Climate Informatics

Picture 1. WCRP 40th Session Image. From WCRP by N. V. Wel, 2017, https://www.wcrp-climate.org/images/WCRP_conferences/JSC40/images/JSC40header_featurebox.jpg About The World Climate Research Programme (WCRP) are group of scientists focused on understanding the climate change. This group differs from CliMA in that WCRP chose to enhance the traditional method of prediction and augment it using machine learning instead of replacing the system from scratch. They host annual meetings with global officials at attendance and provide suggestions to the policy makers on the current state of Earth. References Wel, N. V. (2019, June 18). 40th Session of the Joint Scientific Committee. Retrieved from https://www.wcrp-climate.org/jsc40-about

Personalization The Faceapp is a controversial phone app that manipulates a person’s picture to show how the person would look many years from now (Koetsier, 2019). Controversies aside, it is an interesting app that has sparked an explosive interest from the general population by showing them a personal and likely future of its user. In many ways, Schmidt and his colleagues had a very similar idea in their effort to gain public interest on climate change: show a personal and likely future of its user (Schmidt, et al., 2019). Instead of face, the team used house, and instead of just few years of aging, the team showed the future after 50 years with likely climate change and the related natural disaster in mind (Schmidt, et al., 2019). Figure 1: "Before" and "After" Pictures. From Visualizing the consequences of climate change using cycle-consistent adversarial networks by V. Schmidt, et al., 2019 Conclusion This is a refreshing effort by scientists to nud...

About In this 2018 paper, “Predicting weather forecast uncertainty with machine learning” by professor Scher and professor Messori, shows an early effort to test the feasibility of the technology as a potential replacement to the popular ensemble weather model approach. The ensemble weather model approach, as the name suggest, is similar to having a panel of experts instead of just one expert; the approach produces information that is a composite of different information generated by multiple models given the same input data. Each model caters to different specialization and has their own strength and weaknesses, and by combining information from each model, the approach attempts to generate more accurate information. As such, while it increases the accuracy and precision, it became computationally very expensive to perform in a timely manner. The researchers’ goal was to provide a competitive solution to the computationally expensive ensemble weather model approach by using machine ...

Work of Many The paper “Tackling Climate Change with Machine Learning” is a comprehensive, cross-discipline overview of the research in climate change and other related fields. This paper sets its scope beyond simply predicting the future and defines a comprehensive role scientists and society should play to address the current and future challenges using machine learning. It divides the current researches and efforts into two categories: mitigation and adaptation (Rolnick, et al. 2019). In mitigation, the efforts are focused on lessening the effects of the climate change, such as changing the energy industry to consume less fossil fuel or building more sustainable cities (Rolnick, et al. 2019). The adaptation, on the other hand, accepts that certain outcomes are inevitable and focuses on technologies that would allow humans to survive, including weather prediction, social infrastructure, and education (Rolnick, et al. 2019). The paper also provides some guidance to prioritizing d...

AI for Earth In the Microsoft blog called “Researchers turn to AI in a bid to improve weather forecasts” by Roach, the author highlights some of the company's contribution to the research that benefits mankind. Through the AI for Earth initiative, the company funds various challenging research projects that studies Earth. The blog also highlights the social activities, such as hackathons, that illustrates some of the current trends of using machine learning to solve difficult problems. References Roach, J. (2019, May 20). Researchers turn to AI in a bid to improve weather forecasts. Retrieved May 26, 2019, from https://blogs.microsoft.com/ai/ai-subseasonal-weather-forecast/

Upgrade or Replace There are two approaches when upgrading an existing system: improve the current system or replace the system. The former enjoys the extensive amount of previous work, but it also faces possible short-comings inherit to a design based on outdated technologies. The latter option provides an opportunity to design a solution based on the latest technologies, it but may prove costly to replace old system. Most of the researchers deemed replacement too expensive and opted to supplement the existing system by solving the existing problems. Professor Tapio Schneider and his colleagues decided otherwise (Perkins, 2018). They created a team called CliMA (Perkins, 2018). Picture 1. Professor Tapio Schneider. From New Climate Model to Be Built from the Ground Up by S. Diani, 2019, https://www.sciencemag.org/sites/default/files/styles/inline__699w__no_aspect/public/350cs_80727X_Tapio_0.jpg?itok=6HRPTU8g New Climate Model This young but ambitious project aims to replace ...

Cloud formation and parameterization (Voosen, 2019); T. SCHNEIDER ET AL., GEOPHYSICAL RESEARCH LETTERS 44, 12,396 (2017), ADAPTED BY N. DESAI/SCIENCE Clouds The clouds in the story had been associated with many things. It is often seen as soft, fluffy, and harmless object floating around in the sky. It provides many imaginations to the children, world-wide, while also providing life-giving water to the plant. It is difficult, therefore, to imagine clouds as the most difficult and confounding problem in scientific world of weather prediction (Gentine, et al, 2018). Clouds play a significant role in predicting rain falls, and not being able to provide accurate prediction results in inaccurate prediction of the location and amount of rainfall (Voosen, 2019). Parameterization To better understand the cloud problem, we need to discuss about parameterization in weather prediction. To process data into manageable set of information, the globe is sectioned off into grids, and each...

Figure 1. Coral Reef From Coral Reef by Jürgen Freund, http://d2ouvy59p0dg6k.cloudfront.net/img/web_289583_4_528276.jpg Follow-up Research This is a follow-up experiment to the research , “Neural Network Radiative Transfer for Imaging Spectroscopy”. This time lead by professor Deshpande, many of the previous members joins the research to continue their work on the algorithm that may potentially replace the expensive Radiative Transfer Model (RTM) method. Figure 2. Coral Reef Spread. From WWF by Hugo Ahlenius, UNEP/GRID-Arendal,http://d2ouvy59p0dg6k.cloudfront.net/img/coraldistribution_001_362390.png Context The cause of the rapid and on-going demise of coral reef has long been suspected to be caused by the climate change but drawing a direct link between the greenhouse gases and the phenomena has been difficult (Deshpande, et al, 2019). The paper illustrates that the root cause is has been the inability to process necessary and large scale of data into information. By...

Figure 1. The study zone: the India Peninsula. From Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques, by Y. Dash, 2019. Purpose Professor Dash and his colleagues investigated correlation between northeast monsoon rainfall (NEMR) and sea surface temperature (SST) in hopes of finding an effective way to predict flood and draughts in the Indian peninsular (Dash, et al. 2019). In their approach, they compared three different methods of machine learning: linear regression, artificial neural network, and extreme learning machine (Dash, et al. 2019). Focus This research was had multiple focuses. As with many other researches currently studying the feasibility of using machine learning in the study of climate change, the research aimed to compare the efficiency and scalability of various machine learning algorithms to study weather patterns. Not only that, it also was poised ...

The White House is rolling back emission restrictions. From Trump Administration Hardens Its Attack on Climate Science by B. Thibodeaux, 2019, https://static01.nyt.com/images/2019/05/24/us/politics/00dc-cli-trump1/merlin_151256502_8b48e75c-4479-4e89-a186-ae5f913c1c7b-jumbo.jpg Donald Trump Donald Trump is the 45th president with many less than positive traits. As the most powerful man in the most influential country in the world, his views and opinions have a large impact on how the world is shaped. Therefore, it does not help that he lacks common sense on many facets of the world, and his denial of scientific assessment of the climate change is one of many examples. In fact, he claims the term climate change is at least misleading if not a conspiracy theory (Davenport & Landler, 2019). Events Based on the news article by Davenport and Landler, the president elect has played hardline against the pro-environmentalist. For example, not only has he infamously retreate...

Origin The “AI and climate: On the bleeding edge with a pioneering researcher” by journalist Crowder is a dialogue between the journalist and professor Monteleoni. The professor is credited to coining the term, ‘climate informatics’ in 2012 (Crowder, 2018). Crowder interviewed the professor after 6 years to find out more about the new-born field of research and just how much the field has grown since. Picture 1. Professor Monteleoni. From Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques by L. Crowder, 2018. https://thebulletin.org/wp-content/uploads/2018/02/cmontel-680x1024.jpg In the conversation, the professor described climate informatics as “innovation at the intersection of data science and climate science” (Crowder 2018). This is similar to bioinformatics that became popularized more than a decade ago which combined biological data with data science, and professor...

Previously In the article , “AI for Earth observation and numerical weather prediction”, by Boukabara, the community identified the need to boost the growth of data refinement technology. Furthermore, based on the trend and performances observed in other disciplines with similar challenges, Boukabara suggested a strong candidate for the solution: AI. The research by Professor Bue and his colleagues support Boukabara’s argument. Spectroscopy The spectroscopy is essentially the study of light crossed with chemistry. When a chemical is treated with certain stress, such as heat, it emits light. This is like a finger print to a person: each chemical emits light with a specific wavelength. The spectroscopy is a branch of science that studies the light wave to the chemical that is associated with it. It is widely used to identify the chemical constituent of an unknown compound or a distant star system, among other things. It is also used to understand the chemical composition of a ...

Background: Data Science Before talking about “AI for Earth observation and numerical weather prediction” by Sid-Ahmed Boukabara, I need to talk a little bit about data science. One of interesting distinction in the data science world is the separation of data and information: the former refers to the raw facts without context while the latter is a processed data to give it a meaning. In another words, raw data is not very useful in that state until it is distilled into information. This distinction formed the foundation of the article by Boukabara, the acting deputy director of the NOAA NESDIS Center for Satellite Applications and Research. Bottleneck Boukabara’s article describes a lop-sided advancement in technology: the human infrastructure to collect data, such as an array of satellites, has far surpassed our ability to process the data in a timely manner. This imbalance is so huge that “only 3-5 percent of satellite observations are actually used in preparing numerical we...

Social Social aspect of the topic covers events or communication aimed towards the general public. The posts in this category consists of various social and political events that pertains to climate change and climate informatics.

Technology If the general science forms the basis of our understanding, technology is the application of our understanding. This series compiles information on some of the latest technological advancements on climate informatics.

General Science As with any scientific improvement, general research of the topic forms the basis of our understanding. This series compiles various research findings that highlights the field, such as novel approaches and other research topics.

Media Boukabara, S. (2019, April 17). AI for Earth observation and numerical weather prediction. Retrieved May 26, 2019, from https://spacenews.com/ai-for-earth-observation-and-numerical-weather-prediction/ Crowder, L. (2018, June 28). AI and climate: On the "bleeding edge" with a pioneering researcher. Retrieved May 26, 2019, from https://thebulletin.org/2018/02/ai-and-climate-on-the-bleeding-edge-with-a-pioneering-researcher/ Davenport, C., & Landler, M. (2019, May 27). Trump Administration Hardens Its Attack on Climate Science. The New York Times. Retrieved June 23, 2019, from https://www.nytimes.com/2019/05/27/us/politics/trump-climate-science.html Evarts, H. (2018, June 19). Machine Learning May Be a Game-Changer for Climate Prediction. Retrieved May 26, 2019, from https://engineering.columbia.edu/press-releases/machine-learning-climate-prediction Koetsier, J. (2019, July 18). Viral App FaceApp Now Owns Access To More Than 150 Million People's Faces An...

Introduction This blog focuses on the current research and trend regarding climate informatics. It aims to compile information from three perspective: general science, applied engineering and technology, and politics. Climate Informatics The climate informatics refers to a combined discipline between the computer science, information science, and weather science: it deals with big data, that uses the power of computers (e.g. machine learning) to produce information and does so with respect to the weather. (Easterbrook, 2013) It is a emerging field that is growing rapidly with great potential to improve our lives. References Easterbrook, S. (2013, October 24). What is Climate Informatics? Retrieved July 15, 2019, from https://www.easterbrook.ca/steve/2012/09/what-is-climate-informatics/