scholarly journals Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

2020 ◽  
Vol 12 (18) ◽  
pp. 3060
Author(s):  
Kao-Shen Chung ◽  
Hsien-Jung Chiu ◽  
Chian-Yi Liu ◽  
Meng-Yue Lin
Keyword(s):  
Radiative Transfer ◽  
Wrf Model ◽  
Skill Score ◽  
Radiative Transfer Model ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Transfer Model ◽  
High Cloud ◽  
Weather Research

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend.

scholarly journals Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

2018 ◽  
Vol 57 (3) ◽  
pp. 493-515 ◽  
Author(s):  
S. K. Mukkavilli ◽  
A. A. Prasad ◽  
R. A. Taylor ◽  
A. Troccoli ◽  
M. J. Kay
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Global Climate ◽  
Weather Prediction ◽  
Wrf Model ◽  
Global Climate Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Dust Event ◽  
Direct Normal Irradiance

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.

scholarly journals Orography-Induced Gravity Wave Drag Parameterization in the Global WRF: Implementation and Sensitivity to Shortwave Radiation Schemes

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Hyeyum Hailey Shin ◽  
Song-You Hong ◽  
Jimy Dudhia ◽  
Young-Joon Kim
Keyword(s):  
Gravity Wave ◽  
Wrf Model ◽  
Sensitivity Study ◽  
Wave Drag ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Weather Research And Forecasting ◽  
Cold Bias ◽  
Transfer Model ◽  
The Impact

This paper describes the implementation of the orographic gravity wave drag (GWDO) processes induced by subgrid-scale orography in the global version of the Weather Research and Forecasting (WRF) model. The sensitivity of the model simulated climatology to the representation of shortwave radiation and the addition of the GWDO processes is investigated using the Kim-Arakawa GWDO parameterization and the Goddard, RRTMG (Rapid Radiative Transfer Model for GCMs), and Dudhia shortwave radiation schemes. This sensitivity study is a part of efforts of selecting the physics package that can be useful in applying the WRF model to global and seasonal configuration. The climatology is relatively well simulated by the global WRF; the zonal mean zonal wind and temperature structures are reasonably represented with the Kim-Arakawa GWDO scheme using the Goddard and RRTMG shortwave schemes. It is found that the impact of the shortwave radiation scheme on the modeled atmosphere is pronounced in the upper atmospheric circulations above the tropopause mainly due to the ozone heating. The scheme that excludes the ozone process suffers from a distinct cold bias in the stratosphere. Moreover, given the improper thermodynamic environment conditions by the shortwave scheme, the role of the GWDO process is found to be limited.

scholarly journals The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator

2021 ◽  
Author(s):  
Cheng-Hsuan Lu ◽  
Quanhua Liu ◽  
Shih-Wei Wei ◽  
Benjamin T. Johnson ◽  
Cheng Dang ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Lower Atmosphere ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Thermal Window ◽  
Aerosol Absorption ◽  
Tropical Troposphere ◽  
Aerosol Module ◽  
Aerosol Radiative Effects

Abstract. The Community Radiative Transfer Model (CRTM), a sensor-based radiative transfer model, has been used within the Gridpoint Statistical Interpolation (GSI) system for directly assimilating radiances from infrared and microwave sensors. We conducted numerical experiments to illustrate how including aerosol radiative effects in CRTM calculations changes the GSI analysis. Compared to the default aerosol-blind calculations, the aerosol influences reduced simulated brightness temperature (BT) in thermal window channels, particularly over dust-dominant regions. A case study is presented, which illustrates how failing to correct for aerosol transmittance effects leads to errors in meteorological analyses that assimilate radiances from satellite IR sensors. In particular, the case study shows that assimilating aerosol-affected BTs affects analyzed temperatures in the lower atmosphere significantly in several different regions of the globe. Consequently, a fully-cycled aerosol-aware experiment improves 1–5 day forecasts of wind, temperature, and geopotential height in the tropical troposphere and Northern Hemisphere stratosphere. Whilst both GSI and CRTM are well documented with online user guides, tutorials and code repositories, this article is intended to provide a joined-up documentation for aerosol absorption and scattering calculations in the CRTM and GSI. It also provides guidance for prospective users of the CRTM aerosol option and GSI aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are briefly discussed.

Application of the Weather Research and Forecasting Model for Air Quality Modeling in the San Francisco Bay Area

2013 ◽  
Vol 52 (9) ◽  
pp. 1953-1973 ◽  
Author(s):  
Raphael E. Rogers ◽  
Aijun Deng ◽  
David R. Stauffer ◽  
Brian J. Gaudet ◽  
Yiqin Jia ◽  
...  
Keyword(s):  
Air Quality ◽  
San Francisco ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Transfer Model ◽  
Bay Area ◽  
Model Configuration ◽  
Weather Research

AbstractThe Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim–Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.

Optimizing Weather Model Radiative Transfer Physics for Intel’s Many Integrated Core (MIC) Architecture

2016 ◽  
Vol 26 (04) ◽  
pp. 1650019 ◽  
Author(s):  
John Michalakes ◽  
Michael J. Iacono ◽  
Elizabeth R. Jessup
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Wrf Model ◽  
Multiscale Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Weather Model ◽  
Hardware Performance Counters ◽  
S Peak ◽  
Many Integrated Core

Large numerical weather prediction (NWP) codes such as the Weather Research and Forecast (WRF) model and the NOAA Nonhydrostatic Multiscale Model (NMM-B) port easily to Intel's Many Integrated Core (MIC) architecture. But for NWP to significantly realize MIC’s one- to two-TFLOP/s peak computational power, we must expose and exploit thread and fine-grained (vector) parallelism while overcoming memory system bottlenecks that starve oating-point performance. We report on our work to improve the Rapid Radiative Transfer Model (RRTMG), responsible for 10-20 percent of total NMM-B run time. We isolated a standalone RRTMG benchmark code and workload from NMM-B and then analyzed performance using hardware performance counters and scaling studies. We restructured the code to improve vectorization, thread parallelism, locality, and thread contention. The restructured code ran three times faster than the original on MIC and, also importantly, 1.3x faster than the original on the host Xeon Sandybridge.

Sign in / Sign up

Export Citation Format

Share Document