scholarly journals Implementation of a roughness sublayer parameterization in the Weather Research and Forecasting model (WRF version 3.7.1) and its evaluation for regional climate simulations

2020 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
Junhong Lee ◽  
Jinkyu Hong ◽  
Yign Noh ◽  
Pedro A. Jiménez
Keyword(s):  
Air Temperature ◽  
Surface Wind ◽  
Winter Season ◽  
Atmospheric Modeling ◽  
Roughness Sublayer ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Climate Simulations ◽  
Weather Research

Abstract. The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s−1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion.

scholarly journals Implementation of a roughness sublayer parameterization in the Weather Research and Forecasting model (WRF version 3.7.1) and its validation for regional climate simulations

2019 ◽  
Author(s):  
Junhong Lee ◽  
Jinkyu Hong ◽  
Yign Noh ◽  
Pedro Jiménez
Keyword(s):  
Air Temperature ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Winter Season ◽  
Roughness Sublayer ◽  
Rainfall Simulation ◽  
Climate Simulation ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Weather Research

Abstract. The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach to the gray zone of turbulence in the energy containing range and the lowest model layer is located within the RLS. In this perspective, the RSL has an important implication in atmospheric modelling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s−1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm month−1 of RMSE), although its impact is not as substantial as that to wind speed. Our findings suggest that the RSL must be properly considered both for better weather and climate simulation and for the application of wind energy and atmospheric dispersion.

scholarly journals Why do models perform differently on particulate matter over East Asia? A multi-model intercomparison study for MICS-Asia III

2020 ◽  
Vol 20 (12) ◽  
pp. 7393-7410 ◽  
Author(s):  
Jiani Tan ◽  
Joshua S. Fu ◽  
Gregory R. Carmichael ◽  
Syuichi Itahashi ◽  
Zhining Tao ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
East Asia ◽  
Dust Emission ◽  
Sulfur Oxidation ◽  
Atmospheric Modeling ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Air Quality Prediction ◽  
Weather Research

Abstract. This study compares the performance of 12 regional chemical transport models (CTMs) from the third phase of the Model Inter-Comparison Study for Asia (MICS-Asia III) on simulating the particulate matter (PM) over East Asia (EA) in 2010. The participating models include the Weather Research and Forecasting model coupled with Community Multiscale Air Quality (WRF-CMAQ; v4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ; v4.7.1 and v5.0.2), the Weather Research and Forecasting model coupled with chemistry (WRF-Chem; v3.6.1 and v3.7.1), Goddard Earth Observing System coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS) and the NASA-Unified WRF (NU-WRF). This study investigates three model processes as the possible reasons for different model performances on PM. (1) Models perform very differently in the gas–particle conversion of sulfur (S) and oxidized nitrogen (N). The model differences in sulfur oxidation ratio (50 %) are of the same magnitude as that in SO42- concentrations. The gas–particle conversion is one of the main reasons for different model performances on fine mode PM. (2) Models without dust emission modules can perform well on PM10 at non-dust-affected sites but largely underestimate (up to 50 %) the PM10 concentrations at dust sites. The implementation of dust emission modules in the models has largely improved the model accuracies at dust sites (reduce model bias to −20 %). However, both the magnitude and distribution of dust pollution are not fully captured. (3) The amounts of modeled depositions vary among models by 75 %, 39 %, 21 % and 38 % for S wet, S dry, N wet and N dry depositions, respectively. Large inter-model differences are found in the washout ratios of wet deposition (at most 170 % in India) and dry deposition velocities (generally 0.3–2 cm s−1 differences over inland regions).

scholarly journals Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3670
Author(s):  
Denis E.K. Dzebre ◽  
Muyiwa S. Adaramola
Keyword(s):  
Surface Layer ◽  
Surface Wind ◽  
Resource Assessment ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Wind Speeds ◽  
Parameterization Schemes ◽  
Pbl Scheme ◽  
Level 3 ◽  
Weather Research

This paper examines the impacts of five planetary boundary layer (PBL) parameterization schemes paired with several compatible surface layer (SL) parameterization schemes in the Weather Research and Forecasting Model on wind hindcasts for resource assessment purposes in a part of Coastal Ghana. Model predictions of hourly wind speeds at 3 × 3 km2 and 9 × 9 km2 grid boxes were compared with measurements at 40 m, 50 m, and 60 m. It was found that the Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3) PBL scheme generally predicted winds with a relatively better combination of error metrics, irrespective of the SL scheme it was paired with. When paired with the Eta surface layer scheme, it often produced some of the relatively fewest errors in estimated mean wind power density (WPD) and Weibull cumulative density. A change in the simulation grid size did not have a significant impact on the conclusions of the relative performance of the PBL-SL pairs that were tested. The results indicate that the MYNN3 PBL and Eta SL pair is probably best for wind speed and energy assessments for this part of coastal Ghana.

scholarly journals Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

2021 ◽  
Author(s):  
Alessio Golzio ◽  
Silvia Ferrarese ◽  
Claudio Cassardo ◽  
Gugliemina Adele Diolaiuti ◽  
Manuela Pelfini
Keyword(s):  
Boundary Layer ◽  
Land Use ◽  
Land Cover ◽  
Mixing Layer ◽  
Local Area ◽  
Heat Fluxes ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Mountainous Terrain ◽  
Weather Research

AbstractWeather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

Parallelization Strategies for the GPS Radio Occultation Data Assimilation with a Nonlocal Operator in the Weather Research and Forecasting Model

2014 ◽  
Vol 31 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
Xin Zhang ◽  
Ying-Hwa Kuo ◽  
Shu-Ya Chen ◽  
Xiang-Yu Huang ◽  
Ling-Feng Hsiao
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Observation Operator ◽  
Gps Radio Occultation ◽  
Phase Observation ◽  
Excess Phase ◽  
Weather Research

Abstract The nonlocal excess phase observation operator for assimilating the global positioning system (GPS) radio occultation (RO) sounding data has been proven by some research papers to produce significantly better analyses for numerical weather prediction (NWP) compared to the local refractivity observation operator. However, the high computational cost and the difficulties in parallelization associated with the nonlocal GPS RO operator deter its application in research and operational NWP practices. In this article, two strategies are designed and implemented in the data assimilation system for the Weather Research and Forecasting Model to demonstrate the capability of parallel assimilation of GPS RO profiles with the nonlocal excess phase observation operator. In particular, to solve the parallel load imbalance problem due to the uneven geographic distribution of the GPS RO observations, round-robin scheduling is adopted to distribute GPS RO observations among the processing cores to balance the workload. The wall clock time required to complete a five-iteration minimization on a demonstration Antarctic case with 106 GPS RO observations is reduced from more than 3.5 h with a single processing core to 2.5 min with 106 processing cores. These strategies present the possibility of application of the nonlocal GPS RO excess phase observation operator in operational data assimilation systems with a cutoff time limit.

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