Weather Research and Forecasting model simulations of a rare springtime bow echo near the Great Salt Lake, USA

2014 ◽  
Vol 22 (3) ◽  
pp. 301-313 ◽  
Author(s):  
L. Zhao ◽  
S.-Y. Wang ◽  
J. Jin ◽  
A. J. Clark
Keyword(s):  
Salt Lake ◽  
Great Salt Lake ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Model Simulations ◽  
Bow Echo ◽  
Weather Research

scholarly journals The OWLeS IOP2b Lake-Effect Snowstorm: Mechanisms Contributing to the Tug Hill Precipitation Maximum

2017 ◽  
Vol 145 (7) ◽  
pp. 2461-2478 ◽  
Author(s):  
Leah S. Campbell ◽  
W. James Steenburgh
Keyword(s):  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Land Breeze ◽  
Eastern Shore ◽  
Model Simulations ◽  
Lake Effect ◽  
Precipitation Enhancement ◽  
Weather Research

Lake-effect storms frequently produce a pronounced precipitation maximum over the Tug Hill Plateau (hereafter Tug Hill), which rises 500 m above Lake Ontario’s eastern shore. Here Weather Research and Forecasting Model simulations are used to examine the mechanisms responsible for the Tug Hill precipitation maximum observed during IOP2b of the Ontario Winter Lake-effect Systems (OWLeS) field program. A key contributor was a land-breeze front that formed along Lake Ontario’s southeastern shoreline and extended inland and northeastward across Tug Hill, cutting obliquely across the lake-effect system. Localized ascent along this boundary contributed to an inland precipitation maximum even in simulations in which Tug Hill was removed. The presence of Tug Hill intensified and broadened the ascent region, increasing parameterized depositional and accretional hydrometeor growth, and reducing sublimational losses. The inland extension of the land-breeze front and its contribution to precipitation enhancement appear to be unidentified previously and may be important in other lake-effect storms over Tug Hill.

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.

Simulations of Coastal Fog in the Canadian Atlantic with the Weather Research and Forecasting Model

2021 ◽  
Author(s):  
Reneta Dimitrova ◽  
Ashish Sharma ◽  
Harindra J. S. Fernando ◽  
Ismail Gultepe ◽  
Ventsislav Danchovski ◽  
...  
Keyword(s):  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Coastal Fog ◽  
Weather Research

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).

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