Evaluation of thermally driven local winds in the Swiss Alps simulated by a high-resolution NWP model

2020 ◽  
Author(s):  
Juerg Schmidli ◽  
Abouzar Ghasemi
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Swiss Alps ◽  
Free Atmosphere ◽  
Near Surface ◽  
Wind Climatology ◽  
Thermally Driven ◽  
Nwp Model ◽  
Alpine Valleys ◽  
Local Winds

<p>In fair weather conditions, thermally driven local winds often dominate the wind climatology in deep Alpine valleys resulting in a unique wind climatology for any given valley. The accurate forecasting of these local wind systems is challenging, as they are the result of complex and multi-scale interactions. Even more so, if the aim is an accurate forecast of the winds from the near-surface to the free atmosphere, which can be considered a prerequisite for the accurate prediction of mountain weather.  This study investigates the skill of a high-resolution numerical weather prediction (NWP) model, the most current version of the COSMO-DWD model,  at 1.1 km grid spacing in simulating the thermally driven local winds in the Swiss Alps for a month-long period in September 2016. The study combines the evaluation of the surface winds in several Alpine valleys with a more detailed evaluation of the wind evolution throughout the valley depth for a particular site in the Swiss Rhone valley. The former is based on a comparison with observations from the operational measurement network of MeteoSwiss, while the latter uses data from a wind profiler stationed at Sion airport.</p>

scholarly journals Evaluation of thermally driven local winds in a deep Alpine valley in a high-resolution numerical weather prediction model

2021 ◽  
Author(s):  
Juerg Schmidli ◽  
Julian Quimbayo-Duarte
Keyword(s):  
Weather Prediction ◽  
Swiss Alps ◽  
Grid Spacing ◽  
Valley Wind ◽  
Near Surface ◽  
Wind Climatology ◽  
Thermally Driven ◽  
Rhone Valley ◽  
Alpine Valleys ◽  
Local Winds

<p>In fair weather conditions, thermally driven local winds are dominant feature of the atmospheric boundary layer over complex terrain. They may dominate the wind climatology in deep Alpine valleys resulting in a unique wind climatology for any given valley. The accurate forecasting of these local wind systems is challenging, as they are the result of complex and multi-scale interactions. Even more so, if the aim is the accurate forecasting of the winds from the near-surface to the free atmosphere, which can be considered a prerequisite for the accurate prediction of mountain weather.  This study investigates the skill of the COSMO model at 1.1 km grid spacing in simulating the thermally driven local winds in the Swiss Alps for a month-long period in September 2016. The study combines the evaluation of the surface winds in several Alpine valleys with a more detailed evaluation of the wind evolution throughout the depth of the valley atmosphere for a particular location in the Swiss Rhone valley, the town of Sion. The former is based on a comparison with observations from the operational measurement network of MeteoSwiss, while the latter uses data from a wind profiler stationed at Sion airport. It is found that the near-surface valley wind is generally well represented for the larger Alpine valleys, except for the Rhone valley at Sion. The reasons for the poor skill at Sion are investigated and shown to be attributable to several factors. One of which is a too strong cross-valley flow reaching down to the valley floor and displacing the daytime up-valley wind. A second factor is the particular local valley geometry. It is shown that an increase of the initial soil moisture and the use a finer horizontal grid spacing results in an improved simulation of the diurnal valley wind at Sion.</p>

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

scholarly journals A hindcast study of the Piedmont 1994 flood: the CIMA Research Foundation hydro-meteorological forecasting chain

2020 ◽  
Author(s):  
Antonio Parodi ◽  
Martina Lagasio ◽  
Agostino N. Meroni ◽  
Flavio Pignone ◽  
Francesco Silvestro ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Warning System ◽  
Atmospheric Model ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
Nwp Model ◽  
North Western ◽  
Discharge Curves

AbstractBetween the 4th and the 6th of November 1994, Piedmont and the western part of Liguria (two regions in north-western Italy) were hit by heavy rainfalls that caused the flooding of the Po, the Tanaro rivers and several of their tributaries, causing 70 victims and the displacement of over 2000 people. At the time of the event, no early warning system was in place and the concept of hydro-meteorological forecasting chain was in its infancy, since it was still limited to a reduced number of research applications, strongly constrained by coarse-resolution modelling capabilities both on the meteorological and the hydrological sides. In this study, the skills of the high-resolution CIMA Research Foundation operational hydro-meteorological forecasting chain are tested in the Piedmont 1994 event. The chain includes a cloud-resolving numerical weather prediction (NWP) model, a stochastic rainfall downscaling model, and a continuous distributed hydrological model. This hydro-meteorological chain is tested in a set of operational configurations, meaning that forecast products are used to initialise and force the atmospheric model at the boundaries. The set consists of four experiments with different options of the microphysical scheme, which is known to be a critical parameterisation in this kind of phenomena. Results show that all the configurations produce an adequate and timely forecast (about 2 days ahead) with realistic rainfall fields and, consequently, very good peak flow discharge curves. The added value of the high resolution of the NWP model emerges, in particular, when looking at the location of the convective part of the event, which hit the Liguria region.

Adopting Citizen Observations in Operational Weather Prediction

2019 ◽  
Vol 101 (1) ◽  
pp. E43-E57 ◽  
Author(s):  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Cristian Lussana ◽  
Jørn Kristiansen ◽  
Øystein Hov
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Weather Prediction ◽  
Urban Heat Islands ◽  
Production Chain ◽  
Near Surface ◽  
High Resolution Data ◽  
Baltic Countries ◽  
Operational Systems ◽  
Weather Stations

Abstract Citizen weather stations are rapidly increasing in prevalence and are becoming an emerging source of weather information. These low-cost consumer-grade devices provide observations in real time and form parts of dense networks that capture high-resolution meteorological information. Despite these benefits, their adoption into operational weather prediction systems has been slow. However, MET Norway recently introduced observations from Netatmo’s network of weather stations in the postprocessing of near-surface temperature forecasts for Scandinavia, Finland, and the Baltic countries. The observations are used to continually correct errors in the weather model output caused by unresolved features such as cold pools, inversions, urban heat islands, and an intricate coastline. Corrected forecasts are issued every hour. Integrating citizen observations into operational systems comes with a number of challenges. First, operational systems must be robust and therefore rely on strict quality control procedures to filter out unreliable measurements. Second, postprocessing methods must be selected and tuned to make use of the high-resolution data that at times can contain conflicting information. Central to resolving these challenges is the need to use the massive redundancy of citizen observations, with up to dozens of observations per square kilometer, and treating the data source as a network rather than a collection of individual stations. We present our experiences with introducing citizen observations into the operational production chain of automated public weather forecasts. Their inclusion shows a clear improvement to the accuracy of short-term temperature forecasts, especially in areas where existing professional stations are sparse.

scholarly journals On the Consequences of PBL Scheme Diffusion on UTLS Wave and Turbulence Representation in High-Resolution NWP Models

2020 ◽  
Vol 148 (10) ◽  
pp. 4247-4265 ◽  
Author(s):  
Domingo Muñoz-Esparza ◽  
Robert D. Sharman ◽  
Stanley B. Trier
Keyword(s):  
High Resolution ◽  
Great Plains ◽  
Gravity Waves ◽  
Weather Prediction ◽  
Vertical Mixing ◽  
Small Scale ◽  
Vertical Diffusion ◽  
Model Simulations ◽  
Pbl Scheme ◽  
Nwp Model

AbstractMesoscale numerical weather prediction (NWP) models are routinely exercised at kilometer-scale horizontal grid spacings (Δx). Such fine grids will usually allow at least partial resolution of small-scale gravity waves and turbulence in the upper troposphere and lower stratosphere (UTLS). However, planetary boundary layer (PBL) parameterization schemes used with these NWP model simulations typically apply explicit subgrid-scale vertical diffusion throughout the entire vertical extent of the domain, an effect that cannot be ignored. By way of an example case of observed widespread turbulence over the U.S. Great Plains, we demonstrate that the PBL scheme’s mixing in NWP model simulations of Δx = 1 km can have significant effects on the onset and characteristics of the modeled UTLS gravity waves. Qualitatively, PBL scheme diffusion is found to affect not only background conditions responsible for UTLS wave activity, but also to control the local vertical mixing that triggers or hinders the onset and propagation of these waves. Comparisons are made to a reference large-eddy simulation with Δx = 250 m to statistically quantify these effects. A significant and systematic overestimation of resolved vertical velocities, wave-scale fluxes, and kinetic energy is uncovered in the 1-km simulations, both in clear-air and in-cloud conditions. These findings are especially relevant for upper-level gravity wave and turbulence simulations using high-resolution kilometer-scale NWP models.

Temperature monitoring in mountain regions using reanalyses: Lessons from the Alps

2020 ◽  
Author(s):  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Reanalysis Data ◽  
Swiss Alps ◽  
Mountain Regions ◽  
Near Surface ◽  
The Alps ◽  
Regional Reanalysis ◽  
High Elevations

<p>The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20 to almost 60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies of the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.</p><p> </p>

scholarly journals Current gust forecasting techniques, developments and challenges

2018 ◽  
Vol 15 ◽  
pp. 159-172 ◽  
Author(s):  
Peter Sheridan
Keyword(s):  
Structural Damage ◽  
Weather Prediction ◽  
Regional Scale ◽  
Surface Wind ◽  
Learning Approaches ◽  
Probabilistic Prediction ◽  
Near Surface ◽  
Advantages And Disadvantages ◽  
Boundary Layer Turbulence ◽  
Nwp Model

Abstract. Gusts represent the component of wind most likely to be associated with serious hazards and structural damage, representing short-lived extremes within the spectrum of wind variation. Of interest both for short range forecasting and for climatological and risk studies, this is also reflected in the variety of methods used to predict gusts based on various static and dynamical factors of the landscape and atmosphere. The evolution of Numerical Weather Prediction (NWP) models has delivered huge benefits from increasingly accurate forecasts of mean near-surface wind, with which gusts broadly scale. Techniques for forecasting gusts rely on parametrizations based on a physical understanding of boundary layer turbulence, applied to NWP model fields, or statistical models and machine learning approaches trained using observations, each of which brings advantages and disadvantages. Major shifts in the nature of the information available from NWP models are underway with the advent of ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields. This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts on the performance of gust forecasting using current methods. The transition to probabilistic prediction using ensembles at the regional level means that considerations such as these must also be carried through to the aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.

scholarly journals BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

2020 ◽  
Author(s):  
Chun-Hsu Su ◽  
Nathan Eizenberg ◽  
Dörte Jakob ◽  
Paul Fox-Hughes ◽  
Peter Steinle ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
South Australia ◽  
Heavy Rain ◽  
Temporal Variations ◽  
Climate Projections ◽  
Storm Events ◽  
Near Surface ◽  
Light Rain ◽  
Regional Reanalysis

Abstract. The development of convection-permitting models (CPMs) in numerical weather prediction has facilitated the creation of km-scale (1–4 km) regional reanalysis and climate projections. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) also aims to realise the benefits of these high-resolution models over Australian sub-regions for applications such as fire danger research, by nesting them in BARRA's 12 km regional reanalysis (BARRA-R). Four mid-latitude sub-regions are centred on Perth in Western Australia, Adelaide in South Australia, Sydney in New South Wales (NSW), and Tasmania. The resulting 29-year 1.5 km downscaled reanalyses (BARRA-C) are assessed for their added skill over BARRA-R and global reanalyses for near-surface parameters (temperature, wind and precipitation) at observation locations and against independent 5 km gridded analyses. BARRA-C demonstrates better agreement with point observations for temperature and wind, particularly in topographically complex regions and coastal regions. BARRA-C also improves upon BARRA-R in terms of intensity and timing of precipitation during the thunderstorm seasons in NSW, and spatial patterns of sub-daily rain fields during storm events. However, as a hindcast-only system, BARRA-C largely inherits the domain-averaged biases and temporal variations of biases from BARRA-R. Further, BARRA-C reflects known issues of CPMs: overestimation of heavy rain rates and rain cells, and underestimation of light rain occurrence.

Day-Ahead Wind Power Forecast Through High-Resolution Mesoscale Model: Local Computational Fluid Dynamics Versus Artificial Neural Network Downscaling

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Matteo Mana ◽  
Davide Astolfi ◽  
Francesco Castellani ◽  
Cathérine Meißner
Keyword(s):  
High Resolution ◽  
Wind Farm ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Cfd Model ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Nwp Model ◽  
The Impact

Abstract The importance of accurately forecasting the power production of wind farms is boosting the development of meteorological models and their processing. This work is a discussion of different forecast configurations for predicting the day ahead production of a wind farm sited in a moderately complex terrain. The numerical weather prediction (NWP) model MetCoOp Ensemble Prediction System with 2.5 km resolution focusing on the wind farm area is dynamically downscaled by the computational fluid model (CFD) model WindSim. The transfer of the NWP model to the CFD model can be done using NWP results from various heights above ground and using all or parts of the nodes of the NWP model within the wind farm area. In this work, many different forecasting configurations are validated and the impact on the forecast performance is discussed. The NWP-CFD downscaling results are compared to a day ahead forecast obtained through ANN methods and to the observed production. The main result of this work is that a deterministic downscaling method like CFD simulations can perform as good or better than statistical approaches when using high-resolution NWP models and more NWP model data.

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