EXTRACTION AND VISUALIZATION OF WIND SPEED IN A GIS ENVIRONMENT FROM A WEATHER RESEARCH AND FORECASTING (WRF) OUTPUT USING PYTHON

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1 EXTRACTION AND VISUALIZATION OF WIND SPEED IN A GIS ENVIRONMENT FROM A WEATHER RESEARCH AND FORECASTING (WRF) OUTPUT USING PYTHON Jerome T. Tolentino 1, Ma. Victoria Rejuso 1, Jara Kaye Villanueva 1, Loureal Camille Inocencio 1 and Ma. Rosario Concepcion Ang 1 1 Phil-LiDAR 2 REMap: Philippine Renewable Energy Resource Mapping from LiDAR Surveys and UP Training Center for Applied Geodesy and Photogrammetry (UP TCAGP), University of the Philippines, Diliman, Quezon City, 1101, Metro Manila, Philippines, tolentinojeromet@gmail.com KEY WORDS:WRF, GrADS, netcdf, Python, GIS ABSTRACT: Wind is one of the rapidly developing sources of renewable energy in the Philippines. However, determining wind speed and wind pattern poses a tough challenge to developers and researchers. The Weather Research and Forecasting (WRF) model is an open source Numerical Weather Prediction (NWP) system used for determining meteorological data like wind speed in a specific area. The default format of each WRF output is in netcdf which can be viewed in a GIS environment. Even though GIS can handle large files, it will take long processing time. An alternative is to convert the netcdf file into a format that is readable by the Grid Analysis and Display System (GrADS), which is an open source post-processing tool used to access, manipulate and visualize earth science data. However, creating a script for GrADS is very complex. Thus, this study developed a Pythonbased GUI software for extraction of wind speed using the PyGrADS library, a Python interface for GrADS. After extraction, these can be interpolated in a GIS environment to produce a raster file. The extracted and averaged wind speed data are from 2008 (La Niña), 2010 (El Niño) and 2014 of Quezon City WRF output with a domain of 1km by 1km resolution. Given the amount and number of data points, using python script in extracting and averaging wind speed is faster than doing it manually. A GIS environment can now project the extracted output because it has a smaller file compared to the raw netcdf file. It was also able to show that the output visualized in the GIS environment has a similar trend with the output of the previous NREL study. It can be concluded that this study has shown that extracting and averaging wind speed using Python is more flexible than other post-processing softwares. 1. INTRODUCTION 1.1 Background of the Study In past centuries, Archimedes stated that an object immersed in a fluid, wholly or partially, has a buoyant force equal to the weight of the displaced fluid by the object, which was eventually called the Archimedes principle. Furthermore, it became the fundamental of fluid mechanics. Fluid is a substance that deforms with minimal applied shear stress. Nowadays, application of fluids, particularly on wind, can convert its natural energy to other types of energy like mechanical and electrical. Wind is one of the rapidly developing sources of renewable energy in the Philippines. Since wind is classified as fluid, any obstacle can alter its path. Utilization of this resource for electrification requires intensive study of the wind regime in a given locality. Wind is highly variable; thus, it poses a tough challenge to developers and researchers because an accurate model is needed to extract and visualize the wind speed and wind pattern. Numerical Weather Prediction (NWP) is one method to determine a wind potential map in a given domain. NWP uses numerical representation of the atmosphere to forecast future weather behavior based on current weather measurements. Some components of the atmosphere which NWP numerically represent are Newton s Laws, Conservation of mass, energy, and ideal gas law. However, these components are insufficient to characterize the atmosphere because it does not include small physical processes. To improve manipulation of atmosphere, parameterization is used in the model which represents an important physical process that cannot be directly included in the model. It shows adding parameterization schemes in NWP improves weather forecasting. 1.2 Significance of the Study In 2014, National Renewable Energy Laboratory (NREL) made a 1-kilometer resolution wind resource assessment on the Philippines using Weather Research and Forecasting model version 3.5 (WRF 3.5). The output of WRF is in netcdf format which contains different scientific data in an array. This format only allows the visualization of the wind pattern through Panoply netcdf viewer, but extraction of values to output variables were not applicable. In addition, a netcdf file can be viewed in a GIS environment. However, the tool used to open such format cannot handle large (~10 Gigabytes) netcdf files containing the necessary period of data and domain required for wind

2 resource study. Even though the GIS can handle large files, it will take long processing time. An alternative is to convert the netcdf file into a format readable by the Grid Analysis and Display System (GrADS). However, creating a script for GrADS is very complex. Thus, this study explored the use of Python language for extracting and averaging output values such as wind speed. This allows the extracted values to be saved in an Excel format (.xlsx). Moreover, the Python script creates a GUI version for easier interaction with the user. After extracting wind speed values, these can be interpolated in a GIS environment to produce a raster file Objective This study aims to extract wind speed from WRF netcdf output using Python-based GUI software. Conversion, extraction and visualization are three main parts of the study. ARWpost program will be used in conversion part while for extraction, a developed Python-based GUI software will be implemented. Lastly, a GIS environment will be used for visualization. Specifically, the objectives of the study are: 1. To convert netcdf file to GrADS file 2. To extract wind speed from WRF output 3. To determine the reliability of the converted file 4. To develop GUI software 5. To visualize the extracted wind speed in a GIS environment 1.4. Scope and Limitation The study focuses on extraction of wind speed from netcdf file using Python. As input file for the software, GrADS file (ctl and dat) was used. The extracted and averaged wind speed data are from 2008 (La Niña), 2010 (El Niño) and 2014 of Quezon City WRF output with a domain of 1km by 1km resolution. Its output in multi-point extraction type was averaged every month. All the conversion and extraction was done in Ubuntu platform. 2. RRL 2.1. Grid Analysis and Display System (GrADS) The Grid Analysis and Display System (GrADS) is an open source post-processing tool used to access, manipulate and visualize earth science data which is used worldwide. It is composed of two data models in managing gridded and station data. The data file formats that GrADS supports are as follows: binary (stream or sequential), GRIB (version 1 and 2), NetCDF, HDF (version 4 and 5), and BUFR (for station data) (Doty, 1995). GrADS handles a 5-Dimensonal data environment: longitude, latitude, vertical level, time and an optional dimension that is used for ensembles (Doty, 1995). Data descriptor file is used to arrange the data sets in the 5-D space. The data can be displayed in graphs like line, bar, scatter plot and contour plots which can be saved as image file. Also, GrADS has a programmable interface for advanced analysis and display. In addition, GrADS can extract certain files and write it to a text file (Doty, 1995). The sample script is shown in Figure 1. The default format of GrADS is in.ctl and.dat file. ARWpost can convert meteorological outputs such as WRF output to a file readable by the GrADS. Also, GrADS is the easiest post processing tool for WRF model.

3 2.2 Python in Earth Sciences Figure 1. Sample script using GrADS language (Da Silva, 2008b) Python is an object-oriented open source programming language. Being object-oriented, its command is more understandable by the user and its syntax is more compact than other programming language (i.e. Java or C++). Nowadays, Python is widely used by researchers and software engineers. In addition, developers created several packages which helps the users solve their problem particularly in sciences. For example, Python was used in atmospheric science for its ability to handle arrays (Lin, 2012). Despite its flexibility, Python code is relatively slower than compiled code and its scientific modules are limited compared to Fortran language. Pyhton may have disadvantages but for science applications those weaknesses are diminished. Earth sciences community adopts python environment that leads to handling file formats like netcdf, HDF4 and GRIB 1 and 2. Furthermore, python can behave as a scripting language for scientific application such as OpenGrADS (Python interface GrADS) and ArcGIS (Lin, 2012). PyGrADS is a Python interface of GrADS that makes scripting easier. One advantage is that for a user who knows Python would not need to learn a new scripting language. Moreover, the user can take advantage of the unique capabilities of Python like data storage in MySQL database and creation of graphical user interface. Still, it allows the usage of GrADS scripting language (OpenGrADS, 2014). Sample script is shown in Figure 2 wherein it only takes a few lines to extract one variable to the input file compared to GrADS script. Figure 2. Sample script using Python language (PyGrADS) (Da Silva, 2008a)

4 2.3 Application of Python in netcdf and GIS software Tropical Marine Science Institute (TMSI) generated compressed large (~700 Megabytes) netcdf data daily including hydrodynamic models. The file contains 4D (x, y, z and time) values. A netcdf file can only be handled by special software programs for extraction and visualization of datasets. Rainer s (n.d.) study was to create a hydrodynamics visualization framework which will be used by TMSI researchers. To obtain its objective, a Python environment was used to extract and read netcdf data, and then, converted it to a shapefile because it has a compatibility with a GIS software. The Python code consists of netcdf data handling, array operations, shapefile conversion and a GUI module. After extracting and converting netcdf files, the created shapefiles developed by Rainer (n.d.) was used in ArcGIS ModelBuilder script to develop necessary visualization results. Under the ArcGIS ModelBuilder script, ArcPy (ArcGIS Python module) was used as the main library. Lastly, the hydrodynamic map will be animated with tidal currents per certain time. The workflow of Hydrodynamics Visualization Framework is shown in Figure 3. Figure 3. Hydrodynamics Visualization Framework workflow Zhao, Bryan, King, Song & Yu (2012) determined the improvement of efficiency of spatial analysis for model data assembly using high performance computing techniques. An algorithm was made to define a summary statistics for a climate data set with a long time-series. It sets as input for crop simulation model. The programming language used in the algorithm is Python with netcdf4, GDAL and PIL. In addition, they used parallel processing in the algorithm to have faster computation. As a result, the created algorithm takes only one second to calculate one climate data. Comparing to the use of GIS processing with 180 second per climate data, the created algorithm is much faster. 3. METHODOLOGY The study has three main parts: conversion, extraction and visualization. ARWpost was used to convert the WRF output into GrADS file. Then, data values were extracted in GrADS file and eventually stored into an Excel file. Lastly, GIS environment was used in visualizing output. A 3-year 1 km resolution Quezon City WRF output was used in the study. The dates were 2008 (La Niña), 2010 (El Niño) and The flowchart is shown below.

5 Figure 4. Wind Speed Extraction workflow 3.1 ARWpost ARWpost is a program made in Fortran language that converts WRF input and output files to a GrADS output files. Since the default format of WRF output is in netcdf, it will not affect the study. The converted GrADS file was configured thus, each output file has an hourly data every month. The reason is that the WRF output (netcdf) file has output every 30 days. 3.2 Extraction Type Single Point and Multi-Point are two types of extraction types. Single point type extracts the values per iteration in a given point (latitude and longitude). The values that can be extracted are x-wind speed, y-wind speed, wind direction (degrees), wind speed and wind direction at 10 m height and time. The wind direction is determined using the table below. Cardinal Direction Degree Direction N NNE NE ENE E ESE SE SSE S SSW SW WSW

6 W WNW NW NNW Table 1. Wind Direction Chart Multi point type extracts values in all points in a domain and average all the output in a file. As mentioned earlier, the converted GrADS file was in terms of hourly output every month. Thus, the multi-point type output is averaged every month. For wind speed, 20 and 40 meters was calculated using the wind speed profile equation. V 2 = V 1 ( Z 2 Z1 ) (1) Where V 2 is wind speed at height Z 2 (in meters), V 1 is known wind speed at height Z 1 (in meters) and exponent α is the power-law exponent. 3.3 Graphical User Interface Tkinter library in Python was used in creating the GUI software named Wind Extraction Software. The main window of the GUI shows Save Path, Directory Path, Extraction Type and Data to Extract. In Directory Path, the user can choose a file directory to which the converted GrADS file will be stored. The software has two types of extraction type: Single Point and Multi Point. If the user chooses the Single Point, input for latitude and longitude will be enabled, but will be disabled if Multi-Point is chosen. For description of the software, the user can press the About button. Figure 5. Wind Extraction Software Main Window The GUI was programmed such that the user must fill out all the necessary lines first before the extraction. It will give an error message to indicate the step that the user is missing. If the user s latitude and longitude input was invalid, it will display the range of the latitude and longitude available in a given file. Sample error message box is was shown in Figure 6.

7 Figure 6. Error message box for invalid input of longitude/latitude After filling up the needed inputs, the wind speed buttons can be clicked to extract the files with its corresponding value. The extracted data is stored in an Excel file using the xlsxwriter library. 4. RESULTS AND DISCUSSION Using python script, the average time of extraction and storage of data was 7.5 seconds per month. In GIS environment, it took around 3 minutes but still its output is not in average. This shows that using the python script in extracting and averaging wind speed is faster than doing it in GIS environment. One reason for efficiency of the script is that the software is more lightweight compared to a GIS environment. A GIS environment can now project the extracted output easily because it has a smaller file (Kilobytes) compared to the raw netcdf file (Gigabytes). A sample wind extraction output for single point and multi-point type were shown in Figure 7. Figure 7. Sample wind extraction output for single point (above) and multi-point (below) type To determine the reliability of the output, WRF output was viewed in Panoply netcdf viewer. Then, the extracted wind speed in GrADS file was interpolated using GIS software. As observed in Figure 8, no changes involved in converting netcdf file into GrADS file. Basically, the domain was not exactly Quezon City but rather domain containing Quezon City.

8 Figure 8. Projected extracted wind speed (left) vs. Projected netcdf file (right) To further analyze the extracted data, an annual mean wind speed domain was made. As shown in Figure 9, the annual mean wind speed resource map for Quezon City from 2008, 2010 and 2014 have similar trend to the output of the previous NREL study. Figure 9. a.) Quezon City 2008 domain, b.) Quezon City 2010 domain, c.) Quezon City 2014 domain and d.) Annual Quezon City domain by NREL

9 5. CONCLUSION NetCDF file was converted to GrADS file without altering its data. Then, Wind Extraction Software was developed using Python which can extract wind speed with 2 different output from GrADS file successfully. The single point extracted output is applicable for validation of simulated data to the observed data; whereas, the multi-point extracted output is applicable for wind resource assessment which determines the places that have high wind potential. High wind potential is one key for developing a wind farm. Since the output is in Excel file, manipulating present data is easier and flexible. For example, wind power density can be calculated from wind speed. The developed software has faster extraction than the GIS environment software. It can be concluded that this study has shown that extracting and averaging wind speed using Python is more flexible than other post-processing softwares. 6. ACKNOWLEDGEMENT The study was under full consultation with Dr. Gerry Bagtasa of Institue of Environmental Science and Meteorology, University of the Philippines-Diliman. This research is also part of the Phil-LiDAR 2 Program under the Department of Science and Technology. REFERENCE Da Silva A., 2008a. Using PyGrADS to save variable data to a text file, Retrieved July 1, 2015, fromhttp://cookbooks.opengrads.org/index.php?title=recipe- 003:_Using_PyGrADS_to_save_variable_data_to_a_text_file Da Silva A., 2008b. Saving GrADS Variable data to a text file, Retrieved July 1, 2015, from Doty, B., The grid analysis and display system. GRADS Manual, 10, 148. Lin, J. W. B., Why python is the next wave in earth sciences computing.bulletin of the American Meteorological Society, 93(12), McNamara J., Creating Excel files with Python and XlsxWriter, Retrieved July 1, 2015 from OpenGrADS, Python Interface to GrADS, Retreived July 1, 2015, from ython Rainer, M. Multidimensional Marine Environmental Data Conversion and Visualization Using Python and GIS. Shipman, J. W., Tkinter reference: a GUI for Python. Technical report, New Mexico Tech Computer Center. Wang, W., Bruyere, C., Duda, M., Dudhia, J., Gill, D., Lin, H. C.,...& Mandel, J. (2015). ARW version 3 modeling system user s guide. Mesoscale&Miscroscale Meteorology Division. National Center for Atmospheric Research (January 2015), Zhao, G., Bryan, B. A., King, D., Song, X., & Yu, Q., Parallelization and optimization of spatial analysis for large scale environmental model data assembly. Computers and electronics in agriculture, 89,