AS1.35 | Mountain Weather and Climate
EDI
Mountain Weather and Climate
Co-organized by CL4
Convener: Stefano Serafin | Co-conveners: Maria Vittoria GuarinoECSECS, Sven Kotlarski, Douglas Maraun, Anna NapoliECSECS
Orals
| Tue, 16 Apr, 16:15–17:55 (CEST)
 
Room M2
Posters on site
| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30
 
Hall X5
Orals |
Tue, 16:15
Wed, 10:45
Mountains cover approximately one-quarter of the total land surface on the planet, and a significant fraction of the world’s population lives in their vicinity. Orography critically affects weather and climate processes at all scales and, in connection with factors such as land-cover heterogeneity, is responsible for high spatial variability in mountain weather and climate. Due to this high complexity, monitoring and modeling the atmosphere and the other components of the climate system in mountain regions is challenging both at short (meteorological) and long (climatological) time scales. This session is devoted to a better understanding of weather and climate processes in mountain and high-elevation areas around the globe, as well as their modification induced by global environmental change.

We welcome contributions describing the influence of mountains on the atmosphere on meteorological time scales, including terrain-induced airflow, orographic precipitation, land-atmosphere exchange over mountains, forecasting, and predictability of mountain weather. Contributions connected with the TEAMx research programme (http://www.teamx-programme.org/) are encouraged.

Furthermore, we invite studies that investigate climate processes and climate change in mountain areas and its impacts on dependent systems, based on monitoring and modeling activities. Particularly welcome are contributions that merge various sources of information and reach across disciplinary borders (atmospheric, hydrological, cryospheric, ecological, and social sciences) and that connect to the Elevation-Dependent Climate Change (EDCC) working group of the Mountain Research Initiative (see https://www.mountainresearchinitiative.org/activities/community-led-activities/working-groups).

Orals: Tue, 16 Apr | Room M2

Chairpersons: Maria Vittoria Guarino, Sven Kotlarski, Anna Napoli
Mountain boundary layer
16:15–16:25
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EGU24-11038
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ECS
|
On-site presentation
Nicolai Krieger, Christian Kühnlein, Michael Sprenger, Heini Wernli, Philipp Bättig, Maxime Hervo, and Ulrich Krieger

During a winter storm in January 2007, a train derailed due to strong winds in a narrow valley in northeastern Switzerland. The accident was attributed to the Laseyer, a local windstorm characterized by flow reversal that manifests as easterly to southeasterly winds at the valley floor during strong prevailing northwesterly winds above. We analyze case studies of the local windstorm using sonic anemometer and Doppler lidar measurements. The data reveal a highly turbulent flow in the narrow valley and extreme wind speeds exceeding 45 m/s during Laseyer conditions.

Additionally, we use a newly developed large-eddy simulation (LES) atmospheric model to improve our understanding of the local windstorm. The model is implemented in a Python environment with the GT4Py (GridTools for Python) domain-specific library to enable performance portability.  Robust and efficient solution of the nonhydrostatic compressible equations is achieved using a finite-volume semi-implicit discretization following ECMWF’s IFS-FVM. LESs are performed above the highly complex terrain of northeastern Switzerland, which leads to extremely steep slopes exceeding 70°. With these LESs, we identify the mechanism behind the local windstorm, study its sensitivity to ambient flow conditions, and characterize the flow conditions in the narrow valley.

How to cite: Krieger, N., Kühnlein, C., Sprenger, M., Wernli, H., Bättig, P., Hervo, M., and Krieger, U.: Investigating a local windstorm using measurements and large-eddy simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11038, https://doi.org/10.5194/egusphere-egu24-11038, 2024.

16:25–16:35
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EGU24-12631
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On-site presentation
Katrin Sedlmeier, Meinolf Kossmann, Ivan Paunovic, Astrid Eichhorn-Müller, Oliver Nitsche, Ronny Leinweber, Eileen Päschke, and Gudrun Mühlbacher

Previous studies have found a pronounced nocturnal low-level jet at the exit of the Inn Valley north of the valley contraction near Schwaigen which reaches into the Alpine foreland (e.g. Pamperin and Stilke, 1985 as part of the MERKUR experiment or a model study by Zängl, 2004). The exit jet forms under nocturnal stably stratified atmospheric conditions and is interpreted as a transition from subcritical to supercritical hydraulic flow.

As part of the pre-campaign of the TEAMx programme in June-August 2022, we have conducted measurements to corroborate the previous findings on the formation and maintenance of the Inn valley exit jet and learn more about its turbulence structure, which has not been studied in previous experiments. For this purpose, a wind lidar was deployed in Brannenburg, north of the valley constriction. TKE profiles were derived from the Lidar measurements using the method described in Smalikho and Banakh (2017).  Furthermore, 3-hourly radiosondes were launched at the site of the wind lidar, accompanied by drone measurements during an IOP (18/19 July 2022) in high pressure weather conditions with low cloud cover.  

Upper air and surface wind measurements during the IOP captured a well pronounced Inn valley exit jet which is analyzed in detail in this contribution. Additionally, a statistical analysis of the occurrence and characteristics of nocturnal low-level jets within the whole pre-campaign period is presented.

 

References:

TEAMx: http://www.teamx-programme.org/

Smalikho, I.N., and V.A. Banakh. "Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer." Atmospheric Measurement Techniques 10.11 (2017): 4191-4208.

Pamperin, H., and G. Stilke. "Nächtliche Grenzschicht und LLJ im Alpenvorland nahe dem Inntalausgang." Meteorologische Rundschau 38.5 (1985): 145-156

Zängl, G. "A reexamination of the valley wind system in the Alpine Inn Valley with numerical simulations." Meteorology and Atmospheric Physics 87.4 (2004): 241-256.

How to cite: Sedlmeier, K., Kossmann, M., Paunovic, I., Eichhorn-Müller, A., Nitsche, O., Leinweber, R., Päschke, E., and Mühlbacher, G.: The Inn Valley exit jet: results of the TEAMx pre-campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12631, https://doi.org/10.5194/egusphere-egu24-12631, 2024.

16:35–16:45
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EGU24-6607
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ECS
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On-site presentation
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Brigitta Goger and Anurag Dipankar

The horizontal grid spacing of numerical weather prediction models keeps decreasing towards the hectometric range, where topography, land-use, and other static parameters are well-resolved. Still, models have to be evaluated over complex terrain, because it cannot be assumed that higher horizontal resolution automatically yields better model performance. In this study, we perform limited-area simulations with the ICON model across horizontal grid spacings (1 km, 500 m, 250 m, 125 m) in the Inn Valley, Austria. Simulations are ran with two turbulence schemes - a 1D parameterization and a 3D Smagorinsky-type scheme. We evaluate the model across scales with observations of the valley boundary layer from the CROSSINN measurement campaign. This allows us to investigate whether increasing the horizontal resolution automatically improves the representation of the thermally-induced circulation, surface exchange, and other mountain boundary layer processes. Results suggest that the valley topography is already well-represented at the kilometric range, but the simuations in the hectometric range show a more detailed representation of the vertical valley atmosphere structure and the up-valley flow. Across resolutions, the model struggles with the correct representation of interactions between larger and smaller scales. The two turbulence schemes show a similar performance, but the 3D Smagorinsky scheme simulates a delayed evening transition of the up-valley flow. It is argued that the major difference between schemes actually emerges from the different surface transfer schemes, and the choice of boundary layer parameterization is secondary.

How to cite: Goger, B. and Dipankar, A.: Modelling the mountain boundary layer: Does higher resolution improve model performance?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6607, https://doi.org/10.5194/egusphere-egu24-6607, 2024.

16:45–16:55
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EGU24-18557
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ECS
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On-site presentation
Katharina Perny, Herbert Formayer, and Imran Nadeem

Persistent inversions and associated low wind situations during the winter months often lead to air pollution problems in alpine basins and valleys, regardless of emission levels. The aim of this work is to determine how well high-resolution simulations with the Weather Research and Forecasting (WRF) model are able to reproduce the occurrence and weather conditions during temperature inversions in complex topography.

The city of Graz in south-eastern Austria often experiences increased strength and persistence of winter inversions due to its location and local topography. Experiments in this area with the WRF model show a better reproduction of these weather conditions when the shortwave radiation scheme Dudhia is used instead of the RRTMG, while variations in the microphysics and planetary boundary schemes did not lead to relevant changes in the model results.

In a next step, additional basins and alpine valleys will be investigated to determine the influence of shape and extent of the topography on the results.

The model is forced with the ECMWF-IFS analysis data with a spatial resolution of 9 km. Two one-way nested domains with resolutions of 3 and 1 km are used to investigate what resolution is required to adequately represent the local topographic effects. The model results are compared with station and radiosonde observations as well as with the analysis and nowcasting system INCA for Austria.

How to cite: Perny, K., Formayer, H., and Nadeem, I.: Testing the capability of the WRF model on representing temperature inversions in alpine basins and valleys, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18557, https://doi.org/10.5194/egusphere-egu24-18557, 2024.

Orographic precipitation
16:55–17:05
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EGU24-20124
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On-site presentation
Deanna Hence and Scott James

Analysis of surface precipitation accumulations upstream, near-shore, and adjacent to the Olympic mountains from the 17 December 2015 case during OLYMPEX using Weather Research and Forecasting (WRF) simulations, the NPOL dual-polarization radar, and high-resolution soundings investigates the role of low-level blocking on upstream precipitation enhancement. Past work shows that frontal systems often slow while approaching complex terrain if the Froude number is sufficiently low. Low-level blocking of stable air ahead of a front can modify precipitation distributions by frontal deformation, slowing, splitting, or merging. Observed coastal sounding-derived vertical stability profiles indicate high levels of low-level stability and significant vertical wind shear, which showed little change while a warm front propagated northeastward and stalled as the stable air mass likely dammed against the terrain. Radial velocity from the NPOL radar and simulated wind fields indicate strong down-valley flow coupled with a frontal jet also contributed to long-lasting Kelvin-Helmholtz (KH) waves extending offshore.

Using WRF simulations along with OLYMPEX observations, we examined the evolution of precipitation upstream of complex terrain by breaking down the distribution of pre-frontal and frontal precipitation accumulations as the warm front approached the Olympic Peninsula. Through dividing the event into regions upstream of NPOL and into timeframes relative to landfall, results indicate pre-warm frontal precipitation accumulations decrease with distance upstream of the coast with the highest accumulations present over the terrain. As the front's translation speed slowed and eventually stalled, the warm frontal period accumulations are highest far upstream of the coast and over the terrain, with lesser accumulations in the middle region. These results indicate that upstream precipitation enhancement upstream is an indirect effect of the terrain influencing the frontal shape and propagation, resulting in enhanced frontal precipitation accumulations.

How to cite: Hence, D. and James, S.: Evolution of Surface Precipitation Accumulations Upstream of the Olympic Mountains using Observations and Simulations: An OLYMPEX Case Study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20124, https://doi.org/10.5194/egusphere-egu24-20124, 2024.

17:05–17:15
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EGU24-10043
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ECS
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On-site presentation
Siddharth Gumber, Andrew Orr, Paul Field, Hamish Pritchard, Federico Covi, Pranab Deb, Marc Girona-Mata, Martin Widmann, and Emily Potter

Complex mountain orography induces sharp gradients in precipitation accumulation locally. The associated complexity in understanding these events depends on local orographic, microphysical, and dynamical conditions, which makes simulating snowfall a major challenge for regional atmospheric models. This study addresses these deficiencies by using a unique repository of snowfall measurements at a range of ‘super sites’ in the European Alps and Himalayas, which are used to produce a precipitation-optimised version of the atmosphere-only UK Met Office Unified Model (MetUM) at a spatial resolution of 1.5 km. The snowfall measurements involve using the winter time-series of water pressure in frozen lakes to measure the mass of falling snow during extreme precipitation events directly over the lake area, which are comparable in size to the model’s grid cells. Development of the precipitation-optimised version of the MetUM involves undertaking a series of model sensitivity experiments focused on varying the physical representation of cloud and precipitation microphysics, with the aim of better capturing the onset and end periods, and amounts of received snowfall during these extreme events. The MetUM is configured to use a double moment cloud microphysical scheme (CASIM: Cloud AeroSol Interacting Microphysics) with prescribed hydrometeor spectral attributes necessary to quantify both the auto-conversion rates and thresholds for the cloud conversion to take place. Results from these experiments suggest that local microphysical processes, often subsumed within small spatial scales, can influence dynamics at larger scales, impacting gradients in precipitation. Cloud radiative properties, including the hydrometeor effective radii and optical depths are further validated against satellite-based observations.

How to cite: Gumber, S., Orr, A., Field, P., Pritchard, H., Covi, F., Deb, P., Girona-Mata, M., Widmann, M., and Potter, E.: Using Novel Lake-based Snowfall Measurements in the Alps and Himalayas to optimise Cloud and Precipitation processes in a Regional Atmospheric Model (MetUM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10043, https://doi.org/10.5194/egusphere-egu24-10043, 2024.

17:15–17:25
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EGU24-2067
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Virtual presentation
Xuelong Chen, Dianbin Cao, Qiang Zhang, Xin Xu, and Yaoming Ma

The Yarlung Tsangbo Grand Canyon (YGC), one of the world’s deepest canyons, is located in the southeastern Tibetan Plateau (SETP). The YGC exhibits the highest frequency of convective activity in China. Due to frequent rainstorms in the wet season, natural disasters such as landslides and debris flows frequently occur, and often block traffic corridors. Thus, understanding the relationship between water vapor changes, convective cloud activity, and extreme rainfall events in the YGC is critical. A comprehensive observation network for water vapor variations, cloud activity, local circulation, and land-air interactions in the YGC was installed to help us to determine the relationship between the water vapor transport and heavy precipitation in the YGC and the physical process that determines the precipitation intensity, especially for cases of strong precipitation.

More than three years data collected from a rain gauge network, disclose that the spatial pattern of rainfall distribution. There are two regions (500 m and 2500 m AMSL) with high precipitation in the YGC. Diurnal cycles showed some variations among sites, but a clear floor was visible around afternoon and peak values exhibited in the early morning. The monthly precipitation in the YGC region shows two peaks in April and July. Vertical convection and vapor transport are important for extreme rainfall in this region.

We analyzed 35 years observation data of daily precipitation to objectively classify the weather systems responsible for the SETP heavy precipitation. Hierarchical clustering method divided the atmospheric circulation of the regional heavy precipitation into two representative patterns: the Tibetan Plateau vortex type (TPVT, accounting for 56.6% of the heavy precipitation events) and the mid-latitude trough type (MLTT,43.4%). The comprehensive analysis of the two patterns shows a clear connection between the heavy precipitation and positive vorticity anomaly, moisture convergence and the southeastward shift of the westerly jet core. Specifically, TPVT heavy precipitation events are caused by potential vorticity dry-to-wet processes during its eastward movement, while MLTT events are associated with the intrusion of deeply extratropical trough-ridge circulations into the SETP.

We used the Weather Research and Forecasting (WRF) model to simulate the water vapor flux during extreme rainfall events. The general shortcoming of the WRF precipitation simulation nudged with the European Centre for Medium-Range Weather Forecasts’ reanalysis dataset version 5 (ERA5), is that it cannot capture strong rainfall period. We tested many WRF parameterization schemes at a 1 km grid resolution. It was found that when an optimized combination of parameterization schemes in WRF can better capture the variations in the wind and water vapor concentration in the YGC channel, the model produced the best simulation results for extreme rainfall in the YGC.

These analyses have help us understanding the impacts of YGC valley on the water vapor transport and extreme rainfall outbreak mechanism.

How to cite: Chen, X., Cao, D., Zhang, Q., Xu, X., and Ma, Y.: Extreme rainfall characteristics and its simulations in the Yarlung Tsangbo Grand Canyon, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2067, https://doi.org/10.5194/egusphere-egu24-2067, 2024.

Climate
17:25–17:35
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EGU24-2463
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ECS
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On-site presentation
Fangying Wu and Qinglong You

The Tibetan Plateau (TP) directly heats the middle tropospheric atmosphere, and accurate simulation of its surface temperature is of great concern for improving climatic prediction and projection capabilities, but climate models always exhibit a cold bias. Based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in-situ observations during 1981-2014, this study elucidates the impact of the snow overestimation on the temperature simulation over the TP in CMIP6 from the perspective of local radiation processes and atmospheric circulation. On the one hand, more snow in the CMIP6 models not only directly cools the surface more, but also makes the surface receive less shortwave radiation due to the higher surface albedo, and thus has lower ground surface temperature (GST), and the more snow/albedo-low temperature process is particularly evident in the westerly region due to more uncertainty at high elevations. This process contributes 87% to the annual GST cold bias. Lower GST corresponds to less latent heat transfer and thereby lower surface air temperature (SAT). In addition, the more snow in the CMIP6 models leads to the weaker the South Asian summer monsoon and the westerlies, and brings less warm and moist air (less integrated water vapor flux), as well as less clear-sky downward longwave radiation (less water vapor amount and lower tropospheric air temperature) to the TP (contributing 58% to the annual GST cold bias). These processes will result in less both precipitation and surface latent heat loss, which offsets a 35% annual GST cold bias. Besides, the physical mechanism of snow on GST and SAT differs with season over the westerly and monsoon regions of the TP. The research highlights the importance of topography and snow parameterization schemes for optimizing CMIP6 models.

How to cite: Wu, F. and You, Q.: Understanding of CMIP6 surface temperature cold bias over the westerly and monsoon regions of the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2463, https://doi.org/10.5194/egusphere-egu24-2463, 2024.

17:35–17:45
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EGU24-19545
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On-site presentation
Luis Durán, Álvaro González-Cervera, and Belén Rodríguez-Fonseca

Mountains play a crucial role in the climate system at various temporal and spatial scales. Additionally, they serve as vital sources of resources, such as fresh water, and host a diverse range of biodiversity. This influence on development and natural ecosystems is particularly significant in semi-arid regions like the Sierra de Guadarrama. This mountain range is located in the Iberian Peninsula and has been the subject of official meteorological observations since the mid-20th century. Nevertheless, there is a gap in the knowledge of the rainfall and temperature variability and its drivers in this important region. TROPA-UCM group has been intensively observing and studying this range since 1998 and recently, a methodology has been developed to extend the observations from 1900 to present using data from the ERA20C and in-situ observations. This has enabled longer time series and a deeper analysis of large-scale teleconnection patterns and climate variability, unlike ever before. The analysis includes trends in temperature, snow precipitation, and snowpack duration. Variations in precipitation and temperature have been identified, providing valuable information for estimating potential changes in seasonal runoff and rainfall intensity. This information can be of great use to organizations responsible for the management of this area, for developing adaptation strategies for new scenarios and to improve seasonal to decadal predictions.

 

 

How to cite: Durán, L., González-Cervera, Á., and Rodríguez-Fonseca, B.: Analysis of climate variability and teleconnection patterns in Sierra de Guadarrama (Iberian Peninsula) using 120-year observed and reconstructed time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19545, https://doi.org/10.5194/egusphere-egu24-19545, 2024.

17:45–17:55
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EGU24-13294
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ECS
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Virtual presentation
Giulio Bongiovanni, Michael Matiu, Alice Crespi, Anna Napoli, Bruno Majone, and Dino Zardi

Several observational products of key climate variables have been widely used to evaluate the extent of the ongoing effects of climate change in the Alpine area, one of the most vulnerable and sensitive regions to the continuous warming of climate. However, a limited spatial coverage in most observational products and quality issues of data may strongly impact climate and hydrological studies results in terms of reliability, accuracy and precision. Even though the collection and management of meteorological data for the whole Alpine area is a challenging task due to strong fragmentation and diversity of data sources, further efforts need to be dedicated to produce new harmonised, high-quality and high-resolution products able to permit a more robust assessment of climate change and its impacts.  

Here we present a new observational dataset gathering in-situ measurements of meteo-climatic variables provided by a variety of meteorological and hydrological services within the extended Alpine region. The observational network consists of about 10000 in-situ weather stations, measuring key climate variables up to 2020 at daily time resolution, resulting in an extended and homogeneous coverage, both in space and elevation. Data collected are screened, inspecting the presence of most important critical issues in terms of data quality. A deep quality control of collected time series has been performed by checking internal, temporal and spatial consistency of time series, exploiting the problem of outlier removal. Inhomogeneities in time series are detected by a multi-methods approach and significant inhomogeneous periods are corrected. 

A climatological and trend analysis, in terms of both mean and extreme values, was carried out on a selection of homogenised time series extending over the period 1961-2020. The most common climate indices and statistics are used to perform the analysis at different time frequencies and spatial scales. A further analysis concerned the relationship between climate variables and main teleconnection patterns.

The present dataset addresses the most important issues affecting state-of-the-art observational products and it represents a powerful tool for better understanding Alpine climate changes over the last decades and improving the reliability of future scenarios.

How to cite: Bongiovanni, G., Matiu, M., Crespi, A., Napoli, A., Majone, B., and Zardi, D.: A new dataset of daily observations from a dense network of weather stations covering the Extended Alpine Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13294, https://doi.org/10.5194/egusphere-egu24-13294, 2024.

Posters on site: Wed, 17 Apr, 10:45–12:30 | Hall X5

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 12:30
Chairperson: Stefano Serafin
Climate
X5.13
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EGU24-14933
Orographic complexity and local microclimatic variations in the Dolomites (north-eastern Italy)
(withdrawn)
Massimiliano Fazzini, Teodoro Georgiadis, Letizia Cremonini, Flavio Tolin, and Stefano Zamperin
X5.14
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EGU24-19261
Ramón Viloria and Verónica Tricio

Several studies and observations suggest that global warming processes are more prevalent in mountain areas, showing higher rates of warming and more pronounced changes in precipitation than in average land data. Snow and ice are highly sensitive to variations in climate. The importance of mountain areas as water reservoirs for the surrounding land and valleys at lower altitudes justifies paying special attention to this type of behaviour and to the changes brought about by global warming. This interest is enhanced by the special environmental sensitivity of mountain ecosystems, and the difficult balance between these fragile ecosystems and their use as tourist resources or winter resorts.

In this paper we analyse climate data collected at mountain weather stations in Spain in time series up to 80 years. Stations in mountain areas are not numerous and are sometimes very scattered; nevertheless, we have selected the available data on temperatures, precipitation and other meteorological variables at stations located in the various mountain ranges throughout the Iberian Peninsula. For the selected stations, trends in temperatures (mean, maximum and minimum) have been studied and a seasonal analysis has been carried out. In addition, the data were processed with RClimDex, statistical and climatic software package, to evaluate Climate Extreme Indices. Cooling and warming patterns have been detected, and changes in precipitation have been analysed, trying to address the distinctive characteristics of mountain areas in the studies conducted. Monthly and seasonal assessments have also been carried out to detect changes in behaviour patterns. In general, good agreement with previously published data has been obtained, although not many studies have been carried out systematically in Spain, except in the Pyrenees area.

How to cite: Viloria, R. and Tricio, V.: Analysis of surface temperature and precipitation trends and climate indices in Spanish mountain areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19261, https://doi.org/10.5194/egusphere-egu24-19261, 2024.

X5.15
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EGU24-13935
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ECS
Jhoana Agudelo, Jhan-Carlo Espinoza, Clementine Junquas, and Paola A. Arias

The South American Altiplano is a high-altitude plateau (3800 m MSL) located in the central Andes between 15ºS and 22ºS. Bounded to the west by the coastal desert of Peru-Chile and to the east by the hyper-humid lowlands of Peru-Bolivia, the Altiplano exhibits a semi-arid climate, following a pronounced annual cycle, with over 70% of rainfall occurring during the austral summer (December-January-February). Associated with factors such as convective activity occurring west of the Amazon Basin, the generation of convective clouds over the central Andes occurs when eastward winds encounter the orographic barrier on the eastern slope of the Andes. This process represents the primary mechanism governing precipitation variability in the Altiplano. Previous studies analyzing future projections anticipate that the central Andes will become warmer during the 21st century, impacting the population, ecosystems, and glaciers of the South American Altiplano. This is particularly relevant since agriculture is the main economic activity in this region and depends directly on precipitation.

Summer precipitation over the Altiplano has shown a strong dependence on the magnitude of zonal flow in the free troposphere (200 - 300hPa). Nevertheless, General Circulation Models (GCMs) suggest a continuous increase in westerly flow over the central Andes, hindering moisture transport from the interior of the continent. Minvielle and Garreaud (2011) suggest a significant reduction (10%-30%) in Altiplano precipitation by the end of this century under moderate to strong greenhouse gas emission scenarios. More recently, Segura et al., (2020) found that precipitation variability in the Altiplano is also associated with upward motion over the western Amazon (WA). Thus, DJF precipitation over the Altiplano seems to respond directly and primarily to the upward motion over the WA, since the early 21st century.

Using a set of 13 GCMs, this study aims to explore possible future projections in precipitation processes under the SSP3-7.0 scenario. This study focuses on the evolution of two previously established mechanisms driving austral summer precipitation over the Altiplano: 1) easterly winds at upper levels over the central Andes, and 2) upward motion over the WA. As preliminary conclusions of this work, models indicate that both mechanisms appear to weaken for the future period analyzed (2050-2084), suggesting a reduction in summertime precipitation by the mid-21st century. Additionally, models project a more stable atmosphere over the central Andes for the future period, also indicating a reduction in precipitation in the region, reinforcing the initial conclusion.

How to cite: Agudelo, J., Espinoza, J.-C., Junquas, C., and Arias, P. A.: Future Projections of Summer precipitation-driving Mechanisms over the South American Altiplano, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13935, https://doi.org/10.5194/egusphere-egu24-13935, 2024.

X5.16
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EGU24-9427
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ECS
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Simon Zitzmann, Benjamin Fersch, and Harald Kunstmann

Mountain regions, such as the Alps, play a crucial role in providing ecosystem services, e.g., by acting as ‘water towers’ and substantially contributing to the discharge of the main European rivers. However, global warming is causing significant changes to the cryosphere, biodiversity and ecosystems in these regions. Hence, understanding the microclimatic changes in mountainous areas is essential, particularly the phenomenon of elevation-dependent warming (EDW), describing an amplified warming trend predominantly at higher elevations compared to adjacent lowlands. In the scientific community multiple drivers of EDW are being discussed, among them snow-albedo feedback, changes in cloud properties, and aerosols. The contribution of the individual drivers varies regionally and the role of surface energy balance components, especially ground heat flux, is rarely examined.

Therefore, this study focuses on investigating the elevation-dependency of temperature trends and surface energy balance components, as well as its driving mechanisms in the Berchtesgaden National Park, Germany. This area in the northern limestone Alps is characterized by a highly variable topography, diverse landscapes and numerous ecosystems. Preliminary results from this ongoing study are presented, emphasizing the methodological approach and initial insights gained:

Extensive data from the meteorological measuring station network, covering elevations from 600 to 2700 m.a.s.l., is analyzed to identify EDW patterns in the national park and its surroundings.

Additionally, from fall 2023 to 2025, a transect of three meteorological stations is established at different elevations (600 to 2000 m.a.s.l.) for a detailed investigation of land surface energy balance. Besides measuring the radiative components in highly variable terrain, the field observations focus especially on ground heat flux, obtained at multiple positions within each station site to capture the small-scale variance and aspect dependency of ground heat flux. Additionally, at one of the stations the turbulent heat fluxes are assessed, using the Modified Bowen Ratio Method.

To gain a holistic picture of the processes within the national park, the land surface model Noah-MP is employed to simulate the surface energy exchange processes at a high spatial resolution of 100 m. To improve the understanding of the development over time, model runs covering several decades in the past and a run during the measurement period (2023–2025) are performed, with results validated against the observational data.

How to cite: Zitzmann, S., Fersch, B., and Kunstmann, H.: Climatic drivers of elevation-dependent warming (EDW): A concerted field and modeling assessment for an alpine national park, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9427, https://doi.org/10.5194/egusphere-egu24-9427, 2024.

X5.17
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EGU24-6068
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ECS
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Anna Napoli, Michael Matiu, Sven Kotlarski, Dino Zardi, Alberto Bellin, and Bruno Majone

Climate change is a global phenomenon with regionally varying peculiarities. It is well known that mountainous regions are highly sensitive to climate change. Furthermore, the complex orography exerts a strong control on the expected impacts that often depend on several controlling factors such as elevation, slope, land use etc.. In addition, climate models introduce errors in reproducing local physical processes due to their coarse spatial resolution and partly poorly constrained parameterisations.

Elevation Dependent Climate Change has been observed in the European Alps as a consequence of the interplay of global warming and the specific Alpine orography. The Alpine region is considered as a climate change hot-spot given that a large portion of this region has warmed about twice as much as the global average with warming rates characterised by a strong dependence on elevation. On the contrary, observed precipitation trends show very high spatial variability, sometimes with significant dependence on the elevation. In this study we analyse these complex Alpine temperature and precipitation change patterns with the elevation in the EURO-CORDEX ensemble of regional climate models at 0.11° resolution including CORDEX-Adjust (bias-adjusted CORDEX simulation) and compare these results to different model outputs characterised by coarse grid resolution (GCMs, e.g. CMIP5 ) and selected convection permitting models. The future trends of climate indices covering both the mean the extremes are explored across spatial scales and different RCPs. This study includes also analysis of the effects of different bias-adjustment techniques on the trend reproduction.

How to cite: Napoli, A., Matiu, M., Kotlarski, S., Zardi, D., Bellin, A., and Majone, B.: 21st Century climate change in the European Alps and its elevation dependency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6068, https://doi.org/10.5194/egusphere-egu24-6068, 2024.

X5.18
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EGU24-5260
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ECS
Xuechen Dong, Daoyi Gong, and Cuicui Shi

The variation of near surface wind speed is a key dynamic parameter in the orographic effect of precipitation over eastern China. In this study, we used the latest high-resolution outputs from six GCMs in CMIP6-HighResMIP to evaluate the performance of high-resolution models in simulating the orographic precipitation characteristics of typical mountainous areas in summer over eastern China. Combined with observational results, the orographic precipitation under warming scenarios was projected and constrained. The results indicated that during the contemporary climate reference period (1979-2009), although the relationship between model-simulated near surface wind speed and the orographic light rain frequency was consistently stable, the sensitivity of the orographic light rain frequency to surface wind variability was generally underestimated, with a deviation approximately 24.1% lower than the observational values. Comparison of model-simulated wind speed with observational records showed that the negative bias of the sensitivity value was mainly contributed by the overestimated wind speed in models. Based on observed near-surface wind speed to constrain and correct the orographic light rain frequency, the constrained estimates revealed a 36.1% reduction in orographic light rain frequency under a 1.5°C warming scenario, which is 8.6 times greater than the original predictions (4.2%). The MRI-AGCM3-2-S model, with a longer dataset, demonstrated a relatively stable reduction in orographic light rain frequency under different warming scenarios (1.5°C, 2°C, 3°C, and 4°C) after wind speed constraints, all of which are exceeding the original predictions.

How to cite: Dong, X., Gong, D., and Shi, C.: The frequency of summer light rain projections in typical terrain over eastern China constrained by surface wind speed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5260, https://doi.org/10.5194/egusphere-egu24-5260, 2024.

X5.19
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EGU24-3382
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ECS
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Marco Bongio, Carlo De Michele, and Riccardo Scotti

Air temperature is a pivotal factor influencing numerous chemical, physical, and biological processes. However, there is a notable scarcity of long-term data, especially at high elevations, exceeding 2000 m a.s.l. This study focuses on reconstructing the daily maximum, mean, and minimum temperatures at Jungfraujoch (3571 m a.s.l.) since 1864. The approach involves daily data from 10 meteorological stations within the ECA&D (6) and Meteo Swiss (4) databases. All selected stations are situated above 2000 m a.s.l. (in the range 2140-3109 m a.s.l.), providing uninterrupted observations spanning at least from 1961 to 2022. The methodology includes these steps: 1) for each meteorological station, in the calibration period 1980-1999, it was modeled the daily temperature at Jungfraujoch as the sum of the temperature at the selected station plus a deterministic and a stochastic component; the deterministic component is the product of the temperature lapse rate (TLR) and the elevation difference between the reference and selected station, and the stochastic component is a “noise” which comes from the statistical distribution of the residuals. The seasonality requires parameters with monthly variability which are different considering minimum, mean and maximum temperature. The calibration phase consists in the estimation of TLR and the statistical distribution of residuals (among Normal, GEV, Stable and Tlocscale distributions). The evaluation of model performances was based on the calculation of Pearson correlation coefficients (ρP) and Root mean squared errors (RMSE) within the two validation periods (1961-1979 and 2000-2022). High correlation coefficients (greater than 0.9 in both calibration and validation periods) and low values of RMSE (from 1.56°C to 3.32 °C in the calibration period and from 1.56°C to 3.42°C in the validation) confirm the model’s accuracy. The same high performances were found before (1961-1979) and after (2000-2022) the calibration period, for every meteorological stations. 2) Then the 10 simulated time series at Jungfraujoch were sorted according to the lowest values of the RMSE, and the first three was mediated to define an “ensemble” daily temperature time series, which was able to obtain these performances: (ρP=0.96,0.98,0.97; RMSE=1.97,1.46,1.68 °C respectively for max, mean and min temperature). The study was then extended from the year 1864. Comparing the results with the existing literature we highlighted: i) high performances without the need of modeling the observed trend due to the climate change (subjected to high uncertainty in the future), ii) very parsimonious model without the need of any other variables (relative humidity, cloud cover, wind velocity, weather patterns); iii) the importance of selecting high stations elevations (above 2000 m a.s.l.) rather than considering closer stations but subjected to the thermal inversion phenomena; iv) maximum temperature is affected by higher errors, especially from 2000-2022 which is probably due to the higher increasing of the summer and winter temperatures at high elevation accordingly to an elevation warming dependence; v) This method could be easily extended in many regions of the world and these results could be used to make a back ward analysis of many environmental processes (glacio-hydrological and permafrost), within the Jungfrau-Aletsch UNESCO World Heritage Site. 

How to cite: Bongio, M., De Michele, C., and Scotti, R.: An ensemble of meteorological stations for estimating daily air temperature time series at Jungfraujoch since 1864, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3382, https://doi.org/10.5194/egusphere-egu24-3382, 2024.

X5.20
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EGU24-1999
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ECS
Ian Castellanos, Martin Ménégoz, Juliette Blanchet, and Julien Beaumet

Regional imprints of global warming have to be investigated to predict the impact of climate change on a local scale and inform mitigation and adaptation policies. The rate of warming as a function of elevation in mountainous regions is yet to be fully characterized and understood. This study aims to identify elevation-dependent warming features in the Alps as well as its physical drivers, using MAR (Modèle Atmosphérique Régional) simulations with a 7kmx7km resolution over 1961-2100, under different climate scenarios. Different seasonal patterns have been found, most notably a maximum of warming at intermediate elevation (~1500m to 1800m) in Spring related to earlier snow melting in future projections. This maximum of warming moves towards higher elevations over the XXIst century. Elevation-dependent warming is found to be different in the free-atmosphere and along the slopes of the mountains, highlighting the major impact of surface processes, such as changes in albedo, in the drivers of climate change in the Alps.

How to cite: Castellanos, I., Ménégoz, M., Blanchet, J., and Beaumet, J.: Elevation-dependent warming in the Alps estimated from MAR simulations over 1961-2100, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1999, https://doi.org/10.5194/egusphere-egu24-1999, 2024.

Weather
X5.21
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EGU24-4274
Spatio-temporal characteristics of rainfall over different terrain features in the middle reaches of the Yangtze River basin during the warm seasons of 2016-2020
(withdrawn)
Jianhua Sun and Qian Wei
X5.22
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EGU24-8337
Thermally-driven orographic convection initiation is sensitive to terrain steepness
(withdrawn)
Stefano Serafin, Matthias Göbel, and Mathias W. Rotach
X5.23
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EGU24-16067
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ECS
Daniele Corradini, Claudia Acquistapace, and Paula Bigalke

As climate change advances, the Alps are expected to experience increasingly intense thunderstorms, which are likely to cause more damage due to floods and landslides. This study aims at evaluating extreme precipitation in weather models over complex terrains where orography causes the hardest challenges to precipitation prediction. 

Our preliminary analysis assessed which infrared and visible satellite channels are most effective in predicting precipitation, by examining the MSG satellite channels and radar-derived rain products. This assessment considered the influence of terrain by comparing data from flatlands and more complex topographies.

We will then use a combination of the selected channels to train a self-supervised machine learning (ML) algorithm for both observations and model outputs. We will exploit the space where cloud classes are identified, known as feature space, in two distinct ways to evaluate the ICON-GLORI model. Firstly, we pinpoint significant cases of extreme precipitation and simulate them using the ICON-GLORI model. This data is then input into the observation-trained ML algorithm to determine the cluster within the feature space where the simulated cases will be categorized. Secondly, we construct a feature space using the ensemble ICON-GLORI model. A showcase of the ML algorithm trained using the cloud optical thickness from 2015 imagery over Germany will demonstrate the potential of this approach.

How to cite: Corradini, D., Acquistapace, C., and Bigalke, P.: High-resolution model evaluation with self-supervised neural network approach targeted on severe storms over the Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16067, https://doi.org/10.5194/egusphere-egu24-16067, 2024.

X5.24
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EGU24-8087
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ECS
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Manuel Saigger and Thomas Mölg

Redistribution of snow by the wind has been shown to greatly influence local snow accumulation in alpine terrain. Due to the small-scale nature of this process, previous studies either concentrated on short case studies over small areas or relied on highly simplified wind fields. To bridge the gap towards an assessment of the importance of snow drift over alpine glaciers on seasonal scales we present a new approach using simulations with the Weather Research and Forecasting (WRF) model and deep learning as a computationally efficient downscaling tool for near-surface winds and snow redistribution over complex topography.

We created a training data set of high-resolution (dx=50 m) WRF simulations coupled to a novel drifting-snow module that is representative for winter-time alpine environments. The idealized setup allows us to control the degrees of freedom that the final model has to learn. We developed a new technique to create synthetic topographies with similar spectral information as real terrain employing inverse Fourier transforms of scaled fields of random noise. Initial conditions for the WRF simulations are taken to represent the distribution of atmospheric and snow conditions over a winter season. This training data set we feed into a U-Net shape architecture using convolutional neural networks.

Here we present first results using a training data set with a reduced number of degrees of freedom as a prove of concept. Future developments will involve adding more complexity to the initial conditions as well as applying it to real-world settings. For this we will couple the model to a glacier mass balance model and run it with real-world atmospheric fields in order to asses the overall importance of drifting snow for alpine glaciers.

How to cite: Saigger, M. and Mölg, T.: Welcome to Fourier-Land: Deep-Learning based downscaling of near-surface winds and drifting snow using WRF simulations over synthetic topographies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8087, https://doi.org/10.5194/egusphere-egu24-8087, 2024.

X5.25
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EGU24-14519
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ECS
Yuliya Kazachkova and Annette Miltenberger

Quantiative precipitation forecasting remains a major challenge even for kilometre-scale ensemble forecasating systems. However, operational high-resolution ensemble systems provide a large data-set from which - if combined with observational data - insight into systematic issues in the model physics can be gained. Here, we explore statistical methods to automatically identify systematic error patterns and their relation to the larger-scale conditions at example problem of precipitation at the Harz mountain range in northern Germany. For the analysis COSMO-D2-EPS forecasts for the years 2011-2018 are combined radar-derived and station-calibrated surface precipitation estimates provided by the German Weather Service (DWD). For the identification of common precipitation error patterns, empirical orthogonal function (EOF) analysis has been employed. For the winter season the leading order principal components show error features located on the elevated topography in the Harz region. Analysis of large-scale conditions (derived from ERA5) for each principal component shows systematic differences in upstream wind direction and speed, temperature, and specific humidity. In the summer seasons patterns are less localised, but some regional structure is maintained especially for the first principal component. Also the differentiation in large-scale conditions between EOFs is less. The challenges in summer are presumably related to a large contribution of convective precipitation. Overall, the leading 5 principal components explain 70,4% (48,2%) of the variance in winter (summer). To gain a better understanding of the relationship of error models to larger-scale conditions, as well as the physical mechanisms of model errors, simulations of precipitation at representative dates for principal components 1 and 2 were performed using the ICON-D2 model.

How to cite: Kazachkova, Y. and Miltenberger, A.: Automatic identification of systematic model failures in ensemble precipitation forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14519, https://doi.org/10.5194/egusphere-egu24-14519, 2024.

X5.26
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EGU24-14813
Seungwoo Lee, Jin-Woo Park, Sun-Jin Mo, and Ji-Young Gu

Complex terrain is often characterized by mechanical sources, which force the initiation of convection in unstable atmospheric conditions, resulting in severe weather such as thunderstorms and hail, etc. With respect to the synoptic prevailing wind, the terrain can also cause heavy precipitation by converging flow and stationary rain bands. Rain gauges can provide a direct measure of accurate precipitation at a point station. However, low accessibility and high maintenance costs limit the ability to install high-resolution rain gauge networks in mountainous regions. Thus, quantitative precipitation estimation (QPE) through remote sensing using weather radar plays an important role in securing observation information on rainfall amounts in mountainous areas. 
In this study, we first analyzed the statistics of hazardous weather events retrieved from radar-based rainfall estimates over the Korean Peninsula according to complex topography. In order to analyze the frequency of occurrence of each type of hazardous weather such as torrential rain, hail, and snowfall according to orographic conditions, we used radar-based QPE. We investigated the correlation between topographic characteristics such as terrain altitude, wind direction, and downwind side and the frequency of occurrence and development of hazardous weather phenomena. 
We also examined the accuracy of QPE relating to rainfall mechanisms including radar echo top height for each warm and cold season. We analyzed the accuracy of QPEs according to various methods using radar reflectivity, dual-polarization parameters, and radar attenuation. We analyzed precipitation estimation error factors that may be caused by terrain shielding and high radar beam height in mountainous areas. We explored QPE errors based on radar beam height and echo intensity to improve the accuracy of QPE. 
Furthermore, in order to provide hazardous weather information specialized for the mountainous region, we have planned to develop an algorithm to estimate the probability of severe weather due to topographic characteristics by merging terrain altitude, atmospheric instability, and radar echo intensity by calculating Froude number using radar-based three-dimensional wind (Wind Synthesis System using Doppler Measurements, WISSDOM). In conjunction with the technology for calculating stationary precipitation information using radar echo image processing techniques, we aim to strengthen the ability to respond to dangerous weather by providing information on possible areas of heavy rain, snowfall, and extreme wind due to complex terrain.

 

How to cite: Lee, S., Park, J.-W., Mo, S.-J., and Gu, J.-Y.: Statistical Analysis of Radar-based Quantitative Precipitation Estimation over Complex terrain in Korea and Development of User-oriented Services for Mountainous Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14813, https://doi.org/10.5194/egusphere-egu24-14813, 2024.