bluuebunny/crossref_metadata_2025 huggingface dataset:
An Intelligent Forecasting Method for Near-Surface Extreme Wind Speed Based on K-Nearest Neighbor Mutual Information Feature Selection
Authors: Huang Xiao Yan , Zhao Hua Sheng , Wu Yu Shuang , He Li , Huang Ying
Published: July 2024

This study focused on predicting the near-surface maximum wind speed using the eXtreme Gradient Boosting (XGBoost) model based on k-nearest neighbor mutual information feature selection. The data from 93 meteorological stations in Guangxi Province, with a temporal resolution of 3 h, were used for the prediction. By examining the effects of various dynamic and thermal factors, such as high altitudes and surface variables, on the prediction of maximum wind speed, a novel XGBoost-based prediction model for maximum wind speed was proposed. The model incorporates the k-nearest neighbor mutual information feature selection algorithm to choose the most relevant factors for accurate wind speed prediction. In the design of the prediction model, there are two main areas of improvement. First, a stepwise variable selection algorithm based on k-nearest neighbor mutual information estimation was employed, which selects relevant variables and removes weakly relevant variables through two steps, effectively eliminating redundant prediction characteristics that affect accuracy by screening the primary predictors and retaining important forecasting factors. Second, the Bayesian optimization algorithm was used to optimize the parameters in the XGBoost model, significantly enhancing the model's generalizability. The optimized and improved prediction model was utilized to model and research the near-surface maximum wind speed for 6 forecast lead times (12-72 h) at 93 meteorological stations. Comparative results of various forecast experiments using independent prediction samples from 2020 to 2021 demonstrated that the new model reduced the average mean absolute error (MAE) evaluation metric by 18.9% to 30.06% for the prediction results of the 93 stations. The root mean square error (RMSE) metric decreased by 40.18% to 65.83%. For the prediction of maximum wind speeds exceeding level 6, the MAE was reduced by 40.41%, 25.93%, 19.96%, 21.39%, 12.39%, and 8.55% for the 6 forecast lead times, respectively. The RMSE evaluation metric also decreased by 30.92%, 18.67%, 12.29%, 12.21%, 7.92%, and 2.39% for the respective lead times.
Future changes in the global teleconnections of extreme El Niño events
Authors: Trascasa-Castro Paloma , Ruprich-Robert Yohan , Maycock Amanda
Published: July 2024

Model simulations show a robust increase in El Niño-Southern Oscillation (ENSO)-related precipitation variability in a warmer climate, but there remains uncertainty in whether the characteristics of ENSO events themselves may change in the future. Our study aims to disentangle these effects by addressing how the global impacts of observed large El Niño events would change in under present and future background climate conditions.Pacemaker simulations with the EC-Earth3-CC model were performed replaying the 3 strongest observed El Niño events from the historical record (1982/83, 1997/98, 2015/16). Model tropical Pacific sea surface temperature (SST) anomalies were restored towards observations, while imposing different background states, mimicking present and future climate conditions (following the SSP2-4.5). All variables outside the restoring region evolve freely in a coupled-atmosphere ocean transient simulation. For each start date, 30 ensemble members with different initial conditions were run for 2 years. Using this approach we ask ‘what impacts would arise if the observed El Niño occurred in the past or future’?In response to the same imposed El Niño SST anomalies, precipitation anomalies are shifted towards the Eastern equatorial Pacific in the future compared to the present day, leading to changes in the extratropical response to El Niño. Some examples are an amplification of the surface temperature response over north-eastern North America, northern South America and Australia in boreal winter. We link the changes of El Niño related tropical Pacific precipitation to a decrease in the climatological zonal SST gradient in the equatorial Pacific, as we move from past to future climatologies, which potentially leads to a higher convection sensitivity to SST anomalies over the Central and Eastern equatorial Pacific in the future. Changes in the future climate response to extreme El Niño events are not homogeneous among regions. For example, cold and hot anomalies driven by extreme El Niño over North America and Australia in the future are amplified, whereas the response over southern Africa gets muted due to shifts in atmospheric circulation. Our study aims to attribute the absolute future climate response to extreme El Niño events to either changes in El Niño teleconnections or climate change.
Characterization of the surface energy balance in complex terrain
Authors: Destro Martina , Rotach Mathias W. , Lehner Manuela
Published: July 2024

The surface energy balance (SEB) is a physical principle that represents how the energy is distributed between the lowest layer of the atmosphere and the Earth's surface. Its understanding is pivotal not only for determining the meteorological and climatological conditions in the atmospheric boundary layer, but also for applications such as weather and climate modeling, agricultural and forest management, air pollution modeling, ecosystem carbon budget, and biological modeling. Indeed, all the mentioned applications assume a balance between the energy fluxes. Yet, even over flat and horizontally homogeneous terrain, the SEB closure is rarely observed. For an ideal massless layer at the surface, the SEB formulation states that net radiation is balanced by the sum of the turbulent fluxes of sensible and latent heat, and the soil heat flux (Rn = H + LE + G). Previous research suggests that the persistent lack of closure either results from measurements limitations or from omitted processes. In fact, measurements are usually not taken at the interface between the atmosphere and the surface, but at a certain distance above (Rn, H and LE) or below the ground (G). Consequently, fluxes should be referred to a volume rather than a surface. SEB studies identified the storage of heat in the volume of air/soil above/below the measurement point as a contributing factor to the SEB residual. Nowadays, it is widely recognized that non-turbulent, advective fluxes derived from surface heterogeneities are the main reason behind the SEB non-closure. We systematically assessed this ideal formulation using data collected from the sites of the i-Box network, a long-term measurement setup in the Inn Valley (Austria). These stations represent different topographic categories (valley floor, mountain top, and slopes) and present different slope angle, orientation and land-use characteristics. Different to assessing SEB closure (or non-closure) over ideal surfaces, the goal of the present study is to characterize the SEB residual in complex, mountainous terrain. In other words it is investigated which processes contribute to what degree to the expected non-closure of the SEB in this type of environment. Specifically, we determined the relation between the SEB imbalance and different flow regimes (thermally-driven valley-slope wind circulations and foehn events), stability classes, seasons and topographic categories. Furthermore, the effects of specific post-processing procedures on the magnitude of the SEB residual were assessed for a valley-floor and a north-facing slope site. We compared the turbulent fluxes using double rotation, planar-fit and sectorial-planar-fit coordinate rotation methods. In general, these procedures lead to small differences among fluxes such that they do not impact the relative importance of different processes to explain the SEB non-closure. However, this comparison allowed us to evaluate the uncertainty of the magnitudes of the turbulent fluxes and therefore of the SEB residual. 
How should the anthropogenic heat be treated in the urban surface energy balance: revisit of the definition of QF
Authors: Kikegawa Yukihiro , Sugawara Hirofumi , Kobayashi Takuto , Maruyama Naoya
Published: July 2024

  The most widely accepted concept of energy balance in the volume of the urban canopy layer is expressed by the following equation,Q* + QF = QH + QE + ΔQS + ΔQAwhere Q* is the net all-wave radiation, QF  the anthropogenic heat flux, QH the turbulent sensible heat flux, QE the latent heat flux, ΔQS the net storage heat flux and ΔQA the net advective heat flux.Through observations and simulations targeting a residential area in Tokyo, this study proposes revisions to the above conventional concept for energy balance in the following two respects.1. QF  should be interpreted not as the anthropogenic exhaust heat released to the urban atmosphere but as energetic bulk input to the urban canopy where QF  is equivalent to heat resulting from fuels combustion and electricity consumption with usually negligible metabolic heat there.2. Instead, actual anthropogenic exhaust heat should be treated as components of the turbulent heat fluxes (QH and QE) using the following equation,QH = QHb + QHt + QHswhere QHb is the net building anthropogenic heat consisting of waste heat from HAC (heating and air conditioning) systems and exchanged heat between indoors and outdoors through ventilation, QHt the anthropogenic heat from traffic and QHs the turbulent sensible heat flux (convection) from the urban surfaces. The same concept holds true for the latent heat.   The above revisions are applied in this study to the full energy flow analysis for the residential urban canopy in Tokyo using the eddy covariance CO2 flux and O2 & CO2 concentrations measurements, together with fine resolution (time and space) inventory data of electricity use and car traffic. Through the analysis, we estimate QF  by source (electricity, liquid and gas fuel) and each component of the turbulent heat fluxes (QHb, QHt and QHs). Finally, those observation-based estimates are compared with the simulations with a focus on QHb. The authors’ multi-layer urban canopy model coupled with the building energy model is used for the simulations. Resultantly, observation-based components of urban energy balance show good agreement with those from simulations including QHb, suggesting the validity of the authors’ revisions to the conventional urban surface energy balance concept.
A Fuzzy Neural Network Bagging Ensemble Forecasting Model for 72-hour Forecast of Low-temperature Chilling Injury
Authors: Lu Hong , Ou Yi , Qin Chuan , Jin Long
Published: July 2024

On the basis of the daily temperature and precipitation data of Guangxi and the NCEP/NCAR reanalysis data and forecast field data, the paper aims to determine the significant nonlinearity and temporal variability of the forecast quantity series and the overfitting that can easily appear in the forecast modeling of a single fuzzy neural network model and many adjustable parameters that are difficult to determine objectively. Thus, an ensemble forecasting model of fuzzy neural network bagging for 72-hour forecast of low-temperature chilling injury is developed. The forecast results of independent samples show that under the same forecast modeling sample (N = 299) and forecasting factor (M = 9), the fuzzy neural network bagging ensemble forecasting model obtains a mean absolute error of 13.91. By contrast, the mean absolute errors of the single fuzzy neural network forecasting model and the linear regression forecast are 15.82 and 18.13, respectively. The fuzzy neural network bagging ensemble forecast error is lower by 12.07% and 23.27%, respectively, compared with the latter two methods, showing a better forecasting skill. This improved performance is mainly due to the ensemble individuals of the fuzzy neural network bagging ensemble forecasting model with playback sampling. Different ensemble individuals are obtained. Analyses of the new scheme suggests that the forecast accuracy of the ensemble prediction model has been improved by enhancing the prediction ability and population diversity of the individual ensemble members. Therefore, the generalization capacity of the intelligent computing ensemble prediction model has been significantly enhanced. The ensemble enhances the generalization performance and forecast stability of the fuzzy neural network bagging ensemble forecasting model. Thus, this model has better applicability in forecasting nonlinear low-temperature chilling injury.
Designing future Met Office regional ensemble prediction systems: the impact of initial and later boundary conditions on spread-skill relationship for heavy precipitation forecasts.
Authors: Cafaro Carlo , Webster Stuart
Published: July 2024

Many factors need to be considered when designing limited-area ensemble models to forecast at short spatio-temporal scales. However, since the main motivation to run them is to capture the inherent forecast uncertainty at small spatio-temporal scales, especially for severe weather cases, representing such uncertainty is key for producing skillful probabilistic forecasts. Arguably, initial and lateral boundary conditions are a major source of uncertainty for limited-area ensemble forecasts. Many strategies have been explored to initialise convective-scale ensembles but a configuration that is objectively better than others has not been found yet, whereas lateral boundary conditions are provided by a coarser ensemble. Also, limited attention has been given to the size of domains for limited-area models. A domain that is too small could prevent the nested model to generate finer scale and more physically realistic structures which arise from local surface forcing.To this end, at the Met Office, we have started to run some tests to better understand the sensitivity of our convective-scale ensembles to the presence of high-resolution information at initialisation as well as to the size of the domain, this as a function of lead time and regime dependence.hourly time-lagged ensemble with initial conditions centred around the high-resolution deterministic to mimic the current operational ensemble (MOGREPS-UK), 
downscaler from a global ensemble on the same domain as the hourly-cycling,
downscaler from a global ensemble on an increased size domain. 
In all the different designs the ensemble consists of 18 members and runs four times a day. However, in the downscaler all the members are generated from a 6-hourly cycle with initial perturbations coming from the same parent ensemble forecast range, whereas in the hourly-cycling three new members are generated every hour, with five additional sets of initial conditions perturbations and high-resolution deterministic analyses.  The aim of this study is then to assess the sensitivity of the probabilistic skill of heavy precipitation forecasts to these three different designs, both through a series of case studies and aggregate statistics over a nine months period (November 2022-August 2023), using ad-hoc verification measures, focussing on the spatial spread-skill relationship. By looking at different cases we will also assess whether there is any link between the probabilistic skill and the synoptic weather patterns, in order to make more informed decisions about which ensemble is the most skilful.
Optimizing roofing strategies to mitigate urban climate risks using a deep learning-based surrogate method
Authors: Kim JiHyun , Choi Suyeon , Panahi Mahdi , Kim Yeonjoo
Published: July 2024

The anticipated increases in urban climate vulnerabilities resulting from climate shifts have led to various mitigation efforts, such as adopting green or cool roofs in urban areas. Therefore, determining the most effective roofing strategies, including type and distribution, is crucial given associated costs. This preliminary assessment typically involves employing statistical or numerical models with diverse scenario simulations, which are computationally intensive and time-consuming. In this study, we introduce a deep learning-based surrogate model designed to optimize roofing strategies for urban climate risk mitigation in the Greater Seoul area, South Korea. First, we implemented the Weather Research and Forecasting model coupled with Urban Canopy Modeling (WRF-UCM) while assigning one type of roof (e.g., 100% green roof scheme or cool roof schemes ranging from 25% to 100%) to urban grids within the study region under the business-as-usual climate scenario (RCP8.5) during the period 2090-2099. Using the outputs from the WRF-UCM model, we calculated three objective indices (heat stress index, flash flood index, and wind speed index), and trained a deep learning algorithm, Multi-residual networks (Multi-ResNet), to construct a surrogate model of the WRF-UCM. To reduce the total number of scenarios, we applied the Mini Batch K-mean method to cluster 379 urban grids into nine. Afterwards, we generated multi-type roof scenarios by assigning each roof scheme to every urban cluster (four roof schemes across nine clusters, totaling 0.3M scenarios). We then employed the surrogate model to compute the three objectives (i.e., heat, flood, and wind) for each multi-type roof scenario. Finally, we present the optimal roof configurations lying on the Pareto front, reflecting the trade-offs among objectives including cost reduction, heat mitigation, flash flood prevention, and wind speed enhancement. Our results demonstrate the potential of deep learning-based surrogate models as an effective framework for urban planning to mitigate climate risks.This study is supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2020R1A2C2007670, 2020R1C1C1014886, and 2022R1C1C2009543) and Korea Environment Industry & Technology Institute (KEITI) through the R&D Program for Innovative Flood Protection Technologies against Climate Crisis funded by the Korean Ministry of Environment (MOE) (No. RS-2023-00218873). 
Comparison of land surface albedo between MODIS and ground-based measures at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland
Authors: Tosco Monica , Meloni Daniela , di Sarra Giorgio Alcide , Calì Quaglia Filippo , Muscari Giovanni , Di Iorio Tatiana , Pace Giandomenico
Published: July 2024

The land surface albedo is one of the key parameters and a driver in climate and weather models since it regulates the shortwave radiation absorbed by the Earth’s surface. In the Polar Regions, the high albedo of snow and ice helps to maintain low surface temperature by reflecting most of the incident solar energy. As the temperature increases and the snow and ice melt, the absorption of solar radiation increases, leading to more warming and melting. This positive ice-albedo feedback is partially responsible for the amplified warming in the Arctic compared to lower latitudes.In remote areas, where in-situ instruments are absent, satellites are crucial to measure surface albedo changes. In this work, a comparison of satellite and in-situ measurements of surface albedo is conducted. The area of interest selected is around the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it) on the North-western coast of Greenland (76.5°N, 68.8°W), where the measurements of downwelling and upwelling solar irradiance have been started in 2016. The used radiometers are regularly calibrated, and corrections for thermal offset are applied.Albedo determinations based on  MODIS observations from both Terra and Aqua (MODIS MCD43A3 dataset), consisting of daily values with a spatial resolution of 500 m, have been compared with the ground-based measurements.The analysis has been carried out through five successive steps: the choice of the size of the area for averaging the satellite data; the application of data selection methods; the determination of Blue Sky Albedo by weighting Black and White Sky Albedo; the selection of clear sky conditions based on in-situ measurements; and the comparison of the ground-based and satellite albedo measurements for different areas (1 km x 1 km, 2 km x 2 km and 4 km x 4 km centred at THAAO and including only land surface) and sky conditions (all-sky and cloud-free)Moreover, filters based on the quality flag have been applied to select the highest-quality data.The albedo measurements were compared only for cloud-free cases selected using the in-situ solar irradiance measurements (332 cases out of 2922 in the period July 2016 – October 2023).The results show an underestimation of albedo measurements from satellite compared to the ground-based measurements at the THAAO over a large part of the period considered. The best agreement is found in the summer when there is no snow around the observatory, and the mean measured albedo value is 0.202. The mean bias during this season is around 0.004 for cloud-free conditions and 0.016 for all sky conditions. In spring, when the in-situ albedo values are highly variable between (0.350 and 1), the mean bias is around 0.051 for cloud-free conditions and 0.076 for all sky conditions.
Unraveling the lidar-turbulence paradox
Authors: Peña Alfredo , Yankova Ginka , Malini Vasiliki
Published: July 2024

The meteorological community, and in particular the wind energy community, have been trying to establish a methodology to correct/convert turbulence measures derived from measurements performed with Doppler wind lidars. The idea with these corrections is that lidar-based turbulence measures become equivalent or comparable to those turbulence measures that we normally retrieve using traditional in-situ instrumentation such as cup anemometers on masts. Part of the reason of this quest is the ability of Doppler wind lidars to be both versatile and affordable, apart from the most important characteristic of these instruments, which is their high accuracy and precision in measuring winds within and beyond the limits of meteorological masts. Particularly, for offshore applications, floating Doppler wind lidars are nowadays the standard for wind resource assessment, since deploying and maintaining instruments on tall meteorological towers offshore is currently too expensive. With regards to versatility, Doppler wind lidars are used for different applications ranging from, e.g., atmospheric modeling, wind turbine wake studies, and wind turbine control, among others. The wind energy community in particular is therefore making efforts in the development of recommended practices and standards to enhance the adoption of measurements from lidars. Corrections for lidar-based turbulence measures have been however investigated for decades. The major difficulties for establishing a turbulence correction are related to two main points: turbulence contamination and turbulence filtering due to probe-volume averaging. These two points lead to what we here refer to as the lidar-turbulence paradox, which basically means that in order to determine the ratio between the lidar turbulence measure to that of a cup anemometer, we need to know the characteristics of atmospheric turbulence; but these characteristics of turbulence are the ones we want to measure with our lidars.We circumvent the paradox using a physics-based lidar-turbulence model, which serves for the training of a number of neural networks. The measurements we study are from continuous-wave Doppler lidar wind profilers, which were deployed besides a tall 250-m meteorological mast at the Østerild test station for wind turbines in northern Denmark. Metek USA-1 sonic anemometers on the masts match four lidar measurement levels and are used as reference for the turbulence measures. The prediction of the neural networks, which has the lidar measures as inputs, are compared with the measurements from independent Doppler wind lidars.
Optimising orography for global high-resolution simulations
Authors: Sützl Birgit , van Niekerk Annelize , Beljaars Anton , Maciel Pedro , Choulga Margarita , Janoušek Martin , Vannière Bennoît , Forbes Richard , Balsamo Gianpaolo , Sandu Irina , Dueben Peter
Published: July 2024

The model’s mean orography acts as the boundary condition for the model dynamics and the drag from resolved orographic gravity waves can have a significant impact on the large-scale atmospheric circulation in weather and climate models. As we approach km-scale horizontal resolutions in global models, more of the orographic spectrum and the impact of orography becomes resolved. Benefits of increased resolution, for example better prediction of orographic rain, can only be harvested if the resolution of the mean orography field is also increased. However, even at kilometre scale, some of the orographic variance will not be represented on the model grid and must be parameterised.Initial simulations for Destination Earth’s global Digital Twin at 4.4 km horizontal resolution have shown a negative wind bias over Eastern Asia and increasing forecast error compared to the operational 9 km model, indicating that the higher resolution of resolved orography causes additional small horizontal-scale orographic gravity waves breaking above the mid-latitude jet, which affects the global circulation. Therefore, we focused on improving the mean orography processing and sub-grid scale orography parameterisations with the aim to find a scale-independent formulation that maintains good forecast skill at operational model resolutions and profits from increased resolution at kilometre scale.The processing of the source data was simplified and harmonised across resolutions using conservative interpolation of the source dataset.  A new source dataset for surface elevation with 30 m resolution is used. A small increase to the spectral filtering of mean orography showed an improvement in forecast skill over the Tibetan plateau, and the updated fields describing sub-grid orographic features yield a more consistent behaviour across different resolutions. However, the sub-grid orography has significantly changed, and the parameters of the orographic parameterisation schemes needed to be optimised again considering an appropriate formulation across resolutions. We present a new approach to this parameter re-tuning, using Bayesian parameter optimisation, which enables an efficient workflow for simultaneously optimising several interdependent parameters.
Testing automatic observation quality control for assimilation in a non-hydrostatic simulation in the framework of the MAGDA HE Project
Authors: Lagasio Martina , Uboldi Francesco , Oberto Elena , Milelli Massimo
Published: July 2024

Climate change poses significant challenges for agriculture, impacting crop production with rising temperatures, altered precipitation patterns, and more frequent extreme weather events. In this context, data assimilation techniques in Numerical Weather Prediction (NWP) models could become essential tools. By integrating real-time observations into forecasts, data assimilation enhances accuracy, providing farmers with valuable insights to adapt to climate change effectively. Thus, observations from high-resolution networks are of interest for non-hydrostatic model simulations both for verification purposes and for data assimilation. Such observations can be influenced by sub-grid scale processes that cannot be represented in the model dynamics; moreover they can be subject to gross errors. Furthermore, initial errors can grow, during a non-hydrostatic simulation, along a large number of independent modes, influencing a variety of dynamic scales. Suitable automatic data quality control techniques are then necessary and their application can improve assimilation results by enabling the representation of weather features that the model can effectively simulate.The MAGDA (Meteorological Assimilation from Galileo and Drones for Agriculture) Horizon Europe project (https://www.magdaproject.eu), started in 2022, has been developing a toolchain aimed at providing valuable weather and irrigation information to agricultural operators. At the scientific core of MAGDA activity lies the assimilation in non-hydrostatic NWP models of various sources of high-resolution observations, including in situ observations, GNSS (Global Navigation Satellite System), weather radar and meteodrones .In this work we study the effect of assimilating high-resolution in situ observations in a short-range simulation with the WRF model of a convective event of interest for MAGDA agricultural applications. We aim to assess the impact of preliminary quality control checks on observations being assimilated. In particular, the Spatial Consistency Test based on Optimal Interpolation and Cross Validation can be effective in rejecting data affected by gross errors or large representativeness errors that could otherwise introduce detrimental noise in the model simulation.This type of application paves the way for the inclusion of low-cost IoT (Internet of Things) sensors in the assimilation procedure, the "metIS hub" used in the Magda project for instance, but it can also be utilized for various other applications using low-cost sensors as observational tools.
Assessing changes in the intensity and dynamics of extreme cold spells in France from CMIP
Authors: Cadiou Camille , Yiou Pascal
Published: July 2024

Extreme cold winters have been projected to decrease in the future, although their impacts on society are still significant. The goal of this study is to assess whether climate change affects the atmospheric mechanisms leading to cold winters.
We first explore the dynamics of 15-day winter cold spells in France, as observed since 1950. We find that the most extreme events tend to have the same atmospheric circulation pattern, consisting of an eastward-shifted NAO- dipole. We calculate an atmospheric index that characterizes this dipole. Then, using a stochastic weather generator with importance sampling, we show that this is a sufficient condition to trigger extremely cold temperatures in France, and that it performs better than a classical North Atlantic Oscillation index. This suggests that a dipole of atmospheric circulation is a necessary and sufficient condition leading to extreme cold spells in France.
We use this atmospheric index to select the CMIP6 models that best reproduce the identified dynamics leading to extreme cold spells of 15 days. Using a stochastic weather generator with importance sampling, we run simulations of worst-case winter cold spells from 2015 to 2100, following different emission trajectories for the selected models. 15-day winter cold spells in France will reach less extreme temperatures at the end of the century, especially in the case of a high-emission scenario (SSP5-8.5). However, the simulated ensembles of extreme cold spells do not show the same warming trend as the mean temperature, and very extreme cold spells are still possible in the near future. The atmospheric circulation prevailing during these events is analyzed and compared with the circulation observed during previous events.
years since the biggest avalanche tragedy in Slovakia in the Rybô village - reanalysis of weather conditions
Authors: Mikulova Katarina , Vojtek Martin , Ivaňáková Gabriela , Polčák Norbert , Faško Pavel
Published: July 2024

The winter at the turn of the years 1923 and 1924 in the highest elevated areas of the Slovak mountains was exceptionally rich in snow, leading to an increased occurrence of avalanches. The deadliest avalanche not only in that season but in the entire history of Slovakia occurred on the night of February 6, 1924, from the southeast slope of Mount Krížna (1,574 m above sea level) in the Veľká Fatra mountain range. In the presented contribution, we try to analyse the meteorological and snow conditions that led to this tragic event. The assessment of the weather situation was carried out based on the NOAA-CIRES reanalysis of the 500 hPa geopotential, surface pressure field, and temperature at 850 hPa. In addition, 103 precipitation and 10 climatological stations were used for the analysis of snow cover height and for the validation of the NOAA-CIRES reanalysis.According to available data, the deepening of low-pressure systems over the Baltic states played a crucial role in creating suitable conditions for avalanches, associated with a frontal passage over Slovakia from February 2nd to 6th, 1924. A northwest cyclonic situation prevailed, during which a significant amount of snow fell on the northwest (lee) slopes of the mountains due to orographic lifting in a relatively short period. This effect was observed on the ridge of the Veľká Fatra and also in the area of Krížna, where around 60 cm of new snow fell in 4 days. Due to the absence of trees or other vegetation that could increase surface roughness in this location, the snow was easily transported by the northwest wind to the leeward side of the mountains, creating conditions for snow cover accumulation on the eastern slopes.The temperature at the 850 hPa level was below freezing from February 2nd to 5th. However, on February 5th, 1924, a warm front passed through, leading to a short-term significant warming, combined with extremely intense snowfall driven by west to northwest winds, which transported snow into the release zone with predominantly east exposure. Here, the previously above-average snow cover height created suitable conditions for avalanche initiation.The avalanche in the settlement of Rybô destroyed 3 houses and damaged 2 others. It buried 22 people, of whom 18 (including 15 children) did not survive. The amount of snow in the avalanche was so significant that it did not melt even during the summer. The avalanche was approximately 2.5 km long and descended a vertical distance of 760 m. It generated very high impact pressure and speed (up to 180 km/h). According to today's international avalanche classification, this avalanche would be classified as a very large avalanche, which is the largest size category.
Dynamics of pan-Atlantic winter compound extremes in ERA5 and CMIP6 models.
Authors: Leeding Richard , Messori Gabriele
Published: July 2024

Recent work has provided robust evidence for the systematic co-occurrence of wintertime cold spells in North America and wet and windy extremes in Europe, which we term compound pan-Atlantic extremes. Both cold spells and wet and windy extremes are individually highly impactful, and their concurrence further amplifies their effects for actors with international exposure who are vulnerable to correlated losses. This study aims to investigate further the atmospheric processes associated with compound pan-Atlantic cold, wet and windy extremes and how these processes are represented in CMIP6 models.On aggregate, cold spells in different parts of North America statistically co-occur with wind and precipitation extremes in specific European regions. However, North American cold spells can arise from multiple dynamical pathways, altering the location and timing of the associated European extremes for individual cold spells. Here, we use ERA5 reanalysis data (1940-2014) to identify North American wintertime cold spells in three different regions as well as the co-occurring wet and windy extremes in northern and southern Europe. We relate these compound pan-Atlantic extreme occurrences to dynamical pathways based on Pacific and Atlantic weather regimes. We then compare the CMIP6 historical simulation model data (1940-2014) with the ERA5 results. First, we discuss the performance of the models in capturing the weather regime frequencies and dynamical pathways to cold spells. Second, we review the ability of the models to replicate the spatial and temporal pattern of pan-Atlantic extremes for the three cold spell regions.The results of this study contribute to the evaluation of the model fidelity in reproducing pan-Atlantic compound extremes and the associated circulation, with direct implications for the assessment of climate projections.
Machine learning-driven assessment and prediction of sub-seasonal forecast skill
Authors: Mârza Ana-Cristina , Domeisen Daniela I.V. , Meyer Angela
Published: July 2024

Certain atmospheric conditions provide windows of opportunity for sub-seasonal prediction, such that forecasts initialized at these times maintain good predictive skill further out into the future than usual. Such windows of opportunity are for example given by certain phases of the Madden-Julian oscillation and the state of the stratospheric polar vortex at forecast initialization.These findings come from retrospective analyses of forecast skill that often investigate only one or two drivers of sub-seasonal predictability at a time. What is lacking is investigating multiple drivers and comparing their relative importance, which we propose to achieve by means of explainable machine learning (ML) techniques. Furthermore, whereas previous ML-based studies of atmospheric predictability quantified uncertainty in terms of deterministic error or ensemble spread as a proxy for forecast skill, we here aim to predict the probabilistic forecast skill itself. The result is an operational decision support tool that can inform users, at forecast initialization time, of the skill expected at sub-seasonal lead times.Therefore we are presenting, for the first time to our knowledge, an explainable ML model (based on random forests) that learns to predict directly the skill of the ECMWF extended-range forecasts at several weeks’ lead time, given the atmospheric state at initialization. We use reanalysis data as ground truth and focus on the skill of weekly aggregated statistics of geopotential height at 500 hPa, a variable of particular interest to the renewable energy industry since it tracks the synoptic-scale weather patterns controlling European energy production and demand.We expect that our forecast skill prediction model will enable enhanced risk management for sub-seasonal forecast users, such as stakeholders in the renewable energy industry, and thereby accelerate the transition to a decarbonized energy system. From a scientific perspective, interrogating our ML model with explainability techniques should yield new insights into the sources of sub-seasonal predictability.
Maximizing added value in extreme weather forecasting: insights from case studies with the Continuous Global and On-Demand Extremes Digital Twins within the Destination Earth initiative
Authors: Gascón Estíbaliz , Yang Xiaohua , Benacchio Tommaso , Baordo Fabrizio , Alerskans Emy , Vannière Benoît , Sandu Irina
Published: July 2024

Destination Earth initiative of the European Commission is creating several digital replicas (digital twins) covering different aspects of the Earth system and based on state-of-the-art simulations and observations. One of the digital twins aims to develop two components of Earth's Digital Twin on Weather-induced Extremes (Extremes DT), with both continuous global high-resolution forecasts, developed by ECMWF and on-demand regional simulations, funded by ECMWF and led by Météo-France.The Global Continuous Extremes DT is engineered to forecast extreme weather events worldwide with unparalleled precision, providing continuous km-scale global high-resolution forecasts. Currently, the Continuous (Global) Extremes DT uses ECMWF's Integrated Forecasting System (IFS) cycle 48r1 with approximately 4.4 km grid-spacing (TCo2559). On the other side, the On-Demand Extremes (DT) offers configurable digital twin engines for forecasting environmental extremes at a sub-km scale regionally and when necessary. It aims at providing an on-demand workflow with co-design of very high-resolution predictions about extreme weather events combined with decision making support for impact sectors. This On-Demand Extremes DT aims to run a hectometric scale forecasting models with a typical resolution of 750 or 500 m, exploring a finer resolution of up to 200 m for special applications where it is meaningful. In the first phase, capability demonstration aims to explore the added values with co-designing workflow combining the continuous and the on-demand Extremes DT, event- or user-driven solutions with weather forecasting on the one hand, and impact sectors on the other hand. As part as this activity is the focus by examining a range of carefully selected high-impact weather events in both Extremes DT to understand the synergies and the added values that each one provides inside of the Extremes DT.In this presentation, we will showcase a range of extreme weather events where we demonstrate the predictability capacity of both, the Continuous and the On-Demand extremes DT, and the added value of progressive increment of model horizontal resolution. These cases will be focus on European severe weather, like Mediterranean and Atlantic cyclones and the extreme values in surface variables like wind gusts or precipitation, especially convective precipitation, which is generally better represented at sub-grid km-scale. These selected cases will also provide some insights of how useful is to use the Continuous global Extremes DT to initialise the On-Demand DT compared to the current 9km deterministic IFS.
Enhancing Extreme Weather Forecasts: Diagnostic Strategies in ECMWF's Destination Earth Initiative  
Authors: Gascón Estíbaliz , Maier-Gerber Michael , Vannière Benoît , Sützl Birgit , Magnusson Linus , Sandu Irina
Published: July 2024

This presentation will showcase diagnostic activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) as part of the Destination Earth initiative of the European Commission. This initiative aims to develop a continuous global component of Earth's Digital Twin on Weather-induced and Geophysical Extremes (Extremes DT) to forecast and monitor extreme weather events worldwide within a five-day range with unprecedented precision. Currently, the Continuous (Global) Extremes DT uses ECMWF's Integrated Forecasting System cycle 48r1 with approximately 4.4 km grid-spacing (TCo2559). 

Analysing continuous forecast runs and emerging extreme weather events (e.g., mid-latitude storms, tropical cyclones, medicanes, heatwaves, strong precipitation/flooding events) in the daily Extremes DT runs allows us to evaluate the performance of the km-scale Extremes DT model, particularly for extreme events. We compare the performance of DestinE with ECMWF's operational deterministic 9km forecasts and. All forecast resolutions are validated with high-density observations and model analyses to assess quality and physical realism. This analysis provides insight into which types of extreme events benefit from higher model resolutions for more accurate prediction. But it also highlights aspects in the model where improvements have not yet been achieved, or even led to some degradation in model skill compared to our current deterministic model with 9km resolution. Those issues underscore the need for improvements in other components of global models alongside increased resolution. 

Diagnostic work on these model shortcomings is critical for identifying causes and testing possible solutions. Variables such as 2m temperature, 10m wind speed, or precipitation have been diagnosed using various tools to understand general underestimation of extreme values when compare with observations (i.e. extreme cold temperature or large precipitation accumulations). As part of the diagnosis and proposal of improvements, different experiments have been conducted to analyse small changes in the model (i.e. small modifications in the parametrizations or the initial analysis) that could lead to significant performance improvements. 

This presentation will showcase some shortcomings of the Extremes DT forecasts and the diagnostic techniques applied to identify the underlying causes, especially related to extreme event prediction. This evaluation and diagnostic work aim to provide a better understanding of necessary model developments required to reduce identified model errors, which are crucial at km-scale for representing extreme events and thereby improving the quality of the Continuous Extremes DT simulations. 
Getting to the core of winter frosts in central South America through decision trees
Authors: Collazo Soledad , García-Herrera Ricardo
Published: July 2024

Agricultural production in Central South America (CSA) is highly sensitive to frost events. This study aims to provide a detailed characterisation and quantification of the physical processes leading to frost conditions in CSA between 1979 and 2022, using gridded observational data. To accomplish this objective, we focus on three innovative elements: a thorough examination of regional frost properties, a novel multi-parametric method for the description of upper-level jets, and the quantification of the underlying mechanisms through decision trees. We selected this methodology due to its high interpretability, particularly advantageous for users within the agricultural sector, owing to its visually intuitive representation.After regionalisation and statistical description of the frosts, we have been able to identify five homogeneous regions in CSA. Regarding the synoptic mechanisms that promote the occurrence of extensive frosts, we have found that the physical processes are similar across all regions. These processes are triggered by the passage of a cold front and the establishment of a migratory post-frontal anticyclone, which is reinforced by the subsidence generated on the southern flank of the entrance region of the Subtropical Jet (STJ). We have computed six parameters to characterise the STJ and seven for the Polar Front Jet (PFJ). They consider the latitude, intensity, height, tilting, longitudinal extent, and branches of the jets. By employing decision trees, we have effectively discerned a subset of jet parameters that are closely linked to frost events. The most relevant of these are the latitude of the STJ, the longitudinal extent of the Atlantic branch of the STJ, and the tilt of the Atlantic branch. In general, when the STJ is positioned north of 31°S, the likelihood of frost in the CSA rise. However, in more northern areas, the probability of frost occurrence further increases with a slight northward shift in its location. In addition, extensions of the Atlantic STJ ranging from 25 to 55°, coupled with a negative tilt, are two characteristics often linked to frost occurrences. These features are especially accentuated in events occurring in the northernmost regions. The application of decision trees has allowed us to summarise this information for every region of CSA in a user-friendly way, helpful for decision-makers in the agricultural sector. Acknowledge: The SAFETE project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847635