Three-dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting-Chemistry model and Grid-point Statistical Interpolation

2010 ◽  
Vol 136 (653) ◽  
pp. 2013-2024 ◽  
Author(s):  
M. Pagowski ◽  
G. A. Grell ◽  
S. A. McKeen ◽  
S. E. Peckham ◽  
D. Devenyi
Keyword(s):  
Particulate Matter ◽  
Data Assimilation ◽  
Grid Point ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Fine Particulate ◽  
Statistical Interpolation ◽  
Weather Research

scholarly journals Optimization of the WRFV3.7 adjoint model

2018 ◽  
Author(s):  
Qiang Cheng ◽  
Juanjuan Liu ◽  
Bin Wang
Keyword(s):  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Time Cost ◽  
Input Output ◽  
Adjoint Model ◽  
New Strategy ◽  
Weather Research

Abstract. This work focused on a new strategy for productively improving the performance of adjoint models. By using several techniques including the push/pop-free method, careful Input/Output (IO) analysis and the use of the conception of adjoint locality, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUS) by almost half on different numbers of processors especially with a slight decrease in total memory. Several experiments are conducted using the four-dimensional variational data assimilation (4DVar) method. The results show that the total time cost of running a 4DVar application is decreased by approximately 1/3.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

scholarly journals Possible warming effect of fine particulate matter in the atmosphere

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Shau-Liang Chen ◽  
Sih-Wei Chang ◽  
Yen-Jen Chen ◽  
Hsuen-Li Chen
Keyword(s):  
Particulate Matter ◽  
Fine Particles ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Mie Scattering ◽  
Dust Particles ◽  
Long Wavelength ◽  
Inorganic Aerosols ◽  
Fine Particulate ◽  
Difference Time

AbstractParticulate matter emitted through human activities not only pollutes the air, but also cools the Earth by scattering shortwave solar radiation. However, coarser dust particles have been found to exert a warming effect that could, to some extent compensate for the cooling effect of fine dust. Here we investigate the radiative effects of sulfate containing aerosols of various sizes and core/shell structures using Mie scattering and three-dimensional finite difference time domain simulations of the electromagnetic fields inside and around particulate matter particles. We find that not only coarse dust, but also fine non-light-absorbing inorganic aerosols such as sulfate can have a warming effect. Specifically, although the opacity of fine particles decreases at longer wavelengths, they can strongly absorb and re-emit thermal radiation under resonance conditions at long wavelength. We suggest that these effects need to be taken into account when assessing the contribution of aerosols to climate change.

scholarly journals Improving Incremental Balance in the GSI 3DVAR Analysis System

2009 ◽  
Vol 137 (3) ◽  
pp. 1046-1060 ◽  
Author(s):  
Daryl T. Kleist ◽  
David F. Parrish ◽  
John C. Derber ◽  
Russ Treadon ◽  
Ronald M. Errico ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Normal Mode ◽  
Three Dimensional ◽  
Substantial Improvement ◽  
Environmental Modeling ◽  
Variational Data Assimilation ◽  
North American Data ◽  
Analysis System ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract The gridpoint statistical interpolation (GSI) analysis system is a unified global/regional three-dimensional variational data assimilation (3DVAR) analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS, respectively). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a tangent-linear normal-mode constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation at NCEP.

scholarly journals Pollutant emission reductions deliver decreased PM<sub>2.5</sub>-caused mortality across China during 2015–2017

2020 ◽  
Author(s):  
Ben Silver ◽  
Luke Conibear ◽  
Carly L. Reddington ◽  
Christoph Knote ◽  
Steve R. Arnold ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Pollutant Emission ◽  
Weather Research And Forecasting ◽  
Minor Role ◽  
Ozone Concentrations ◽  
Exposure Response ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
A Minor

Abstract. Air pollution is a serious environmental issue and leading contributor to the disease burden in China. Rapid reductions in fine particulate matter (PM2.5) concentrations and increased ozone concentrations have occurred across China, during 2015 to 2017. We used measurements of particulate matter with a diameter  1000 stations across China along with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. The measured nationwide median PM2.5 trend of −3.4 µg m−3 year−1, was well simulated by the model (−3.5 µg m−3 year−1). With anthropogenic emissions fixed at 2015-levels, the simulated trend was much weaker (−0.6 µg m−3 year−1), demonstrating interannual variability in meteorology played a minor role in the observed PM2.5 trend. The model simulated increased ozone concentrations in line with the measurements, but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM2.5 concentrations with an exposure-response function to estimate that reductions in PM2.5 concentrations over this period have reduced PM2.5-attribrutable premature morality across China by 150 000  deaths year−1.

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