scholarly journals Numerical Study of the Impact of Complex Terrain and Soil Moisture on Convective Initiation

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 871
Author(s):  
Beilei Zan ◽  
Ye Yu ◽  
Longxiang Dong ◽  
Jianglin Li ◽  
Guo Zhao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Complex Terrain ◽  
Numerical Study ◽  
Dominant Role ◽  
Deep Convection ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Local Circulation ◽  
Convective Initiation ◽  
The Impact

The relative importance of topography and soil moisture on the initiation of an afternoon deep convection under weak synoptic-scale forcing was investigated using the weather research and forecasting (WRF) model with high resolution (1.33 km). The convection occurred on 29 June 2017, over the Liupan Mountains, west of the Loess Plateau. The timing and location of the convective initiation (CI) simulated by the WRF model compared well with the radar observations. It showed that the warm and humid southerly airflow under 700 hPa was divided into east and west flows due to the blockage of the Liupan Mountains. The warm and humid air on the west side was forced to climb along the slope and enhanced the humidity near the ridge. The accumulation of unstable energy in the middle and north of the ridge led to a strong vertical convergence and triggered the convection. Sensitivity experiments showed that terrain played a dominant role in triggering the convection, while the spatial heterogeneity of soil moisture played an indirect role by affecting the local circulation and the partition of surface energy.

Impact of Soil Moisture Initialization on Temperature Extreme Detection in the context of Regional Climate Modeling

2020 ◽  
Author(s):  
Matilde García-Valdecasas Ojeda ◽  
Juan José Rosa-Cánovas ◽  
Emilio Romero-Jiménez ◽  
Patricio Yeste ◽  
Sonia R. Gámiz-Fortis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Modeling ◽  
Weather Research And Forecasting ◽  
Temperature Extremes ◽  
Temperature Extreme ◽  
The Impact

<p>Land surface-related processes play an essential role in the climate conditions at a regional scale. In this study, the impact of soil moisture (SM) initialization on regional climate modeling has been explored by using a dynamical downscaling experiment. To this end, the Weather Research and Forecasting (WRF) model was used to generate a set of high-resolution climate simulations driven by the ERA-Interim reanalysis for a period from 1989 to 2009. As the spatial configuration, two one-way nested domains were used, with the finer domain being centered over the Iberian Peninsula (IP) at a spatial resolution of about 10 km, and nested over a coarser domain that covers the Euro-CORDEX region at 50 km of spatial resolution.</p><p>The sensitivity experiment consisted of two control runs (CTRL) performed using as SM initial conditions those provided by ERA-Interim, and initialized for two different dates times (January and June). Additionally, another set of runs was completed driven by the same climate data but using as initial conditions prescribed SM under wet and dry scenarios.</p><p>The study is based on assessing the WRF performance by comparing the CTRL simulations with those performed with the different prescribed SM, and also, comparing them with the observations from the Spanish Temperature At Daily scale (STEAD) dataset. In this sense, we used two temperature extreme indices within the framework of decadal predictions: the warm spell index (WSDI) and the daily temperature range (DTR).</p><p>These results provide valuable information about the impact of the SM initial conditions on the ability of the WRF model to detect temperature extremes, and how long these affect the regional climate in this region. Additionally, these results may provide a source of knowledge about the mechanisms involved in the occurrence of extreme events such as heatwaves, which are expected to increase in frequency, duration, and magnitude under the context of climate change.</p><p><strong>Keywords</strong>: soil moisture initial conditions, temperature extremes, regional climate, Weather Research and Forecasting model</p><p>Acknowledgments: This work has been financed by the project CGL2017-89836-R (MINECO-Spain, FEDER). The WRF simulations were performed in the Picasso Supercomputer at the University of Málaga, a member of the Spanish Supercomputing Network.</p>

Impact of revegetation of the Loess Plateau of China on the regional growing season water balance

2020 ◽  
Author(s):  
Weidong Guo ◽  
Andrew Pitman ◽  
Jun Ge ◽  
Beilei Zan ◽  
Congbin Fu
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Wrf Model ◽  
Growing Season ◽  
Weather Research And Forecasting ◽  
The Loess Plateau ◽  
Deep Soil ◽  
Bare Land ◽  
Biophysical Changes ◽  
The Impact

<p>To resolve a series of ecological and environmental problems over the Loess Plateau, the was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend with soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau which has raised questions whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using high resolution simulations and multiple realisations with the Weather Research and Forecasting (WRF) model. Results suggests that land cover change since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to increase evapotranspiration further, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements, without any significant compensation from rainfall.</p>

scholarly journals Impact of revegetation of the Loess Plateau of China on the regional growing season water balance

2019 ◽  
Author(s):  
Jun Ge ◽  
Andrew J. Pitman ◽  
Weidong Guo ◽  
Beilei Zan ◽  
Congbin Fu
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
The Loess Plateau ◽  
Deep Soil ◽  
Grain For Green ◽  
Bare Land ◽  
Biophysical Changes ◽  
The Impact

Abstract. To resolve a series of ecological and environmental problems over the Loess Plateau, the Grain for Green Program (GFGP) was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend with soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau which has raised questions whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using high resolution simulations and multiple realisations with the Weather Research and Forecasting (WRF) model. Results suggests that land cover change since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to increase evapotranspiration further, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements, without any significant compensation from rainfall.

scholarly journals Impact of revegetation of the Loess Plateau of China on the regional growing season water balance

2020 ◽  
Vol 24 (2) ◽  
pp. 515-533 ◽  
Author(s):  
Jun Ge ◽  
Andrew J. Pitman ◽  
Weidong Guo ◽  
Beilei Zan ◽  
Congbin Fu
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
The Loess Plateau ◽  
Deep Soil ◽  
Grain For Green ◽  
Bare Land ◽  
Biophysical Changes ◽  
The Impact

Abstract. To resolve a series of ecological and environmental problems over the Loess Plateau, the “Grain for Green Program” (GFGP) was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend, which has resulted in soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau, which has raised questions regarding whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using relatively high-resolution simulations and multiple realizations with the Weather Research and Forecasting (WRF) model. Results suggest that revegetation since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to further increase evapotranspiration, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration, our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements and the lack of any significant compensation from rainfall.

scholarly journals Simulation of Flash-Flood-Producing Storm Events in Saudi Arabia Using the Weather Research and Forecasting Model*

2015 ◽  
Vol 16 (2) ◽  
pp. 615-630 ◽  
Author(s):  
Liping Deng ◽  
Matthew F. McCabe ◽  
Georgiy Stenchikov ◽  
Jason P. Evans ◽  
Paul A. Kucera
Keyword(s):  
Heavy Rainfall ◽  
Extreme Values ◽  
Flash Flood ◽  
Deep Convection ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Storm Events ◽  
Convective Systems ◽  
The Impact ◽  
Weather Research

Abstract The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite, in situ, and reanalysis data. Here, we focus on characterizing the initial synoptic features and examining the impact of model parameterization and resolution on the reproduction of a number of flood-producing rainfall events that occurred over the western Saudi Arabian city of Jeddah. Analysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data suggests that mesoscale convective systems associated with strong moisture convergence ahead of a trough were the major initial features for the occurrence of these intense rain events. The WRF Model was able to simulate the heavy rainfall, with driving convective processes well characterized by a high-resolution cloud-resolving model. The use of higher (1 km vs 5 km) resolution along the Jeddah coastline favors the simulation of local convective systems and adds value to the simulation of heavy rainfall, especially for deep-convection-related extreme values. At the 5-km resolution, corresponding to an intermediate study domain, simulation without a cumulus scheme led to the formation of deeper convective systems and enhanced rainfall around Jeddah, illustrating the need for careful model scheme selection in this transition resolution. In analysis of multiple nested WRF simulations (25, 5, and 1 km), localized volume and intensity of heavy rainfall together with the duration of rainstorms within the Jeddah catchment area were captured reasonably well, although there was evidence of some displacements of rainstorm events.

scholarly journals A Study on Microscale Wind Simulations with a Coupled WRF–CFD Model in the Chongli Mountain Region of Hebei Province, China

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 731
Author(s):  
Shaohui Li ◽  
Xuejin Sun ◽  
Shan Zhang ◽  
Shijun Zhao ◽  
Riwei Zhang
Keyword(s):  
Wind Field ◽  
Complex Terrain ◽  
Wrf Model ◽  
Mountain Region ◽  
Weather Research And Forecasting ◽  
Cfd Model ◽  
Improved Method ◽  
Comprehensive Understanding ◽  
Cfd Models ◽  
Computational Fluid Dynamics Cfd

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.

scholarly journals An Automated, Multiparameter Dryline Identification Algorithm

2015 ◽  
Vol 30 (6) ◽  
pp. 1781-1794 ◽  
Author(s):  
Adam J. Clark ◽  
Andrew MacKenzie ◽  
Amy McGovern ◽  
Valliappa Lakshmanan ◽  
Rodger A. Brown
Keyword(s):  
Wrf Model ◽  
Large Datasets ◽  
Probability Of Detection ◽  
Weather Research And Forecasting ◽  
High Plains ◽  
Identification Algorithm ◽  
Algorithm Performance ◽  
Convective Initiation ◽  
Operational Forecasting ◽  
Very Large Datasets

Abstract Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.

scholarly journals Impact of Coupling an Ocean Model to WRF Nor’easter Simulations

2015 ◽  
Vol 143 (12) ◽  
pp. 4997-5016 ◽  
Author(s):  
Stephen D. Nicholls ◽  
Steven G. Decker
Keyword(s):  
Storm Track ◽  
Wrf Model ◽  
Ocean Model ◽  
Weather Research And Forecasting ◽  
Model Coupling ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Simulated Precipitation ◽  
Ocean Atmosphere ◽  
The Impact

Abstract The impact of ocean–atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor’easter did tend to cool more strongly during the early season (October–December) and were more likely to warm late in the season (February–April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31–0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean–atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%–2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations.

scholarly journals Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation

2010 ◽  
Vol 49 (2) ◽  
pp. 268-287 ◽  
Author(s):  
Pedro A. Jiménez ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Juan P. Montávez ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Wrf Model ◽  
Spatial Dimension ◽  
Weather Research And Forecasting ◽  
Observational Period ◽  
Spatiotemporal Resolution ◽  
Wind Variability ◽  
Spatial Coverage

Abstract This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

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