Wind is a core state variable of the atmosphere. Accurate weather predictions and a better understanding of atmospheric dynamics, transport and dispersion require improved wind information, especially inside the turbulent planetary boundary layer (PBL). The Doppler lidar technique provides advanced capabilities for remote sensing of wind using laser radiation. It has been widely deployed using ground-based and airborne platforms in the last decades. Extending the capabilities of ground-based measurement systems, airborne Doppler lidar (ADL) onboard research aircraft allows for targeted and spatially resolved wind measurements, which is crucial for localized severe weather events or in inaccessible regions such as over water and complex terrain.

This contribution introduces a novel design for ADL systems, aiming to revolutionize the field of airborne wind measurements by providing higher measurement accuracy and spatial resolution, in combination with a simplified and more robust system design. Up to now, ADL systems use a single Doppler lidar attached to a scanner to provide multi-angle measurements. Due to cost and size reductions of Doppler lidar units over the recent years, it has now become possible to construct an ADL system that uses multiple lidars with fixeddirection beams, instead of a single lidar with a scanning beam.

The new system design uses five eye-safe, compact and lightweight Doppler lidars pointing at fixed beam directions. LES-based simulation results have demonstrated that a fixed-beam system will have approximately one order of magnitude improved spatial wind measurement resolution as well as higher accuracy, compared to existing scanning systems. Further, these simulation results allow us to determine an optimal beam number and orientation. Due to the high resolution and accuracy, the retrieval of turbulence properties, such as vertical wind variance and turbulent kinetic energy becomes possible. Besides improved measurements, the greatly simplified ADL system design allows for more cost-effective manufacturing, certification, and application on a wider range of aircraft. For example, the more light-weight construction without scanner may enable measurements onboard smaller aircraft. Furthermore, routine measurements aboard larger commercial aircraft could be established.

Overall, the higher measurement quality and order of magnitude higher spatial resolution present an important step forward to meet urgent needs to improve wind measurements. Atmospheric research and weather forecasting are expected to benefit through better process understanding and data assimilation. Additionally, through the availability of ADL measurements on a wider range of airborne platforms, a significant impact on weather forecast and aviation safety can be anticipated.

How to cite: Gasch, P., Kasic, J., and Wang, Z.: A novel airborne Doppler lidar system design for highresolution wind measurements, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-694, https://doi.org/10.5194/ems2023-694, 2023.

The mountain cryosphere is so large, varied, inhospitable and changeable that we must rely on models of snowfall to map and manage this water resource and to predict how it will evolve. This is an important goal because snowmelt released each summer is an extraordinary generator of wealth and wellbeing in rich and poor countries, but it is among the most sensitive of all major ecosystem-services to climate change. Unfortunately, the water content of snowfall is notoriously difficult to measure accurately on a large enough scale, and conventional gauges are too sparse, small, and bias-prone to constrain precipitation climatologies or weather models. As a result, there are large biases in state-of-the-art assessments of mountain precipitation worldwide, and future projections are even more uncertain.

I present an innovative approach to measuring snowfall that solves many of the problems of existing instruments. I show that lakes, which are common in the mountain cryosphere, can be used as pressure-sensing surfaces to yield accurate observations of the water content of snowfall simply, cheaply, autonomously, and over areas that are thousands to billions of times larger than conventional gauges. Using a standard water-pressure sensor submerged on a lakebed, this approach quantifies directly, as a pressure signal, the mass of winter precipitation as it reaches the lake surface. The measurement precision is readily quantifiable and comparable to, or better than, that of commercial precipitation gauges. More importantly, it avoids the measurement biases of other instruments (like pluviometers or snow pillows) that interact with snow as it falls or accumulates. Crucially, it also senses snowfall averaged over the whole hydrostatic lake surface above, over large areas comparable in scale to model grid cells. The largest single lake tested to date, for example, has an area 100,000 times greater than all the world’s conventional pluviometers combined. In winter conditions, this new approach can therefore largely eliminate both the measurement biases and scaling biases of the global array of conventional instruments, biases that until now have propagated into errors in the output of models developed, calibrated and validated on flawed observations. Initial tests of advanced operational forecast models Arome Arctic and COSMO-1 against these novel measurements reveal model biases of up to 100%, demonstrating the potential for this new approach to transform our ability to measure and model snowfall.

How to cite: Pritchard, H.: Lakes as snowfall sensors: solving the precipitation problem inthe mountain cryosphere, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-693, https://doi.org/10.5194/ems2023-693, 2023.

Extreme weather events can wreak havoc on human health, infrastructure, and economy, accentuating the need for timely detection and accurate prediction. Current sub-seasonal and seasonal NWP forecast models, e.g. the ECMWF extended range and SEAS5 products, lack the spatial resolution necessary for applications operating at a smaller scale. Dynamic downscaling can improve spatial and temporal granularity, but is computationally expensive, whereas traditional statistical downscaling requires high-resolution target data. The application of machine learning has shown promise in weather forecast downscaling, but most methods are not transferable to new regions, disregard physical boundaries, and often oversimplify the downscaled fields, thereby diminishing the representation of extreme events.

Our proposed project idea for the Harry-Otten Prize aims at developing a novel, computationally inexpensive, transferable, and physics-aware post-processing methodology including a pre- and post-processing framework addressesing these challenges. We aim to develop a low-cost computational setup, incorporating physics-informativeness and tail-awareness, establish trustworthiness within the scientific community and the public, and assess transferability to untrained regions.

Our proposed methodology combines ideas from a statistical residual fitting approach with local information (topography, land-use and physics-informed
machine learning techniques. More specifically, we suggest a GAN-setup consisting of a ConvLSTM and a physics-aware UNET. Using specified loss
functions accounting both for physics-awareness and upper/lower tails of the distribution, aiming at improving the prediction of extreme events, as well as
handling computations more efficiently. The pre-trained model can be transferred to untrained regions and enable usage of in regions with only a few local data or few computational resources.

This project has the potential for significant societal impacts, particularly in the agricultural and renewable energy sectors. By providing more accurate, highresolution predictions, it supports decision-making for optimizing harvests and reliable energy production in region that are severely affected by extreme weather and climate events. The transferable, low-cost methodology allows for easy adaptation to specific regions and applications, which is very valuable in regions with limited access to HPC resources. To this end, our framework can help enhancing societal resilience to adverse weather conditions worldwide.

Expected outcomes include an open-source deep learning framework for postprocessing and downscaling sub-seasonal and seasonal weather predictions, an improved accuracy in representation of extreme weather events, and an evaluation of the benefits of dynamical downscaling. The proposed solution is unique in its ability to increase spatial resolution to convection permitting scale and its transferability, both in terms of application to different regions and computational platforms.

How to cite: Schicker, I., Bügelmayer-Blaschek, M., and Lampert, J.: Weather impacts on renewables agriculture:explainable AI for hyperresolution downscalingfor S2S, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-692, https://doi.org/10.5194/ems2023-692, 2023.