Topography in WPS

Two weeks ago, I tried to run CMAQ. However, it was really beyond my expectation that the pollution emission source data were so difficult to deal with, and finally we had to give up.

SMOKE is a set of programs responsible for the emission data. It takes in pollution inventory and turns it into CMAQ permitted format. In addition, it also divides the inventory spatially into grids and temporally into hours and days as most inventory data we get are annual. The problem is, SMOKE is developed by Americans and for USA use. The standards and administrative divisions are totally different. To take advantage of SMOKE, a specific methodology to adapt to the local is needed. Spanish scholars (R. Borge, et al)  have done that. In China, Prof. Zheng Junyu from South China University of Technology spent two years finishing that.

We are lucky that on 2nd March, Prof. Zheng came here and gave us a speech, exactly about pollution inventory preparation. He promised to give us his fruit so that we can go further. This Wednesday we are going there to learn about the skills.

Last week, I tried to replace topography data for WPS with my own data. WPS is the pre-processing software of WRF, preparing geographical and meteorological data for real-case weather simulation. It consists of three sub-programs, among which geogrid is responsible for interpolating static geographical data into grids. WPS is equipped with global geographical data issued by USGS, with 10m, 5m, 2m, 30s resolutions. For better precision, we have to add customized data to geogrid. We've got 1s (30 meters) PRD DEM data, and we can replace the topography data with it. Later, we are going to collect landuse data and do the same replacement.

The work is not so difficult as it seems. Both DEM data and geogrid formatted data are in grids. All I have to do is to read DEM into memory and then output it to static data. GDAL facilitates us greatly with dealing with DEM and WPS provides the output routine. We have to pay attention to obeying the WPS program interface. Also, data sequence in these two data formats are different. GDAL tends to read data from the first row (from north to south), but geogrid routine writes data from the south to the north. 

Ater the static data are ready, some configurations have to be modified to instruct geogrid to use the data we specified. After geo_em.d* have been output, we can use NCL to view the result. A sample ncl script can be downloaded here. 

In this way, terrain data with better resolution can be applied to WRF, which should affect the simulation result, like wind direction. Of course, the simulation grid is important in determining the significance of this work. Simulation with large domains and low resolution care less about course geography.

Fig1. Terrain with USGS 10m data

Fig2. Terrain with 1s data