As part of our 20 week capstone project, we were approached by NASA Jet Propulsion Laboratory to create a tool that helps scientists understand weather on Mars. The overarching goal is to reduce the back and forth between scientists and engineers to enable operations teams to meet an aggressive five hour mission planning timeline for the Mars 2020 rover mission.
How might we help scientists understand and predict the impact of atmospheric opacity (𝞃) on instrumentation and data?
Angela Yung, John Sykes, Yomna Hawas, Tyler La
Low-hi fidelity wireframes, video prototyping, storyboarding, visual design, usability testing, user research, secondary research
NASA Jet Propulsion Laboratory
Mentored by Lyle Klyne
Long-term Routing and Rover Instrumentation(L-RROI) is the first tool to help mission teams predict and visualize atmospheric opacity levels and its effect on instruments
We realized that the given topic would require extensive secondary research in order to have enough background knowledge to understand the needs in this space. We conducted a literature review to gather context about rover mission foundations, relevant instruments involved, weather variables that they observe on these missions, collaboration methods and challenges, rover operations decision making processes, data visualization practices, and the structure of the Mars team at JPL.
Through our literature review we learned about atmospheric opacity, also known as tau, which is a measure of optical depth or how much sunlight can penetrate the atmosphere. Understanding atmospheric opacity is critical for rover planning and overall success of the mission.
We then conducted a competitive analysis to assess 13 of the currently available products used for collaboration, communication, mapping and data visualization. The competitive analysis helped our team explore existing tools in various domains, assess their pros and cons, and draw insights on how we might utilize existing features for our own design. Based on our research, we identified five important criteria to measure the tools against: transparency, customization, shareability, comparability, and annotation.
Once we had a basis of knowledge about the space we were designing for, we began conducting user research to uncover the typical mission planning workflow, and understand the challenges associated with this line of work. We formed a few questions to guide us through the research process:
What weather information does the mission operation team need to know and how do they use this information in making time-sensitive decisions?
What is the typical workflow for mission planning and what collaborative and operational challenges do they face?
What tools and technology does the mission operation team currently use, or plan to use, to collaborate and assess weather information for mission planning?
We then conducted 18 user interviews with mars specialists which included a combination of engineers, atmospheric scientists, geologists, rover planners, designers.
Semi-structured interviews were conducted to better understand team member experiences
Directed Storytelling was used to get context around their experiences and understand the pain points, communication breakdowns, and motivations
Iterative Diagramming was conducted to get a better sense of the structure of rover planning meetings to improve the accuracy.
After conducting our user and expert interviews, we did coded our transcripts and did several rounds of research synthesis. We began with sorting our codes into categories. We then created themes from our categories. From our themes we were able to derive research insights.
Meeting the proposed 5-hour operational timeline is unattainable unless rover teams shift focus to long-term goals.
Weather no longer poses a critical risk to rover safety, but still must be considered due to its impact on instruments and power constraints.
Weather on Mars is relatively predictable, however, there is no weather forecasting despite its potential applications to longterm planning purposes.
In the conflict between mission groups, the only shared language is data. Even then, every specialty has its own dialect, leading to misunderstandings.
Data is more revealing when contextualized with other observations. Existing tools do not have this capability, hindering scientific discovery.
Show How Things Fit Into the Big Picture
Scientists and engineers should understand how their work impacts each other and the overall mission. This can be done through contextualizing data to provide a comprehensive view of the situation.
See Through The Same Lens
Scientists are using their own tools and this could lead to misunderstandings about the meaning of their output. As a result, enabling shared mental models is crucial in order to ground conversations
Understanding the history of data is just as important as understanding what it means. To scientists, knowing the provenance of data facilitates trust, exposes caveats, and helps ground the conversations.
The advancement of scientific knowledge stands to benefit from disagreement as dissent breeds productive debate in order to reconcile conflicts. Our tool needs to facilitate healthy discussion between differing points of view in order to drive scientific advancement.
From our research results, we derived 2 design opportunities to guide us as we moved forward with our ideation process:
1. Express the impact of atmospheric opacity on instrumentation
2. Use the predictability of Martian weather to help teams shift from short-term to long-term planning
With a new narrowed focus on helping scientists understand the impact of tau on the mission, we began ideating, resulting in a total of 120 concepts. We then categorized these concepts and began the down selection process. With our top 4 concepts, we created storyboards to further explore use cases for these concepts.
After much deliberation, we landed on a concept to move forward with. Our tool would aim to give visibility about what tau is going to be at a certain time in a mission and how that impacts the instruments aboard the rover.
We created a paper prototype to test out some features. We wanted to incorporate a map view in the tool in order to give scientists context around the rover's path. We experimented with overlays, hover states, and panels at the bottom of the screen that gave additional information such as tau's impact on instrument, tau forecast , and weather data.
We then created a low fidelity prototype and began testing our concept with 5 scientists who work with Tau in their research. This took the form of rapid iterative testing to improve on our features, meaning that we continuously recruited participants and made incremental changes through out the process, testing each new iteration and increasing fidelity as we went along. Our usability studies led us to several focused areas:
1. Using graphs to visualize tau
2. Showing instrument favorability
3. Rover-view feature
Using graphs to visualize tau
One of the features that we iterated on continuously was the seasonal tau graph, which shows scientists tau trends for a given period time and lets them compare data across several martian years.
In early versions we attempted to create an easy way to view instrument condition favorability over time. After testing this version, we learned that this was confusing to our users because they were not sure what the graph was showing. There were also concerns regarding accessibly due to colors.
In an iteration of this panel, we explored the use of height to show the favorability. This was more clear to users and allowed them to understand the graph without needed to rely on color. However, the colors we chose for this graph had some overlap with other visualizations which caused confusion.
In our final iteration, we removed the colors and kept the graph blue to avoid any confusion.
Showing instrument favorability
Another feature that we heavily iterated on was the instrument favorability chart, which is meant to show how favorable it would be to use a given instrument over a given period of time given the tau conditions.
Early iterations showed messy graphs stacked on top of one another. There was not much color variation which made the graphs difficult to distinguish. We also learned that scientists had a hard time trusting the data because there was no source named.
In our final iteration, we chose to use small multiples to help users compare data. This data visualization method has a proven record in enabling comparability and readability. We also added a slider to allow users to further explore within a more specific date range.
The rover-view feature also underwent several changes in response to research findings. Rover-view helps scientists visualize how tau impacts the images they may take using cameras on the rover. So for example they can play around with tau values and see how that would affect an image.
In our first iteration, a single photo was shown and users were able to use a slider to adjust the tau. After testing, our users explained that while this is helpful in visualizing conditions, being able to compare multiple images would allow them to better understand the impact of tau.
In our final iteration, we decided to dedicate a side panel that pops out when rover-view is activated. This allows the user to select a reference photo to compare tau at different values, and provides useful context.
L-RROI allows the user to customize their view to highlight a specific date range or target tau levels, helping scientists get a holistic view of the mission itself in the context that they choose.
The tool offers the user a customizable list of relevant instruments. Users can see a detailed view of the rover’s instruments, upcoming optimal usage dates and solar longitudes, and explanation of the weather’s effect on those instruments.
The forecast tau feature allows users to query, visualize, and export tau data for their own needs. Using the previous Martian year overlay, scientists can quickly display tau information in small multiples, allowing them to compare it against previous years to look for emergent patterns or anomalies.
Users can drop a moveable pin on the map so users are able to see an image of the mars terrain given the corresponding tau value. Users are also able to select a reference photo to compare to the selected photo. The contextualization of data through imagery helps scientists deepen their understanding of atmospheric opacity and it’s impact.