter serves as validation for the analysis.
The resulting analysis from the Hybrid-3DEnVar configuration outperforms the operational 3DVAR of GeoSphere Austria, indicating a substantial leap forward in forecast accuracy of convective scale weather within Austria’s complex terrain. However, the optimal weight to the ensemble-based covariances for the optimal analysis strongly depends on the weather phenomenon investigated.
Keywords: AROME-Austria, Hybrid-3DEnVar, a 50-member ensemble, convective scale, and non-linear error growth.
 
 
 

How to cite: Jyoti, K., Weissmann, M., Griewank, P., and Meier, F.: Testing Hybrid-3DEnVar in the convective scale NWP model AROME-Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5305, https://doi.org/10.5194/egusphere-egu24-5305, 2024.

X5.42
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EGU24-8069
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ECS
Towards the assimilation of dual-polarization radar data 
Tatsiana Bardachova, Maryam Ramezani Ziarani, and Tijana Janjic

The forecast accuracy of numerical weather prediction models is strongly determined by the precision of the initial conditions, especially for storm and convective-scale weather prediction. Since radars allow to capture the internal structure and important microphysical and dynamical processes in convective systems, they are crucial instrument for improvement of weather forecasts on these scales. Dual-polarization radar, in contrast to a prevalent single-polarization radar, also provides information on the types and sizes of hydrometeor particles. As a result, polarimetric radar data (PRD) proves to be a valuable data source for data assimilation. However, direct assimilation of PRD is not used in current operational non-hydrostatic convection-permitting numerical models. This is associated with several difficulties, such as model error estimation, which require further study.

The current focus of our study is to directly assimilate PRD in an idealized setup. For that purpose, observation system simulation experiments (OSSEs) were performed that simulate the development of a long-lived supercell using the ICON model with two-moment microphysics scheme. In OSSE, the Kilometer-scale Ensemble Data Assimilation (KENDA) system, which comprises the Local Ensemble Transform Kalman Filter (LETKF) was used. The new polarimetric radar forward operator EMVORADO-POL developed at Deutscher Wetterdienst (DWD) was incorporated in the setup. The first steps towards the direct assimilation of differential reflectivity, in addition to non-polarimetric variables, have been implemented and will be presented. Proper thresholds and model equivalents of polarimetric data were examined. Results were compared to reference experiments assimilating non-polarimetric variables such as reflectivity and radial velocity.

How to cite: Bardachova, T., Ramezani Ziarani, M., and Janjic, T.: Towards the assimilation of dual-polarization radar data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8069, https://doi.org/10.5194/egusphere-egu24-8069, 2024.

X5.43
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EGU24-8810
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ECS
Investigating the life-cycle of convective clouds from 4D observational data 
Sarah Brüning and Holger Tost

Convective clouds play a crucial role for understanding the Earth’s climate. Current advancements of remote sensing instruments allow us to obtain valuable information on the spatio-temporal dynamics of convective clouds on multiple scales. Nevertheless, a continuous coverage of high-resolved 4D observational data to investigate the 3D properties of rapidly developing convective clouds is generally not available.

In this study, we leverage 4D radar reflectivities (in dBZ) derived from the extrapolation of passive and active remote sensing sensors with machine learning to close this gap. Using data with a spatial resolution of 3 km and a temporal resolution of 15 minutes, we receive a continuous perspective on the evolution of the cloud vertical column along the different stages of the cloud life-cycle. For this purpose, we apply an object-based algorithm to detect the centroid of convective cores and their anvil at each time step. Based on these centroids, we extract the 3D cloud field and track the horizontal and vertical movement through space and time. Afterwards, we filter all tracks using the vertical extension and maximum reflectivity of the associated cloud field to exclude erroneous features.

Here, we present an evaluation of the algorithm and its ability to investigate the 4D spatio-temporal properties of convective clouds. We set out to compare convective systems of different sizes over both oceans and continents to analyze the impact of varying environmental conditions on the cloud vertical motions along the cloud life-cycle.

How to cite: Brüning, S. and Tost, H.: Investigating the life-cycle of convective clouds from 4D observational data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8810, https://doi.org/10.5194/egusphere-egu24-8810, 2024.

X5.44
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EGU24-8868
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ECS
Situation-Dependent Localization for All-Sky Satellite Observations 
Tobias Necker, Takumi Honda, Philipp Griewank, Takemasa Miyoshi, and Martin Weissmann

This study aims to improve the localization and assimilation of satellite observations in the visible and infrared spectral ranges to enhance predictions of clouds and convective processes. Understanding correlation structures between satellite observations and atmospheric state variables is crucial for successful data assimilation. We focus on examining vertical ensemble-based correlations from Himawari-8 channels (VIS0.64 or IR7.35) and tackle the challenge of vertical observation-space localization. Traditional distance-based localization methods are often suboptimal due to the multi-layered origin of observed radiation. We present empirical optimal localization (EOL) functions derived from a 1000-member ensemble convective-scale simulation to address this issue. Our research highlights the need for channel-specific and variable-specific localization strategies, emphasized by our analysis of two summer case studies that exhibit substantial situational variability in correlation structures, especially in the visible spectral range. Further, we explore various predictors for formulating dynamic, situation-specific vertical localization strategies, offering insights into their effectiveness and potential for advancing convective-scale satellite data assimilation.

How to cite: Necker, T., Honda, T., Griewank, P., Miyoshi, T., and Weissmann, M.: Situation-Dependent Localization for All-Sky Satellite Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8868, https://doi.org/10.5194/egusphere-egu24-8868, 2024.

X5.45
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EGU24-12221
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ECS
Preparing AROME assimilation experiments for cloud-affected satellite observations 
Sandy Chkeir, Martin Weissmann, Philipp Griewank, Florian Meier, and Adhithiyan Neduncheran

Despite the abundance of satellite observations, their assimilation in all-sky scenarios remains difficult, which hinders the use of high-resolution information in forecast models. In this work, we focus on the direct assimilation of satellite radiances (visible 0.6 μm, infrared IR 6.2 and 7.3 μm of the Seviri instrument) under all-sky conditions into the convection-permitting Numerical Weather Prediction (NWP) model AROME, which is in operation at Geosphere Austria, the Austrian weather service. Our research aims to exploit the potential of assimilating visible and IR channels under all-sky conditions making use of convection-permitting weather models that can explicitly resolve deep convection. In particular, we are looking at the use of the Radiative Transfer for TOVS (RTTOV), as an observational operator, to generate synthetic images for each channel. We endeavor to optimize the operator settings for running simulations within the convective-scale AROME model. Our first experiment focuses on testing IR synthetic images generated under all-sky conditions during a summer period (August) over Austria. 

How to cite: Chkeir, S., Weissmann, M., Griewank, P., Meier, F., and Neduncheran, A.: Preparing AROME assimilation experiments for cloud-affected satellite observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12221, https://doi.org/10.5194/egusphere-egu24-12221, 2024.

X5.46
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EGU24-15473
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ECS
The assimilation of surface observations in mountainous terrain in the WRFDA system 
Giorgio Doglioni, Stefano Serafin, Martin Weissmann, Gianluca Ferrari, and Dino Zardi

Assimilating surface observations in convective scale data assimilation (DA) systems is not straightforward, since these observations may be affected by small-scale effects not represented in the model, and the model itself might not be able to accurately represent the features of the atmosphere close to the surface. These issues are particularly evident in mountainous terrain. In variational DA systems, such as the Weather Research and Forecasting, Data Assimilation (WRFDA) suite, the available background error (BE) models produce BE variances and covariances that vary smoothly over long distances. Therefore, for instance, assimilating a valley-floor surface observation typically leads to large analysis increments even at nearby mountain tops, which are physically unwarranted and cause high levels of gravity-wave noise. 

Such problems can be partially mitigated in WRFDA by modeling the BE using the Alpha Control Variable Transform (Alpha CVT). 

Like other BE models in WRFDA, this technique derives BE statistics from an ensemble of differences between forecasts with different initial and identical valid times (NMC method), and it makes use of a control variable transform (CVT). Differently from other BE models in WRFDA, it computes analysis increments as a linear combination of the NMC ensemble members.

In this work we consider simulations with a grid spacing of 3.5 km over a domain encompassing the European Alps. We first use pseudo-observation tests to show how different BE specifications in WRFDA affect the assimilation of surface observations of temperature, specific humidity, pressure and horizontal winds components in complex terrain.

We then present real-case assimilation experiments with a limited set of surface observations. Considering the consistency between the variances of innovations and the assigned observation and background errors, we demonstrate the positive impact of the Alpha CVT.

How to cite: Doglioni, G., Serafin, S., Weissmann, M., Ferrari, G., and Zardi, D.: The assimilation of surface observations in mountainous terrain in the WRFDA system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15473, https://doi.org/10.5194/egusphere-egu24-15473, 2024.

X5.47
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EGU24-15891
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ECS
Optimizing Kilometer-Scale Climate Modeling: Refining Cloud Microphysics Using Machine Learning and Satellite Correlation 
Hannah Marie Eichholz, Jan Kretzschmar, Josefine Umlauft, and Johannes Quaas

The modeling of the Earth Climate System has undergone outstanding advances to the point of resolving atmospheric and oceanic processes on kilometer-scale, thanks to the development of high-performance computing systems. In the preparation phase of the global kilometre-resolution coupled ICON climate model, there's a critical need to fine-tune cloud microphysical parameters. Our approach involves investigating the optimal calibration of these parameters using machine learning techniques.

Our initial focus involves calibrating the autoconversion scaling parameter by correlating it with satellite-derived top-of-atmosphere and bottom-of-atmosphere radiation fluxes. This calibration process entails conducting limited area simulations specifically within the North Atlantic and South Pacific region using ICON. Through these simulations, various adjustments to cloud microphysical parameters are made, aiming to assess their potential impacts on radiation flux output.

How to cite: Eichholz, H. M., Kretzschmar, J., Umlauft, J., and Quaas, J.: Optimizing Kilometer-Scale Climate Modeling: Refining Cloud Microphysics Using Machine Learning and Satellite Correlation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15891, https://doi.org/10.5194/egusphere-egu24-15891, 2024.

X5.48
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EGU24-17871
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ECS
Accurate Representation of Dual-Polarized Radar Parameters with Data Assimilation 
Ji-Won Lee, Ki-Hong Min, and GyuWon Lee

To enhance the accuracy of heavy rainfall prediction, the assimilation of radar data (DA) is crucial. Single-polarized radar variables, such as reflectivity and Doppler velocity, offer insights into raindrop quantity and speed. Dual-polarization (dual-pol) radar variables, including differential reflectivity (ZDR), specific differential phase (KDP), and co-polar correlation coefficient (ρhv), provide additional details about hydrometeor phase, size, and liquid water content. Assimilating dual-pol radar variables into a Numerical Weather Prediction (NWP) model can enhance the accuracy of predicting both large-scale and rapidly developing mesoscale precipitation events. Therefore, the development and application of an accurate radar observation operator for DA, considering the microphysical information of an NWP model with dual-pol radar data, is necessary.

In this study, we developed a dual-pol radar operator based on microphysical variables such as the mixing ratio and total number concentration of hydrometeors. The enhanced method can accurately replicate the characteristics of dual-pol radar variables in the melting layer, improve the underestimation of hydrometeors mixing ratio for liquid and ice particles. Enhancing the estimation of hydrometeor increments further refines the prediction of mesoscale precipitation effects. This study aims to demonstrate improvements in microphysical processes and enhanced accuracy in rainfall predictions through dual-pol radar DA.

※ This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT)(No. 2021R1A4A1032646, 2022R1A6A3A13073165) and the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740.

How to cite: Lee, J.-W., Min, K.-H., and Lee, G.: Accurate Representation of Dual-Polarized Radar Parameters with Data Assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17871, https://doi.org/10.5194/egusphere-egu24-17871, 2024.

X5.49
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EGU24-18115
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ECS
Influence of data assimilation on tropical waves 
Yvonne Ruckstuhl, Tijana Janjic, Hyunju Jung, Peter Knippertz, and Robert Redl

Precipitation forecasts in the tropics are poor due to large model and initial condition errors, leaving ample room for improvement. In particular, it has been hypothesized that the coupling of tropical waves and convection offers a source of predictability, suggesting that capturing these waves accurately in the model and in initial conditions could lead to improved precipitation forecasts. In this work, we investigate whether standard data assimilation (DA) algorithms like the Ensemble Kalman Filter (EnKF) are fundamentally capable of recovering tropical waves and thereby provide initial conditions that lead to skillful precipitation forecasts. To capture the essence of tropical dynamics without contamination of land-sea contrasts, sea-surface temperature gradients and influences from the extra-tropics, we use a tropical aqua channel configuration at 13km grid-spacing with the ICON numerical weather prediction model. Further, to isolate the role of the initial conditions provided by DA, we assume a perfect model. In our setup, Kelvin waves dominate over other wave types and primarily modulate precipitation.  In addition, there is evidence of a Madden-Julian-Oscillation (MJO)-like feature that appears coupled to large-scale convective activity. We show that when sufficient wind observations are assimilated, the DA can reduce the errors in the representation of the Kelvin waves sufficiently to provide accurate precipitation forecasts up to several weeks. Surprisingly, even the MJO-like rainfall event, which starts after a forecast lead time of 10 days, is captured by the forecast ensemble. Further, we find that accurate initial conditions for humidity are important to slow down error growth for all model variables. Like emphasized by several other studies, we conclude that wind observations are by far the most important input to achieve skillful tropical forecasts. A secondary challenge is to improve initial conditions of humidity by developing DA algorithms to account for non-Gaussian error statistics. Investigating the role of model error in DA and the resulting forecasts is left for future work. 

How to cite: Ruckstuhl, Y., Janjic, T., Jung, H., Knippertz, P., and Redl, R.: Influence of data assimilation on tropical waves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18115, https://doi.org/10.5194/egusphere-egu24-18115, 2024.

X5.50
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EGU24-2790
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ECS
The preliminary assimilation of three-dimensional horizontal winds from geostationary hyperspectral infrared measurements
(withdrawn)
Ruoying Yin