A mapping tool for climatological applications - Wiley Online Library

METEOROLOGICAL APPLICATIONS Meteorol. Appl. 18: 230–237 (2011) Published online 17 September 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/met.233

A mapping tool for climatological applications Olaf Matuschek and Andreas Matzarakis* Meteorological Institute, Albert-Ludwigs-University Freiburg, Freiburg, Germany

ABSTRACT: Modern GIS tools are inclined to be complex and not well suited to climatological applications. To redress this situation, a free tool for the creation of maps at different spatial resolutions for biometeorological and climatological purposes has been developed for use by GIS users and non-specialists alike. The method produces grids and isolines on maps at the same time using a climate mapping tool (CMT), which employs two different algorithms for deriving isolines from gridded data. CMT allows the creation and mapping based on ASCII and CSV files as well as the import of the most common GIS data file formats. The algorithms and the mapping tool developed can be used for a variety of applications, such as mapping of bioclimate indices. A case study using physiologically equivalent temperature (PET) is presented to demonstrate the features of CMT. Copyright  2010 Royal Meteorological Society KEY WORDS

climate mapping tool; isolines; grid data; bioclimate; physiologically equivalent temperature

Received 15 June 2009; Revised 16 April 2010; Accepted 21 July 2010

1.

Introduction

Construction of climatological and biometeorological maps is frequently required in climate research (Unkaˇsević and Radinović, 2000; Daly et al. 2002; Chapman and Thornes, 2003; Matzarakis et al., 2007a, 2007b). The tools required for this are particularly relevant to both climate change studies and simple climate mapping tasks (Matzarakis et al., 1998; Daly et al. 2002). Traditional mapping techniques are usually based on the interpolation of point data (usually from meteorological and climatological networks) in space (Saurer und Behr, 1997; ESRI, 2005; Neteler and Mitasova, 2008). Nowadays, there is a demand for interpolation and mapping of output from climate models (Jacob et al., 2001; Zebish et al., 2005; Wunram, 2005; Böhm et al., 2006; Will et al., 2006). Data from regional climate model simulations cover a range of spatial resolutions (from 10 km to more than 200 km). In order to visualize such data in meaningful ways, several options have to be provided to users. Moreover, it is often necessary to blend together two or more types of information (Saurer and Behr, 1997). Existing Geographical Information System (GIS) methods and software packages offer such possibilities (Chapman and Thornes 2003; ESRI, 2005; Haberman, 2005; Shipley, 2005; Neteler and Mitasova, 2008). However, for many types of climatological studies, the routines in these packages are too complex. Consequently, the process is difficult and time consuming. GIS information is often comprised of spatial databases that represent aspects of the cultural and physical environment of a particular geographic region. GIS applications * Correspondence to: Andreas Matzarakis, Meteorological Institute, Albert-Ludwigs-University Freiburg, Werthmannsr. 10, D-79085 Freiburg, Germany. E-mail: [email protected] Copyright  2010 Royal Meteorological Society

are used to generate graphical and statistical products from these databases (Saurer and Behr, 1997; Bill, 1999). A typical GIS, therefore, consists of three modules: data input, data management and visual or statistical output. To visualize different types of information, usually one or more layers of information can be superimposed. For climatological mapping, it is convenient to allow for at least two layers, such as elevation and the relevant climate parameter or variable. With this in mind, the aim of the current work is to develop an easy to use tool that combines the three steps of data input, data management and visualization into one single step and, at the same time, provide a visualization of two or more layers of information (Matzarakis et al., 2007b). The paper is organized as follows. First, relevant meteorological applications are reviewed. Next, development of the climate mapping tool is described, followed by a description of the development of algorithms for the creation of isolines. Finally, a case study is presented, which involves the creation of a maps of the biometeorological thermal index Physiologically Equivalent Temperature (PET). The PET index describes the effect of climate on the human organism based on future regional climate scenarios runs for Europe. 2.

Methods

The focus of the method presented here is to develop a user friendly tool for the generation of meteorological or climatological maps. Although these maps can be created using a standard GIS package, they are expensive and require specialized knowledge and considerable time to complete the mapping task. The aim here is to produce a user friendly, easy-to-use mapping tool that is freely available via the Web, for example along the lines

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provided by ‘RayMan’ (Matzarakis et al., 2007c). As the programming language used is C#, calculation routines can be run on any standard Microsoft Windows-based computer. The approach allows data of different formats and structures to be read (imported) and then processed so as to produce maps using a variety of different visualization techniques. The user is given different options, but in each case gridded and isoline output can be mapped at the same time. The technique developed to draw isolines is one of the most challenging yet important features of this tool. Details are presented in Section 2.3. 2.1. Existing meteorological applications Existing meteorological visualization mapping applications include ArcGIS (ESRI, 2005), GRASS and GrADS. These are all complex software packages that can be used via Windows (ArcGIS, GRASS) or Linux (GRASS, GrADS) operating systems. While ArcGIS has a graphical user interface, the latter two software packages are largely operated through the command line or a scripting interface. All three software packages require the user to have acquired considerable experience with the software and its procedures and are several steps away from a ‘switch-on-and-use’ mapping product. Meteorological and climatological data for modelling are typically processed in tab-delimited text format, or as comma separated values (CSV) (Matzarakis et al., 2007c). Each line contains the latitude, longitude and height, as well as other relevant parameters such as air temperature and water vapour pressure. These text formats have the advantage of being ‘human readable’. They can be easily processed by a variety of standard

software tools such as Microsoft Excel or the SPSS statistics package. Working with text files is possible with all three above mentioned visualization packages, though certain limitations apply. ArcGIS has a text import filter that allows the user to convert specially prepared text files to ‘point-shape’ files. With further processing steps, these shape files can be converted and visualized as raster grids, or in any of the various options provided by ArcGIS. Similar text-import filters exist in Excel and SPSS packages as well. The steps for use involve first importing several text files (for example, one with the special distribution of mean air temperature, and one with the calculated PET), then further pre-processing steps in which the user is able to visualize the output for comparison (for example, by presenting one air temperature as raster, and PET on top as isolines). Although visualization of biometeorological or climate model data is possible using any of the three software packages mentioned above, acquiring the software is costly and using it is quite involved. 2.2. Climate mapping tool The climate mapping tool (CMT) generates maps from meteorological and climatological grid data. CMT allows the visualization of several data sets by overlaying multiple layers. In the lower layer grid, data can be visualized by defining a colour gradient. In the overlaid layers isolines can be drawn using the algorithms described below. Figure 1 shows a screenshot of the program displaying the topography of the Black Forest in southwest Germany. A legend is included on the right hand side. A typical workflow with CMT is as follows (Figure 1). First, the desired dataset is loaded by clicking on ‘Add

Figure 1. ‘Climate Mapping Tool’ software. This figure is available in colour online at wileyonlinelibrary.com/journal/met Copyright  2010 Royal Meteorological Society

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Data Source’ and selecting the appropriate file. Raster file formats that are supported include CSV and ASCII data, produced with Excel or SPSS, or, for example, ‘RayMan’ (Matzarakis et al., 2007c). CMT includes an algorithm to detect encoding, column separator and number format of these files automatically, so that the time consuming step of manually entering these values found in most other software packages is not necessary. Furthermore, CMT automatically detects the structure of rows and columns of CSV and ASCII files. Other file formats that are supported include GeoTIFF and several formats produced by output from weather forecast and climate models, though certain restrictions apply. GIS data such as rivers or state boundaries can be loaded as well, to give a more detailed or realistic picture of the geographical structures. Typical GIS file formats such as shape files and E00 files are supported. After selecting the data, the results will show up in the data layer list on the upper right side of Figure 1. The second step is to configure the visualization. This includes selecting certain data from the data source to be visualized, which can be the column in CSV files, or the time step found in GRIB files. The type of visualization is selected through ‘Display Mode’, which for grid data can be as raster through a colour gradient or as a collection of isolines. The third step is to configure map generation options, including generation of map title, ticks and north arrow. If selected, map ticks are drawn automatically at logical positions. A specially developed algorithm calculates this position, so that the step of entering tick position manually found in most other GIS is no longer necessary. In addition, geographical projections can be applied in order to produce more realistic maps for the user. The last step consists of the generation of the map by selecting the ‘Generate’ button. The maps can be saved in PNG or JPG format and printed by any graphics software. 2.3.

Raster and vector concepts

Spatial data in GIS are represented either as grid or as vector data (Saurer and Behr, 1997). A raster divides an area in elements (cells) of specific size, usually in squares or rectangles. Each cell can be addressed with its unique coordinate X/Y. A cell contains information such as total precipitation amount or mean annual air temperature at a specific height, usually at 2 m above sea level. This information, the value of the cell, can be continuous or discrete. For example, precipitation would be continuous, but climate classification would be discrete. Grid points represent a similar concept, but differ from grid cells insofar as here its value is associated with the point on the edges, not with the area of the cell. Gridded (raster) data are best used for spatially continuous data. On the other hand, vector data are most suitable for spatially discrete data, such as the location of cities, the run of a river, or the borders and area of a country. Vector data can also be used to represent spatially continuous data, for example through triangular irregular Copyright  2010 Royal Meteorological Society

nets or isolines. Isolines can be seen as a different representation of the same underlying information as raster data. Both representations can, therefore, be transformed into each other. 2.4.

Deriving isolines from raster data

Isolines are represented in a GIS as multiple vectorized lines. Each line connects points with the same nominal value, e.g. 20 ° C. Both algorithms developed here work on the basis of deriving each isoline independently one after the other. Therefore, only the creation of a single isoline is discussed below. The first algorithm developed, the point connecting algorithm, (a) first finds all raster cells that belong to an isoline, and, (b) connects these cells to an isoline. Detecting raster cells is done by checking the four corners of each cell. If there are values higher and lower than the value of the isoline, it will certainly cross this cell. Figure 2 shows the basics of this algorithm. Each line is scanned for line segments, that is points lying next to each other (Figure 2(a)). All segments identified are connected to segments on the previous line (Figure 2(b)). Depending on location of start points and end points, a segment might have to be swapped when connected. In Figure 2(e), the segment 10-11-12 has to be connected as 12-11-10 to point 8. Finally, partial isolines have to be checked and closed if appropriate (Figure 2(f)). There is a couple of special cases to consider when connecting the points. One such case is shown in Figure 3. After connecting the points 35 and 36 in Figure 3(a), the next step with the algorithm described above would be to connect the segment 41-40-39-38 to point 35. After this, a connection to point 36 is not possible. Such potentially ambiguous cases are detected by checking all partitioned isolines after the processing of each row, if their start and endpoint are on the same row. If this is not the case, the last added segments are checked if they can be broken up to satisfy this condition (Figure 3(c)). A few other special cases may exist with segments consisting of only one, two or three points, where segments have to be broken up as well (see Figure 3(c), point 37). The second algorithm for deriving isolines, the edge interpolation algorithm, is based on Snyder (1978), who presents an algorithm to output isolines derived from a raster on a plotter. While this algorithm as a whole is not suitable for the problem, its determination of isoline segment proves most useful. To find parts of an isoline, four adjoining raster points representing a rectangle are evaluated at a time. Each grid edge is then tested for an intersection with an isoline through linear interpolation. Three cases have to be distinguished: (1) no intersections, no isoline segments, (2) two intersections, the two intersecting points are connected with an isoline segment, and, (3) four intersections, depending on the location on the intersection points on the upper and lower edge two isoline segments are connected. After finding the segments going through each cell, they can be relative straightforwardly connected to isolines: segments that Meteorol. Appl. 18: 230–237 (2011)

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Figure 2. Connection between point and isolines (simple case).

Figure 3. Connection between point and isolines (special cases).

have to be connected share the same point on a grid edge. There are eight ambiguous cases to consider, and these occur when one or more grid point values equal the isoline value. During the development of the isoline algorithms, a graphical interface for testing the algorithms was developed. This graphical interface allows for the testing of the algorithm row by row by visualizing their output. This immediate visual feedback allowed assessing mapping problems that might arise at each stage.

3.

Results with examples

A variety of possible options is presented by (a) by testing and comparing the two different kinds of algorithms for isolines drawn (identified), and, (b) the application Copyright  2010 Royal Meteorological Society

and visualization of a thermal index (PET) for the assessment of the thermal environment of humans based on results of regional climate models for the IPCC future climate scenario A1B. 3.1. Comparison of algorithms The comparison of the algorithms used is shown in Figure 4. The study area in this case is the German North Sea coast. The cells contain the number of days per year with temperature >20 ° C for a specific period and the 35 day isoline calculated. Cells with a black background have been detected by the first algorithm step (a) to belong to an isoline. Cells with a grey background are not to be crossed by an isoline, as determined by this algorithm. Cells in white are those with no data. Meteorol. Appl. 18: 230–237 (2011)

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Point connecting algorithm, with smoothing (b)

Edge interpolating algorithm, no smoothing

Figure 4. Comparison of algorithms for the creation of isolines.

Both algorithms successfully connect all highlighted raster cells. Algorithm (a) is successful in connecting all special cases in the upper right corner: however, isolines always go through the middle of each cell. Despite smoothing (Armstrong, 2005), it appears blocky. Isolines generated by algorithm (b) run through interpolated points and seem to be more exact. In the upper right corner both algorithms generate isolines of a different shape. Again algorithm (b) generates more accurate results, since it can operate on more information: it does not connect contextless points as algorithm (a), but premade parts of an isoline. 3.2. Comparison of overlay techniques and interpolation considerations There are three important techniques for combining two or more climate/meteorological parameters on one map for visual comparison. These are mapematics (i.e. map algebra), transparency and isolines. Mapematics is an important methodology for combining raster data by applying an algebraic formula to cell values from two or more layers (ESRI, 2005). Its application is best suited to task where in-depth data analysis is required. Mapematics require exact knowledge of the calculation process and data interpretation. CMT is intended to be a simple generation tool for climate and climate modelling data in raster formats. Therefore, mapematics are not included. Algebraic calculations on the input data can be performed before using CMT. Transparency has not been integrated into CMT in favour of isolines, which seems to be the most straightforward way to overlay two types of data. Copyright  2010 Royal Meteorological Society

An often requested feature for CMT is the generation of maps from irregularly spaced locations, e.g. weather stations. While this would allow for easy visualization of measured meteorological parameters, a few theoretical problems arise. Drawing maps from irregular spaced locations requires spatial interpolation between these locations. There is no general algorithm to transfer point information to space for any meteorological/climatological parameter (Mesquita and Sousa, 2009). Different approaches and limitations exist for the different meteorological parameters, and for different regions of the world (Mesquita and Sousa, 2009). For air temperature, for example, a digital elevation model (DEM) at least has to be considered. 3.3.

Application of CMT for PET mapping

In this section a regional climate scenario run based on the CLM – model (Wunram, 2005; Böhm et al., 2006; Will et al., 2006) is used. The daily data from the A1B scenario are used for the periods 1991–2000 and 2091–2100, and calculated PET based on VDI (1998), Höppe (1999) and Matzarakis et al. (1999), in order to quantify the human thermal bioclimate for the whole of Europe. PET builds a valuable method in order to quantify the effect of the thermal environment on humans based on the energy exchange of the human body and quantifies the heat effects, i.e. heat stress conditions (Höppe, 1999). The calculation of PET is produced by the RayMan model (Matzarakis et al., 2007c). Daily data from CLM (air temperature, air humidity, wind speed, global radiation, albedo and Bowen’s ratio) have been imported into the RayMan and PET calculation routines Meteorol. Appl. 18: 230–237 (2011)

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for standard PET thermophysiological conditions (VDI, 1998). From the PET results the PET >35 ° C days has been accounted and used as import values for CMTmapping. Figure 5 shows the PET conditions for the period 1991–2000 based on the CLM model. Figure 6 shows the difference of PET conditions from 1991 to 2000 to the decade 2091–2100. The spatial resolution of each grid point in Figures 5 and 6 is 18 km. The scale in Figure 5 is linear and the most heat stress days extend over 80 days in the Mediterranean region. In the higher elevation areas and in areas with latitudes of 50° and above, heat stress is not an issue. There is variability in the amount of days with PET >35 ° C closer to the sea. The PET >35 ° C conditions show an increase for the period 2091–2100 for areas with latitude less than 55 ° C this amount increases to greater than 30 days for most of the parts of southern Europe and the whole Mediterranean region. PET is a widely used index for characterizing the thermal bioclimate and allows the evaluation of thermal conditions in a physiologically significant manner. This consideration is often not appropriately included in biometeorological and applied meteorological studies especially epidemiology and medicine, when accurately investigating the relationship of the influence of weather and climate on humans. PET contains the effects of climatic variables, not only air temperature and wind speed or air humidity such as wind chill or thermal heat index (THI). PET can be helpful for diverse applications concerning climate impact research i.e. detection of areas with extreme heat waves (Matzarakis et al., 2007b) or quantification of extreme events.

4.

Discussion

4.1. Isoline generation algorithms In the analysis it was found that, with certain theoretical or non realistic input data, the edge interpolation algorithm (Section 2.3) might not produce optimal results. A combination of edge interpolation and point connecting algorithm produces appropriate results. Further improvement might also be achieved by incorporating smoothing algorithms presented by Mohamed and Ottmann (2007) in passive form. Especially with the point connecting algorithm, the isolines do not follow all detected points exactly, but follow them only by approximation. The second presented algorithm, the edge interpolation, delivers the most appropriate results. This algorithm has been implemented in the Climate Mapping Tool. In comparison to existing commonly used methods, such as by Snyder (1978) and Bill (1999), the approach presented here produces results faster because of the straightforward method of plotting and visualizing results on screen. 4.2. Usability comparison with other GIS Usability of the CMT in comparison to standard GIS software differs significantly, and is shown here for the generation of contour lines. ArcGIS does permit the computation of contour lines from raster data, though this is a separate working step and cannot be directly performed on the map. After selecting the function ‘contour’ in the ArcGIS-Toolbox, choosing the input raster and output shape file, and the desired iso values, ArcGIS generates a new shape file. The new shape file can be opened in ArcGIS, and connected with a desired symbology (ESRI, 2005). Open Source GIS GRASS

Figure 5. Number of days with PET >35 ° C for Europe for the period 1991–2000. No. of Days 0 80 160 Height (m) 250 600 1200. Copyright  2010 Royal Meteorological Society

5

10

20

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Figure 6. Difference in number of days with PET >35 ° C for Europe for the period 2091–2100 in comparison to 1991–2000. No. of Days 0.0 2.0 4.0 8.0 16.0 32.0 64.0 Height (m) 250 600 1200.

supports the generation of isolines from raster data as well (Neteler and Mitasova, 2008). The process is similar, but even more complicated than with ArcGIS. Isoline generation is still an extra step with production of a new shape file. This also means that all steps have to be repeated from the beginning when changes are required or desired. More complications may be experienced as GRASS is still operated almost exclusively from the command line and generally has a steep learning curve. In contrast, generating isolines in the presented CMT means only to select ‘isolines’ from the ‘display mode’ drop down box (Figure 1). Results can then be immediately verified and corrected. 4.3. Case study on physiologically equivalent temperature The case study (PET for Europe, Figures 5 and 6) presented here, which is based on results of regional climate models for the A1B scenario, was produced with existing climate simulations. The results are along the line of what is to be expected. They have a spatial resolution of 18 km grid size and are based on daily data for specific time frames 1991–2000 and 2091–2100. This is a relatively fine resolution when compared to other studies on future climatic conditions based on global circulation models expressed as PET (Matzarakis, 2006; Matzarakis and Amelung, 2008). Visualization of the results using CMT and the developed algorithms for isolines is straightforward. 5.

Conclusion

The results presented here show that CMT is an attractive tool compared to others. Data in climatology and Copyright  2010 Royal Meteorological Society

meteorology can be spatially represented and visualized using GIS techniques, but these are expensive to acquire and not easy to use. CMT can generate maps based on ASCII files, which are commonly used in climate science and its applications. In addition, binary data (e.g. GRIB, GeoTIFF) can be processed, and the option to overlay other GIS data, such as state boundaries, exists. Multiple datasets can be imported and processed simultaneously as grid or isolines in CMT. Colour and isolines plots or combined graphs can be created. The processing of data is easy and user friendly. This feature of showing or visualizing data from CMT is straightforward and easy to use. This, together with CMT’s other features, makes it an inexpensive way for dealing with climate mapping issues. CMT is freely available on request from the authors of this paper. References Armstrong J. 2005. Hermite Curves. Singularity [online] http://www. algorithmist.net/ [Accessed November 2007]. Bill R. 1999. Grundlagen der Geo-Informationssysteme. Band 1 Hardware, Software und Daten. Wichmann: Heidelberg, Germany. Böhm U, Kücken M, Ahrens W, Block A, Hauffe D, Keuler K, Rockel B, Will A. 2006. CLM – the climate version of LM: Brief Description and long term application. COSMO Newsletter 6: 225–235. Chapman L, Thornes JE. 2003. The use of geographical information systems in climatology and meteorology. Progress in Physical Geography 27: 313–330. Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research 22: 99–113. ESRI – Environmental Systems Research Institute. 2005. ArcGIS 9: What is ArcGIS 9.1? ESRI: Redlands, CA. Haberman T. 2005. What is GIS (for Unidata). Bulletin of the American Meteorological Society 86: 174–175. Höppe P. 1999. The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology 43: 71–75. Meteorol. Appl. 18: 230–237 (2011)

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