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Using Crowdsourced Hail Observations to Link C-Band Radar-Based Differential Reflectivity Columns to Hail Events in Switzerland


Core Concepts
Differential reflectivity columns (ZDRC) derived from C-band weather radar data correlate with hail events and show potential for nowcasting hail, particularly severe hail, even in complex terrain like the Swiss Alps.
Abstract
  • Bibliographic Information: Aregger, M., Martius, O., Germann, U., & Hering, A. (2024). Differential reflectivity columns and hail – linking C-band radar-based estimated column characteristics to crowdsourced hail observations in Switzerland. arXiv preprint arXiv:2410.10499v1.
  • Research Objective: This study investigates the relationship between differential reflectivity columns (ZDRC) detected in C-band weather radar data and crowdsourced hail observations in Switzerland, aiming to assess the potential of ZDRC for hail detection, size estimation, and nowcasting.
  • Methodology: The researchers implemented an adapted version of the Snyder et al. (2015) ZDRC detection algorithm on a 3D composite of ZDR data from five Swiss weather radars. They linked ZDRC characteristics (area, maximum ZDR value, height, and volume) to hail reports categorized by size (small, medium, large) and compared their findings to existing radar-based hail products (MESHS and POH). The study also analyzed the timing of peak ZDRC characteristics relative to hail reports.
  • Key Findings:
    • ZDRCs were present in most hail-producing storms, with higher frequencies in storms producing severe hail.
    • Significant differences were found in ZDRC characteristics between hail-producing and non-hail-producing storms, particularly in maximum ZDR value and volume.
    • Peak ZDRC characteristics were most often measured 5-10 minutes before the first hail reports.
    • While ZDRC characteristics showed potential for hail nowcasting, their intermittent nature posed challenges.
  • Main Conclusions: ZDRC characteristics derived from C-band radar data correlate with hail events and hold promise for nowcasting hail, especially severe hail. The study highlights the potential of combining ZDRC analysis with crowdsourced data for improved hail detection and nowcasting in complex terrain.
  • Significance: This research contributes to the understanding of ZDRC as a hail indicator and its applicability for C-band radar observations in mountainous regions. The findings have implications for developing more accurate and timely hail warning systems.
  • Limitations and Future Research: The study acknowledges limitations due to the intermittent nature of ZDRCs and the reliance on crowdsourced data, which may be biased by population density. Future research could explore combining ZDRC with other radar variables and investigating the microphysical processes within ZDRCs to enhance hail size estimation and nowcasting accuracy.
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Stats
ZDRCs were found in 81% of all hail storms. ZDRCs were found in 74% of small hail storms. ZDRCs were found in 93% of medium hail storms. ZDRCs were found in 95% of large hail storms. ZDRCs were found in 61% of no-hail storms. The best ZDRC characteristic for separating no-hail storms from hail storms was the maximum ZDR value within the column, with a Heidke Skill Score of 0.32 using a threshold of 4.4 dB. The best ZDRC characteristic for separating small hail storms from severe hail storms was the ZDRC volume, with a Heidke Skill Score of 0.3 using a threshold of 51.8 km3. Peak ZDRC characteristics were most frequently measured close to the first reported hail, but with a wide spread of more than 50 minutes before and after. In the 20 minutes preceding the first hail report, 47% of hail storms exhibited a continuous ZDRC. 18% of hail storms did not exhibit a ZDRC before the first hailfall.
Quotes

Deeper Inquiries

How might machine learning techniques be employed to improve the prediction of hail events based on ZDRC characteristics and other relevant meteorological data?

Machine learning (ML) techniques hold significant potential for enhancing hail event prediction by leveraging the complex relationships between ZDRC characteristics and other meteorological variables. Here's how: 1. Predictive Modeling: Algorithm Selection: Supervised ML algorithms, particularly those adept at classification and regression tasks, are well-suited for hail prediction. Classification: Algorithms like Random Forests, Support Vector Machines (SVMs), and Neural Networks can be trained to classify storms as hail-producing or non-hail-producing based on ZDRC features (area, height, maximum ZDR, volume) and other predictors. Regression: For hail size estimation, regression algorithms like Linear Regression, Decision Trees, or Gradient Boosting can learn the mapping between ZDRC characteristics and observed hail sizes. Feature Engineering: Beyond raw ZDRC characteristics, ML models benefit from carefully engineered features. This might include: Temporal Trends: Rates of change in ZDRC features over time (e.g., growth rate of ZDRC height) can signal intensifying updrafts. Spatial Patterns: Spatial characteristics of ZDRCs, such as their shape complexity or proximity to other convective cells, could be informative. Combined Predictors: Integrating data from other sources, such as thermodynamic variables (CAPE, CIN, wind shear), lightning data, or even numerical weather prediction model output, can significantly enhance model accuracy. 2. Model Training and Evaluation: Data Splitting: A large dataset of ZDRC observations, hail reports (or a proxy like hail damage reports), and other meteorological variables is essential. This dataset should be divided into training, validation, and testing sets to prevent overfitting and ensure model generalizability. Performance Metrics: Model performance should be rigorously evaluated using metrics relevant to the specific task: Classification: Probability of Detection (POD), False Alarm Rate (FAR), and Heidke Skill Score (HSS) are commonly used. Regression: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficients can assess hail size estimation accuracy. 3. Operational Implementation: Real-time Prediction: Once trained and validated, ML models can be integrated into nowcasting systems to provide real-time hail probabilities or size estimates based on incoming radar data. Ensemble Approaches: Combining predictions from multiple ML models, each trained on different subsets of data or using different algorithms, can further improve forecast skill and robustness. Challenges and Considerations: Data Imbalance: Hail events, especially large hail, are relatively rare compared to non-hail events. Addressing this class imbalance in the training data is crucial to prevent models from being biased towards the majority class. Generalizability: ML models trained on data from one geographic region or radar system might not generalize well to others due to variations in topography, climate, or radar characteristics. Transfer learning techniques can help adapt models to new environments. Interpretability: While some ML models are inherently interpretable (e.g., Decision Trees), others, like Neural Networks, can be more opaque. Understanding the decision-making process of ML models is important for building trust and identifying potential biases.

Could the observed relationship between ZDRC characteristics and hail be influenced by regional factors other than topography, such as prevailing wind patterns or microclimates?

Yes, the observed relationship between ZDRC characteristics and hail can be modulated by regional factors beyond topography, including: Prevailing Wind Patterns: Steering Storms: Wind patterns at different levels of the atmosphere can influence storm motion and organization. Storms moving quickly in the direction of the mean wind might have less time to develop large hail, even with strong updrafts indicated by ZDRCs. Conversely, storms moving slowly or against the mean wind might have longer residence times over a given area, potentially enhancing hail growth. Wind Shear: Vertical wind shear, the change in wind speed and/or direction with height, is a crucial factor in hailstorms. Strong wind shear can tilt the updraft, promoting hailstone growth by increasing the time spent in the growth zone of the storm. Regions with typically higher wind shear might see a stronger link between ZDRC characteristics and large hail. Microclimates: Local Temperature and Moisture Variations: Microclimates, driven by factors like proximity to water bodies, elevation changes, or land cover, can create localized areas of enhanced instability or moisture. These variations can influence the intensity of updrafts within storms and, consequently, the relationship between ZDRCs and hail. For example, a storm passing over a warmer, more humid microclimate might experience a temporary intensification of its updraft, potentially leading to larger hail even if the overall ZDRC characteristics are not exceptionally strong. Aerosol Concentrations: Ice Nuclei Availability: The availability of ice nuclei, particles that promote ice crystal formation, can vary regionally. Higher concentrations of ice nuclei might lead to more efficient hailstone growth at colder temperatures, potentially influencing the relationship between ZDRC characteristics (which primarily reflect large liquid drops above the freezing level) and hail size at the ground. Investigating Regional Influences: Comparative Studies: Conducting comparative studies across regions with contrasting wind patterns, microclimates, and aerosol regimes can help disentangle the influence of these factors on the ZDRC-hail relationship. Numerical Modeling: High-resolution numerical weather models can be used to simulate storms under different environmental conditions, allowing for controlled experiments to isolate the impact of specific regional factors. Data Collection: Expanding hail observation networks, particularly in regions with limited data, is crucial for better understanding regional variations in hail characteristics and their relationship to ZDRC features.

If ZDRCs represent updrafts strong enough to support hail growth, what are the implications for understanding the dynamics and predictability of severe convective storms in a changing climate?

The link between ZDRCs, as indicators of strong updrafts, and hail growth has significant implications for understanding severe convective storms in a changing climate: 1. Enhanced Updraft Strength and Hail Growth: Warmer Atmosphere, More Moisture: Climate change projections suggest a warmer atmosphere capable of holding more moisture. This increased moisture availability can fuel more intense updrafts within convective storms. If ZDRCs accurately reflect these stronger updrafts, it suggests a potential increase in the frequency or intensity of hailstorms in a warmer climate. Shifts in Hail Size Distributions: Stronger updrafts could potentially lift larger hailstones before they fall out of the storm, leading to a shift towards larger hail sizes. This has implications for hail damage potential, as even small increases in hailstone diameter can significantly increase damage to crops, property, and infrastructure. 2. Improved Predictability of Severe Convection: Early Warning Systems: If ZDRCs prove to be reliable indicators of hail-producing updrafts, they could be incorporated into early warning systems for severe convection. Real-time monitoring of ZDRC characteristics, particularly their growth rates and maximum values, could provide valuable lead time for issuing hail warnings. Nowcasting Applications: The rapid evolution of ZDRCs makes them potentially useful for nowcasting hail threats. By tracking ZDRC characteristics in real-time, forecasters could better anticipate the development and movement of hailstorms, improving short-term (0-2 hour) predictions. 3. Challenges and Uncertainties: Complex Interactions: Climate change is likely to alter multiple aspects of the atmosphere simultaneously, including temperature, moisture, wind shear, and atmospheric stability. These changes can interact in complex ways, making it challenging to isolate the specific impact of enhanced updrafts on hailstorms. Regional Variations: The response of severe convective storms to climate change is expected to vary regionally. Factors like changes in large-scale weather patterns, regional warming trends, and local microclimates will influence how updrafts and hailstorms evolve in different parts of the world. Continued Research: Further research is needed to fully understand the relationship between ZDRCs, updrafts, and hail growth in a changing climate. This includes: Long-term ZDRC Observations: Collecting long-term datasets of ZDRC characteristics across diverse geographical regions is crucial for establishing climatologies and detecting trends. High-Resolution Modeling: Using high-resolution numerical models to simulate convective storms under different climate change scenarios can help quantify the potential impacts on updraft strength and hail production. Improved Hail Observations: Expanding hail observation networks and developing new remote sensing techniques for hail detection and size estimation are essential for validating model predictions and improving our understanding of hailstorms in a changing world.
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