Chemical Transport Model Data Sample Clauses

Chemical Transport Model Data. SENAMHI produces WRF-CHEM (Weather Research and Forecast with Chemistry) simulations for air quality forecasts in Lima at 5 km spatial resolution. WRF- CHEM is a next generation CTM (atmospheric chemical transport model) developed by NOAA (National Oceanic and Atmospheric Administration) and NCAR (National Center for Atmospheric Research) [24]. CTMs simultaneously simulates the emission, turbulent mixing, transport, transformation, and fate of trace gasses and aerosols using a combination of meteorological fields, topography data and emission modules based on measurements of emission factors and ambient concentrations [24]. WRF-CHEM data outputs were packaged in monthly files with 26 vertical levels in the atmosphere every 6 hours (00:00, 06:00, 12:00 and 18:00 UTC). Parameters including in each WRF-CHEM files include: surface pressure, temperature, u- and v- wind components, simulated PM2.5, and planetary boundary layer height (PBL, a measure of earth’s lower atmosphere where surface radiative forces causes turbulent mixing of chemicals) in HDF format. WRF- CHEM HDF files were processed through Interactive Data Language (IDL) to extract daily averaged measurements of parameters of interests at layer 0 (the lowest layer in the 26 vertical levels of the atmosphere). Parameters of interest including pressure, PBL, PM2.5 aerosol dry mass, precipitation, temperature, and both wind components (u and v) were each extracted and analyzed for monthly, seasonal and yearly trends from 2010 to 2016 and for vertical xxxxx 0, 0, 0, 00, 00 and 24. For the purpose of modeling PM2.5, only parameters in vertical level 0 were used as it was the level closest to earth’s surface. Furthermore, due to the coarseness of the data at 5km spatial resolution, interpolation to 1 km spatial resolution using an inverse distance weighting method in statistical software R was used to create a smoother surface of the WRF-CHEM parameters. Figure 6 shows the contrast of temperature before and after interpolation on April 1 2015. Table 4 shows the correlations between ground measurements of PM2.5 and WRF-CHEM simulated PM2.5 for all SENAMHI stations, with N representing the number of days with available data between 2014 and 2016. Figure 7 shows the yearly average simulated PM2.5 in µg/m3 from the WRF-CHEM output. Figure 8 shows a comparison between daily, monthly mean, and yearly mean PBL height in meters from WRF-CHEM in vertical layer 0. a sample of the monthly and yearly average of the pa...
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