Meddy Vanotti
email: meddy.vanotti@femto-st.fr

Virginie Blondeau-Patissier

David Rabus

Jean-Yves Rauch

Sylvain Ballandras

Selective Detection of Hydrogen with Surface Acoustic Wave Devices Using Palladium Layer Properties

Keywords: Gas sensors, Hydrogen, Palladium layer, SAW device, Rayleigh waves

For an increasing number of applications, hydrogen represents a solution of the future as it is the most common element in the Earth. However, due to its unstable properties in gas phase, a particular care must be dedicated to control possible gaseous leaks close to tanks and facilities using this resource. In this paper, surface acoustic wave sensors are proposed for detecting gaseous hydrogen in standard environmental conditions (atmospheric pressure and room temperature). The proposed Surface Acoustic Wave sensors consists in two Rayleigh-wave delay lines built on Quartz, one equipped with a palladium overlay and the other exhibiting a free path between the two interdigited transducers. A specific gas test cell has been developed to test various sensor configurations submitted to hydrogen-composed atmospheres. A particular care was paid to avoid hydrogen leakage in the working environment and to perform the regeneration of the gas absorbing layer. The developed device allows for identifying different concentrations of hydrogen (in the 1-4% range) diluted in nitrogen and is also able to detect hydrogen in current atmosphere. Surface Acoustic Wave devices exploiting hydrogen absorption capabilities of palladium thin films have been here used to make the detection and the identification of hydrogen concentrations in the 1-4% range and the influence of outer parameters such as temperature and relative humidity variations on the sensor operation is also reported.

Introduction

The raising shortages of fossil energy resources added to the increasing concern towards environmental issues have led to consider hydrogen as one of the most promising energy resource. This odorless and colorless gas being highly explosive over 4% concentration in air, the availability of a fast and accurate detection system close to storing facilities and equipping hydrogen-operated machines is mandatory for obvious security reasons. Such a system must exhibit a significant selectivity as it must detect the presence of gaseous hydrogen in air with concentrations smaller than the above-mentioned critical limit at standard conditions (room temperature and atmospheric pressure) as well as in harsher environment (very low or significantly high temperature). Although, some solutions have been proposed [START_REF] Katti | Passivated thick lm catalytic type H2 sensor operating at low temperature[END_REF] [2] [START_REF] Simon | Thermal and gas-sensing properties of a micromachined thermal conductivity sensor for the detection of hydrogen in automotive applications[END_REF] [4] [START_REF]Mems-based hydrogen gas sensors[END_REF] [6] [START_REF] Zhao | Alloptical hydrogen sensor based on a high alloy content palladium thin lm[END_REF], the current availability of such a detection system meeting modern specifications of hydrogen use and storage is still questionable. The main improvements for such sensors are their sensitivity, their selectivity and their reliability together with sensor size, cost reduction, energetic needs and response time [START_REF] Boon-Brett | Identifying performance gaps in hydrogen safety sensor technology for automotive and stationary applications[END_REF]. Many methods of detection of hydrogen and a comprehensive review can be found in the literature [START_REF] Hubert | Hydrogen sensors a review[END_REF], providing a substantial material base to try and address the above challenges. Among the possibilities, SAW (Surface Acoustic Wave) sensors have been widely studied in the last decades because of their attractive capabilities [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] [26] [START_REF] Hines | Passive SAW-Based Hydrogen Sensor And System[END_REF]. Indeed, SAW devices exhibit high sensitivity to surface perturbation since most of the energy propagates in a region whose thickness is a few times the wavelength of the propagating acoustic wave. It is also a mature technology, SAW devices do exhibit limited size (less than 1 cm²) and they allow for wireless use [START_REF] Friedt | A wireless interrogation system exploiting narrowband acoustic resonator for remote physical quantity measurement[END_REF]. Initial works were made by D'Amico et al. [START_REF] D´amico | Palladium surface acoustic wave interaction for hydrogen detection[END_REF] using the properties of palladium layer to trap the targeted gas. Since this pioneer work, innovations concerning the selectivity and stability of sensitive layers versus external parameter have been proposed to improve hydrogen detection using SAW devices [START_REF] Jakubik | Bilayer structure for hydrogen detection in a surface acoustic wave sensor system[END_REF] [13] [14] [START_REF] Ippolito | Layered SAW hydrogen sensor with modified tungsten trioxide selective layer[END_REF] [16] [START_REF] Phan | Surface acoustic wave hydrogen sensors based on ZnO nanoparticles incorporated with a pt catalyst[END_REF] [18] [START_REF] Fardindoost | Pd doped wo3 films prepared by solgel process for hydrogen sensing[END_REF]. In this paper, a SAW sensor is proposed for detecting gaseous hydrogen in standard environmental conditions (atmospheric pressure and room temperature). The proposed SAW sensor consists in two Rayleigh-wave delay lines built on quartz, one equipped with a palladium (Pd) overlay and the other exhibiting a free path between the two interdigitated transducers (IDTs) used to excite and detect the acoustic wave. These IDTs are built using aluminum electrodes, as this metal is of low density and acts as a limited acoustic reflector. An innovative aspect of the proposed sensing system consists in the open-loop strategy for phase changes monitoring [START_REF] Rabus | A high sensitivity open loop electronics for gravimetric acousticwave-based sensors[END_REF]. Moreover, delay lines are monitored in parallel using a synchronous detection approach that provides high frequency measurement resolution and that permits a systematic characterization of the device before starting measurement. Along this approach, the impact of changes of intrinsic properties of the devices such as working frequency drift with aging can be minimized. These sensors have been tested in a specific gas cell developed to experiment with various sensor configurations submitted to hydrogen composed atmospheres. A particular care has been dedicated to avoid hydrogen leakage in the working environment and to promote the gas absorbing layer regeneration. The developed device allows for identifying different concentrations of hydrogen diluted in N 2 and is also able to detect H 2 in current atmosphere. The first section of the paper presents the SAW sensor as well as the exploited monitoring system. Experimental validation of H 2 detection then is reported, with a description of the chemical test bench and detection results for various H 2 concentrations. An analysis of the influence of H 2 adsorption on SAW propagation is proposed to provide routes for the proposed device optimization. In the same purpose, the last section of the article is devoted to characterizing the influence of external parameters such as temperature and relative humidity variations on the sensor operation, yielding conclusive discussions.

SAW sensor and associated monitoring system

Selective detection of hydrogen at room temperature and pressure have been achieved using SAW delay lines exploiting Rayleigh waves on AT-cut quartz, as the corresponding first order temperature coefficient of frequency (TCF) is close to zero, yielding frequencytemperature compensation for the above-mentioned operating conditions. The sensor structure correspond to a differential set-up in, which a sensitive track is achieved by depositing a Pd layer in between two IDTs whereas the reference track surface is left free to detect nonspecific gas/surface interaction. Along this approach, one can significantly increase the sensitivity of the device and its robustness to correlated perturbations (temperature, vibration, non-specific adsorption). The configuration of both generation and detection IDTs used for the sensor consist in 50 fingers pairs with a grating period of 10µm and path length of 5mm (the reactive surface). The wavelength is 40µm, yielding a frequency operation in the vicinity of 78MHz as the wave velocity approaches 3100m.s -1 . The Pd film was deposited by thermal evaporation on a single run and shaped by a lift-off technique. Its length along the propagation path was 3mm and its thickness equal to 300nm. The device configuration is shown on Fig. 1. Using a network analyzer, the transfer function of the device can be easily determined and hence the phase shift induced by gas absorption has been first monitored that way. However, the use of a dedicated electronics has been implemented and delivers similar information. That is represented on Fig. 2. This system includes two separate phase measurement schemes: a rough characterization stage using the AD8302 phase and magnitude detector, and a high sensitivity phase measurement using dedicated phase detectors and variable gain, low noise operational amplifiers as presented in another work [START_REF] Rabus | A high sensitivity open loop electronics for gravimetric acousticwave-based sensors[END_REF]. By increasing the gain on the operational amplifier, one can gradually improve the phase measurement resolution while remaining at a constant operating point. However, this approach also reduces the phase range due to the limited voltage range of the analog to digital converter (ADC). While the AD8302 is tuned to generate a voltage within the ADC range as the phase rotates from +90° to -90°, increasing the gain of the high sensitivity stage yields measurement ranges reduced to a fraction of the angle range. Thus, a tracking scheme is implemented in which the phase to frequency (locally linear) relationship is identified, and whenever the phase reaches a condition close to the boundary of the operating range of the ADC, the RF source frequency is tuned so that the phase moves back to the middle of the ADC range. This implementation is similar to a digital oscillator, since the frequency tracks a phase condition, with the added advantage than a precise phase measurement is provided as long as the voltage remains within the operating range of the ADC. The sensitivity of the set-up allows for some tens millidegrees resolution and is easily transportable. The response of the bare device and the functionalized one are respectively measured. This configuration has been used so as to make a systematic characterization of each new device used for H 2 detection. Fig. 3. illustrates the way the phase shift measurement is achieved.

Detection of hydrogen at room temperature and atmospheric pressure

Hydrogen detection in the percent order has been achieved at room conditions. Figs. 4 and5 present experimental results when using either nitrogen or air as carrier gas. As it can be observed in Fig. 5., the detection of about 95%vol of hydrogen in air at 35% RH and 20°C can be achieved with a response delay of about 20 seconds considering that the determination of the H 2 concentration in the melting gas is derived from the phase shift velocity during the exposure and not from the steady state. It is notable that this value depends on a large part on the performance of the hydrogen generation setup. Therefore, the intrinsic response delay of the device is expected to be shorter. Fig. 4. shows that the device allows for identifying different concentrations of hydrogen diluted in a nitrogen flow of 100sccm.

Analysis of hydrogen absorption on Pd films and its influence on SAW propagation

Absorption of hydrogen into Pd layer induces modifications on the SAW propagation conditions. The effective physical characteristics of the wave then vary with the amount of absorbed H 2 (especially its phase velocity). It has been shown [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] that the two main parameters that influence the phase velocity change of elastic waves on quartz are mass-and elastic-loading as it can be seen on the equation bellow [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF]. Since only devices using quartz substrates have been used here, changes in electrodes conductivity are not considered as a possible origin of the observed phase velocity drift and therefore were not taken in account in the proposed analysis. Electromechanical coupling of Rayleigh waves on quartz substrates is actually small enough to consider conductivity changes negligible. The following equation has been derived for materials whose crystal structure is "cfc" and does not directly apply for the experiment presented in this paper but it provides a clear illustration of the two main principles governing the observed variations and an (although simplified) analytical formula for estimating the importance of each contribution (elastic and mass loadings).

(1)

In case of H 2 adsorption on Pd layer of thickness h through which a SAW of wavelength λ propagates, both the mass density ρ and the elastic constants C11 decrease, whereas the C44 constant increases. The values of normalized mechanical displacement A i for palladium are reported in [START_REF] Auld | Acoustic fields and waves in solids[END_REF] and [START_REF] Slobodnik | Microwave acoustic handbook[END_REF]. Numerical calculations of hydrogen absorption in Pd layers [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] predict an increase of C44 elastic constant, yielding an increase of the phase velocity of the Rayleigh wave propagating under such an overlay. These calculations are in agreement with the experimental observations presented here.

Theoretical study and simulation results

Accurate calculations have been achieved, based on a set of programs dedicated to the analysis of stratified structures used as elastic wave guides. The basics the corresponding developments have been presented in [START_REF] Reinhardt | Scattering matrix method for acoustic waves in piezoelectric, fluid, and metallic multilayers[END_REF] [START_REF] Pastureaud | Stable scattering-matrix method for acoustic waves in piezoelectric multilayers[END_REF]. The calculation principle is based on the derivation of a surface or interface effective permittivity yielding numerous information about the wave propagation characteristics such as the wave velocity, the mode coupling and possible propagation losses. The model accounts for all the known linear properties of the materials constitutive of the layered structure which directly condition the accuracy of the results as no assumption is made on the device operation (provided flat interfaces). Here are presented simulation results about mass loading effect. One can see on Fig. 6. the influence of a growing water film at the surface of the sensor. A phase decrease is observed. It is noticeable that the phase shift observed on Fig. 7. that is relative to the exposure of the sensor to humidity is consistent with the theoretical result. Indeed, that phase decrease corresponds to a reduction of the velocity of 133.10 -3 m.s -1 which would correspond to the presence, at the top, of the device of a water film that thickness is 4nm which is realistic. On another hand, calculations of theoretical velocity variation of the wave consecutive to changes of elastic properties of the palladium layer have been made. Simulation results presented on Fig. 8. were obtained on the basis of [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] results. We can see here the influence of each elastic parameter on the velocity of the acoustic wave. According to [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF], we considered a decrease of 0,1% and 10% of the density (red solide curve) and the C11 (green triangles) coefficient of the palladium layer respectively. A increase of 40% of the C44 (blue circles) and the induced modification of non diagonals parameters (black squares) were also simulated. The global modification of the acoustic wave velocity is shown on the dashed black curve. One can notice that the velocity increases, upon a complete hydrogenation Fig. 6. Simulated condensation of water on the sensitive zone. (corresponding to X=0.66 (PdH X )), by 1.7m.s -1 . This value takes in account the modification of both Cij and density modifications of the palladium layer. It is also necessary to take into account the gravimetric effect due to H 2 adsorption. This phenomenon, shown on Fig. 9., results in a decrease of the wave velocity of 0.5m.s -1 when considering a complete hydrogenation. As a consequence, the global velocity change is an increase of about 1.2m.s -1 . This result is in accordance with the observation made on Fig. 4. Indeed, the phase shift observed on this figure corresponds to an increase by 2.6m.s -1 of the acoustic wave velocity. The orders of magnitudes are consistent and the difference between these to values (2.6m.s -1 for the experiments and 1.2m.s -1 from the simulation) can be explained by the fact that the palladium layer used here has quite different geometrical properties than the one that was used in [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] for the determination of the elastic properties modifications. Moreover, the sensitive layer has been here deposited using sputtering method contrary to [START_REF] Anisimkin | Elastic properties of thin-film palladium for surface acoustic wave (SAW) sensors[END_REF] where thermal evaporation was used.

The influence of humidity and temperature on hydrogen detection

As SAW devices are known to suffer from interference due to humidity and temperature, the influence of these parameters on the sensor operation have been experimentally investigated and the corresponding results are exposed here. The observations reported in this section are expected to provide information allowing for the improvement of the differential acquisition setup. One can see in Fig. 7. that an increase of the relative humidity (RH) of the injected gas causes a massloading effect that results in a decrease of the measured phase of the delay line. Indeed, the condensation of water onto the surface of the device naturally leads to a raise of the mass at the surface of the device (mass loading) yielding the decrease of the experimentally observed phase velocity. As said in the previous section, that behavior is sustained by theoretical observation. Furthermore, a damping phenomenon can clearly by seen on the transfer function of the device when exposed to humidity. Fig. 10. exhibits the impact of temperature variations on the capability of the SAW sensor to detect hydrogen at atmospheric pressure. Temperature changes lead to a decrease of the delay line synchronicity frequency characterized by a sensitivity of -219.10 -3 °.°C -1 . That phase decrease totally compensates the phase shift toward the high frequency observed when detecting hydrogen in absence of any temperature changes. It appears that the delay line used as a reference does not undergo any phase shift when exposed to heated gas since the device is temperature compensated. However, the TCF of the Pd detection channel notably changes as the Pd overlay tends to lower the 1 st order TCF. This behavior currently represents an obstacle to accurate hydrogen detection but can be avoided by the use of an appropriate reference (covered by a layer inert to hydrogen which induces the same TCF change as experienced here for the detection channel). As one can see on the second part of the graph, the detection of H 2 at 2%vol in N 2 is still clearly possible considering a stable operation temperature at room conditions.

Conclusion

In this paper, a differential SAW sensor based on Rayleigh wave on quartz has been developed and successfully tested. The sensor consist of two adjacent delay lines, one being used as a reference and the second one being functionalized with a Pd overlay, a metal known to be affected by gaseous hydrogen. The observation of specific phase shifts on the sensitive track compared to reference in presence of gaseous hydrogen using different gas carrier did assess the operation of the sensor. The use of the Pd overlay actually allows segregating the nature of the gas inserted in the reaction cell and provides quantitative information about its composition. The impact of temperature and humidity on the sensor operation also were analyzed. Although the differential nature of the sensor is expected to reject such interferences, it appears necessary to control this parameters at very minimum. Concerning the analysis of the sensor operation itself, some work still has to be carried out even if strong convictions arise from the experiment that the main change is related to the elastic properties of the Pd film due to hydrogen absorption. The crystalline structure of the film having an effective influence on the way hydrogen interact with the metallic film, it still has to be determined in order to improve simulation accuracy of the leverage of hydrogen absorption on the SAW device response. Toward this aim, further work will consist in the elaboration and characterization of palladium layers with different crystalline structures. Simulation of SAW device exhibiting different sensing layers will also be deepened in order to provide a comprehensive analysis of the experimental observations and to predict the structure of the delay-line to use in order to improve the differential acquisition setup. These investigations are expected to enhance the performance of the proposed sensor in terms of sensitivity and selectivity toward outer parameters such as temperature and humidity.

Fig. 1 .

 1 Fig. 1. Scheme of a SAW delay line using Rayleigh surface acoustic wave at 78MHz.

Fig. 2 .

 2 Fig. 2. Scheme and photograph of a SAW devices probing card.

Fig. 3 .

 3 Fig. 3. Phase shift measurement principle using a dedicated instrumentation.

Fig. 4 .

 4 Fig. 4. Detection of hydrogen at high concentration in air.

Fig. 5 .

 5 Fig. 5. Detection of hydrogen in the range 4% to 1%vol in N 2 .

Fig. 8 .

 8 Fig. 8. Wave velocity shift induced by changes in C11 (green triangles), C44 (blue circles), C12 (black squares), density (red solide line), global shift (black dashed line).

Fig. 9 .

 9 Fig. 9. Phase velocity variation due to hydrogen massloading effect.

Fig. 7 .

 7 Fig. 7. Influence of humidity on the detection of hydrogen at constant room temperature (19.3+/-0.1°C).

Acknowledgements

The authors want to address a special thank to J.M FRIEDT and G. MARTIN for their contributions to the development of the probing electronics. Fig. 10. Influence of temperature on the detection of hydrogen (RH in the 3-8% range).


Nesra Yannier

Cagatay Basdogan

Serdar Tasiran

Omer Lutfi Sen

Using Haptics to Convey Cause and Effect Relations in Climate Visualization

Keywords: Data and information visualization, climate simulation, haptic exploration, multi-modal user interfaces Index Terms-H.1.2 [Models and Principles]: User/Machine Systems -Human factors H.5.2 [Information Interfaces and Presentation]: User Interfaces---Haptic I/O, I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism---virtual reality, I.6.3 [Simulation and Modeling]: Applications, I.2.9 [Artificial Intelligence]: Robotics---operator interfaces, commercial robots and applications

We investigate the potential role of haptics in augmenting the visualization of climate data. In existing approaches to climate visualization, different dimensions of climate data such as temperature, humidity, wind, precipitation, and cloud water are typically represented using different visual markers and dimensions such as color, size, intensity, and orientation. Since the number of dimensions in climate data is large and climate data needs to be represented in connection with the topography, purely visual representations typically overwhelm users. Rather than overloading the visual channel, we investigate an alternative approach in which some of the climate information is displayed through the haptic channel in order to alleviate the perceptual and cognitive load of the user. In this approach, haptic feedback is further used to provide guidance while exploring climate data in order to enable natural and intuitive learning of cause and effect relationships between climate variables. As the user explores the climate data interactively under the guidance of wind forces displayed by a haptic device, she/he can understand better the occurrence of events such as cloud and rain formation and the effect of climate variables on these events. We designed a set of experiments to demonstrate the effectiveness of this multimodal approach. Our experiments with 33 human subjects show that haptic feedback significantly improves the understanding of climate data and the cause and effect relations between climate variables as well as the interpretation of the variations in climate due to changes in terrain.

INTRODUCTION

In this study, we investigate the integration of haptics and information visualization techniques in the exploration of complex, multi-dimensional data. Our focus is the discovery and mastery of cause and effect relationships between patterns in data. The interactive exploration of climate data is our application domain. The large number of dimensions and constraints particular to this domain make it hard to devise a compelling, purely visual representation that allows for an intuitive display of information and efficient exploration.

Traditional techniques in the visualization of atmospheric or climatic data make the processing of information difficult for scientists and for non-experts who wish to learn about climate using real data. Data produced by climate simulation programs typically consists of values for a set of some ten climate variables of primary interest such as temperature, humidity, wind intensity and direc-tion, precipitation, and cloud water for a set of grid points in the atmosphere. Patterns in and relationships among these variables need to be examined in order to understand climate phenomena. Because climate data includes a large number of dimensions, different parameters are typically shown on different maps, requiring the integration of information from multiple maps for the interpretation of data. This may be especially difficult for nonexperts (i.e. students).

In meteorology, atmospheric parameters are usually depicted on hypothetical surfaces where pressure is constant at every point. A constant pressure surface is generally illustrated using contours in terms of its height from the mean sea level. These surfaces are not flat, and they include ridges and troughs, but contour plots inherently make the perception of these features difficult.

Figure 1a 1 shows a relatively simple meteorological map, an 850 hPa geopotential height map (contours), for a particular day, obtained from a regional climate model simulation for the region surrounding Turkey. The plot also includes a depiction of the topography used in the model (color shade). An experienced meteorologist is expected to know how the wind blows on this surface, and may guess where clouds form and possibly where this system generates precipitation. For those unfamiliar with such maps, it may be difficult to grasp and interpret even 1 The online version of the paper contains color copies of all figures. Details are more visible in the color version.

---------------- March 13-14, 2008 the basic aspects of the surface represented by these contours, let alone speculate about the wind characteristics and their effects. Figure 1b shows 850 hPa wind fields (arrows) and precipitation (color shade) added to the plot given in Figure 1a. In this figure, topography is excluded to avoid further complication. It would be useful for this plot to include more atmospheric parameters and surface features, such as specific humidity, air temperature, cloud water, vorticity, and topography; however, in order to retain clarity, it is not desirable to include more than two or three parameters on the same plot. Instead, several separate plots for different parameters are generated. For example, Figure 1c illustrates specific humidity (shade) and air temperature (contour) at 850 hPa level for the same day. When all of the plots in Figure 1 are analyzed together, they will provide an experienced observer with insight into the atmospheric conditions of this region on that specific day. However, the overwhelming visual information in these graphs makes it difficult for a nonexpert to integrate and interpret the data and come to a correct understanding of the meteorological conditions in the region on that day.

An earlier version of this work was presented orally at the IEEE Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems,

In addition to traditional visualization tools, such as the Grid Analysis and Display System (GrADS) [START_REF] Tsai | A Prototype Java Interface for the Grid Analysis and Display System[END_REF] and the tools developed by the National Center for Atmospheric Research (NCAR) [2], which generate data such as those presented above, there are also interactive forecast visualization tools such as IGrADS [START_REF] Telfeyan | Interactive Forecast Visualization Tools for the US Armed Forces[END_REF] used by the US Armed Forces or CLIMVIS (The Climate Visualization System) [START_REF]CLIMVIS (The Climate Visualization System[END_REF] used by NASA. Although these systems display information more effectively, they do not attempt to show all relevant data on one map, which would make it easier for users to identify and understand connections between the data without being overwhelmed.

Attempts have also been made to provide for more intuitive analysis of meteorological data through 3D Visualization Systems. Gateau [START_REF] Joe | A 3D Visualization System for Intuitive Analysis of Atmospheric Science[END_REF], which allows atmospheric scientists to view 3D grid data without 3D programming knowledge, is one such tool. Its straightforward interface allows the user to interactively view atmospheric data in 3D [START_REF] Joe | A 3D Visualization System for Intuitive Analysis of Atmospheric Science[END_REF]. Another method uses brush strokes to represent multidimensional data elements. In this method, the attributes of different data elements like precipitation, temperature, wind speed, or pressure are used to vary the appearance of the brush stroke's color, size, and orientation or texture [START_REF] Healey | Formulizing Artistic Techniques and Scientific Visualization for Painted Renditions of Complex Information Spaces[END_REF]. Although these tools are successful at displaying more multidimensional data on the same graph, they do not adequately support user exploration, or the discovery and understanding of the patterns in the data.

Multi-modality may serve to communicate data to users more effectively by reducing the cognitive load placed on any one sense. Lawrence et al. have suggested that rendering modes that combine haptic and visual interfaces allow users to interactively explore scalar, vector and tensor fields and facilitate their understanding of the data [START_REF] Lawrence | Synergistic Visual/Haptic Rendering Modes for Scientific Visualization[END_REF]. Harding et al. [START_REF] Harding | Visualization of Very Large Datasets: A Multi-Sensory System for the Investigation of Geoscientific Data[END_REF] have proposed combining 3D graphics, sonification and haptics into multi-sensory systems in order to manipulate geoscientific data interactively, which they suggest would facilitate many complicated tasks faced by geoscientists in academia and industry. Jeong et al. have studied the feasibility of adding haptic and auditory displays to traditional visual geographic information systems [START_REF] Jeong | Multimodal Geographic Information Systems: Adding Haptic and Auditory Display[END_REF].

While these multi-modal systems serve the needs of scientists, Gordin and Pea have suggested that scientific visualization also has the potential to make science education more accessible and provide a means for scientific inquiry [START_REF] Gordin | Prospects for Scientific Visualization as an Educational Tool[END_REF]. Edelson and Gordin [START_REF] Edelson | Visualization for Learners: A Framework for Adapting Scientists' tools[END_REF] explain that scientific visualization technologies allow students to visualize scientific concepts using real, complex data and to apply their knowledge in a discovery environment. They have applied this framework to the design of ClimateWatcher, a scientific visualization environment currently used in middle school, high school and university settings [START_REF] Edelson | Visualization for Learners: A Framework for Adapting Scientists' tools[END_REF]. ClimateWatcher displays surface temperature at a given time using color mapping. Although this software displays surface temperature effectively, it is not multimodal and does not show multiple levels of climate data at the same time. It also does not attempt to explain the cause and effect relationships among different climate data.

Although multi-modal systems have been used to represent multidimensional data more intuitively, no attempt has been made to use a multi-modal climate visualization system for education. Lee et al. have suggested that haptics can be used to display meteorological information [START_REF] Lee | Haptic Weather[END_REF], and Omata et al. have represented wind in climate data using vibrations and forces [START_REF] Omata | Haptizing Wind on a Weather Map with Reactive Force and Vibration[END_REF]. Our work also utilizes a multi-modal approach, but is distinct in that our goal is to convey cause-effect relationships in the domain of climate visualization and to enhance intuitive learning in this domain. The advantages of a multi-modal approach in learning have been demonstrated in other fields. Haptic interfaces have been used to augment the teaching of physics, especially simple machines in elementary school [START_REF] Williams | Haptics-Augmented Simple-Machine Educational Tools[END_REF], and the teaching of basic chemistry and structural biology has also been aided by haptic feedback enabling the user to touch different sizes and complexities of known molecular structures [START_REF] Sankaranarayanan | Role of Haptics in Teaching Structural Molecular Biology[END_REF]. Feygin et al have shown that haptic guidance helps the users remember a given task better by giving them a kinesthetic understanding [START_REF] Feygin | Haptic Guidance: Experimental Evaluation of a Haptic Training Method for a Perceptual Motor Skill[END_REF]. Keeping in mind the role of haptics in education, and in reducing the cognitive load in multidimensional data, we suggest that a multi-modal climate visualization system can be constructed that helps users understand the different dimensions of data as well as the cause and effect relationships among them by taking advantage of the natural guidance provided by haptic feedback.

We present CEVIZ (Climate Exploration and VIsuali-Zation), a system for the presentation and interactive exploration of climate data [START_REF] Yannier | Using Haptics to Convey Cause and Effect Relations in Climate Visualization[END_REF]. CEVIZ is a prototype tool built to demonstrate how visual and haptic representations and haptic guidance can be used to improve upon purely visual representations. Design choices in CEVIZ were made in order to test the hypotheses given below. These design choices are based on the earlier findings that action and perception are coupled in haptics [START_REF] Lederman | Hand Movements: A Window into Haptic Object Recognition[END_REF][START_REF] Lederman | Action for Perception: Manual Exploratory Movements for Haptically Processing Objects and Their Features[END_REF] and humans can successfully memorize and cognize haptic stimuli [START_REF] Kahol | Neuro-Cognitively Inspired Haptic User Interfaces[END_REF][START_REF] Morris | Haptic Feedback Enhances Force Skill Learning[END_REF]. In addition, the research conducted by Ernst and Banks [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF] shows that human brain combines visual and haptic information in an optimal manner. While there was no strict rule followed in selecting the representations, generally, visual representations were determined to be more appropriate for the presentation of overview information while haptic representations were more useful in the representation of local phenomena. I. Haptics can be used to guide exploration along certain paths that correspond to cause-and-effect relationships, and precedence relationships in time, and, in this way, improve the discovery, learning and retention of these relationships II. Haptic representations of certain kinds of data can reduce visual overload. Furthermore, augmenting visual representations with haptics improves the perception of data dimensions that do not stand out in purely visual representations. III. Haptics can be used to help guide and/or confine exploration to areas where interesting phenomena occur. This improves the perception and recall of relationships between these phenomena. IV. Haptic representations provide significant improvement upon purely visual representations by enabling users to notice and remember relationships between different kinds of data variables when they are not paying explicit attention.

In addition to displaying the climate data to the user through visual and haptic channels, our work makes use of a number of information visualization techniques, for instance, the transparent information cube [START_REF] Rekimoto | The Information Cube: Using Transparency in 3D Information Visualization[END_REF]. We discuss these techniques in Section 3 as we present how each climate data dimension is represented visually and/or using haptics. We do not introduce any novel visual representations; instead, we focus on picking a suitable combination of visual and haptic representations optimized to illustrate cause and effect relationships and minimize perceptual and cognitive overload.

We designed a set of human experiments in order to test the hypotheses listed above. In these experiments, the group that received visual and haptic feedback together consistently performed better at tasks than the other two groups received visual feedback only. This and other results support our hypotheses.

PRELIMINARIES

Climate Data, Cause and Effect Relationships

Climate data is provided by simulation programs at points that lie on pressure surfaces in the atmosphere. Conventionally, pressure surfaces at 500mb, 700mb, 850mb and 925mb are used to study climate data. These pressure surfaces may intersect with the terrain.

The output of a climate simulation program is a set of data points at a certain point in time (most often a given day) on a particular pressure surface. Each data point consists of the following dimensions:

x and y coordinates (degrees): the latitude and longitude of the point (shown from now on by (x,y))

p-level height (gpm/geopotential meter, the z coordinate, in km): the height (altitude) of the pressure surface at (x,y) Precipitation (mm): rain, snow and other forms of water falling at (x,y)

Air temperature (K): measure of the heat content of the air at (x, y, z)

Zonal wind (m/s): The component of the wind velocity at (x, y, z) along the local latitude, i.e., the x component.

Meridional wind (m/s): The component of the wind velocity at (x, y, z) along the local longitude.

Specific moisture (kg/kg): Humidity of the air at (x,y,z)

Cloud water (kg/kg): The total concentration of ice and liquid water particles in a cloud, if one exists, at (x, y, z)

Vorticity (K m 2 kg -1 s -1 ): The force defined as the curl of the wind velocity representing the amount of circulation or rotation. Vorticity is a force perpendicular to the plane along which the wind velocity is given. The vorticity makes the wind rise or fall due to the circulation of the air.

The following is a list of noteworthy interactions and cause and effect relationships among climate data variables and the terrain.

-When wind has enough humidity and enters into a relatively cooler region, clouds form.

-

Clouds occur in regions where winds collide with mountains.

-Clouds occur in areas of humidity where there is counterclockwise wind circulation in the northern hemisphere and where there is clockwise wind circulation in the southern hemisphere.

-For precipitation to occur there must be vorticity and humid air must rise. Furthermore, there must be a significant temperature drop from a higher pressure level (closer to earth surface) to a lower pressure level.

Learning and internalizing these basic climate-related principles becomes important in two settings. First, while climate non-experts are being taught climate phenomena, they need to develop a working knowledge of these principles in order to be able to use them without much cognitive cost. This is especially the case when they are interpreting climate patterns and climate data at different points in time or for different geographical configurations. Second, while climate experts are analyzing and comparing results of climate simulations, it is desirable to convey to them particular occurrences of the above relationships in a way that does not demand too much of their conscious cognitive attention. This allows them to instead focus on what is different about a particular set of climate data. Demonstrating that haptics-augmented visualization techniques facilitate this learning and internalization is the primary contribution of our work.

Climate Simulation

For the experiments we report in this paper, we use climate data provided by the simulation program RegCM3 [START_REF]Regional Climate Model[END_REF]. RegCM3 makes use of a three-dimensional, sigma-coordinate, primitive equation climate model. The simulation program RegCM3 takes boundary and initial conditions as input, which are the specific data values in the region of interest corresponding to the day the climate simulation is started. These include the description of the terrain in the form of a height map, i.e. a set of points of the form (x, y, z) where z is the altitude of the terrain (in km) above sea level at coordinate (x, y) and x and y are given as grid points along west-east and northsouth directions respectively. The boundary and initial conditions, including the height map and the vegetation cover for the region we want to investigate were provided by the Eurasia Earth Sciences Institute (AYBE) at the Istanbul Technical University. RegCM3 can simulate the evolution of the data variables for various lengths of time.

In order to investigate the effect of vegetation or certain terrain features, climatologists run simulations of the same geographic area with different vegetation or at different points in time, or with particular terrain features modified. The results of such sets of simulations are data files of the same format. In such cases, climatologists are interested in comparing macro patterns in climate data and finding differences in climate events.

THE CEVIZ SYSTEM

Figure 2. Block diagram of the CEVIZ system.

The CEVIZ system takes as input the climate data files produced by RegCM3, and by using a combination of haptics and information visualization techniques, presents them in an interactive way to the user (Fig. 2). Details of how each kind of climate data is presented are provided in Section 3.1. The design choices in the CEVIZ system were arrived at after some experimentation with earlier prototypes. The choice of where and how to use haptics, where to use point rather than overview information, as well as the particulars of how each dimension was represented was made so that a comparison between a purely visual system and a haptics-augmented system would allow us to make conclusions about hypotheses I-IV explained in the Introduction section.

Representation of Climate Data

For each kind of climate data listed below, we explored several visual, haptic and combined representation alternatives. Due to space limitations, in the following we discuss only a few of the alternatives that were considered and discarded. In all cases, the mapping from climate variables to visual and haptic quantities was linear.

Height map: A three-dimensional mesh representation of topography is formed by connecting the height values for each grid point using the height map provided by AYBE. Height values are mapped to color shades ranging from blue (sea level) to gray. The height map is also used for haptic rendering.

Pressure Surface: The p-level height data is used to construct a pressure surface. This surface is not shown visually because it blocks the user's view of the terrain too much. Instead, it is mapped as an invisible haptic surface that the user can feel and move along (see the review of haptic rendering concepts in [START_REF] Basdogan | Haptic rendering in virtual environments[END_REF]). Only the parts of the terrain above the pressure surface being explored can be felt by the user.

Visual feedback

Haptic device

Wind: A combination of visual and haptics representations is used for winds. The visual representation consists of conical arrows at grid points (Fig. 3). The apex of the cone points in the direction of the wind, and the radius and height of the cone are both proportional to the magnitude of the wind. At the beginning of exploration, none of the wind arrows are displayed. The wind arrows at grid points within a certain distance of the cursor are made visible as the terrain is explored. Forces rendered through the haptic device lead the cursor and guide the user to follow the path of the winds. This is implemented by dividing the map into gridlines on the x-y plane and assigning a force vector to each point in the grid. The zonal and the meridional winds are mapped as forces in the x and y directions. Forces at points intermediate between grid points are computed by a linear interpolation of the forces at the neighboring four vertices on the grid. We initially experimented with displaying all of the wind data at once statically throughout the entire map and found that, rather than allowing the user to grasp wind overview information rapidly, this representation instead overloads the user and makes it more difficult to retain this information. We found that when the entire overview is presented at once, the user typically is unable to immediately absorb the overview, and instead serially explores the map.

We tested if displaying wind information gradually while the user is guided by the wind forces displayed through a haptic device gives the impression of traveling with the wind and finding out information about other climate variables simultaneously with wind-guided exploration. As our experiments demonstrate, this was instrumental in communicating cause-effect relationships because, as explained in Section 2.1, the effect is always located downstream in the wind and happens after the phenomenon that causes it.

Cloud water: Cloud water is represented graphically by white spheres mapped over the pressure surface with radii directly proportional to the cloud water value at that point (Fig. 4). In a quick overview of the terrain, the regions where the spheres are largest and most frequent easily stand out as the regions where there is the largest amount of cloud water.

Cloud water is also represented haptically using bump textures mapped onto the pressure surface [START_REF] Ho | Efficient Point-Based Rendering Techniques for Haptic Display of Virtual Objects[END_REF]. The size of the bumps is proportional to their radii. The information cube lets the user observe these variables at specific points rather than giving an overview for the whole map. This draws the user's attention to changes in climate data while the cursor motion is guided by the haptic forces. The user experiences these changes in humidity and temperature that are caused by or happen concurrently with the wind motion. This design choice was made in order to convey cause-and-effect relationships more explicitly. The information cube has a shadow (Fig. 6) directly beneath it on the terrain which is used to show the exact location of the information box over the xy plane. Rain: Rain is illustrated by animated small blue cones with radii directly proportional with the precipitation value (mm) at that point (Fig. 7).

Vorticity and Rain: When there is a counterclockwise circulation in the northern hemisphere or when the wind collides with the mountains air tends to rise. In our system, vorticity is represented by a resistive spring force F=-k*∆z at each point, the magnitude of the spring constant k being inversely proportional to the vorticity force and ∆z representing the distance the cursor lifts up from the pressure surface. As a result, it is easier for the user to lift the cursor from a pressure surface in regions where vorticity is high. We chose to render vorticity only in the areas where vorticity and precipitation are high. In these areas, vorticity causes air to rise. These areas were delineated by blue cylinders (Fig. 7).

When the cursor is outside but close to the boundary of one of the cylinders, haptic forces pull the cursor into the cylinder with a spring force proportional to the distance of the cursor from the cylinder surface. Once inside the cylinder, lateral spring forces help keep the cursor confined within the cylinder. As the user tries to lift up the cursor while inside the cylinder, he/she moves inside a tunnel (a virtual fixture) along a vertical line going up from that point.

When the cursor enters the cylinders, if there is enough humidity, rain starts to fall. The temperature and drop in humidity as the cursor rises in the cylinder are represented by the changes in the color and size of the information cube. Since pressure is directly proportional to temperature by the ideal gas law, as the air rises, the pressure drops and causes the temperature to drop as well.

In the cylinders where rain forms, as the user lifts the cursor from the surface inside the cylinder he sees rain drops moving as he moves up and down with the information cube. When the cursor arrives at a pressure surface the rain drops disappear and when it is lifted, they reappear. This design choice was made to make more explicit the fact that rain forms when the wind rises. We experimented with other ways of representing vorticity but found them to be counterintuitive. For instance, we tried rain tunnels which had a force pulling the cursor up along the axis of the cylinder and moving him to a pressure surface above the current one, at which point the climate data in the newly-reached pressure surface would be displayed. We found that the pull felt very abrupt to users and it was very difficult for them to notice that they had moved up to a pressure surface at a higher altitude. They would be confined to this new pressure surface and haptic forces would work against them if they wanted to repeat the transition from one pressure surface to the other. Users felt this was counterintuitive.

EXPERIMENTAL EVALUATION AND DISCUSSION

In order to test our hypotheses, we designed five experiments where the user explores the climate system around Turkey for different time periods and attempts to figure out the cause and effect relationships between different geographic and climatic variables.

We used climate data files produced by RegCM3 for the region surrounding Turkey. Climate data variables were provided on a grid in the (x,y) plane with 24 km in between each grid point. There were 169 grid points along the x direction and 115 grid points along the y direction.

33 human subjects participated to the experiments. Subjects were divided into three groups (11 in each group). Only visual feedback (V) was provided to the subjects in Groups 1 and 2 whereas both visual and haptic feedback (V+H) were provided to the subjects in Group 3. The subjects in Group 1 (represented by V1 in the text) held the stylus of the haptic device and freely explored the surface of the map with no force feedback. For subjects in Group 3, haptic feedback guided exploration and certain climate parameters and the terrain were rendered using haptics.

To subjects in Group 2 (appears as V2 in the text), instead of allowing them free and unguided visual exploration, the climate data was presented along the paths of cause-and-effect relationships in a sequence that reflected the occurrence and progress of these relationships in time. This was accomplished by showing them selected videos of subjects from Group 3. For this reason, in the experiments below, we do not provide a separate description for subjects in Group 2.

The purpose of the experiments performed by subjects in Group 2 was to test whether the improvement brought about by haptics is almost completely due to the fact that exploration is guided along particular paths, or whether haptics provides observational and learning benefits beyond this.

Experiment 1: Wind

Goal: The goal of this experiment is to investigate whether haptics helps a user observe and remember wind patterns better.

Stimuli: In the visual version of this experiment (V1), as the user explores the surface, he sees the arrows appearing gradually on the screen. The user's goal is to explore the whole surface until all the arrows are visible on the screen (Fig. 8).

In the haptic-enhanced version (V+H), in addition to seeing the wind arrows, the user feels the wind forces in real time through the haptic device. Also, haptic feedback snaps the cursor onto the pressure surface during the exploration by applying a spring force when the user wants to move the cursor away from the pressure surface. Procedure: The users are instructed to explore the whole surface until all the arrows are visible on the screen. At that point, the visual and haptic displays are turned off, and the users are given six different maps representing different wind patterns (see Figure 8 for examples) and asked to choose and rank the three maps that most closely match the one they explored.

Results:

The columns of the table below titled 1st, 2nd, and 3rd represent the number of subjects in each group who chose the correct answer as their first, second and third choice. The column titled raw score is the number of subjects that chose the right map in any one of their three choices. The penalty-based score is calculated by assigning 3, 2, 1 points to the 1 st , 2 nd , and 3 rd choices.

Experiment 2: Cloud Formation Goal:

The statement "When wind has enough humidity and enters a relatively cooler region, clouds form." specifies a cause and effect relationship between climate variables. The goal of this experiment was to test whether the combined haptic and visual approach improves upon a purely visual approach in learning this relationship and later being able to use it to make inferences.

Stimuli: In the visual version of this experiment (V1), as the user explores the surface, wind and arrows start appearing. Five locations are marked by numbers and off-white spheres on the map (Fig. 10), and the user is asked to choose the regions where clouds are likely to occur. In the haptic version of the experiment, the same visual representations are used for temperature and humidity with the addition of the haptic feedback for representing the winds. The wind forces guide the user to follow the path of the winds and observe the changes in the climate variables along these paths.

Procedure:

The subjects are given a handout explaining that they will investigate the cause and effect relationships between humidity, wind, temperature and formation of clouds in the experiment. They are given the statement "Wind carries humidity. If the wind has enough humidity and enters into a relatively cooler region, clouds form." The subjects are then asked to explore the pressure surface and decide in which of the five regions (provided to them in the beginning of the experiment) clouds are likely to form. Subjects are allowed to choose as many regions as they like and winds are rendered throughout the experiment.

Results:

The results of this experiment are shown in the table below. The perfect scores represent the number of participants that gave the perfect answer (1-3-4) while the raw scores are based on the number of correct answers only. The penalty based scores are calculated by assigning "+1" to each region chosen correctly and "-1" to each wrongly-chosen region.

Experiment 3: Locating Clouds Goal:

The goal of this experiment is to investigate whether haptic feedback helps the user construct a link between the cloud location, the wind patterns, and the terrain. Differently from experiment 2, no statement is given to the subjects about the relations between climate variables in order to test whether haptics helps users learn without explicitly paying attention.

Stimuli: All versions of this experiment (V1, V2, and V+H) consist of a learning phase followed by a testing phase.

In the learning phase of the visual version of the experiment (V1), the user observes clouds appearing on the surface together with the wind arrows. First, the user explores the whole surface until all the arrows and clouds are visible on the screen (Fig. 11). In the testing phase, the user is given the same system, but this time without the clouds appearing on the surface and is asked to guess the location of three cloud clusters in five trials each. During this second part of the experiment where the user's learning is being tested, as the user explores the surface, only the wind arrows appear on the screen gradually. The user is asked to find the location of the cloud clusters.

In the haptic version, haptic feedback enables the user to feel the winds, the pressure surface, and the terrain. Also, a bump texture is haptically mapped (as ex-plained in Section 3) onto the surface to represent the clouds during the exploration along with the visual sphere representations. The testing phase of the haptic version is the same as that of the visual version. Haptic feedback is on throughout the experiment.

Results:

The correct answer for the first part of the experiment was the choices "b" and "e". The distribution of the subjects' responses is given below. The penalty based score was calculated by assigning "+1" to each correct answer (i.e. to each "b" or "e" chosen by the user) and "-1" is assigned to each wrong answer (i.e. to each "a", "c" or "d" chosen by the user).

Experiment 4: Rain Goal:

The aim of this experiment is to investigate whether haptic feedback helps the user realize that the air must rise (vorticity being positive and high) in order for rain to occur and observe the changes in other climate variables, like temperature and humidity, during the rain formation.

Stimuli: Five cylinders in vertical position are displayed to the user on the screen (Fig. 7 cursor enters a cylinder the user can investigate whether or not rain forms in the cylinder. The five cylinders are located so that each has different characteristics in terms of humidity, vorticity, wind pattern, and rain formation.

, Section 3). If the

In the haptic version of the experiment, as described in Section 3, when the cursor is close to a cylinder, haptic forces draw the cursor inside the cylinder. While inside the cylinder, as the user moves the cursor up, he feels side forces inhibiting him from going out of the cylinder creating a tunnel effect. In the cylinders where the vorticity is high and the forces allow him to lift the cursor, rain starts to fall if the air has enough humidity, i.e., if the information box is large. The user is also able to feel the circulating wind forces around the cylinder if the cursor steps outside the cylinder.

The visual version of the experiment (V1) is similar, except that the user is not able to feel any forces. In order to create a visual effect that matches the haptic interaction as closely as possible, we adjust the positional displacements of the visual cursor based on the vorticity values inside cylinders. When the vorticity force is weak, the visual cursor does not move up much in response to the movements of the user's hand. If vorticity is low, until a certain cursor displacement threshold is exceeded, the visual cursor does not move at all in response to the movement of the stylus.

Procedure: At the end of the exploration, the user is asked to answer the following questions for each cylinder: Q 4.4.1: Why does/doesn't rain occur in Cylinder "n"? A.

The temperature difference between the pressure surfaces is/isn't significant. B.

The humidity when the cylinder is entered is high/low. C.

The wind inside the cylinder is/is not able to rise There is counterclockwise wind circulation.

c)

There is no circulation.

d)

The wind collides with the terrain.

e)

The wind doesn't collide with the terrain. Then, a new set of climate data on the same terrain, this time with four different cylinders is displayed. The user is asked to pick in which of the cylinders rain is likely to form. The user is also given the picture of the previously explored system (i.e. the training system) and is asked to respond to the following question for each cylinder in the new system.

Results:

The percentage of correct answers for each question is given for both categories of subjects below. In questions Q4.4.1, Q4.4.2, Q4.4.4, and Q4.4.5, the standard deviations show the variations in subjects' response among cylinders. In Q4.4.3, the standard deviations show the variations in subjects' response to the six different questions given above. In Q4.4.5, the first number is the raw score while the second one is the penalty-based score. The penalty-based score is calculated by assigning "+2" to each correct answer chosen as the best match and "+1" to each correct answer chosen as the second match.

Experiment 5: Terrain Manipulations Goal:

The visual representations of climate data "before" and "after" terrain manipulation shown on the same map overwhelm the user. This experiment investigates if haptic feedback helps the user understand and interpret the differences between two maps.

Stimuli: In experiment 5.1, the mountains in the North East region are removed. In experiment 5.2, the heights of the mountains in the Antalya region (south coast of Turkey on the Mediterranean Sea) are increased (see Fig. 12). The information appearing on the first window is data for the unmodified terrain. As the user explores the surface, the representation of the climate data corresponding to the modified terrain replaces the original data in areas within certain proximity of the cursor. The user can reset the map to display the original data at any time. He can also turn on/off the visual representations for clouds, rain and wind. In the haptic version of the experiment, haptic feedback guides the user to explore climate changes along paths of strong winds. As usual, the user is able to feel the terrain surface in addition to the winds.

Procedure: The user is initially given the original system and asked to explore the surface to observe the changes. As the user explores the terrain, new climate data is rendered in the areas explored by the cursor. After the user is finished exploring, she/he is asked to explain the changes by answering the following question, based on the knowledge he has gathered from the earlier experiments.

What feature in the "after" version of the climate data do you think played a role in the changes that occurred? (You can choose more than one answer)

DISCUSSION

The results of Experiment 1 show that haptic feedback helps the user remember the direction of the winds better. The task was very challenging since the user had to remember the direction of two wind circulations and five other major wind patterns. It was observed that the visual explorers reacted completely differently from the haptic explorers when they were given the question after the exploration of winds. Most V subjects indicated that they had not paid any attention to the directions of the winds whereas V+H subjects remembered some of the wind patterns quite well, especially at the locations where they were exposed to a strong force feedback.

We believe that V+H subjects remembered the map better because their motor senses were stimulated and they committed to memory the direction and the magnitude of the forces better. For example, it was observed that the haptic user remembered that his arm was being circulated in the clockwise direction, whereas it was harder for the visual user to remember which direction the circulation was. Even though the users were not told to pay attention to wind patterns, they built a haptic memory that they were later able to recall.

In Experiment 2, we investigated whether haptic feedback helps the user understand the cause and effect relations between humidity, wind, temperature and formation of clouds. It was observed that V+H subjects were significantly better at identifying the locations where the temperature dropped along the path of the wind and where the wind entered into a relatively cooler region allowing the clouds to form. V subjects were able to approach to the same locations from any direction since they were not guided by the wind forces. For example, if the user follows the path A-B-C (along the direction of winds) in Figure 10 to explore the location number 3 under the guidance of forces, he/she can quickly observe that the color of the cube changes dark to light indicating a drop in temperature and its size gets bigger indicating an increase in humidity. Hence, clouds form in location number 3. On the other hand, if the force feedback is turned off and the user approaches the same location by following the path C-B-A for example, the same conclusion cannot be derived. Other paths of exploration that do not coincide with paths of cause-and-effect relationships make it almost impossible for the user to discern any patterns.

The results of this experiment indicate that the lack of haptic guidance made it difficult for the V subjects to detect and retain the temperature and humidity patterns along the wind paths. This supports our hypothesis that haptic guidance leads users to observe changes in climate variables in a particular sequence that corresponds to the cause and effect relationships and although the same information is there in the purely visual model, the unguided exploration makes it difficult to notice and/or retain these relationships. While V2 subjects observed the relationships along the same paths, they did not appear to learn and retain these relationships as well as the V+H subjects.

The results of Experiment 3 were consistent with the results of Experiments 1 and 2. It was observed that V+H subjects learned that clouds formed in areas of counterclockwise circulation and where the circulating wind was colliding with the terrain better than V subjects. It was observed that V+H subjects paid attention to the bumpy texture mapped on the surface at cloud locations, whereas, the V subjects reported making their decisions about the cloud locations based on their geographic knowledge and visual memory. The results confirm that haptics helped the user form a link between cloud formation, wind structure and terrain even though the user was not explicitly told to pay attention to these variables.

In Experiment 4, all of the V+H subjects noticed that wind inside some of the rain cylinders was able to rise whereas V subjects reported answering the related question randomly. In the training portion of this experiment, subjects in the V+H group were able to recognize the relation between the wind rise and the resistive force inside the cylinders. We believe V+H subjects performed better at remembering the changes in temperature and humidity inside the cylinders better than V subjects because during exploration, the haptic feedback allowed the cursor to move in a more controlled way inside the rain tunnels. Results of this experiment show that the haptic users were significantly better than both V1 and V2 subjects in remembering the cause and effect relationships in rain formation even though they were not told to pay atten-tion on it. These results confirm that the benefits of a haptic representation were not purely due to controlled exploration but that haptics aids learning and retention in other ways as well.

The second, "testing" part of Experiment 4 asked the users to consider a new map and to identify in which of the cylinders rain was likely to form by applying what they had learned in the training part. It was observed that the V+H group was significantly more successful in locating the cylinders in which rain formed and matching the new cylinders with the ones in the first part. This lends further support to our hypothesis that when certain differences are rendered in haptics, they are detected and remembered better.

In Experiment 5, we investigated if haptics improves the understanding and interpretation of the differences between two maps overloaded with climate information. Given the task of interpreting the changes in climate data when topography was manipulated, we observed that the V+H subjects performed significantly better. The difference in performance was especially pronounced in the Antalya case (Section 4.5), where rain increases when the mountains are made higher as a result of counterclockwise circulating wind colliding with the manipulated terrain.

The experimental results strongly support hypotheses I-IV in the introduction and indicate that carefully designed haptic guidance and haptic rendering of certain data dimensions can be very instrumental in conveying cause-and-effect relationships in a high-dimensional data space. The correct response average of the subjects to all questions is reported below in percentage (raw-scores are used in the calculations when there is more than one type of score). The standard deviations show the variations among the questions. The difference between the groups is highly significant. It appears that observing the guided movements of a haptic user (V2) leads to better results than the visual exploration with no haptic feedback (V1). However, (V+H) is significantly better than V2, indicating that the arm movements guided by the haptic device in physical space has additional benefits in human perception and learning. It could be argued that once these cause-and-effect relationships and their manifestations in a certain map are known, it is possible to find better visual representations for them, such as animating the winds and the clouds. The experiments carried out by Group 2 were conducted to provide an approximate answer to this question and demonstrated that even when exactly the same exploration was carried out, with haptics learning and retention improved. It is noteworthy that our haptics-augmented system allows the user to naturally notice the manifestations of these relationships on a map and internalize them while allowing him to direct the exploration. Our system accomplishes this without needing to pre-process, interpret and detect patterns in the data first.

CONCLUSION

We designed, built and experimented with a prototype that combines haptics and information visualization in order to improve the discovery, learning, and retention of cause-and-effect relationships in a multidimensional data space. Our experiments confirmed that the inclusion of haptics significantly improves results. The existing system may be further improved by integrating a thermal display to render temperature changes, an auditory display for rendering gusty winds, or by displaying other haptic effects such vibration to simulate the start of rain.

Figure 1 .

 1 Figure 1. Simulated weather conditions for a region covering southeastern Europe and part of the Middle East on a particular day created by Grid Analysis and Display System (GrADS). (a) Topography (m; shade) and 850 hPa geopotential height (gpm; contours), (b) 850 hPa geopotential height (gpm; contours), 850 hPa winds (m/s; arrows) and precipitation (mm/day; shade), and (c) Specific moisture (g/kg; shade) and air temperature (K; contours).

Figure 3 .

 3 Figure 3. Representation of the winds in the CEVIZ system.

Figure 4 .Figure 5 .

 45 Figure 4. Cloud water represented by the sizes of white spheres. Humidity and Air Temperature: The cursor in CE-VIZ is an information box. Humidity (specific moisture) is represented by the size of the information box. There is a linear mapping from the moisture values to the size of the box. Temperature values are represented by the color of the information box. Air temperature is divided into eleven intervals, and each interval is assigned a color (Fig. 5).

Figure 6 .

 6 Figure 6. The shadow of the information cube shows the location of the cursor on the terrain.

Figure 7 .

 7 Figure 7. The representation of rain cylinders and raindrops. The cylinders represent the areas where vorticity and precipitation are high. Within the cylinders, a spring force inversely proportional to vorticity pulls the cursor towards the pressure surface.

Figure 8 .

 8 Figure 8. The user exploring the patterns of wind in experiment 1.

Figure 9 .

 9 Figure 9. Two of the six wind pattern map choices in experiment 1.

Figure 10 .

 10 Figure 10. Winds and clouds in experiment 2. The addition of haptics introduces a sense of direction for understanding causalities in the climate data. For example, if the user follows the path A-B-C (along the direction of winds), the color of the cube changes dark to light indicating a drop in temperature and its size gets bigger indicating an increase in humidity. Hence, clouds form in location number 3.

Figure 11 .

 11 Figure 11. Winds and clouds in experiment 3.

Q 4 . 4 . 2 :

 442 Which of the below are true for Cylinder "n"? a) There is clockwise wind circulation. b)

Q 4 . 4 . 3

 443 The user is asked to fill in the following questionnaire for cylinder "n":Air can/cannot rise There is clockwise/counterclockwise circulation Wind collides with terrain/There is no circulation and wind does not collide with the terrain There is/is not a significant temperature drop Wind has/does not have significant humidity When rain occurs, humidity a) drops b) rises c) stays the same.

Q 4 . 4 . 4 Q 4 . 4 . 5

 444445 Cylinder `?`: No/little/a lot of rain will occur. Which cylinder shown in the training system is similar to this cylinder? Give two answers you think match best.

Figure 12 .

 12 Figure 12. Cloud and rain patterns in experiment 5.2. The heights of the mountains in the south coast of Turkey on the Mediterranean Sea (see the darker region on the map) are intentially increased to change the climate conditions.

  The percentage of correct answers by V and V+H subjects in experiments 5.1 and 5.

  , Reno, Nevada, USA.• N.Y. is with the College of Engineering, Koc University, Istanbul, 34450, Turkey. E-mail: nesnes@stanford.edu.

• C.B. is with the College of Engineering, Koc University, Istanbul, 34450, Turkey. E-mail: cbasdogan@ku.edu.tr. • S.T. is with the College of Engineering, Koc University, Istanbul, 34450, Turkey. E-mail: stasiran@ku.edu.tr. • O.L.S. is with the Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, 34469, Turkey. E-mail: senomer@itu.edu.tr.

  2 are shown in the table below.

		Exp. 5.1	Exp. 5.2
	V1	7/11 (64%)	6/11 (55%)
	V2	9/11 (82%)	7/11 (64%)
	V+H	9/11 (82%)	11/11 (100 %)