PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data

This paper introduces Precipitation Attention-based U-Net (PAUNet), a deep learning architecture for predicting precipitation from satellite radiance data, addressing the challenges of the Weather4cast 2023 competition. PAUNet is a variant of U-Net and Res-Net, designed to effectively capture the large-scale contextual information of multi-band satellite images in visible, water vapor, and infrared bands through encoder convolutional layers with center cropping and attention mechanisms. We built upon the Focal Precipitation Loss including an exponential component (e-FPL), which further enhanced the importance across different precipitation categories, particularly medium and heavy rain. Trained on a substantial dataset from various European regions, PAUNet demonstrates notable accuracy with a higher Critical Success Index (CSI) score than the baseline model in predicting rainfall over multiple time slots. PAUNet's architecture and training methodology showcase improvements in precipitation forecasting, crucial for sectors like emergency services and retail and supply chain management

Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global sensitivity analysis method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation

Indian heatwaves in a future climate with varying hazard thresholds

India has experienced remarkable changes in temperature extremes in recent decades due to rapid global warming leading to extreme heat events with disastrous societal impacts. In response to continuing global warming, this study investigates summertime (March–June) heatwave characteristics over India in the present and future climate. During 1951–2020, India Meteorological Department observational data show rising trends in heatwave characteristics such as frequency, intensity, duration, and season length, mainly over India’s northwest, central, and south peninsular regions. Further, the present study explores the changes in future heatwave characteristics using the state-of-the-art statistically downscaled bias-corrected climate models data from Coupled Model Intercomparison Project Phase 6 (CMIP6) of the Shared Socioeconomic Pathway scenario. This study uses varying hazard thresholds, namely fixed (time-invariant historical climatological threshold) and decadal moving thresholds (time-varying future climatological threshold), to define heatwaves and examine the future changes in heatwave characteristics over India. Results show a significant increase in mean summertime heatwaves defined using fixed thresholds in terms of their frequency, duration, number, amplitude, cumulative magnitude, and season length in the near future (NF) (2025–2054) and the far future (FF) (2065–2094) compared to the baseline period (1985–2014) over much of India, with the most substantial increases seen in the FF. However, heatwaves defined using the decadal moving thresholds show no significant changes in their characteristics during the NF but a substantial change in the FF over many parts of India. This work is the first attempt to use bias-corrected CMIP6 models data to project heatwave characteristics utilising the concept of the varying hazard thresholds across India. Overall, this study provides a comprehensive assessment of climate change’s impact on Indian heatwaves, which can help in planning better adaptation and mitigation strategies

Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation