Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part I: Model Initialization and Simulation Validation

2021 ◽  
Author(s):  
David S. Nolan ◽  
Brian D. McNoldy ◽  
Jimmy Yunge
Keyword(s):  
Wind Field ◽  
Urban Areas ◽  
Initial Conditions ◽  
Surface Wind ◽  
South Florida ◽  
Wind Fields ◽  
Near Surface ◽  
Hurricane Wilma ◽  
Wind Analysis ◽  
Forecasting System

AbstractWhile global and regional dynamical models are used to predict the tracks and intensities of hurricanes over the ocean, these models are not currently used to predict the wind field and other impacts over land. This two-part study performs detailed evaluations of the near-surface, over-land wind fields produced in simulations of Hurricane Wilma (2005) as it traveled across South Florida. This first part describes the production of two high-resolution simulations using the Weather Research and Forecasting Model (WRF), using different boundary layer parameterizations available in WRF: the Mellor-Yamada-Janjić (MYJ) scheme and the Yonsei University (YSU) scheme. Initial conditions from the Global Forecasting System (GFS) are manipulated with a vortex bogussing technique to modify the initial intensity, size, and location of the cyclone. It is found possible through trial and error to successfully produce simulations using both the YSU and MYJ schemes that closely reproduce the track, intensity, and size of Wilma at landfall. For both schemes the storm size and structure also show good agreement with the wind fields diagnosed by H*WIND and the Tropical Cyclone Surface Wind Analysis (TCSWA). Both over water and over land, the YSU scheme has stronger winds over larger areas than MYJ, but the surface winds are more reduced in areas of greater surface roughness, particularly in urban areas. Both schemes produced very similar inflow angles over land and water. The over-land wind fields are examined in more detail in the second part of this study.

Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part II: Surface Winds, Inflow Angles, and Boundary Layer Profiles

2021 ◽  
Author(s):  
David S. Nolan ◽  
Brian D. McNoldy ◽  
Jimmy Yunge ◽  
Forrest J. Masters ◽  
Ian M. Giammanco
Keyword(s):  
Boundary Layer ◽  
Wind Field ◽  
Initial Conditions ◽  
Surface Wind ◽  
Storm Track ◽  
South Florida ◽  
Modeling Framework ◽  
Near Surface ◽  
Hurricane Wilma ◽  
Wind Events

AbstractThis is the second of a two-part study that explores the capabilities of a mesoscale atmospheric model to reproduce the near-surface wind fields in hurricanes over land. The Weather Research and Forecasting Model (WRF) is used with two planetary boundary layer parameterizations: the Yonsei University (YSU) and the Mellor-Yamada-Janjić (MYJ) schemes. The first part presented the modeling framework and initial conditions used to produce simulations of Hurricane Wilma (2005) that closely reproduced the track, intensity, and size of its wind field as it passed over South Florida. This part explores how well these simulations can reproduce the winds at fixed points over land by making comparisons to observations from airports and research weather stations. The results show that peak wind speeds are remarkably well reproduced at several locations. Wind directions are evaluated in terms of the inflow angle relative to the storm center, and the simulated inflow angles are generally smaller than observed. Localized peak wind events are associated with vertical vorticity maxima in the boundary layer with horizontal scales of 5-10 km. The boundary layer winds are compared to wind profiles obtained by velocity-azimuth display (VAD) analyses from National Weather Service Doppler radars at Miami and Key West; results from these comparisons are mixed. Nonetheless the comparisons to surface observations suggest that when short-term hurricane forecasts can sufficiently predict storm track, intensity, and size, they will also be able to provide useful information on extreme winds at locations of interest.

scholarly journals Reconstruction of Near-Surface Tornado Wind Fields from Forest Damage

2010 ◽  
Vol 49 (7) ◽  
pp. 1517-1537 ◽  
Author(s):  
Veronika Beck ◽  
Nikolai Dotzek
Keyword(s):  
Wind Field ◽  
Surface Wind ◽  
Forest Damage ◽  
Wind Fields ◽  
Tree Model ◽  
Translation Speed ◽  
Near Surface ◽  
Additional Information ◽  
Secondary Vortices ◽  
Surface Wind Field

Abstract Tornado intensity is usually inferred from the damage produced. To foster postevent tornado intensity assessments, the authors present a model to reconstruct near-surface wind fields from forest damage patterns. By comparing the structure of observed and simulated damage patterns, essential parameters to describe a tornado near-surface wind field are derived, such as the ratio Gmax between circular and translational velocity, and the deflection angle α between peak wind and pressure gradient. The model consists of a wind field module following the Letzmann analytical tornado model and a tree module based on the mechanistic HWIND tree model to assess tree breakage. Using this method, the velocity components of the near-surface wind field, the track of the tornado center, and the spatial distribution of the Fujita scale along and across the damage path can be assessed. Necessary requirements to apply the model are knowledge of the tornado translation speed (e.g., from radar observations) and a detailed analysis of the forest damage patterns. One of the key findings of this analysis is that the maximum intensity of the tornado is determinable with an uncertainty of only (Gmax + 1) times the variability of the usually well-known tornado translation speed. Further, if Letzmann’s model is applied and the translation speed of the tornado is known, the detailed tree model is unnecessary and could be replaced by an average critical velocity for stem breakage υcrit independent of the tree species. Under this framework, the F3 and F2 ratings of the tornadoes in Milosovice, Czech Republic, on 30 May 2001 and Castellcir, Spain, on 18 October 2006, respectively, could be verified. For the Milosovice event, the uncertainty in peak intensity was only ±6.0 m s−1. Additional information about the structure of the near-surface wind field in the tornado and several secondary vortices was also gained. Further, this model allows for distinguishing downburst damage patterns from those of tornadoes.

scholarly journals Challenges and Opportunities with New Generation Geostationary Meteorological Satellite Datasets for Analyses and Initial Conditions for Forecasting Hurricane Irma (2017) Rapid Intensification Event

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1200
Author(s):  
Russell L. Elsberry ◽  
Joel W. Feldmeier ◽  
Hway-Jen Chen ◽  
Melinda Peng ◽  
Christopher S. Velden ◽  
...  
Keyword(s):  
Wind Field ◽  
Initial Conditions ◽  
Motion Vector ◽  
Surface Wind ◽  
Rapid Intensification ◽  
Constant Intensity ◽  
Near Surface ◽  
Tc Model ◽  
Hurricane Irma ◽  
Better Than

This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most important modifications are a more time-efficient dynamic initialization technique and adding a near-surface wind field adjustment as a low-level constraint on the distribution of deep convection relative to the translating center. With the new technique, the Coupled Ocean/Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model initial wind field at 12.86 km elevation quickly adjusts to the cirrus-level GOES-16 AMVs to better detect the Irma outflow magnitude and areal extent every 15 min, and predicts direct connections to adjacent synoptic circulations much better than a dynamic initialization with only lower-resolution hourly GOES-13 AMVs and also better than a cold-start COAMPS-TC initialization with a bogus vortex. Furthermore, only with the GOES-16 AMVs does the COAMPS-TC model accurately predict the timing of an intermediate 12 h constant-intensity period between two segments of the Irma RI. By comparison, HWRF model study of the Irma case that utilized the same GOES-16 AMV dataset predicted a continuous RI without the intermediate constant-intensity period, and predicted more limited outflow areal extents without strong direct connections with adjacent synoptic circulations.

scholarly journals Analysis of Total Column CO<sub>2</sub> and CH<sub>4</sub> Measurements in Berlin with WRF-GHG

2019 ◽  
Author(s):  
Xinxu Zhao ◽  
Julia Marshall ◽  
Stephan Hachinger ◽  
Christoph Gerbig ◽  
Jia Chen
Keyword(s):  
Urban Areas ◽  
Initial Conditions ◽  
Ghg Emissions ◽  
Concentration Field ◽  
Weather Research And Forecasting ◽  
Background Concentration ◽  
Wind Fields ◽  
Simulated Wind ◽  
Global Population ◽  
Good Agreement

Abstract. Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed for mesoscale atmospheric GHG transport, can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4). In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al., 2015). The measured and simulated wind fields mostly demonstrate good agreement and the simulated XCO2 agrees well with the measurement. In contrast, a bias in the simulated XCH4 of around 2.7 % is found, caused by relatively high initialization values for the background concentration field. We find that an analysis using differential column methodology (DCM) works well for the XCH4 comparison, as corresponding background biases then cancel out. From the tracer analysis, we find that the enhancement of XCH4 is highly dependent on human activities. The XCO2 signal in the vicinity of Berlin is dominated by anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high resolution WRF-GHG model to detect and understand sources of GHG emissions quantitatively in urban areas.

scholarly journals Analysis of total column CO<sub>2</sub> and CH<sub>4</sub> measurements in Berlin with WRF-GHG

2019 ◽  
Vol 19 (17) ◽  
pp. 11279-11302 ◽  
Author(s):  
Xinxu Zhao ◽  
Julia Marshall ◽  
Stephan Hachinger ◽  
Christoph Gerbig ◽  
Matthias Frey ◽  
...  
Keyword(s):  
Urban Areas ◽  
Initial Conditions ◽  
Ghg Emissions ◽  
Weather Research And Forecasting ◽  
Wind Fields ◽  
High Background ◽  
Simulated Wind ◽  
Global Population ◽  
Total Column ◽  
Good Agreement

Abstract. Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed for mesoscale atmospheric GHG transport can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4). In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al., 2015). The measured and simulated wind fields mostly demonstrate good agreement. The simulated XCO2 shows quite similar trends with the measurement but with approximately 1 ppm bias, while a bias in the simulated XCH4 of around 2.7 % is found. The bias could potentially be the result of relatively high background concentrations, the errors at the tropopause height, etc. We find that an analysis using differential column methodology (DCM) works well for the XCH4 comparison, as corresponding background biases are then canceled out. From the tracer analysis, we find that the enhancement of XCH4 is highly dependent on human activities. The XCO2 enhancement in the vicinity of Berlin is dominated by anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high-resolution WRF-GHG model to detect and understand major sources of GHG emissions in urban areas.

scholarly journals Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications

2018 ◽  
Vol 27 (4) ◽  
pp. 257 ◽  
Author(s):  
O. Rios ◽  
W. Jahn ◽  
E. Pastor ◽  
M. M. Valero ◽  
E. Planas
Keyword(s):  
High Resolution ◽  
Surface Wind ◽  
Computation Time ◽  
Data Driven ◽  
Wind Fields ◽  
Near Surface ◽  
Finite Set ◽  
Multiple Resolutions ◽  
Feasible Alternative ◽  
Wildfire Spread

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.

scholarly journals One-Way Coupling of the WRF–QUIC Urban Dispersion Modeling System

2015 ◽  
Vol 54 (10) ◽  
pp. 2119-2139 ◽  
Author(s):  
A. K. Kochanski ◽  
E. R. Pardyjak ◽  
R. Stoll ◽  
A. Gowardhan ◽  
M. J. Brown ◽  
...  
Keyword(s):  
Urban Areas ◽  
Surface Wind ◽  
Wrf Model ◽  
Conclusive Evidence ◽  
Dispersion Modeling ◽  
Industrial Complex ◽  
Urban Dispersion ◽  
Canopy Layer ◽  
Near Surface ◽  
Wind Profiles

AbstractSimulations of local weather and air quality in urban areas must account for processes spanning from meso- to microscales, including turbulence and transport within the urban canopy layer. Here, the authors investigate the performance of the building-resolving Quick Urban Industrial Complex (QUIC) Dispersion Modeling System driven with mean wind profiles from the mesoscale Weather Research and Forecasting (WRF) Model. Dispersion simulations are performed for intensive observation periods 2 and 8 of the Joint Urban 2003 field experiment conducted in Oklahoma City, Oklahoma, using an ensemble of expert-derived wind profiles from observational data as well as profiles derived from WRF runs. The results suggest that WRF can be used successfully as a source of inflow boundary conditions for urban simulations, without the collection and processing of intensive field observations needed to produce expert-derived wind profiles. Detailed statistical analysis of tracer concentration fields suggests that, for the purpose of the urban dispersion, WRF simulations provide wind forcing as good as individual or ensemble expert-derived profiles. Despite problems capturing the strength and the elevation of the Great Plains low-level jet, the WRF-simulated near-surface wind speed and direction were close to observations, thus assuring realistic forcing for urban dispersion estimates. Tests performed with multilayer and bulk urban parameterizations embedded in WRF did not provide any conclusive evidence of the superiority of one scheme over the other, although the dispersion simulations driven by the latter showed slightly better results.

The Effects of Vertical Stratification and Land Use Data on Numerical Simulation of Wind Energy Resources

2014 ◽  
Vol 535 ◽  
pp. 135-140
Author(s):  
Yuan Chang Deng ◽  
Zhen Cao Zou
Keyword(s):  
Numerical Simulation ◽  
Land Use ◽  
Wind Field ◽  
Wind Profile ◽  
Surface Wind ◽  
Wrf Model ◽  
Vertical Stratification ◽  
Near Surface ◽  
Wind Field Simulation ◽  
Surface Wind Field

By adjusting the distribution of vertical layers and increasing its number in WRF model, this paper mainly studies the effects of vertical stratification on the near surface wind field and vertical profile simulation. The test outcomes show that moderately increasing vertical layers can effectively improve the near surface wind field simulation results, while it has little influence on the numeral and changing trend of high vertical wind profile. Considering both accuracy and efficiency, it is recommended to set 10~15 layers below 300m. On the basis of this research, instead of USGS data by using the MODIS_30S data, the data underlying surface land in Shenzhen and HK area are updated. Comparative results between the two schemes, due to the roughness and drag coefficient of difference types of surface are not identical; the surface data has a significant impact on wind field and wind profile simulation. Using the MODIS land use data which is more consistent with the actual situation can improve the accuracy of numerical simulation.

scholarly journals Comparison of regional and global reanalysis near-surface winds with station observations over Germany

2015 ◽  
Vol 12 (1) ◽  
pp. 187-198 ◽  
Author(s):  
A. K. Kaiser-Weiss ◽  
F. Kaspar ◽  
V. Heene ◽  
M. Borsche ◽  
D. G. H. Tan ◽  
...  
Keyword(s):  
Information Source ◽  
Surface Wind ◽  
Wind Fields ◽  
Near Surface ◽  
Wind Speeds ◽  
Energy Applications ◽  
The North ◽  
Independent Observations ◽  
Spatio Temporal ◽  
The North Atlantic Oscillation

Abstract. Reanalysis near-surface wind fields from multiple reanalyses are potentially an important information source for wind energy applications. Inter-comparing reanalyses via employing independent observations can help to guide users to useful spatio-temporal scales. Here we compare the statistical properties of wind speeds observed at 210 traditional meteorological stations over Germany with the reanalyses' near-surface fields, confining the analysis to the recent years (2007 to 2010). In this period, the station time series in Germany can be expected to be mostly homogeneous. We compare with a regional reanalysis (COSMO-REA6) and two global reanalyses, ERA-Interim and ERA-20C. We show that for the majority of the stations, the Weibull parameters of the daily mean wind speed frequency distribution match remarkably well with the ones derived from the reanalysis fields. High correlations (larger than 0.9) can be found between stations and reanalysis monthly mean wind speeds all over Germany. Generally, the correlation between the higher resolved COSMO-REA6 wind fields and station observations is highest, for both assimilated and non-assimilated (i.e., independent) observations. As expected from the lower spatial resolution and reduced amount of data assimilated into ERA-20C, the correlation of monthly means decreases somewhat relative to the other reanalyses (in our investigated period of 2007 to 2010). Still, the inter-annual variability connected to the North Atlantic Oscillation (NAO) found in the reanalysis surface wind anomalies is in accordance with the anomalies recorded by the stations. We discuss some typical examples where differences are found, e.g., where the mean wind distributions differ (probably related to either height or model topography differences) and where the correlations break down (because of unresolved local topography) which applies to a minority of stations. We also identified stations with homogeneity problems in the reported station values, demonstrating how reanalyses can be applied to support quality control for the observed station data. Finally, as a demonstration of concept, we discuss how comparing feedback files of the different reanalyses can guide users to useful scales of variability.

scholarly journals Features of Urban Heat Island in Mountainous Chongqing from a Dense Surface Monitoring Network

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Ping Jiang ◽  
Xiaoran Liu ◽  
Haonan Zhu ◽  
Yonghua Li
Keyword(s):  
Urban Heat Island ◽  
Urban Areas ◽  
Surface Wind ◽  
Warm Season ◽  
Monitoring Network ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Near Surface ◽  
Urban Heat ◽  
Surface Monitoring

The spatial and temporal features of urban heat island (UHI) intensity in complex urban terrain are barely investigated. This study examines the UHI intensity variations in mountainous Chongqing using a dense surface monitoring network. The results show that the UHI intensity is closely related to underlying surfaces, and the strongest UHI intensity is confined around the central urban areas. The UHI intensity is most prominent at night and in warm season, and the magnitude could reach ~4.5 °C on summer night. Our quantitative analysis shows a profound contribution of urbanization level to UHI intensity both at night and in summer, with regression coefficient b = 4.31 and 6.65, respectively. At night, the urban extra heat such as reflections of longwave radiation by buildings and release of daytime-stored heat from artificial materials, is added into the boundary layer, which compensates part of urban heat loss and thus leads to stronger UHI intensity. In summer, the urban areas are frequently controlled by oppressively hot weather. Due to increased usage of air conditioning, more anthropogenic heat is released. As a result, the urban temperatures are higher at night. The near-surface wind speed can serve as an indicator predicting UHI intensity variations only in the diurnal cycle. The rural cooling rate during early evening transition, however, is an appropriate factor to estimate the magnitude of UHI intensity both at night and in summer.

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