Characteristics of the Springtime Alpine Valley Atmospheric Boundary Layer Using Self-Organizing Maps

Marwan Katurji; Bob Noonan; Peyman Zawar-Reza; Tobias Schulmann; Andrew Sturman

doi:10.1175/jamc-d-14-0317.1

Characteristics of the Springtime Alpine Valley Atmospheric Boundary Layer Using Self-Organizing Maps

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0317.1 ◽

2015 ◽

Vol 54 (10) ◽

pp. 2077-2085 ◽

Cited By ~ 8

Author(s):

Marwan Katurji ◽

Bob Noonan ◽

Peyman Zawar-Reza ◽

Tobias Schulmann ◽

Andrew Sturman

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Atmospheric Boundary Layer ◽

Learning Algorithm ◽

Potential Temperature ◽

Atmospheric Stability ◽

Weather Conditions ◽

Self Organizing Maps ◽

Alpine Valley ◽

Self Organizing

AbstractVertical profiles of wind velocity and air temperature from a sound detection and ranging (sodar) radio acoustic sounding system (RASS)-derived dataset within an alpine valley of the New Zealand Southern Alps were analyzed. The data covered the month of September 2013, and self-organizing maps (SOM; a data-clustering approach that is based on an unsupervised machine-learning algorithm) are used to detect topological relationships between profiles. The results of the SOM were shown to reflect the physical processes within the valley boundary layer by preserving valley boundary layer dynamics and its response to wind shear. By examining the temporal evolution of ridgetop wind speed and direction and SOM node transitions, the sensitivity of the valley boundary layer to ridgetop weather conditions was highlighted. The approach of using a composite variable (wind speed and potential temperature) with SOM was successful in revealing the coupling of dynamics and atmospheric stability. The results reveal the capabilities of SOM in analyzing large datasets of atmospheric boundary layer measurements and elucidating the connectivity of ridgetop wind speeds and valley boundary layers.

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Influence of boundary layer structure on air quality in Beijing: Long-term analysis based on self-organizing maps

10.5194/acp-2017-1046 ◽

2017 ◽

Author(s):

Zhiheng Liao ◽

Jiaren Sun ◽

Jialin Yao ◽

Li Liu ◽

Haowen Li ◽

...

Keyword(s):

Boundary Layer ◽

Air Quality ◽

Radiative Forcing ◽

Learning Algorithm ◽

Layer Structure ◽

Surface Wind ◽

Atmospheric Stability ◽

Stable Condition ◽

Self Organizing Maps ◽

Self Organizing

Abstract. Self-organizing maps (SOMs; a feather-extracting technique based on an unsupervised machine learning algorithm) are used to classify the atmospheric boundary layer (ABL) types over Beijing by detecting topological relationships among the 4-yr (2013–2016) radiosonde profiles. The resulting ABL types are then examined in relation to air quality, including surface pollutant concentrations and columnar aerosol properties, to understand the regulating effects of different ABL structures on Beijing's air quality. The SOM provides nine ABL types (i.e., SOM nodes), and each type is characterized by distinct dynamic and thermodynamic conditions. On average, SO2, NO2, CO, PM10 and PM2.5 increase 120–220 % from a near neutral (i.e., node 1) to strong stable condition (i.e., node 9). The ABL controls on diurnal cycles of pollutants are as follows: (1) elevated inversion enhances the afternoon baseline; and (2) surface inversion improves the evening increment. Comparing the CO / SO2 ratios for the different ABL types demonstrates that the local contribution increases with enhanced static stability near the ground, and it is the stable ABL stratification rather than weak surface wind that confines the regional contribution. Due to regional transport, node 3 (dominated by elevated inversion with high relative humidity) corresponds to the most severe columnar aerosol pollution, characterized by the highest optical depth (1.22) and volume concentration (0.30 μm3/μm2). The larger aerosol radiative forcing (ARF) within the atmosphere (> 60 W/m2) in nodes 3, 6 and 9 is likely to strengthen the atmospheric stability and thus induce a positive feedback loop for causing high surface pollution. Analysis of the typical pollution period suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contribution of the ABL anomaly to elevated PM2.5 levels are estimated to be 65.8 % (46.2 μg/m3) during January 2013, 46.7 % (20.2 μg/m,sup>3) during December 2015, and 94.6 % (35.3 μg/m3) during December 2016.

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Modeling of the atmospheric boundary layer under stability stratification for wind turbine wake production

Wind Engineering ◽

10.1177/0309524x19880929 ◽

2019 ◽

pp. 0309524X1988092

Author(s):

Mohamed Marouan Ichenial ◽

Abdellah El-Hajjaji ◽

Abdellatif Khamlichi

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

Wind Turbine ◽

Wind Turbines ◽

Large Scale ◽

Wind Farm ◽

Layer Structure ◽

Atmospheric Stability ◽

Weather Conditions ◽

Boundary Layer Structure

The assessment of climatological site conditions, airflow characteristics, and the turbulence affecting wind turbines is an important phase in developing wake engineering models. A method of modeling atmospheric boundary layer structure under atmospheric stability effects is crucial for accurate evaluation of the spatial scale of modern wind turbines, but by themselves, they are incapable to account for the varying large-scale weather conditions. As a result, combining lower atmospheric models with mesoscale models is required. In order to realize a reasonable approximation of initial atmospheric inflow condition used for wake identification behind an NREL 5-MW wind turbine, different vertical wind profile models on equilibrium conditions are tested and evaluated in this article. Wind farm simulator solvers require massive computing resources and forcing mechanisms tendencies inputs from weather forecast models. A three-dimensional Flow Redirection and Induction in Steady-state engineering model was developed for simulating and optimizing the wake losses of different rows of wind turbines under different stability stratifications. The obtained results were compared to high-fidelity simulation data generated by the famous Simulator for Wind Farm Applications. This work showed that a significant improvement related to atmospheric boundary layer structure can be made to develop accurate engineering wake models in order to reduce wake losses.

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Self-organized classification of boundary layer meteorology and associated characteristics of air quality in Beijing

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-6771-2018 ◽

2018 ◽

Vol 18 (9) ◽

pp. 6771-6783 ◽

Cited By ~ 8

Author(s):

Zhiheng Liao ◽

Jiaren Sun ◽

Jialin Yao ◽

Li Liu ◽

Haowen Li ◽

...

Keyword(s):

Boundary Layer ◽

Air Quality ◽

Learning Algorithm ◽

Potential Temperature ◽

Atmospheric Stability ◽

Strong Stability ◽

Average Concentration ◽

Near Surface ◽

Self Organized ◽

Stable Conditions

Abstract. Self-organizing maps (SOMs; a feature-extracting technique based on an unsupervised machine learning algorithm) are used to classify atmospheric boundary layer (ABL) meteorology over Beijing through detecting topological relationships among the 5-year (2013–2017) radiosonde-based virtual potential temperature profiles. The classified ABL types are then examined in relation to near-surface pollutant concentrations to understand the modulation effects of the changing ABL meteorology on Beijing's air quality. Nine ABL types (i.e., SOM nodes) are obtained through the SOM classification technique, and each is characterized by distinct dynamic and thermodynamic conditions. In general, the self-organized ABL types are able to distinguish between high and low loadings of near-surface pollutants. The average concentrations of PM2.5, NO2 and CO dramatically increased from the near neutral (i.e., Node 1) to strong stable conditions (i.e., Node 9) during all seasons except for summer. Since extremely strong stability can isolate the near-surface observations from the influence of elevated SO2 pollution layers, the highest average SO2 concentrations are typically observed in Node 3 (a layer with strong stability in the upper ABL) rather than Node 9. In contrast, near-surface O3 shows an opposite dependence on atmospheric stability, with the lowest average concentration in Node 9. Analysis of three typical pollution months (i.e., January 2013, December 2015 and December 2016) suggests that the ABL types are the primary drivers of day-to-day variations in Beijing's air quality. Assuming a fixed relationship between ABL type and PM2.5 loading for different years, the relative (absolute) contributions of the ABL anomaly to elevated PM2.5 levels are estimated to be 58.3 % (44.4 µg m−3) in January 2013, 46.4 % (22.2 µg m−3) in December 2015 and 73.3 % (34.6 µg m−3) in December 2016.

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Variable Eddy Viscosities in the Atmospheric Boundary Layer from Ageostrophic Wind-Speed Profiles

Journal of Mathematical Fluid Mechanics ◽

10.1007/s00021-021-00575-1 ◽

2021 ◽

Vol 23 (2) ◽

Author(s):

Tony Lyons

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Atmospheric Boundary Layer ◽

Ageostrophic Wind

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Inference of Marine Atmospheric Boundary Layer Moisture and Temperature Structure Using Airborne Lidar and Infrared Radiometer Data

Journal of Applied Meteorology ◽

10.1175/1520-0450-37.3.308 ◽

1998 ◽

Vol 37 (3) ◽

pp. 308-324 ◽

Cited By ~ 10

Author(s):

Stephen P. Palm ◽

Denise Hagan ◽

Geary Schwemmer ◽

S. H. Melfi

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

Potential Temperature ◽

Airborne Lidar ◽

Temperature Structure ◽

Marine Atmospheric Boundary Layer ◽

Space Technology ◽

Near Surface ◽

Surface Moisture ◽

Infrared Radiometer

Abstract A new technique for retrieving near-surface moisture and profiles of mixing ratio and potential temperature through the depth of the marine atmospheric boundary layer (MABL) using airborne lidar and multichannel infrared radiometer data is presented. Data gathered during an extended field campaign over the Atlantic Ocean in support of the Lidar In-space Technology Experiment are used to generate 16 moisture and temperature retrievals that are then compared with dropsonde measurements. The technique utilizes lidar-derived statistics on the height of cumulus clouds that frequently cap the MABL to estimate the lifting condensation level. Combining this information with radiometer-derived sea surface temperature measurements, an estimate of the near-surface moisture can be obtained to an accuracy of about 0.8 g kg−1. Lidar-derived statistics on convective plume height and coverage within the MABL are then used to infer the profiles of potential temperature and moisture with a vertical resolution of 20 m. The rms accuracy of derived MABL average moisture and potential temperature is better than 1 g kg−1 and 1°C, respectively. The method relies on the presence of a cumulus-capped MABL, and it was found that the conditions necessary for use of the technique occurred roughly 75% of the time. The synergy of simple aerosol backscatter lidar and infrared radiometer data also shows promise for the retrieval of MABL moisture and temperature from space.

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A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

Wind Engineering ◽

10.1177/0309524x211028754 ◽

2021 ◽

pp. 0309524X2110287

Author(s):

Chantelle Y Janse van Vuuren ◽

Hendrik J Vermeulen ◽

Matthew Groch

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Variable Component ◽

Self Organizing Maps ◽

Wind Speed Data ◽

Main Driver ◽

Clustering Approach ◽

Statistical Time ◽

Data Reduction Technique ◽

Self Organizing

The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.

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Soil Moisture-Boundary Layer Feedbacks on the Loess Plateau in China Using Radiosonde Data with 1-D Atmospheric Boundary Layer Model

Atmosphere ◽

10.3390/atmos12121619 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1619

Author(s):

Yingsai Ma ◽

Xianhong Meng ◽

Yinhuan Ao ◽

Ye Yu ◽

Guangwei Li ◽

...

Keyword(s):

Boundary Layer ◽

Soil Moisture ◽

Atmospheric Boundary Layer ◽

Loess Plateau ◽

Atmospheric Stability ◽

Heat Fluxes ◽

Radiosonde Data ◽

Atmospheric Conditions ◽

The Loess Plateau ◽

The Impact

The Loess Plateau is one land-atmosphere coupling hotspot. Soil moisture has an influence on atmospheric boundary layer development under specific early-morning atmospheric thermodynamic structures. This paper investigates the sensitivity of atmospheric convection to soil moisture conditions over the Loess Plateau in China by using the convective triggering potential (CTP)—humidity index (HIlow) framework. The CTP indicates atmospheric stability and the HIlow indicates atmospheric humidity in the low-level atmosphere. By comparing the model outcomes with the observations, the one-dimensional model achieves realistic daily behavior of the radiation and surface heat fluxes and the mixed layer properties with appropriate modifications. New CTP-HIlow thresholds for soil moisture-atmosphere feedbacks are found in the Loess Plateau area. By applying the new thresholds with long-time scales sounding data, we conclude that negative feedback is dominant in the north and west portion of the Loess Plateau; positive feedback is predominant in the south and east portion. In general, this framework has predictive significance for the impact of soil moisture on precipitation. By using this new CTP-HIlow framework, we can determine under what atmospheric conditions soil moisture can affect the triggering of precipitation and under what atmospheric conditions soil moisture has no influence on the triggering of precipitation.

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Stable Atmospheric Boundary Layers and Diurnal Cycles: Challenges for Weather and Climate Models

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-11-00187.1 ◽

2013 ◽

Vol 94 (11) ◽

pp. 1691-1706 ◽

Cited By ~ 245

Author(s):

A. A. M. Holtslag ◽

G. Svensson ◽

P. Baas ◽

S. Basu ◽

B. Beare ◽

...

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Atmospheric Boundary Layer ◽

Climate Models ◽

Model Performance ◽

Weather Forecast ◽

The Arctic ◽

Diurnal Cycles ◽

Low Wind Speed ◽

Weather And Climate

The representation of the atmospheric boundary layer is an important part of weather and climate models and impacts many applications such as air quality and wind energy. Over the years, the performance in modeling 2-m temperature and 10-m wind speed has improved but errors are still significant. This is in particular the case under clear skies and low wind speed conditions at night as well as during winter in stably stratified conditions over land and ice. In this paper, the authors review these issues and provide an overview of the current understanding and model performance. Results from weather forecast and climate models are used to illustrate the state of the art as well as findings and recommendations from three intercomparison studies held within the Global Energy and Water Exchanges (GEWEX) Atmospheric Boundary Layer Study (GABLS). Within GABLS, the focus has been on the examination of the representation of the stable boundary layer and the diurnal cycle over land in clear-sky conditions. For this purpose, single-column versions of weather and climate models have been compared with observations, research models, and large-eddy simulations. The intercomparison cases are based on observations taken in the Arctic, Kansas, and Cabauw in the Netherlands. From these studies, we find that even for the noncloudy boundary layer important parameterization challenges remain.

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Impacts of Modifications to a Local Planetary Boundary Layer Scheme on Forecasts of the Great Plains Low-Level Jet Environment

Weather and Forecasting ◽

10.1175/waf-d-18-0036.1 ◽

2018 ◽

Vol 33 (5) ◽

pp. 1109-1120 ◽

Cited By ~ 3

Author(s):

David E. Jahn ◽

William A. Gallus

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Planetary Boundary Layer ◽

Great Plains ◽

Potential Temperature ◽

Specific Humidity ◽

Low Level ◽

Stable Conditions ◽

Pbl Scheme ◽

Low Level Jet

Abstract The Great Plains low-level jet (LLJ) is influential in the initiation and evolution of nocturnal convection through the northward advection of heat and moisture, as well as convergence in the region of the LLJ nose. However, accurate numerical model forecasts of LLJs remain a challenge, related to the performance of the planetary boundary layer (PBL) scheme in the stable boundary layer. Evaluated here using a series of LLJ cases from the Plains Elevated Convection at Night (PECAN) program are modifications to a commonly used local PBL scheme, Mellor–Yamada–Nakanishi–Niino (MYNN), available in the Weather Research and Forecasting (WRF) Model. WRF forecast mean absolute error (MAE) and bias are calculated relative to PECAN rawinsonde observations. The first MYNN modification invokes a new set of constants for the scheme closure equations that, in the vicinity of the LLJ, decreases forecast MAEs of wind speed, potential temperature, and specific humidity more than 19%. For comparison, the Yonsei University (YSU) scheme results in wind speed MAEs 22% lower but specific humidity MAEs 17% greater than in the original MYNN scheme. The second MYNN modification, which incorporates the effects of potential kinetic energy and uses a nonzero mixing length in stable conditions as dependent on bulk shear, reduces wind speed MAEs 66% for levels below the LLJ, but increases MAEs at higher levels. Finally, Rapid Refresh analyses, which are often used for forecast verification, are evaluated here and found to exhibit a relatively large average wind speed bias of 3 m s−1 in the region below the LLJ, but with relatively small potential temperature and specific humidity biases.

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How is the marine atmospheric boundary layer turbulence organized in the trades ?

10.5194/egusphere-egu21-12339 ◽

2021 ◽

Author(s):

Pierre-Etienne Brilouet ◽

Marie Lothon ◽

Sandrine Bony

Keyword(s):

Boundary Layer ◽

Atmospheric Boundary Layer ◽

Vertical Velocity ◽

Large Scale ◽

Layer Structure ◽

Potential Temperature ◽

Surface Wind ◽

Cloud Amount ◽

Velocity Spectrum ◽

Marine Atmospheric Boundary Layer

Tradewind clouds can exhibit a wide diversity of mesoscale organizations, and the turbulence of marine atmospheric boundary layer (MABL) can exhibit coherent structures and mesoscale circulations. One of the objectives of the EUREC4A (Elucidating the role of cloud-circulation coupling in climate) field experiment was to better understand the tight interplay between the mesoscale organization of clouds, boundary-layer processes, and the large-scale environment.During the experiment, that took place East of Barbados over the Western Tropical Atlantic Ocean in Jan-Feb 2020, the French ATR-42 research aircraft was devoted to the characterization of the cloud amount and of the subcoud layer structure. During its 17 research flights, it sampled a large diversity of large scale conditions and cloud patterns. Multiple sensors onboard the aircraft measured high-frequency fluctuations of potential temperature, water vapour mixing ratio and wind , allowing for an extensive characterization of the turbulence within the subcloud layer. A quality-controled and calibrated turbulence dataset was produced on the basis of these measurements, which is now available on the EUREC4A AERIS data portal.The MABL turbulent structure is studied using this dataset, through a spectral analysis of the vertical velocity. Vertical profiles of characteristic length scales reveal a non-isotropic structure with a stretching of the eddies along the mean wind. The organization strength of the turbulent field is also explored by defining a diagnostic based on the shape of the vertical velocity spectrum. The structure and the degree of organization of the subcloud layer are characterized for different types of mesoscale convective pattern and as a function of the large-scale environment, including near-surface wind and lower-tropospheric stability conditions.&#160;

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