weather app research paper

SMARTPHONES AND WEATHER.
Survey structure.
Analytical methods.
Characteristics of the respondents.
Sources for acquiring weather forecast information.
Reasons for choosing MWAs.
Influence of geographic and demographic factors.
Suggested changes to improve MWAs.
DISCUSSION AND CONCLUSIONS.

Antonić , A. , M. Marjanović , K. Pripužić , and I. Podnar Žarko , 2016 : A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the internet of things . Future Gener. Comput. Syst. , 56 , 607 – 622 , https://doi.org/10.1016/j.future.2015.08.005 .

Search Google Scholar
Export Citation

Bomhold , C. R. , 2013 : Educational use of smart phone technology: A survey of mobile phone application use by undergraduate university students . Program , 47 , 424 – 436 , https://doi.org/10.1108/PROG-01-2013-0003 .

Cahyanto , I. , and L. Pennington-Gray , 2015 : Communicating hurricane evacuation to tourists: Gender, past experience with hurricanes, and place of residence . J. Travel Res. , 54 , 329 – 343 , https://doi.org/10.1177/0047287513517418 .

Chan-Olmsted , S. , H. Rim , and A. Zerba , 2013 : Mobile news adoption among young adults: Examining the roles of perceptions, news consumption, and media usage . J. Mass Commun. Quart. , 90 , 126 – 147 , https://doi.org/10.1177/1077699012468742 .

Cohen , J. , 1960 : A coefficient of agreement for nominal scales . Educ. Psychol. Meas. , 20 , 37 – 46 , https://doi.org/10.1177/001316446002000104 .

Corso , R. A. , 2007 : Local television news is the place for weather forecasts for a plurality of Americans . The Harris Poll, Harris Interactive, accessed 16 January 2014, www.harrisinteractive.com/vault/Harris-Interactive-Poll-Research-Weather-2007-11.pdf .

Dahlstrom , E. , C. Dziuban , and J. Walker , 2012 : ECAR study of undergraduate students and information technology, 2012. EDUCAUSE Center for Applied Research Rep. , 38 pp., https://library.educause.edu/∼/media/files/library/2012/9/ers1208.pdf?la=en .

Davis , F. D. , 1989 : Perceived usefulness, perceived ease of use, and user acceptance of information technology . Manage. Inf. Syst. Quart. , 13 , 319 – 340 , https://doi.org/10.2307/249008 .

Davis , F. D. , R. P. Bagozzi , and P. R. Warshaw , 1989 : User acceptance of computer technology: A comparison of two theoretical models . Manage. Sci. , 35 , 982 – 1003 , https://doi.org/10.1287/mnsc.35.8.982 .

Demuth , J. L. , J. K. Lazo , and R. E. Morss , 2011 : Exploring variations in people’s sources, uses, and perceptions of weather forecasts . Wea. Climate Soc. , 3 , 177 – 192 , https://doi.org/10.1175/2011WCAS1061.1 .

Grotticelli , M. , 2011 : Local TV is main source for weather information. TVTechnology, www.tvtechnology.com/news/local-tv-is-main-source-for-weather-information .

Handmark , 2010 : Handmark releases 2010 mobile media consumption report results. PRNewswire , www.prnewswire.com/news-releases/handmark-releases-2010-mobile-media-consumption-report-results-111445434.html .

Hickey , W. , 2015 : Where people go to check the weather. FiveThirtyEight , http://fivethirtyeight.com/datalab/weather-forecast-news-app-habits/ .

Hsieh , H. , and S. E. Shannon , 2005 : Three approaches to qualitative content analysis . Qual. Health Res. , 15 , 1277 – 1288 , https://doi.org/10.1177/1049732305276687 .

Lazo , J. K. , R. E. Morss , and J. L. Demuth , 2009 : 300 billion served: Sources, perceptions, uses, and values of weather forecasts . Bull. Amer. Meteor. Soc. , 90 , 785 – 798 , https://doi.org/10.1175/2008BAMS2604.1 .

Morss , R. E. , J. L. Demuth , and J. K. Lazo , 2008 : Communicating uncertainty in weather forecasts: A survey of the U.S. public . Wea. Forecasting , 23 , 974 – 991 , https://doi.org/10.1175/2008WAF2007088.1 .

Nagle , A. L. , 2014 : Apps to weather the storm: 10 practical, powerful weather apps for mobile devices . Weatherwise , 67 , 36 – 41 , https://doi.org/10.1080/00431672.2013.839233 .

Oblinger , D. G. , and J. L. Oblinger , 2005 : Educating the Net Generation . Educause , 264 pp., www.educause.edu/ir/library/pdf/pub7101.pdf .

Olmstead , K. , and M. Atkinson , 2015 : The majority of smartphone owners download apps. Pew Research Center , www.pewinternet.org/2015/11/10/the-majority-of-smartphone-owners-download-apps/ .

Pew Research Center , 2018 : Mobile fact sheet. Pew Research Center , www.pewinternet.org/fact-sheet/mobile/ .

Presser , S. , M. P. Couper , J. T. Lessler , E. Martin , J. Martin , J. M. Rothgeb , and E. Singer , 2004 : Methods for testing and evaluating survey questions . Public Opin. Quart. , 68 , 109 – 130 , https://doi.org/10.1093/poq/nfh008 .

Purcell , K. , 2011 : Half of adult cell phone owners have apps on their phone. Pew Research Center , www.pewinternet.org/2011/11/02/half-of-adult-cell-phone-owners-have-apps-on-their-phones/ .

Raento , M. , A. Oulasvirta , and N. Eagle , 2009 : Smartphones: An emerging tool for social scientists . Sociol. Methods Res. , 37 , 426 – 454 , https://doi.org/10.1177/0049124108330005 .

Rainie , L. , and A. Perrin , 2017 : 10 facts about smartphones as the iPhone turns 10. Pew Research Center , www.pewresearch.org/fact-tank/2017/06/28/10-facts-about-smartphones/ .

Rogers , E. M. , 1995 : Diffusion of Innovations . 4th ed. Free Press , 518 pp.

Silver , A. , 2015 : Watch or warning? Perceptions, preferences, and usage of forecast information by members of the Canadian public . Meteor. Appl. , 22 , 248 – 255 , https://doi.org/10.1002/met.1452 .

Smyth , J. D. , D. A. Dillman , L. M. Christian , and M. Mcbride , 2009 : Open-ended questions in web surveys . Public Opin. Quart. , 73 , 325 – 337 , https://doi.org/10.1093/poq/nfp029 .

Stanley , E. , and Coauthors , 2011 : Public Response to Alerts and Warnings on Mobile Devices: Summary of a Workshop on Current Knowledge and Research Gaps . The National Academies Press , 90 pp., https://doi.org/10.17226/13076 .

Willits , F. K. , G. L. Theodori , and A. Luloff , 2016 : Another look at Likert scales . J. Rural Soc. Sci. , 31 ( 3 ), 126 – 139 .

Yoder-Bontrager , D. , J. E. Trainor , and M. Swenson , 2017 : Giving attention: Reflections on severe weather warnings and alerts on mobile devices . Int. J. Mass Emerg. Disasters , 35 , 169 – 190 .

Zabini , F. , 2016 : Mobile weather apps or the illusion of certainty . Meteor. Appl. , 23 , 663 – 670 , https://doi.org/10.1002/met.1589 .

Zickuhr , K. , 2011 : Generations and their gadgets. Pew Research Center , www.pewinternet.org/2011/02/03/generations-and-their-gadgets/ .

Three universities from which surveys were collected.

Respondents’ reasons for choosing MWA.

Respondents’ reasons for switching from default MWA.

View raw image
Download Powerpoint Slide

	All Time	Past Year	Past 30 Days
Abstract Views	0	0	0
Full Text Views	5648	1414	76
PDF Downloads	3728	965	36

Weather on the Go: An Assessment of Smartphone Mobile Weather Application Use among College Students

Displayed acceptance dates for articles published prior to 2023 are approximate to within a week. If needed, exact acceptance dates can be obtained by emailing [email protected] .

Download PDF

Millions of people in the United States regularly acquire information from weather forecasts for a wide variety of reasons. The rapid growth in mobile device technology has created a convenient means for people to retrieve this data, and in recent years, mobile weather applications (MWAs) have quickly gained popularity. Research on weather sources, however, has been unable to sufficiently capture the importance of this form of information gathering. As use of these apps continues to grow, it is important to gain insight on the usefulness of MWAs to consumers. To better examine MWA preferences and behaviors relating to acquired weather information, a survey of 308 undergraduate students from three different universities throughout the southeast United States was undertaken. Analyses of the survey showed that smartphone MWAs are the primary weather forecast source among college students. Additionally, MWA users tend to seek short-term forecast information, like the hourly forecast, from their apps. Results also provide insight into daily MWA use by college students as well as perceptions of and preferential choices for specific MWA features and designs. The information gathered from this study will allow other researchers to better evaluate and understand the changing landscape of weather information acquisition and how this relates to the uses, perceptions, and values people garner from forecasts. Organizations that provide weather forecasts have an ever-growing arsenal of resources to disseminate information, making research of this topic extremely valuable for future development of weather communication technology.

A survey of undergraduate students examines preferences and behaviors relating to modern sources of daily weather forecast information.

The atmosphere is always changing, and its conditions influence our daily lives, influencing what we choose to do and how we go about our day. Weather’s dynamic nature, however, means that factors such as temperature, precipitation, and wind are often constantly in flux. It is no wonder people want to know the individual effects forecast conditions will bring so that they can plan accordingly.

Millions of people in the United States regularly obtain essential information from weather forecasts for a wide variety of reasons ( Lazo et al. 2009 ). With weather being perhaps the most routinely sought-after type of information, it is imperative to understand the many facets of how and why people procure this information, starting with their sources and then how people use their acquired knowledge in day-to-day activities. The rapid growth in mobile device technology has created new contemporary means for people to access weather forecasts, pointing to the need to update past literature in this specific niche of weather research.

With the onset of smartphones and the increasing use of mobile weather applications (MWAs) today, this technology is rapidly becoming the public face of weather forecasting (the entity that the public most associates with weather forecasts). A smartphone is defined as “a cell phone that includes additional software functions (as email or an Internet browser)” ( Merriam-Webster’s Collegiate Dictionary , 11th ed., s.v. “smartphone”). An application (abbreviated as app) is defined as a program downloaded onto smartphones that serves a specific purpose for the user ( Oxford Dictionary Online , s.v. “app,” https://en.oxforddictionaries.com/definition/app ). Therefore, an MWA is a program available on smartphones that can provide weather forecasts and additional related information. Some smartphones may already have an MWA preloaded onto a phone for consumers to use. However, consumers can choose to download any MWA they desire through online marketplaces they access with their smartphones. This study evaluates and works to understand the changing landscape of weather information acquisition and how this relates to the uses, perceptions, and benefits people garner from forecasts. The research addresses the following questions:

Are smartphones the most popular source for weather forecast information among respondents?

What specific reasons do respondents have for choosing their favorite MWA?

How do geographic and demographic factors influence MWA use?

With these research questions, the study hopes to build on past literature relating to sources of weather forecasts and fill the gap in the meteorological literature on our society’s preferences for where they obtain weather information. This knowledge on communicating weather information through mobile smartphone technology will enhance the weather enterprise’s capability to better understand and grasp the quickly changing communication landscape. Additionally, companies and organizations within the weather enterprise that provide weather forecasts have an ever-growing arsenal of resources to disseminate information, making research on this topic extremely valuable for future development in weather communication technology.

Cellular phones and mobile devices are ubiquitous in modern society, and their day-to-day functions are becoming increasingly important for cell phone owners and consumers of information. A 2011 Pew Research Center study found that 95% of the “millennial” generation (ages 18–34) and 85% of all American adults own cellular phones. Today’s college students, who align mostly with the millennial generation, have the highest rate of cell phone use compared to any other generation, with research in 2012 indicating that 62% of undergraduate college students own a smartphone, up from 55% in the previous year ( Dahlstrom et al. 2012 ). Cell phone and smartphone ownership has risen even more in just the last few years. An updated Pew Research Center fact sheet identifies that 100% of young adults (18–29) now own a cell phone, with 94% of the same age group owning a smartphone ( Pew Research Center 2018 ).

With the rise in smartphone use, applications (apps) on these devices are also soaring in popularity. Surveys of the American public found that, between 2009 and 2011, nearly twice as many adults were downloading apps to their phones, increasing from 22% to 38% ( Purcell 2011 ). This number has since soared to 77% of adult smartphone owners, indicating the continued surge in ubiquity of smartphone apps ( Olmstead and Atkinson 2015 ). Adults are most likely to download apps that provide continuous information on news, weather, sports, and finance ( Purcell 2011 ). While most popular mobile apps revolve around games and entertainment, apps for weather come in a close second followed by social media apps and those used for travel and navigation ( Purcell 2011 ). More recent research on app usage by adult smartphone owners is in line with previous studies, while also adding other popular uses for apps including shopping, dating, and reading electronic books ( Rainie and Perrin 2017 ).

Americans, especially younger generations, constantly seek information and expect to have immediate results. The added value of convenience is certainly a motivating factor in what options and sources they choose ( Oblinger and Oblinger 2005 ). Students value convenience over many other factors and therefore turn to their smartphones and mobile devices to quickly access information ( Bomhold 2013 ). Given the smartphone’s advantage in accessibility over other sources of weather information, it is no wonder that MWAs, like other smartphone apps, are rapidly gaining popularity as well ( Hickey 2015 ). Because younger generations will continue their use of smartphone apps, MWAs will experience continued growth in usage, and research into this technology will yield insights into the consumption of MWA information and MWA features that are most useful to consumers.

Information-seeking and -consumption behaviors are rapidly changing as a result of continually evolving technology ( Handmark 2010 ; Zickuhr 2011 ; Pew Research Center 2018 ), and previous research on sources of weather information such as that undertaken by Corso (2007) , Lazo et al. (2009) , Demuth et al. (2011) , and Grotticelli (2011) indicated that television was the most popular medium for weather forecast acquisition. Though the work on the type of information sought from forecasts remains relevant, the research is potentially less applicable today because of their omission of smartphones and mobile devices as a weather forecast source. More recent research has captured smartphone use for retrieving weather information. A study of residents in Ontario found that the use of cell phone apps for weather information was not as popular as other modes, including talking with family and friends, local radio, and The Weather Network, a Canadian cable weather television channel ( Silver 2015 ). A separate survey in 2015 revealed that MWAs are the preferred source for weather information, surpassing the more traditional source of television ( Hickey 2015 ), illustrating the importance of the research undertaken here.

Other recent studies look directly at MWAs and their content. Yoder-Bontrager et al. (2017) analyzed information retrieved from focus groups to better understand the reception of smartphone weather warnings and design of weather warning features on MWAs. They determined that the content of the warning information is important to participants and suggested that future MWA developers focus on the information disseminated in alerts rather than directing attention to increasing ways of alerting the smartphone owner. Additionally, one study looked at 39 of the most popular MWAs from the United States, the United Kingdom, and Italy, analyzing their design and displays of information and relating this to the future of communicating uncertainty information ( Zabini 2016 ).

The use of smartphones to access weather information has certainly shown explosive growth in recent years. Two models, the diffusion of innovations theory (DIT) and the technology acceptance model (TAM), may foster understanding of the rising popularity of smartphones in accessing weather forecasts ( Chan-Olmsted et al. 2013 ). The concepts of relative advantage, complexity, and compatibility from DIT help to explain the adoption of a new product or concept ( Rogers 1995 ). In the case of MWAs, if the apps are seen to be more valuable than a traditional weather source like television or a newspaper, then the app will likely become the preferred choice. Further, if an MWA is easy to use and aligns well with individual lifestyles it is likely to be adopted.

Similar to DIT, TAM emphasizes ideas of relative usefulness and ease of use, both of which have been shown to influence why mobile news applications are widely used by the public ( Davis et al. 1989 ). If the user does not believe the product offers much utility, the new technology will not likely be successful ( Chan-Olmsted et al. 2013 ). Additionally, the perception that a technology or product is easy to use and provides an added benefit to the user strongly correlates not only with current usage rates but also with predicted future use ( Davis 1989 ).

Understanding both where people turn for weather information and the reasons and motivations for how people access and consume weather forecasts is fundamental to learning about how to best communicate weather ( Demuth et al. 2011 ). The landmark study on sources and personal interpretation of weather data by Lazo et al. (2009) found that most people use weather forecasts for the city or area in which they live (87% usually or always). Location, timing, probability, and type of precipitation along with forecast temperatures are seen as most valuable to users ( Lazo et al. 2009 ). This study also found that people use weather forecasts mostly to stay informed about the weather (72% usually or always), but other popular uses include how to dress and how to plan activities that could be affected by the weather ( Lazo et al. 2009 ).

The acquisition, use, and understanding of weather information are all interrelated and affect one another, and factors like gender can certainly play a role in the gathering and interpretation of weather information. In a study looking at sources of weather information during a hurricane evacuation, gender was found to have a significant effect on one’s perception of credibility of sources of information. Females, compared to their male counterparts, exhibited a higher perceived credibility for most sources of weather information, including family and friends, the local tourism office, The Weather Channel, and the newspaper ( Cahyanto and Pennington-Gray 2015 ). Demuth et al. (2011) uncovered differences in how males and females use weather forecasts, where women were more likely to use weather information to plan events, choose appropriate clothing to wear, and stay updated on weather conditions. However, analysis of gender differences in MWA use is missing from the weather communication literature.

The private sector of the weather enterprise has taken advantage of the growing use of mobile apps, with various companies and organizations having introduced some of the most well-known MWAs used by Americans today ( Nagle 2014 ). Since the mid- to late 2000s, a number of companies have joined the mobile technology market, creating their own MWAs. With all signs indicating the continued surge in MWA use among the American public, it is imperative that all areas of the weather enterprise, including the public sector and academia, continue advancing research in weather and communication, especially as it relates to mobile devices. These findings can be used to improve MWAs and increase their appeal and usefulness to a larger demographic. While this study analyzes MWA use and preferences relative to daily weather forecasts, the information provided in this research also lays the foundation for further investigations into the communication of severe weather and other time-sensitive crises via smartphones. Understanding how smartphones and MWAs fit into the weather communication landscape will be of value to many organizations that provide life-saving information to the public.

DATA AND METHODS.

Following approval by the Institutional Review Board (IRB) at East Carolina University (ECU), a 28-item survey was administered to college students in introductory geography courses from East Carolina University, the University of Georgia (UGA), and the University of South Carolina (USC) to gather the data needed to address the research questions ( Fig. 1 ). College students were surveyed because they have a high rate of smartphone usage ( Zickuhr 2011 ; Pew Research Center 2018 ). Additionally, because the undergraduate college student generation will continue using smartphones and other new technologies that arise in the future, it is important to document their use of smartphones and apps because it will be their uses and demands that are most likely to shape future products.

Citation: Bulletin of the American Meteorological Society 99, 11; 10.1175/BAMS-D-18-0020.1

Download Figure
Download figure as PowerPoint slide

Introductory college classes were sampled to ensure that those completing the survey had diverse academic interests rather than sampling from upper-level courses with students who have already declared specific majors. The survey used in this study was administered using the Qualtrics survey software. Emails with a survey link and brief message were sent to professors at each of the three schools, who agreed to assist in the study. They then forwarded the emails to undergraduate students in the introductory geography courses. Participants were self-selected among those who received the invitation email, and no incentives were offered. Because the number of students who received the email is unknown, a response rate cannot be determined.

Before the survey was distributed, it was pretested with a small group of nonmeteorology students at East Carolina University. Feedback was solicited on the content, syntax, and understandability of the survey using methods described by Presser et al. (2004) . The survey was then modified and finalized based on the results of the pretest. Survey responses were analyzed statistically and through content coding for the open-ended responses.

To build on past studies regarding sources of weather information ( Lazo et al. 2009 ; Morss et al. 2008 ), the survey employed similar questions. While a direct comparison between studies is not possible, using similar questions serves to build our knowledge on using MWAs.

The survey solicited demographic information, including age, gender, race, education, family income, and the zip code of the location respondents identify as home. Following these questions, participants were asked about weather forecasts in general, specifically where they acquire forecast information, the importance of different elements or aspects of a weather forecast, and their overall level of confidence in weather forecasts, regardless of source. The next set of questions shifted to mobile devices and MWAs, asking respondents about their ownership of cell phones and smartphones. Respondents were then prompted to select answers that best describe their daily smartphone habits, preferences for MWAs, and perception of and confidence in specific MWA features. For the purposes of this study, the use of “MWA features” refers to different characteristics of MWAs that provide users with information on specific aspects or elements of a forecast. An example of this would be the hourly forecast feature on an MWA, which provides information on forecast temperatures, precipitation chances, and sky cover, three aspects or elements of a general weather forecast. The final survey question asked respondents if they had any suggestions or recommendations for how their MWAs or how MWAs in general could be improved. Most questions consisted of multiple-choice options where respondents chose one answer from a list. Some questions specified “other” as a choice, which allowed participants to supply an answer that was not listed. Strategies from Smyth et al. (2009) were implemented to seek thorough open-ended responses from participants. Other survey questions featured a five-point Likert scale (1 = not at all important, 5 = extremely important) to gauge the level of agreement with the statements provided and for questions involving confidence in MWA forecasts and the level of satisfaction with the MWAs.

To increase the number of completed survey responses, respondents were not required to answer any question before proceeding to the next item in the survey. Therefore, individual survey items have varying numbers of responses, with 308 out of 311 respondents completing a majority of the survey.

Both quantitative and qualitative analytical techniques were employed to analyze the survey data. For the purposes of this research, Likert-scale questions were designated as continuous variables, because while these questions have a specific number of items (categories) from which respondents choose, past research indicates that opposite ends of the Likert spectrum (e.g., “not important at all” and “very important”) are understood by respondents to be a continuum similar to interval-based questions ( Willits et al. 2016 ). To better understand the association between different factors pertaining to the respondents, chi-square tests and nonparametric Kruskal–Wallis and Mann–Whitney U analysis of variance (ANOVA) tests were applied to variables. The chi-square test was used when survey answers were categorical; Kruskal–Wallis was used when these answers were continuous. It should be noted that the Kruskal–Wallis test was used when analyzing three independent groups, while the Mann–Whitney U ANOVA test (a test equivalent to the Kruskal–Wallis test) was used when comparing two independent groups. Kruskal–Wallis and Mann–Whitney U ANOVA tests were employed to analyze continuous Likert-scale variables with universities and gender as independent variables. The Kruskal–Wallis test can signal a significant difference between groups, but it does not explicitly state the relationship of the statistical difference between specific groups. Therefore, the Dunn post hoc test was employed to uncover the particular differences in the independent groups.

Additionally, cross-tabulation analyses comparing two sets of data were used to uncover relationships between variables and answers from respondents. Survey responses that included “not on my app” were not considered in the statistical analysis process because the study considers only respondents who have the relevant experience with specific MWA features. A Cramer’s V post hoc test is undertaken with statistically significant chi-square results to determine if there is an association between the different variables that may explain why the results returned as statistically significant.

With open-ended survey responses, content analyses were performed by two researchers, who coded the answers into categories to gain a clearer picture of main ideas and themes. Categories were determined through directed content-coding strategies, where one coder identified important themes and concepts that were prevalent on respondent answers ( Hsieh and Shannon 2005 ). Initial categories were created, and classes with overlapping ideas were consolidated. After both coders separated responses on their own, a Cohen’s kappa test was used to verify the reliability of the content coding to ensure valid results and inter-rater agreement ( Cohen 1960 ). For Cohen’s kappa, 1.00 represents perfect reliability and 0.00 no reliability. The agreement α was calculated to be 0.955, which shows near-perfect reliability for the dataset.

The analyses of survey responses both with quantitative statistical tests and with qualitative content coding of open-ended suggestions from responses address the research questions for this study.

A total of 308 complete responses were collected between October 2016 and January 2017, with 135 (44%) from East Carolina University, 75 (24%) from the University of Georgia, and 98 (32%) from the University of South Carolina. Most of the student respondents are between the ages of 17 and 22. The predominant race represented is white at nearly 80%, with African American and Asian rounding out the top three. There were more females than males who answered the survey (51.9%). Because most of the respondents are undergraduate students, a large majority had some college credit with no degrees (88.3%), followed by less than a tenth with an associate’s degree (6.5%) or a bachelor’s degree (4.2%). Of the 308 respondents, only 1 person did not own a cell phone and 2 others did not own a smartphone. Most respondents have owned a cell phone for at least 4 years (92.8%), while over 96% of respondents have owned a smartphone for at least 2 years.

Among the college students surveyed, MWAs were overwhelmingly the most frequently used choice to access forecast information, with over 80% checking their MWA at least once a day ( Table 1 ). The second-most favored option was friends and family. Most respondents seldom use the newspaper or the National Oceanic and Atmospheric Administration (NOAA) Weather Radio to retrieve weather forecasts.

Frequency of weather source access by respondents (%).

Including default MWAs that are oftentimes preloaded onto a smartphone, more than half (55%) have only one MWA, while more than 35% have two MWAs. Of those surveyed, 91.8% have never paid for an MWA, and the 25 people who have paid often do not pay more than $3.00 (U.S. dollars).

Participants were asked to identify both the primary reason and secondary reasons for choosing their preferred MWA. Nearly 32% chose their MWA because it is easy to use, while about 23% of people prefer their MWA because it came as the default MWA on their smartphone ( Fig. 2 ). The design and graphics on MWAs seem to be less important to respondents, with only 3.6% picking this as their primary reason.

A critical component of MWA preference among respondents relates to whether they switch from the preloaded MWA on their smartphone. Of the 305 people who responded to this question, 39.3% switched to a different MWA. Nearly 70% of those respondents who switched said they prefer their new MWA more because it offered more information and details, while ease of use, understandability, and graphics were cited as reasons among at least 15% of those who switched ( Fig. 3 ).

In addition to preferred characteristics of MWAs, the perceived importance of various elements of a weather forecast may influence which MWA individuals choose. Survey results indicate that respondents want detailed information on the chance, location, and timing of expected precipitation ( Table 2 ). The type of precipitation was somewhat less important, along with specific details on precipitation amounts. Forecast high and low temperatures were reported to be important or very important, and over 60% of respondents found humidity to be important or very important. Cloud cover and wind direction were of less concern.

Respondents’ perceived importance of aspects of forecasts (%).

The range of forecasts available can influence the choice of an MWA. Three types of forecasts stand out among respondents, with the hourly forecast, forecast chance of precipitation, and five-day forecast being deemed as important or very important by over 80% of respondents ( Table 3 ).

Respondents identifying importance of specific MWA features (%).

The results in Table 3 may, at least in part, relate to how confident respondents are in forecasts overall from all sources and how confident they are in forecasts available on MWAs. Most respondents report that they are confident in a weather forecast, regardless of where they retrieve the information (69.2%), while 21.4% are neutral. For specific MWA features, most respondents trust the hourly forecast, with over 85% being confident or extremely confident ( Table 4 ). For forecasts with longer lead times of more than five days, the decay in confidence for MWA users increases, similar to the findings from previous research ( Lazo et al. 2009 ).

Respondents’ confidence in specific MWA forecast features (%).

The final research question investigates the connection between respondents’ demographics and how this information relates to MWA preferences and usage patterns. Chi-square and Kruskal–Wallis and Mann–Whitney U ANOVA tests were conducted to compare respondent information between schools and between gender. Because age, race, and education level were all relatively uniform in the sample, they were not analyzed.

There are some statistically significant geographic differences between the three schools, as shown in Table 5 . A post hoc analysis found that the perceived importance of precipitation amount by UGA students was lower compared to both ECU and USC. Further, there is a statistically significant result between schools with respect to the perceived importance of the weather video feature (UGA had lower perceived importance in this feature). At the same time, no geographic difference was found with respect to confidence in MWA features, likely reflecting the overall confidence in forecasts discussed above.

Statistically significant Kruskal–Wallis (KW) test differences in MWA preference and use by university. The asterisk indicates statistically significant association at the 0.05 significance level.

In comparing genders, statistically significant results were found such that men perceived wind speed and wind direction to be more important compared to women ( Table 6 ), and more men than women find the satellite and radar features on MWAs to be important. Again, no difference was found with respect to confidence.

Statistically significant Mann–Whitney U (MWU) test differences in MWA preference and use by gender. The asterisk indicates significance at the 0.05 level.

One chi-square test returned as statistically significant with regard to the three universities ( Table 7 ), specifically the primary reason why respondents choose their MWA. A lower percentage of students at USC chose “easy to use” as the most important reason for choosing their MWA compared to UGA and ECU. Additionally, the numbers of students who chose “easy to understand” at UGA and “default” at ECU were smaller compared to the two other schools. However, with a Cramer’s V value of 0.174, this post hoc result reveals schools have a minimal association with respondents’ primary reasons for choosing their favorite MWA. With respect to gender, statistically significant associations were found for respondents who use their MWAs between 0000 and 0600 local time, with women more likely to use their phones during the early overnight hours compared to men ( Table 8 ). Additionally, a statistically significant association was found between gender and the amount of MWAs a respondent reported having on their device, where men reported having more MWAs than women.

Chi-square analyses on MWA preference and use by university. The asterisk indicates significance at the 0.05 level.

Chi-square analyses on MWA preference and use by gender. The asterisk indicates significance at the 0.05 level.

Finally, respondents were prompted to provide suggestions for how they think MWAs could be improved. Of the 308 total surveyed, 256 provided suggestions, totaling 280 suggestions, 46 of which said they would not make changes ( Table 9 ). Respondent suggestions centered on better information or features (24.3%), overall MWA design/customization (18.9%), and improved accuracy (17.9%). While the categories for radar and notifications could have been consolidated with the information and features category, there were a number of responses that targeted these separate items directly. One of the suggestions for radar and notifications included having an enhanced radar that scans the atmosphere more frequently, while a suggestion for the notifications category included having a setting that alerts users when the forecast changes unexpectedly.

Content-coding categories and corresponding examples.

Past research has established the foundation to further explore where people gain information on weather forecasts, but with the rapid growth in mobile device technology that affords much convenience for users, even the most recent studies have been unable to adequately capture the use of MWAs to obtain weather information. This research is aimed at filling the gap in the areas of mobile smartphone technology and its role as a dominant weather source among college students while also updating existing literature on sources of weather information.

Demographic information about respondents revealed a rather homogenous sample. A majority of participants were white, young college students. An overwhelming majority of those surveyed use smartphones regularly for forecasts, while the second-most popular choice was conferring with friends and family. Over 90% do not use newspapers or NOAA Weather Radio for forecasts.

This research uncovered information on what sources of weather information are the most popular among respondents and reasons why specific MWAs were preferred over others. When asked for the single reason respondents prefer their favorite MWA, ease of use, understandability, and being the preloaded default on the device were the top choices. When allowed to expand their reasoning, the level of detail in an MWA along with the design and graphics of an app were viewed as important reasons. While most do not switch from their default MWA, approximately 39% have moved to another app because they were not satisfied with factors like the depth of information or they reported that their current MWA is too complicated. It is important to note that while the research identified which MWAs are most popular among respondents, the specific MWA does not matter as much as the perceived importance and user confidence in MWA features, which are important contributions of this research.

Most respondents found the hourly and 5-day forecasts to be most useful, as well as severe weather alerts and current conditions, and most were also confident in these features. Two complementary questions provide additional information to address MWA preference. Results from a cross-tabulation analysis indicate that perceived importance of weather forecast aspects did not affect which apps participants chose.

The final research question sought to analyze gender and university differences with the many variables analyzed in the survey. Although most analyses using chi-square and the nonparametric Kruskal–Wallis and Mann–Whitney U ANOVA tests were not statistically significant, a statistically significant relationship was found between schools and some MWA use. A Kruskal–Wallis test revealed that students at both ECU and USC placed more importance on information about the amount of precipitation in a forecast than did students at UGA. Additionally, students at ECU were more confident in the pollen count feature on an MWA than UGA students and believed that weather videos were more important than UGA students. For analyses looking at gender, men seemed to find wind speed and direction more important than women; men also place more importance on the satellite and radar feature.

The reasons for these results are not clear and suggest the need for further investigation. While there have been studies addressing gender differences in the use of forecasts ( Demuth et al. 2011 ), the focus was on the importance of attitudes on family roles in a household, thus addressing a different set of users. The data in this study may be a result of subtle differences in weather experiences, an artifact of the survey questions, or a reflection of the interests of survey respondents. Additional research is warranted to sort through these findings.

The fact that most respondents do not switch from their default MWAs signifies that most students are satisfied with the quality of their default MWA and therefore do not feel compelled to switch. Corporations and organizations in the weather enterprise that are able to forge relationships with cell phone service providers or technology companies will likely have the most success with their products, as they are most likely to be used by consumers.

The use of MWAs and MWA choice are important, but information about how people use MWAs helps paint a more complete picture. Respondents want to know about precipitation and temperature. Nearly every aspect of precipitation (chance, timing, location, and type) was perceived as an important aspect of a forecast, while the forecast high and low temperatures and the timing of these temperatures were valuable for those surveyed, which was the case in Lazo et al. (2009) .

Valuable information was gathered from the many suggestions offered by respondents in the open-ended portion of the survey, which asked for suggested changes or additions to MWAs. Some advocated for the addition of new MWA features tailored to active lifestyles that could better pinpoint how the weather would impact them throughout the day. Others proposed features that would provide advice on what to wear and how to prepare based on the forecast. Increased accuracy was another common theme, as well as improved design and the ability to customize an MWA to an individual’s own liking.

The data collected from the analyses of the survey highlight a wealth of information about college students and their use of smartphones and MWAs for acquiring weather forecast information. As a result, this study builds on previous studies by Lazo et al. (2009) and Demuth et al. (2011) on sources of weather forecast information and how respondents use the information daily, in this case focusing on an important demographic segment of weather forecast consumers. Lazo et al. (2009) found that local television and other media were the most common mode for retrieving daily weather information; this study, however, brings to light a younger generation’s habits and the implications that will change the paradigm of communicating weather information well into the future.

With students’ on-the-go lifestyles and their demand for information that allows them to plan for the near future, an MWA offers a compatible, convenient, and useful alternative to local television, radio, and other weather forecast sources, all of which correspond with several aspects from the diffusion of innovations theory (DIT) ( Rogers 1995 ) and the technology acceptance model (TAM) ( Davis et al. 1989 ). MWAs provide the information that respondents find important in a forecast, and the portable nature of smartphones and MWAs allows students to take the forecasts with them wherever they go without having to wait for information that is delivered at specific times on other sources. MWAs are highly accessible, which explains the high usage rates among a majority of respondents. With weather information only a few taps away, little effort is required to obtain valuable forecast details that students can use to plan. MWAs are also often preloaded onto consumers’ phones at the time of purchase, making weather information available to almost everyone with a smartphone who chooses to use a weather app.

This study highlights the potential improvements that can be made to MWAs to garner even more favorability among a young demographic. From the most liked and disliked MWA features to the many suggestions provided by respondents, organizations that want to continually improve their product have important information they can consider when updating their MWAs. Public sector agencies like the National Weather Service may consider using MWA technology to reach a changing demographic that clearly uses mobile technology on a regular basis.

While the focus for this research is on commonplace everyday weather situations, connections can be drawn and applied to severe weather situations that pose a more significant threat to life and property. Many MWAs have special weather alerts that can warn users of impending inclement weather. Additionally, the National Weather Service along with partner government agencies has the capability to send out geographically relevant notifications to cell phone users for extreme severe weather, America’s Missing: Broadcast Emergency Response (AMBER) alerts, and both local and national emergencies in the form of the Wireless Emergency Alerts (WEA) system ( Stanley et al. 2011 ). These warning technologies can serve to benefit from the information in this study relating to MWA usage patterns and preferences.

While the study presents important information, there are several limitations that should be addressed. The information from the research, while valuable, is not generalizable. The study only assesses the use of MWAs by college students who were chosen from specific classes in geography programs in the Southeast. Respondents were similar demographically and geographically, which does not allow for broad conclusions of the American public as a whole. Additionally, the survey was disseminated in the fall and winter months. This could impact survey results as the presence or lack of significant weather events may have affected respondents’ answers to questions.

As mentioned by Lazo et al. (2009) , a more consistent, nationally representative effort to reassess the public’s sources and uses of weather information would be helpful in guiding policy and practices within the weather enterprise. Because the study was limited in its geographic and demographic scope, the study can be expanded to include more participants encompassing a larger study area. Additionally, while surveys are effective tools for social science research, other methods, including qualitative interviews and focus groups, should be considered to extract deeper and richer information from MWA users. There are also new technologies and methods for smartphone research that can help reduce issues of self-reporting biases in surveys and respondent accounts of their actions. Currently, software and other types of mechanisms can extract information directly from smartphones, providing information about the user ( Raento et al. 2009 ; Antonić et al. 2016 ). New strategies of information collection, especially in the realm of smartphone usage, will be of immense value to future researchers in the weather enterprise who continue investigating communication and how to better accommodate the people who use weather app products to stay informed about the weather.

AMS Publications

Get Involved with AMS

Affiliate sites.

Email : [email protected]

Phone : 617-227-2425

Fax : 617-742-8718

Headquarters:

45 Beacon Street

Boston, MA 02108-3693

1200 New York Ave NW

Washington, DC 200005-3928

Privacy Policy & Disclaimer
Get Adobe Acrobat Reader
© 2024 American Meteorological Society
[185.148.24.167]
185.148.24.167

Character limit 500 /500

Universal Design of Mobile Apps: Making Weather Information Accessible

Conference paper
First Online: 16 May 2017
Cite this conference paper

Bruce N. Walker 15 ,
Brianna J. Tomlinson 15 &
Jonathan H. Schuett 15

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10277))

Included in the following conference series:

International Conference on Universal Access in Human-Computer Interaction

3111 Accesses

3 Altmetric

Mobile weather apps are just one class of products that ought to be developed under a Universal Design rubric. However, despite the large number of mobile weather apps available, most have not been developed from the ground up to be more universally accessible. This paper discusses a universally designed weather app that demonstrates how effective universal design can be for a commonly used service.

You have full access to this open access chapter, Download conference paper PDF

Evaluation of Open Source Mobile Phone Weather Applications

Research on the Design of Mobile APP Product—Taking ‘Wei Weather’ APP as an Example

“My Environment” – A Dashboard for Environmental Information on Mobile Devices

Universal design
Accessibility
Weather app
Visually impaired
Sonification
Assistive technology

1 Introduction

Mobile apps are just one class of products that ought to be developed under a Universal Design rubric. However, even very common apps, with truly universal utility, are often not created for as wide an audience as possible. As one example, accessing the current and forecasted weather conditions is a common part of nearly everyone’s day [ 1 ]. As such, a large number of mobile weather apps are available, on all of the mobile platforms, and even on desktop platforms; most have some similar features, along with a slew of unique aspects that try to set them apart in the crowded weather app marketplaces (see Fig. 1 for a few of the many examples). Unfortunately, even though many of these apps may comply with accessibility requirements (largely by inheriting accessibility features of the mobile—or other—operating system), they have not been developed from the ground up to be more universally accessible. This position paper discusses a universally designed mobile weather app that demonstrates just how effective universal design can be. A more extensive discussion of the motivation and our methods is available in the complete description of this project, found in [ 2 ]. Here we frame the problem of universal design as a lack of research and implementation, not a lack of possibility.

Screen capture of just some of the many weather app available in the mobile online marketplaces (left: in this case, iOS App Store), and even desktop apps (right). Most weather apps share some common features, but also have unique attributes.

Coming back to our example, it is particularly important to note that accessing weather conditions is crucial for persons with vision loss and other impairments, because temperature and precipitation have major impacts on the choices they make about their route, clothing, and assistive technology for the day. For example, knowing that there is a chance of rain may allow a person with visual impairment to choose a different white cane, or perhaps bring a raincoat for their guide dog. Heavy rain or snow may cause visually impaired or wheelchair-using commuters to alter their routes altogether [ 3 ].

In the case of low-vision and blind users, screen reader accessibility features on mobile devices can speak aloud the text on the screen, thereby providing some access to a device. However, there is often a much larger issue with mobile apps, in that they are not designed to support the informational needs of users who cannot see the screen. The screen reader typically is forced to present the information in the order it is displayed visually. Often this results in additional time or steps wasted to get to the intended information, if it is even possible. For users with other unique needs, different from the canonical (sighted) user, the specific information that is most important may be quite different from what other users need; it may also be different on different days, or in relation to different tasks (e.g., going to work versus going to the soccer field versus working in the garden) [ 4 ].

To address this larger issue of effective and appropriate access to the needed information, and to serve as a proof of principle for universally designed apps (weather, and otherwise), we designed and developed a weather app from scratch, with universal design—including accessibility for visually impaired users—as prime design directives. We chose to implement a weather app since it is such a common service, and to point out that despite many apps available, there were really none out there that would equally serve the need for those who could and could not see the screen. We started with an Android app (see figures later in this paper, for screen captures), including iterative evaluation and redesign cycles; we have subsequently implemented the final design on iOS, as well.

2 Visual Design Leads to Access Issues

Most existing weather apps display a combination of numbers, text, buttons, and icons on the main page. The most important information (according to the designer), such as current temperature, is often shown in the middle or the bottom of the screen. Typically this is displayed in large text accompanied by visual icons that represent other current weather status. As an example, see Fig. 2 . Such a presentation allows sighted users to perform a quick visual search, drawing their attention to the salient data first [ 5 ]. Weather icons are another way to quickly convey weather information to the typical (sighted) user [ 6 ]. Beyond the initial glance, a user can look for more specific details: other temperatures, wind, rain probabilities, etc. Most weather apps also present a way to check the forecast, including multi-day projections. These are often on different tabs or placed out of sight, accessed by swiping or scrolling. In general, most mobile weather apps are designed to present information in a quick, simple, and visually pleasing way. The emphasis, of course, is on “visual”, since most, if not all, of the apps are clearly designed with canonical sighted users in mind.

Example of a common information layout for the main screen of a mobile weather app. Note that often the information is embedded into an (inaccessible) image.

The user experience is not necessarily as straightforward for someone using a mobile screen reader: the user will generally hear the text items read left to right, top to bottom (or in some scrambled order). In the example presented in Fig. 2 , if the user wants to know what the current temperature is, she might expect to hear the screen reader speak out “Today button” first. Swiping right, she would hear “12 h button,” then swiping again, “10 Day button.” The next swipe might be expected to speak the current weather condition “Fair,” but in many apps the conditions data are embedded into an image, causing the screen reader to ignore the information or say something generic like “image.” A few more swipes and the user may eventually hear the current temperature. This order of information presentation is not suitable for a user (especially one with a disability) who wants to quickly get an update on the current conditions.

Indeed, we have found that the top weather apps for Android and iOS required between 2 and 17 swipes just to get to the current temperature [ 2 ]. We should note that even though some weather apps provide the current conditions within a few swipes, most also chunk additional data together so that the listener has to wade through an extended list of atmospheric conditions before the most relevant data are presented. Blind and visually impaired users routinely waste time with information that is presented out of context, out of order, or unlabeled; or guessing about completely inaccessible items.

3 Broad-Based Needs Analysis

To try to approach an app design from a more universal design perspective, it is imperative to start by performing an information needs analysis with a broader range of potential weather app users than is typically considered. In the weather app design effort we discuss in [ 2 ], blind and low vision users supplemented the typical visual users. In our research and design work, we often use broad-reaching online questionnaires, typically supplemented with follow-up discussions via email. When we ask about issues with current mobile apps, a majority of respondents (from all user categories) tend to report that their major challenge is either the accessibility or appropriateness of the information, or the design of the app interface itself. Users have told us that some weather apps are missing “obvious” features such as providing an accessible hourly or 7-day forecast, or the ability to check details like wind speed, wind direction, and rainfall throughout the day (see [ 2 ], for example). Users with some other disabilities (e.g., hearing loss) have reported that wind direction and speed are also important for safety reasons, and this may be distinct from what “typical” sighted, or even visually impaired, users require.

This demonstrates the need to consider a broad range of uses, and to structure information in a suitable and flexible manner. Figure 3 shows the main page of the new Accessible Weather App, highlighting the simple and straightforward layout, the default high-contrast color scheme, the appropriate and flexible information order, and nested basic/detailed views. In terms of the color scheme, it is crucial to consider the range of needs. The high contrast (white words on a dark background) helps users with low vision read the content with less eye strain [ 7 ]; at the same time it is effective for users without vision impairments. Beyond the look and feel, there is the functionality: for instance, most users, regardless of impairment or lack thereof, want a way to check the weather conditions at a different location. This is, perhaps surprisingly, an issue some have reported as being difficult in existing apps.

Current version of the Accessible Weather App, showing the weather today in overview and detailed view. Note the default high-contrast color scheme, which is both universally appealing and effective. Other color schemes can be selected.

4 Multimodal Weather Display Design

In addition to our goal of creating a weather app that is designed from the ground up to be accessible for mobile screen reader users, in our Accessible Weather App project we were interested in enhancing the user experience for all users, by creating multimodal weather displays that provide functionality similar to that of visual-only weather icons, but in a more broadly available and even more engaging format. Figure 4 shows the visual layout. Note that auditory components are also present to conveyweather data in an efficient and multimodal manner. That is, we created a “glanceable” [ 8 ] way for users of both visual and audio interfaces to find out about the weather condition, by including icons and sonifications , in addition to the speech produced by the screen reader. Sonifications are intentional sounds that use non-speech audio to convey information or data to listeners [ 9 ]. They have been used in many applications and fields (often science) to convey trends and patterns of data, including weather information [ 10 , 11 , 12 ]. In the past, though, sonifications have usually conveyed longer patterns of data. Here, though, we employed short sonifications to support universal glanceability, for all users. The sounds (and the rest of the interface components) were developed through a participatory design process, and thoroughly and systematically evaluated, before being included in the overall multimodal interface design.

Current version of the Accessible Weather App, showing the weather for the next 24 h (left) and with a detailed view (right), with slightly different ordering of data. Note the visual icons that are consistent with other apps, yet have high contrast. Sonifications are also present (though not evident in the visual screen capture), to display data and conditions using sound for those users who prefer it.

Validation is a crucial step, when using interface designs that may be less familiar to some users (but that are necessary to ensure broader access). We often vet our sound designs before deploying them into an application, using variations of participatory design approaches. One example is using sound-sorting tasks [ 13 ] in order to assess candidate sounds. Participants listen to a variety of sounds that we have designed, and indicate which weather conditions they feel those sounds best represent; this helps us understand how the users think about the weather, and about the interface [ 14 , 15 ].

5 Evaluation and Iteration

As with all our software and hardware projects, the Accessible Weather App was evaluated in the field: Blind and sighted smartphone users downloaded and installed the app onto their device, and used it for at least a week (ranging from 1 to 10 times per day). Testers then completed an extensive (63 question) survey, followed up by email discussions with the researchers. We asked about the app (in general), the TTS wording, and their satisfaction and frustration levels for the different features within the app. As a summary of the feedback (again, see [ 2 ] for more details), all respondents stated the app was similar to, or better than, the weather app they had previously preferred. They appreciated that the core features of having access to basic hourly conditions, details, daily, and extended forecasts were available and easy to use (see Fig. 5 , showing 10-day forecasts). Many of the specific comments related to the features being more closely tuned to their diverse needs (reflecting the universal design ethos), and more accessible information.

Current version of the Accessible Weather App, showing the weather for the next 10 days (left) and with a detailed view (right), with slightly different ordering of data. Note the consistency of interaction, and flexibility of data display.

The app has been refined since the main evaluation, with bug fixes and feature requests rolled in. A range of visual preferences have also been incorporated, such as different colors for font and background; high contrast; and location search preferences. Figure 6 shows the Android version of location selection (iOS is also available), leveraging the well-known and oft-used interaction methods (e.g., a side drawer for favorites), extended to include multimodal interface elements. These options address even broader sets of users, such as those who may not use the screen reader function, but still have problems with the typical visual design, such as the seemingly ubiquitous black text on a white background. As seen in Figs. 3 , 4 , 5 and 6 (with the now-default high contrast white-on-black color scheme), the app is very appealing visually, as well as auditorily, and is a very useful weather app for all kinds of users. This, we believe, is a truly successful example of universal design in action!

Current version of the Accessible Weather App, showing favorite locations (left), and details for a given location (e.g., Boston). Note that the app is congruent with the mobile operating system (in this case, Android), but extended to be more universally accessible.

6 Conclusions and Final Thoughts

Accessing weather conditions and forecast is a common part of nearly everyone’s day, and for many users with a variety of needs and limitations, it can be a crucial source of information. Unfortunately, even though many of the multitude of weather apps may comply with accessibility requirements (largely by inheriting default accessibility features of the mobile operating system), they have not been developed from the ground up to be more universally accessible. By focusing on a broader range of users, including those accessing the device through a screen reader, those who may have print disabilities yet do not use a screen reader, and those whose weather data needs might be different from the “typical” weather app user, we were able to create a more fully accessible, and indeed one may say more universal, weather app. Here we show examples of the design features that we have implemented in the Accessible Weather App, which are based on solid evidence, collected through considerable engagement with a wide array of users. However, the message here must not be just about this one app; rather, it is about the approach. The design philosophy we have embraced, the research and development strategies we employ, and the iterative evaluation we routinely conduct with a range of users, can certainly be applied to the creation of any other type of mobile app. Indeed, we strive to employ these universal design approaches in nearly all our projects, well beyond the bounds of mobile devices! We hope that these kinds of success stories will inspire other researchers and developers to follow similar steps to create better, more universal experiences for all users, regardless of any impairment (or lack thereof) those users may have.

Böhmer, M., Hecht, B., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2011), pp. 47–56. ACM (2011)

Google Scholar

Tomlinson, B., Schuett, J., Shortridge, W., Chandran, J., Walker, B.N.: “Talkin About the Weather”: incorporating TalkBack Functionality and Sonifications for Accessible App Design. In: Proceedings of the 18th International Conference on Human-Computer Interaction With Mobile Devices And Services (MobileHCI 2016), pp. 377–386. ACM (2016). doi: http://dx.doi.org/10.1145/2935334.2935390

Williams, M.A., Hurst, A., Kane, S.K.: Pray before you step out: describing personal and situational blind navigation behaviors. In: Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2013), p. 28. ACM (2013)

Rodrigues, A., Montague, K., Nicolau, H., Guerreiro, T.: Getting smartphones to talkback: understanding the smartphone adoption process of blind users. In: Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (ASSETS 2013), pp. 23–32. ACM (2015)

Healey, C.G., Enns, J.T.: Attention and visual memory in visualization and computer graphics. In: IEEE Transactions on Visualization and Computer Graphics, 18(7), pp. 1170–1188. IEEE Press, New York (2012)

Matthews, T.L.: Designing and evaluating glanceable peripheral displays. In: Proceedings of the 6th Conference on Designing Interactive Systems, pp. 343–345. ACM (2006)

WebAIM: Visual disabilities: Low vision. http://webaim.org/articles/visual/lowvision#highcontrast 28 August 2013

Plaue, P., Miller, T., Stasko, J.T.: Is a picture worth a thousand words?: An evaluation of information awareness displays. In: Proceedings of Graphics Interface 2004, pp. 117–126. Canadian Human-Computer Communications Society (2004)

Walker, B.N., Nees, M.A.: Theory of sonification. In: Hermann, T., Hunt, A., Neuhof, J.G. (eds.) The Sonification Handbook, pp. 9–39. Logos Publishing House, Berlin (2011)

Flowers, J.H., Whitwer, L.E., Grafel, D.C., Kotan, C.A.: Sonification of daily weather records: issues of perception, attention and memory in design choices. In: Proceedings of the 2001 International Conference on Auditory Display, pp. 222–226. ICAD (2001)

Schuett, J.H., Winton, R.J., Batterman, J.M., Walker, B.N.: Auditory weather reports: demonstrating listener comprehension of five concurrent variables. In: Proceedings of the 9th Audio Mostly: A Conference on Interaction With Sound. ACM (2014)

Walker, B.N. Kramer, G.: Mappings and metaphors in auditory displays: an experimental assessment. In: Proceedings of the 3rd International Conference on Auditory Display (ICAD 1996), pp. 71–74. ICAD, Palo Alto (1996)

Lewis, B.A., Baldwin, C.: Comparison of traditional psychophysical and sorting methods for in-vehicle display design. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 59 (1), 1312–1315 (2015). SAGE Publications

Article Google Scholar

Barrass, S., Kramer, G.: Using sonification. Multimed. Syst. 7 , 23–31 (1999)

Barrass, S., Vickers, P.: Sonification design and aesthetics. In: Hermann, T., Hunt, A., Neuhoff, J.G. (eds.) The Sonification Handbook, pp. 145–177. Logos Publishing House, Berlin (2011)

Download references

Acknowledgments

Portions of this work were supported by funding from the Na- tional Science Foundation (NSF) and from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR).

Author information

Authors and affiliations.

Georgia Institute of Technology, Atlanta, USA

Bruce N. Walker, Brianna J. Tomlinson & Jonathan H. Schuett

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bruce N. Walker .

Editor information

Editors and affiliations.

Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

Margherita Antona

Foundation for Research and Technology – Hellas (FORTH), University of Crete and Foundation for Research & Technology – Hellas (FORTH), Heraklion, Crete, Greece

Constantine Stephanidis

Rights and permissions

Reprints and permissions

Copyright information

About this paper

Cite this paper.

Walker, B.N., Tomlinson, B.J., Schuett, J.H. (2017). Universal Design of Mobile Apps: Making Weather Information Accessible. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human–Computer Interaction. Design and Development Approaches and Methods. UAHCI 2017. Lecture Notes in Computer Science(), vol 10277. Springer, Cham. https://doi.org/10.1007/978-3-319-58706-6_9

Download citation

DOI : https://doi.org/10.1007/978-3-319-58706-6_9

Published : 16 May 2017

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-58705-9

Online ISBN : 978-3-319-58706-6

eBook Packages : Computer Science Computer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Subscribe to the PwC Newsletter

Join the community, add a new evaluation result row, weather forecasting.

114 papers with code • 2 benchmarks • 13 datasets

Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.

Source: MetNet: A Neural Weather Model for Precipitation Forecasting

Benchmarks Add a Result

--> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		IAM4VP
		SNN Ens U MT Np SpecFE

Most implemented papers

Convolutional lstm network: a machine learning approach for precipitation nowcasting.

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.

Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.

GraphCast: Learning skillful medium-range global weather forecasting

Global medium-range weather forecasting is critical to decision-making across many social and economic domains.

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

stanfordmlgroup/ngboost • ICML 2020

NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.

Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.

Eidetic 3D LSTM: A Model for Video Prediction and Beyond

We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.

Verified Uncertainty Calibration

In these experiments, we also estimate the calibration error and ECE more accurately than the commonly used plugin estimators.

WeatherBench: A benchmark dataset for data-driven weather forecasting

Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.

Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Weather Application on Android App Development

Related Papers

IJERA Journal

Weather forecasting has been an important application for predicting the weather changes and accordingly organizing human activities to prevent any loss. The Weathercast application notifies the user about the weather conditions like min-max temperature, humidity, windspeed, AQI (Air Quality Index), UVI (Ultraviolet Index) of any particular location around the globe. This application has been developed using android studio and the soft wares used are XML (for front-end programming), JAVA (for back-end programming). The app focuses on forecasting weather conditions using historical data. This is done by extracting information from OpenWeathermap API. The app has a salient feature of comparing the weather conditions of two locations making the app more user-friendly. The app also updates the user about the current weather news worldwide and thus the app gives a complete insight of the weather condition.

IOP Conference Series: Materials Science and Engineering

Mohannad Jabbar Mnati

The Meteorological conditions could be with high importance for many applications. Even this information is available in many media resources, but they are not measured for a certain position. In this paper, authors present design for a private weather station that can be established in any place. The design mainly based on a group of sensors used for supplying the information of temperature, humidity, and air speed. An Arduino Uno microcontroller is utilized to process the incoming sensing data and send them via wireless Bluetooth module to mobile phones. The mobile phones are equipped with an Android smart application to display this data. This low-cost design offers an online weather information for any local projects that need such data.

IJIRT Journal

The prediction and forecasting system application has been developed to help humans worldwide to predict historical temporal weather data in their respective region. This system is developed with the goal to supplement the productivity of already available website of India meteorological department. The system is flexible enough to predict current data. It also presents multiple weather scenarios to predict weather data. The weather data which is predicted is available from 1960 to 2010 from different weather stations, maintained by the India Meteorological Department.

TJPRC Publication

Nowadays weather conditions are changing day to day, hence some sort of system has to be designed to measure the weather parameters in an effective way at the place of interest. This paper projects an easy way to measure the dynamic parameters of weather without human interpretation. As this proposed method employs mobile app and IoT technology, collected weather parameters in a remote area can be uploaded to cloud as well as particular mobile app. The uploaded data can be verified and used, at anytime and anywhere in the world. The proposed system uses Raspberry-pi embedded with weather sensors to collect weather conditions. Hence, it provides better support for the weather monitoring and controlling centers, and weather reports for TV and radio stations.

Dr Krushna Chandra Gouda

International Journal of Applied Mathematics, Electronics and Computers

Fatih Başçiftçi

M. P. Raj , International Journal IJRITCC

Citation/Export MLA M. P. Raj, J. P. Davara, R. S. Parmar, H. V. Raval, “Multilingual Android based application for Meteorological Units Conversions and Calculation of empirical relationship.”, January 15 Volume 3 Issue 1 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 76 - 81, DOI: 10.17762/ijritcc2321-8169.150117 APA M. P. Raj, J. P. Davara, R. S. Parmar, H. V. Raval, January 15 Volume 3 Issue 1, “Multilingual Android based application for Meteorological Units Conversions and Calculation of empirical relationship.”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 76 - 81, DOI: 10.17762/ijritcc2321-8169.150117

Raphael Muchovo

International Symposium on Environmental-Life Science and Nanoscales Technology 2019 (ISENT2019)

A weather monitoring system can be described as a facility with instruments and equipment, which provide information to make weather forecasts and to know climate conditions in our neighbouring environment. It will monitor temperature, humidity, rain level and moisture with live reporting of the weather conditions at a particular place and make the information visible anyplace in the world. The purpose of the system is to provide an efficient environmental monitoring system by measuring and monitoring the weather data based on Internet of Things (IoT) technology and to show weather data by using the mobile application with quick and easy access for users. In this study, Arduino MKR WiFi 1010 Board microcontroller is used to collect weather parameters from temperature and humidity sensor (DHT11), digital Barometric Pressure Sensor (BMP180), raindrop module, and UV Sensor (ML8511). Data collected from the sensors are then stored into the Firebase real-time database, a cloud-hosted database and mobile application are developed using MIT App Inventor 2 to show the real-time weather conditions of a particular region in an android mobile phone. The main preferences of such a system include lower cost advantage compared to other similar weather station projects because of using Arduino MKR WiFi 1010 microcontroller, designed to WiFi connectivity for IoT applications module with a cost-effective solution and low power consumption.

Dmitry Korzun

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Special Features

Vendor voice.

Figma pulls AI design tool for seemingly plagiarizing Apple's Weather app

Ai is 'like the fast food of creativity'.

Web and app design toolmaker Figma has temporarily pulled its generative AI "Make Design" feature because it seems to think Apple's Weather app is the be-all and end-all of mobile forecasting.

The AI text-to-app-design feature 's affinity for mimicking Apple's design language was discovered by NotBoring Software founder Andy Allen after he asked the platform to generate a "not boring weather app."

Allen said that the generative app would consistently offer near-replicas of Apple's own weather application, which ships with all iOS devices. The behavior led Allen to speculate that Figma had used existing app designs to train the service. "Figma AI looks rather heavily trained on existing apps," he wrote in a follow up post .

Following the discovery, Figma CEO Dylan Fields wrote in a Xitter thread that the biz would "temporarily disable the Make Design feature" until fixes are made to prevent this behavior.

Fields also attempted to dismiss allegations the service had been trained on popular third-party app designs. "The Make Design feature is not trained on Figma content, community files, or app designs. In other words, accusations around data training in this tweet are false," he wrote in response to Allen's findings.

Yet Fields went on to say the feature is built using off-the-shelf large language models which work in conjunction with "design systems" that Figma commissioned. The problem, he explained, lies with these "design systems," adding that this aping of Apple's weather software could have been prevented with additional quality assurance steps.

We might take that to mean Figma commissioned a bunch of designs to train its generative tool, and some of that design work looked a lot like Apple's, hence the feature's output.

"I hate missing the mark, especially on something I believe is so fundamentally important to the future of design," Fields closed.

Speaking to The Register , Allen said he thought Field's explanation made sense, though noted he never claimed the service was trained on user or community data.

"The real issue between the GenAI companies and creators is that none of the companies have been open about how these models are trained, with what data, and what rights were secured. It's like the fast food of creativity where all the ingredients and processes are hidden away," he lamented.

Allen also made it clear that he isn't opposed to generative AI in app design.

"GenAI in general seems to be fine at making mid stuff for reference, but I find it's mostly not usable for most finished work. It'll get much better over time and has a long future ahead," he said, adding that there are other AI features Figma has introduced – like auto-naming layers and localization – which are fantastic. "The Make Designs feature probably just needed a bit more time to bake."

YouTube confirms it'll pull AI fakes in 48 hours if a complaint's upheld
France poised to bring 'charges against Nvidia'

A friendly guide to local AI image gen with Stable Diffusion and Automatic1111

Anthropic tries 'to enable beneficial uses' of ai by government agencies.

While Fields insists the Make Design feature wasn't trained directly on existing apps – just blueprints that closely resemble them, perhaps – the service's behavior raises questions over who is responsible if a model generates something that arguably violates copyright.

Several tech giants, including Google and Microsoft, have extended limited legal protections against copyright claims for users of its generative AI services.

Figma didn't directly address The Register 's questions regarding how and why the Make Design feature behaved the way that it did, nor what legal protections it currently has or plans to offer users in the future. Instead, a spokesperson directed us to Fields's Xitter post and a page detailing its AI approach.

Considering Make Designs behavior, Allen in a separate missive suggested that users of the service "thoroughly check existing apps, or modify the results heavily" to avoid potential future legal trouble. ®

Development

Narrower topics

Accessibility
Large Language Model
Machine Learning
Neural Networks
Tensor Processing Unit

Broader topics

Self-driving Car

Send us news

What do CTOs hate most about GenAI? Tool changes that break stuff

A friendly guide to containerization for ai work, bake an llm with custom prompts into your app sure here's how to get started, cloudflare debuts one-click nuke of web-scraping ai, meta training ai models on citizen data gets a hard não from brazil, y combinator, startups funnily enough aren't fans of draft california ai safety law.

Advertise with us

Our Websites

The Next Platform
Blocks and Files

Your Privacy

Cookies Policy
Privacy Policy
Ts & Cs

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
About IEEE Xplore
Accessibility
Terms of Use
Nondiscrimination Policy
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Weather monitoring and forecasting system using IoT

August 2021
8(2):008-016

PSG College of Arts and Science
This person is not on ResearchGate, or hasn't claimed this research yet.

Abstract and Figures

Discover the world's research

25+ million members
160+ million publication pages
2.3+ billion citations

THEOR APPL CLIMATOL

Diptendu Bhattacharya
Sanjoy Mitra
Bharat Bhusan
SENSORS-BASEL

Abdulaziz AlShammari

Jaskiran Sobti

Saurav Kumar
Shubham Dabral

S. Supreetha
Harshita Sonarthi

Manas Ranjan Kabat

Christian Icking

Keshav Kumar Singh, S Styline Chirmaxo
Pranjali Wankhede
Sd Gv Satyanarayana
S R Pachpande
J P Chaudari
M B B E E Dr
Recruit researchers
Join for free
Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

IMAGES

Weather ForeCasts App on Behance
How to Install Weather App on Windows 11
(PDF) WEATHER FORECASTING
(PDF) Weather Forecasting Prediction Using Ensemble Machine Learning
Understanding Weather
A React application to show a weather forecast using weather API

VIDEO

Weather app transition #capcut #roadto2k #cruise #royalcaribbean #cruiseship
University of Washington researchers turn develop 'fever app'
Weather App using HTML CSS and JavaScript
How do I change the default weather app on Android?
Build a Stunning Weather App with React, Tailwind CSS, Tremor, and Open Meteo API
How do I get the weather app back on my Samsung phone?

COMMENTS

Weather on the Go: An Assessment of Smartphone Mobile Weather ...
Abstract Millions of people in the United States regularly acquire information from weather forecasts for a wide variety of reasons. The rapid growth in mobile device technology has created a convenient means for people to retrieve this data, and in recent years, mobile weather applications (MWAs) have quickly gained popularity. Research on weather sources, however, has been unable to ...
That's not what my app says: Perceptions of accuracy, consistency, and
1 INTRODUCTION. Smartphone technology and the apps that run on these devices experienced significant uptake during the 2010s (Pew Research Center, 2021).By around 2015, weather apps were becoming go-to sources for weather information alongside the traditional media like television (Hickey, 2015; Silver, 2015).In a study by Phan et al. (), college students (N = 308) attending one of three ...
Weather Forecast Prediction: An Integrated Approach for ...
Weather forecasting is the use of science and technology to predict the condition of the weather for a given area. It is one of the most difficult issues the world over. This project aims to ...
An Eye to the Sky: Describing Characteristics of Weather App Users
weather app users and what those users are looking for is pertinent to research regarding weather communication. While weather is an ongoing phenomenon sought to be understood by people for many decades, the uses and gratifications of engaging in weather communication are under-researched in literature.
(PDF) Weather Forecasting
W eather forecasting is the application of science and technology to predict. the state of the atmosphere for a given location.Ancient weather forecasting. methods usually relied on observed ...
Design and Development of an Efficient and Intelligent Weather
Weather Forecasting App is a based-on web application that provide the. exact the weather data of user's location. In the proposed web application, there are. many parameters used like humidity ...
Mobile weather apps or the illusion of certainty
Portability, permanent connectivity and geolocalization allow location-specific and time-sensitive weather forecasts to be provided. This paper explores the main features emerging in the 39 most popular weather apps in the United States, United Kingdom and Italy, and focuses on the implications in the communication of uncertainty.
Users' Perceptions of Smartphone Weather Applications' Usability
The four themes were (1) user cognitive load, (2) effectiveness, (3) efficiency of use, and (4) user perceptions. Several recommendations were suggested based on the findings which might assist developers in designing highly interactive and usable apps leading to increased user satisfaction. Keywords: user-centered design, usability, smart ...
Weather Forecasting Application Using Web-Based Model-View-Whatever
Weather is the state of the atmosphere at a given place and time in regards to heat, cloudiness, dryness, sunshine, wind, and rain. Of all the geophysical phenomena weather is the most significant one that influences us. Weather can vary greatly and largely depends on climate, seasons and various other factors. The chief goal of this work is to get the weather forecast of any city throughout ...
Universal Design of Mobile Apps: Making Weather Information ...
Abstract. Mobile weather apps are just one class of products that ought to be developed under a Universal Design rubric. However, despite the large number of mobile weather apps available, most have not been developed from the ground up to be more universally accessible. This paper discusses a universally designed weather app that demonstrates ...
Development of a Weather Mobile Application in Android Operating System
The goal of this paper is to find out what are the steps in developing an application, the difficulty of it and future improvements. With this occasion, we chose to develop a weather application. A weather application is a very handy one, with daily usability and can be constantly updated to fit certain needs. Many people now rely on data directly from their smartphones and the weather ...
Weather Forecasting
Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind. Papers With Code provides a comprehensive collection of research papers and code implementations on this topic, covering different aspects such as data sources, neural models, evaluation metrics and applications. Browse the latest papers and compare the state-of-the-art ...
Deterministic weather forecasting models based on intelligent
Abstract. Weather forecasting is the practice of predicting the state of the atmosphere for a given location based on different weather parameters. Weather forecasts are made by gathering data about the current state of the atmosphere. Accurate weather forecasting has proven to be a challenging task for meteorologists and researchers.
PDF Weather on The Go: an Assessment of Smartphone Mobile Weather ...
Related papers. Page number / 90 90
Weather Forecasting Using Deep Learning Algorithms
Weather forecasting aims to predict atmospheric conditions at a particular time and place. Timely alert of weather events is made possible through weather forecasting. For instance, accurate weather predictions enable us to offer early warning of natural disasters that significantly destroy both lives and property, such as cyclones, tsunamis, cloud bursts, etc. The aim of weather scientists ...
PDF Wearable Weather Forecast
Therefore the resulting research question becomes: How can a weather forecast model be implemented and displayed on a mobile application, so that the user intuitively understands the resulting weather forecast? 1.3 Purpose The purpose of this thesis is to present an implementation of a prototype mobile application that has
(PDF) WEATHER FORECASTING APPLICATION USING PYTHON
Weather forecasting is the prediction of the state of the atmosphere for a given location using. the application of science and technolo gy. This includes temperature, rain, cloudiness, wind ...
PDF A React-based Weather Forecasting Web Application
The application's interactive weather maps, powered by Windy.com, provide visual representations of precipitation patterns, enabling users to make informed decisions based on real-time observations.User experience is a top priority in WeatherCastNow's design philosophy. With responsive web design principles and a user- friendly UI/UX, the ...
ARWeather: Weather Forecasting and Visualization using Augmented
Weather Forecasting is the application of AI to predict the state of the atmosphere for a given location. Earlier, weather forecasting methods usually relied on observed patterns of events. Our ancestors predicted the next day weather based on the happenings of the previous day evening. However, those intuitive methods and predictions are not reliable. This paper depicts the design and ...
(PDF) WEATHER PREDICTION BY USING MACHINE LEARNING
aaditya_pillai.scsebtech@galgotias. university.edu.in. Abstract. Weather Project application is a web-based application wher e. you will be able to access all r eports related to weather ...
Weather Application on Android App Development
The Weathercast application notifies the user about the weather conditions like min-max temperature, humidity, windspeed, AQI (Air Quality Index), UVI (Ultraviolet Index) of any particular location around the globe. This application has been developed using android studio and the soft wares used are XML (for front-end programming), JAVA (for ...
Figma pulls AI design feature obsessed with Apple Weather
Web and app design toolmaker Figma has temporarily pulled its generative AI "Make Design" feature because it seems to think Apple's Weather app is the be-all and end-all of mobile forecasting. The AI text-to-app-design feature 's affinity for mimicking Apple's design language was discovered by NotBoring Software founder Andy Allen after he ...
A Cloud-Based Real-Time Weather Forecasting Application
With the advent of Internet of Things (IoT) applications requiring massive streaming data collections, the need for new cloud databases has risen. This work focusses on the implementation of an end-to-end system from real-time data collection to real-time prediction. The storage system is implemented on the International Business Machines (IBM) cloud platform using the Cloudant NoSQL database ...
(PDF) Weather monitoring and forecasting system using IoT
We can easily see weather updates and information from weather stations by using apps. This paper has developed and tested a weather station based on NodeMCU Board and Blynk - IoT technology ...

Related Content