Special Issue "WRF Simulations at the Mesoscale: From the Microscale to Macroscale"

A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: closed (30 November 2017)

Special Issue Editor

Guest Editor
Dr. Jordi Mazon

Department of Physics, Technical University of Catalonia – Barcelona Tech, EETAC, Avda. del Canal Olímpic, Building C3, office 105, 08860 Castelldefels – Barcelona, Catalonia, Spain
E-Mail
Interests: WRF simulation; coastal fronts; precipitation; wind energy; climate change

Special Issue Information

Dear Colleagues,

Weather Research and Forecasting (WRF) is probably the most common numerical model used by atmospheric researchers. This model is applied for studying a wide range of atmospheric topics: precipitation, heat and cold events, pollution, renewable energy, wind cycles, severe storms, etc. These topics range from the macroscale (more than 2000 km and lasting more than several weeks), to the microscale (few hundreds of meters and lasting less than a few hours), and to the mesoscale (from a few to hundreds of kilometres, and from 2–4 hours up to a few days).

This Special Issue calls for contributions showing significant episodes, events, and phenomena simulated through the use of the WRF model on the whole meteorological scale mentioned above, particularly simulations of events at the microscale such as tornadoes, down bursts, turbulence, flash floods, flash heat, and wind gusts, as well as phenomena at the macroscale such as hurricanes, storms, and heat and cold waves. Simulations that study significant and original phenomena and events at the mesoscale are also welcomed.

One of the most significant issues in WRF simulation is the parameterization used as physics options; microphysics, planetary boundary layer (PBL), cumulus, radiation, land surface, shallow convection, urban physics, etc. This Special Issue also encourages researchers to show how different parameterizations that are used during WRF simulation fit to real events (sensitivity analysis). In addition, WRF simulations including the chemical and fire forest modules are also welcomed.

Finally, the WRF model allows us to perform numerical experiments for a better understanding of the atmospheric dynamics behind some episodes or phenomena: By removing or modifying the topography, modifying the sea surface temperature, changing variables (e.g., land use), etc. New findings based on the analysis and interpretation of atmospheric dynamics by applying these types of numerical experiments are also welcomed.

Dr. Jordi Mazon
Guest Editor

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Keywords

  • WRF model

  • Meteorological scales

  • Numerical experiments

  • Sensitivity analysis

Published Papers (8 papers)

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Research

Open AccessArticle
Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System
Atmosphere 2018, 9(3), 106; https://doi.org/10.3390/atmos9030106
Received: 26 January 2018 / Revised: 9 March 2018 / Accepted: 9 March 2018 / Published: 13 March 2018
Cited by 1 | PDF Full-text (4162 KB) | HTML Full-text | XML Full-text
Abstract
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). [...] Read more.
This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Numerical Simulation of Heavy Rainfall in August 2014 over Japan and Analysis of Its Sensitivity to Sea Surface Temperature
Atmosphere 2018, 9(3), 84; https://doi.org/10.3390/atmos9030084
Received: 7 November 2017 / Revised: 22 February 2018 / Accepted: 23 February 2018 / Published: 26 February 2018
Cited by 2 | PDF Full-text (9299 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluated the performance of the Weather Research and Forecasting (WRF) model version 3.7 for simulating a series of rainfall events in August 2014 over Japan and investigated the impact of uncertainty in sea surface temperature (SST) on simulated rainfall in the [...] Read more.
This study evaluated the performance of the Weather Research and Forecasting (WRF) model version 3.7 for simulating a series of rainfall events in August 2014 over Japan and investigated the impact of uncertainty in sea surface temperature (SST) on simulated rainfall in the record-high precipitation period. WRF simulations for the heavy rainfall were conducted for six different cases. The heavy rainfall events caused by typhoons and rain fronts were similarly accurately reproduced by three cases: the TQW_5km case with grid nudging for air temperature, humidity, and wind and with a horizontal resolution of 5 km; W_5km with wind nudging and 5-km resolution; and W_2.5km with wind nudging and 2.5-km resolution. Because the nudging for air temperature and humidity in TQW_5km suppresses the influence of SST change, and because W_2.5km requires larger computational load, W_5km was selected as the baseline case for a sensitivity analysis of SST. In the sensitivity analysis, SST around Japan was homogeneously changed by 1 K from the original SST data. The analysis showed that the SST increase led to a larger amount of precipitation in the study period in Japan, with the mean increase rate of precipitation being 13 ± 8% K−1. In addition, 99 percentile precipitation (100 mm d−1 in the baseline case) increased by 13% K−1 of SST warming. These results also indicate that an uncertainty of approximately 13% in the simulated heavy rainfall corresponds to an uncertainty of 1 K in SST data around Japan in the study period. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Improvement in the Modeled Representation of North American Monsoon Precipitation Using a Modified Kain–Fritsch Convective Parameterization Scheme
Atmosphere 2018, 9(1), 31; https://doi.org/10.3390/atmos9010031
Received: 26 November 2017 / Revised: 8 January 2018 / Accepted: 10 January 2018 / Published: 19 January 2018
Cited by 1 | PDF Full-text (9461 KB) | HTML Full-text | XML Full-text
Abstract
A commonly noted problem in the simulation of warm season convection in the North American monsoon region has been the inability of atmospheric models at the meso-β scales (10 s to 100 s of kilometers) to simulate organized convection, principally mesoscale convective systems. [...] Read more.
A commonly noted problem in the simulation of warm season convection in the North American monsoon region has been the inability of atmospheric models at the meso-β scales (10 s to 100 s of kilometers) to simulate organized convection, principally mesoscale convective systems. With the use of convective parameterization, high precipitation biases in model simulations are typically observed over the peaks of mountain ranges. To address this issue, the Kain–Fritsch (KF) cumulus parameterization scheme has been modified with new diagnostic equations to compute the updraft velocity, the convective available potential energy closure assumption, and the convective trigger function. The scheme has been adapted for use in the Weather Research and Forecasting (WRF). A numerical weather prediction-type simulation is conducted for the North American Monsoon Experiment Intensive Observing Period 2 and a regional climate simulation is performed, by dynamically downscaling. In both of these applications, there are notable improvements in the WRF model-simulated precipitation due to the better representation of organized, propagating convection. The use of the modified KF scheme for atmospheric model simulations may provide a more computationally economical alternative to improve the representation of organized convection, as compared to convective-permitting simulations at the kilometer scale or a super-parameterization approach. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Impact of Grid Nudging Parameters on Dynamical Downscaling during Summer over Mainland China
Atmosphere 2017, 8(10), 184; https://doi.org/10.3390/atmos8100184
Received: 18 July 2017 / Revised: 15 September 2017 / Accepted: 19 September 2017 / Published: 25 September 2017
Cited by 2 | PDF Full-text (3978 KB) | HTML Full-text | XML Full-text
Abstract
The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable has a larger impact [...] Read more.
The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable has a larger impact on the downscaling results; and (2) what is the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the mainland China. To solve these queries, 41 three-month-long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements, while nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable. Despite these findings, it should be noted that the optimum nudging scheme varied with simulated regions and layers, and dedicated research for different regions, seasons, and model configuration is advisable. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Sensitivity Study on High-Resolution WRF Precipitation Forecast for a Heavy Rainfall Event
Atmosphere 2017, 8(6), 96; https://doi.org/10.3390/atmos8060096
Received: 19 April 2017 / Revised: 15 May 2017 / Accepted: 23 May 2017 / Published: 24 May 2017
Cited by 6 | PDF Full-text (11963 KB) | HTML Full-text | XML Full-text
Abstract
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as domain size, sea surface temperature (SST) data, [...] Read more.
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as domain size, sea surface temperature (SST) data, initial conditions, and lead time. The numerical model employs one-way nesting with horizontal resolutions of 5 km and 1 km for the outer and inner domains, respectively. The model domain includes the capital city of Seoul and its suburban megacities in South Korea. The model performance is evaluated via statistical analysis using the correlation coefficient, deviation, and root mean squared error by comparing with observational data including, but not limited to, those from ground-based instruments. The sensitivity analysis conducted here suggests that SST data show negligible influence for a short range forecasting model, the data assimilated initial conditions show the more effective results rather than the non-assimilated high resolution initial conditions, and for a given domain size of the forecasting model, an appropriate outer domain size and lead time of <6 h for a 1-km high-resolution domain should be taken into consideration when optimizing the WRF configuration for regional torrential rainfall events around Seoul and its suburban area, Korea. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Sensitivity of a Mediterranean Tropical-Like Cyclone to Different Model Configurations and Coupling Strategies
Atmosphere 2017, 8(5), 92; https://doi.org/10.3390/atmos8050092
Received: 11 December 2016 / Revised: 15 April 2017 / Accepted: 8 May 2017 / Published: 20 May 2017
Cited by 16 | PDF Full-text (6344 KB) | HTML Full-text | XML Full-text
Abstract
In November 2011, an Atlantic depression affected the Mediterranean basin, eventually evolving into a Tropical-Like Cyclone (TLC or Mediterranean Hurricane, usually designated as Medicane). In the region affected by the Medicane, mean sea level pressures down to 990 hPa, wind speeds of hurricane [...] Read more.
In November 2011, an Atlantic depression affected the Mediterranean basin, eventually evolving into a Tropical-Like Cyclone (TLC or Mediterranean Hurricane, usually designated as Medicane). In the region affected by the Medicane, mean sea level pressures down to 990 hPa, wind speeds of hurricane intensity close to the eye (around 115 km/h) and intense rainfall in the prefrontal zone were reported. The intensity of this event, together with its long permanence over the sea, suggested its suitability as a paradigmatic case for investigating the sensitivity of a numerical modeling system to different configurations, air-sea interface parameterizations and coupling approaches. Toward this aim, a set of numerical experiments with different parameterization schemes and levels of coupling complexity was carried out within the Coupled Ocean Atmosphere Wave Sediment Transport System (COAWST), which allows the description of air-sea dynamics by coupling an atmospheric model (WRF), an ocean circulation model (ROMS), and a wave model (SWAN). The sensitivity to different initialization times and Planetary Boundary Layer (PBL) parameterizations was firstly investigated by running a set of WRF standalone (atmospheric-only) simulations. In order to better understand the effect of coupling on the TLC formation, intensification and trajectory, different configurations of atmosphere-ocean coupling were subsequently tested, eventually including the full coupling among atmosphere, ocean and waves, also changing the PBL parameterization and the formulation of the surface roughness. Results show a strong sensitivity of both the trajectory and the intensity of this TLC to the initial conditions, while the tracks and intensities provided by the coupled modeling approaches explored in this study do not introduce drastic modifications with respect to those resulting from a fine-tuned standalone atmospheric run, though they provide by definition a better physical and energetic consistency. Nevertheless; the use of different schemes for the calculation of the surface roughness from wave motion, which reflects the description of air-sea interface processes, can significantly affect the results in the fully coupled runs. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessFeature PaperArticle
The Influence of an Increase of the Mediterranean Sea Surface Temperature on Two Nocturnal Offshore Rainbands: A Numerical Experiment
Atmosphere 2017, 8(3), 58; https://doi.org/10.3390/atmos8030058
Received: 15 January 2017 / Revised: 10 March 2017 / Accepted: 16 March 2017 / Published: 18 March 2017
Cited by 3 | PDF Full-text (2475 KB) | HTML Full-text | XML Full-text
Abstract
Using the Weather Research and Forecasting (WRF) – Advanced Research WRF (ARW) mesoscale model (WRF–ARW), we investigate how two nocturnal offshore rainbands occurring in the Mediterranean basin are modified in a warmer sea surface temperature (SST). After sunset, the thermal difference between land [...] Read more.
Using the Weather Research and Forecasting (WRF) – Advanced Research WRF (ARW) mesoscale model (WRF–ARW), we investigate how two nocturnal offshore rainbands occurring in the Mediterranean basin are modified in a warmer sea surface temperature (SST). After sunset, the thermal difference between land and sea air increases. Driven by drainage winds or land breeze, the inland cold air interacts with the relatively warmer and moister air over the sea. Vertical movement of sea air over the boundary between the two air masses may induce cloud and rain bands offshore. When an increase of SST is prescribed in the WRF simulations, a change in the precipitation pattern is simulated. The numerical experiments show an increase both in the extension and location of the rainbands and in the precipitation rate. These changes, induced by the modified SST, are analyzed by estimating and comparing several parameters such as the location of level of free convection (LFC), Convective Available Potential Energy (CAPE), or the triggering, deceleration and blockage terms of simplified conceptual models. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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Open AccessArticle
Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season
Atmosphere 2016, 7(11), 145; https://doi.org/10.3390/atmos7110145
Received: 8 October 2016 / Revised: 8 November 2016 / Accepted: 9 November 2016 / Published: 17 November 2016
Cited by 2 | PDF Full-text (34485 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic [...] Read more.
In recent years, the Weather Research and Forecast (WRF) model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI), but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS) over this region. Full article
(This article belongs to the Special Issue WRF Simulations at the Mesoscale: From the Microscale to Macroscale)
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