Climate Informatics

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.