Context: Recently a study was published in the American Meteorological Society’s Journal of Hydrometeorology which reveals that Peninsular River basins face a higher probability of widespread flooding compared to the Ganga and Brahmaputra (transboundary rivers).

Widespread Floods in Indian Peninsular River Basins:Key Findings

• Basis of Study:  The Study  is done based on the occurrence of widespread floods in seven major river basins in the Indian subcontinent, such as Ganga, Brahmaputra, Godavari, Krishna, Mahanadi, Narmada and Kaveri in 1959-2020.
  Flooding Probability: Peninsular Rivers:
  • • Narmada basin (59 per cent)
    • Mahanadi (50 per cent)
    • Godavari (42 per cent)
    • Cauvery (19 per cent)
  • Transboundary Rivers
  • Ganga (21 per cent )
  • Brahmaputra (18 per cent)
• Flooding Events: Mahanadi and Narmada river basins witnessed  40 events  of flooding.
  • Krishna and Godavari basins witnessed more than 20 widespread floods.
• Seasonal Trends: Strong seasonal trends were observed in widespread flood probability in the subcontinental river basins. 
  • For example, during the summer monsoon season, all seven river basins, except Cauvery, experienced widespread flooding in August.

• Drivers of widespread flood:
  • Atmospheric rivers: Atmospheric rivers typically transport moisture from the tropics to the extratropics leading to widespread flood.
  • Example:
    • Floods in the lower Mississippi River in 2008, 2011, and 2015–19
    • Kerala floods in 2018 and 
    • Floods in Pakistan in 2022
• Atmospheric circulations: The study reveals that widespread floods in India are associated with large atmospheric circulations that cause precipitation in the river basin.

Must Read: Urban Flooding: Why Are Our Cities So Flood-Prone?

News Source: Down To Earth

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Context: The Parliamentary Standing Committee on Industry recommends extension of the deadline of the Faster Adoption & Manufacturing of Electric Vehicles/FAME India schemePhase II by  three more years.

• The deadline for the ongoing FAME India Phase II is March 31, 2024, with a budget outlay of Rs 10,000 crore.

Recommendations of Parliamentary Report On FAME India Scheme

• Holistic development of EV industry: Create a National policy on electric mobility, to enable a favorable environment for the EV industry and prevent policy uncertainty to promote a sustainable and clean transportation system in the country.
• Restore the subsidy on electric two-wheelers which was reduced to 15% from the original 40% . It is reported that reduction in subsidy since then has negatively impacted the sale of electric two- wheelers.
• Expand the targets: The government should consider increasing the number of EVs  in the four-wheelers category and include private electric four-wheelers in the domain of the FAME II Scheme, with a cap based on the cost and battery capacity of the vehicle.
• Focus on the whole EV manufacturing ecosystem by establishing dedicated manufacturing hubs and industrial parks for manufacturing of batteries, cells and EV auto components.
• Allocation of Funds: The committee recommended more funds be allocated to BHEL to facilitate popularizing EV mobility.

• Facilitating EV mobility: 
  • Public sector role:  PSU’s and government institutions should  participate in the installation of charging stations in their premises.
  • Incentivise the installation of charging stations to individual investors  to achieve the goal of setting up charging stations pan- India.
  • Women self-help groups and cooperative societies can assist in  opening and operating charging stations with an assured monetary incentive.

FAME INDIA Scheme PHASE- II

• About: The Faster Adoption and Manufacturing of (Hybrid & Electric Vehicles or FAME India Scheme is a part of the National Electric Mobility Mission Plan (NEMMP)
• Nodal Ministry: Ministry of Heavy industries and Public Enterprise.
• FAME India Scheme Features:
  • Electrification of public and shared transportation systems.
  • To incentivize the purchase of approximately 5 lakh three-wheelers, 7000 electric buses, and 35,000 four-wheelers
  • Plans to establish 2700 charging stations in metros, smart cities, and hilly states, with coverage on highways.

Must Read: Battery Energy Storage Systems

News Source: Down to Earth

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Context: An indigenous river model BRAHMA-2D (Braided River Aid: Hydro-Morphological Analyzer) is  developed by the researchers of  IIT Guwahati along  with the Brahmaputra Board, Ministry of Jal Shakti.

IIT Guwahati researchers develop BRAHMA-2D model to understand flow of large braided rivers

• The model was successfully validated on the Brahmaputra River near Majuli Island.
• Findings  of the research were published in the ISH Journal of Hydraulic Engineering earning the   prestigious ‘ISH Jal Vigyan Puraskar’ (Best Paper in ISH Journal) 2023.

About BRAHMA-2D

• BRAHMA-2D is a quasi-3D river flow mathematical model. 
• Aim:  To study and  understand  the pace and movement of water at different depths inside a river and its circulation around a structure like a spur(installed to prevent river bank erosion) for large braided rivers like Brahmaputra.
• It has also been applied to understand habitat suitability of aquatic species, especially endangered species, based on the availability of required depth and flow velocity.
• Application: 
• • It will help understand how fast water moves at different depths in a river
  • It will provide valuable insights to field engineers to design sustainable hydraulic structures like Spurs, Revetment and other river bank protection measures.
  • The model will  help to design bioengineering methods for controlling river bank erosion.

News source: Economic Times

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Context: Recent experiments by a team of Australian scientists show that bees can use “less than” and “greater than” to understand that zero means nothing and they placed zero correctly relative to other numbers; in particular, as less than one.

Bees understand the concept of zero

• Scientists demonstrated that bees could extrapolate the concept of ‘less than’ to order zero numerosity.
• In doing this, they showcased an understanding comparable to that of animals such as the African grey parrot, non-human primates, and even preschool children.

Outcomes of the Experiments 

• Training to grasp the concepts of “less” and “greater”:  In this experiment, they could successfully deduce that five was more than two or three. 
  • Moreover, they exhibited an understanding that zero “lies at the lower end of the numerical continuum.” 
• Identifying number zero as less than one: Bees that previously demonstrated an understanding of “less” when presented with numbers two to five were introduced to numbers one and zero, which they had not encountered before. 
  • Remarkably, the bees consistently chose zero as a lower number than one, a decision which is “challenging for some other animals.”
• Impact of Reward on Decision-Making Process: When presented with the choice between two and zero as potential answers to what is “less than” one, they selected zero only around half the time.
  • This indicates that the potential reward for choosing two, a choice consistently rewarded in the experiment, influenced the bees’ decision-making process.
• Assessing the Impact of Numerical Distance on Bee Behavior: The bees proficiently identified that zero was less than the other available numbers. 
  • However, their decision-making accuracy improved as the numerical gap widened. 
  • This observation indicates a significant impact of numerical distance on their ability to make accurate choices. 

About Bees and Beekeeping

• Bees are an important part of biodiversity, providing high-quality food—honey, royal jelly and pollen — and other products such as beeswax, propolis and honey bee venom.
• Beekeeping:  The rearing of honey bees for the synthesis of honey and bees wax by humans is called apiculture.

Significance of Beekeeping

• Pollination: Bees are crucial in pollinating around 85 per cent of all cultivated crops globally. 
• Tackling Climate Change: Through pollination, bees ensure continuous multiplication of the plants, which are responsible for the purification of the atmosphere by taking in carbon dioxide from the atmosphere and releasing oxygen to it.
• Increasing Crop Productivity: Bee pollination leads to an increase in yields of various crops, for example, the productivity of mustard increased from 128.1% to 159.8%.

Statistics Related to Beekeeping in India

• About 12,699 Beekeepers and 19.34 lakh honey bee colonies are registered with the National Bee Board.
• Production: India produces about 1,33,200 Metric tonnes of Honey (2021-22  2nd advance estimate). 
• Export: India is one of the major honey exporting countries in the World and has exported 74,413 MT of Honey worth Rs. 1221.17 Crores during 2021-22. 
• The major markets for Indian honey are the USA, Saudi Arabia, United Arab Emirates, Bangladesh, Canada, etc.  
• Major Honey-producing states in India: Uttar Pradesh, Gujarat, Punjab, Bihar
• Honey Varieties: Mustard honey, eucalyptus honey, lychee honey, sunflower honey, Pongamia honey, multi-flora Himalayan honey, acacia honey and wild flora honey are some of the major varieties of honey exported from India. 
• Sweet Revolution: It focuses on the holistic development of beekeeping.
• National Beekeeping and Honey Mission (NBHM): It is a Central Sector Scheme for the overall promotion & development of scientific beekeeping and the production of quality honey & other beehive products.
  • It is implemented through the National Bee Board.

News Source: Live Mint

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Context: Recently, the Ministry of Mines launched the National Geoscience Data Repository or NGDR Portal. 

About NGDR Portal

• NGDR Portal is a comprehensive online platform for accessing, sharing, and analyzing geospatial information across the nation. 
• Led By: The Geological Survey of India (GSI) and Bhaskaracharya Institute of Space Applications and Geoinformatics (BISAG-N).
• Significance: It is a significant step towards democratizing critical geoscience data, empowering stakeholders across industries and academia with unprecedented access to invaluable resources.

News Source: PIB

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Context: Recently, the Ministry of Micro, Small & Medium Enterprises has launched three sub-schemes under the aegis of the RAMP (Raising and Accelerating MSME Productivity) programme. 

What are the three new sub-schemes introduced under the RAMP Programme?

• The three new sub-schemes under the RAMP Programme, include- 
  • MSME Green Investment and Financing for Transformation Scheme (MSE GIFT Scheme)
  • MSE Scheme for Promotion and Investment in Circular Economy (MSE SPICE Scheme)
  • MSE Scheme on Online Dispute Resolution for Delayed Payments.

About the RAMP Programme

• It is a World Bank-supported Central Sector Scheme.  
• Administrative and Functional Body in India: National MSME Council 
• Aim: 
  • Improving access to market and credit
  • Strengthening institutions and governance at the Centre and State
  • Improving Centre-State linkages and partnerships
  • Addressing issues of delayed payments
  • Greening of MSMEs
• Key Objectives
  • To accelerate centre-state collaboration in MSME promotion and development
  • Enhance the effectiveness of existing schemes of the MSME Ministry for technology upgradation
  • Strengthen the receivable financing market for MSMEs
  • Enhance the effectiveness of Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE)
  • Promote guarantee for greening initiatives of MSEs and women-owned MSEs

Various Sub-Schemes under RAMP Programme

Also Read: SWAMIH Investment Fund

News Source: PIB

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Context: Indian Finance Minister met the World Bank Group President and discussed the possible role of Multilateral Development Banks in various sectors of India. 

The Chief of the World Bank and the Finance Minister hold talks on the involvement of Multilateral Development Banks in India

• During the G20 Presidency of India, the Independent Expert Group (IEG), recommended a triple agenda to harness the potential of Multilateral Development Banks (MDBs).
  • The Triple Agenda includes
    • Eliminating extreme poverty 
    • Boosting shared prosperity and contributing to global public goods,
    • Tripling sustainable lending levels by 2030, and creating a  flexible funding mechanism. 
• A task force team to study recommendations by the IEG has been formed by the World Bank.

What are Multilateral Development Banks?

• Definition: MDBs are financial institutions that provide financial loans and grants to developing countries for their economic and social growth. 
  • For Example World Bank, the Asian Development Bank, the African Development Bank, the Inter-American Development Bank, etc

Must Read: Governance In Urban Cooperative Banks (UCB)

News Source: Business Standard

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Context: The International Monetary Fund (IMF) reported that the central bank excessively managed the Indian currency in the period December 2022 to October 2023. 

IMF Reclassifies India’s Exchange Rate Regime

• Low Movement: The rupee moved in the range of 81.04 per US dollar and 83.29, which is a band of 2.8%. In contrast, the Euro-Dollar rate moved in a band of 7.3% in the same period. 
• Exchange Rate Regime: India follows a Managed Floating Exchange Rate system, where the currency exchange rate is largely market-determined, and the central bank (RBI) steps in only to contain volatility in the exchange rate or market. 

• Recommendation: However, the IMF has said that Indian currency was excessively managed, making it a ‘stabilized arrangement’ exchange rate. 
  • The IMF has suggested moving towards a system that allows greater flexibility. 
  • Stabilised Arrangement is a situation where the Central Banks intervene frequently to ensure the band of movement of the exchange rate is narrow. 

About IMF

• Establishment: IMF was conceived in 1944 at the United Nations Bretton Woods Conference. 
• Objective: It is a global organization that works to achieve sustainable growth and prosperity for all. 
• Members: 190 countries, including India. 
• Headquarters: Washington DC, United States.
• Activity: Its major role are Policy Advice, Financial Assistance and Capacity Development. 
  • IMF financing helps member countries tackle balance of payments problems. Eg- Sri Lanka, Pakistan etc. 

However, experts are of the view that the IMF analysis is incorrect due to the following reasons: 

• Strengthening  Factors: 
• • India’s trade deficit averaged $20 billion a month, compared to averaged $22.1 billion a month last year. 
  • The Current Account Deficit at $27.3 billion, was almost half of the $53.5 billion previously. 
  • There was an increase in foreign portfolio investment (FPI) by around $45 billion. 
  • The forex reserves also saw a modest increase from $563 billion to $586 billion. .

Factors that can lead to an increase in volatility

• US Policy: An increase in the US Federal Reserve’s policy funds rate made the dollar stronger, which had negative effects on other currencies, including Indian Rupees. 

Types of Foreign Exchange Regime

• Fixed Exchange Rate: An exchange rate regime where the government or the central bank determines the country’s official exchange rate with another currency. For Example – The value of 1 Pound to INR in 1947 was fixed at 13.33. 
• Floating Exchange Rate: A system where the exchange rate is determined by the market forces of supply and demand.
• Managed Floating Rate: In this system, the country’s central bank occasionally intervenes to ensure the currency is not too volatile. India currently follows this model. 

To read more on EXCHANGE RATE SYSTEM

News Source: Live Mint

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Context: R21/Matrix-M malaria vaccine added to the WHO’s list of prequalified vaccines.

• The R21 Malaria vaccine is the second malaria shot approved by WHO, following the RTS,S/AS01.

About R21/Matrix-M Malaria Vaccine

• It is developed by the University of Oxford and manufactured by Serum Institute of India.
• Recommended Usage: To prevent Malaria in children under 5 years of age.
• Efficacy: It can reduce malaria cases by 75% over a year.
• Price: It will be priced around $5 per dose at high volumes. Hence making it cheaper and accessible to a larger population

About Malaria

• About: It is caused by Plasmodium parasites, which spread through the bites of infected female Anopheles mosquitoes.
  • It is caused in humans by five species of parasite, but 2 amongst them – Plasmodium falciparum and Plasmodium vivax  constitute the bulk of the incidence of infection.
• Malaria is not contagious and cannot spread from one person to another,  also, it is preventable and curable.
• In 2021, an estimated 247 million people contracted malaria in 85 countries, and the disease claimed approximately 619,000 lives.
• India accounts for 3% of the global Malaria burden.

Also Read: 

• World’s First Vaccine For Chikungunya Approved
• GEMCOVACOM: India’s First MRNA Vaccine

News source: Down to Earth

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Context: Missing for 42 years, the Namdapha flying squirrel resurfaces in Arunachal Pradesh.

• The Namdapha flying squirrel was last described in 1981 based on a single individual found in the Namdapha Tiger Reserve in Arunachal Pradesh. 

About Namdapha Flying Squirrel

• Namdapha Flying Squirrel: It is an arboreal, nocturnal flying squirrel endemic to Arunachal Pradesh. 
  • As the Namdapha flying squirrel has rarely been recorded, there is little information available regarding its biology or ecology, but it is likely to be similar to other flying squirrels. 
• Scientific Name: Biswamoyopterus Biswasi.
• Population and Habitat: No population estimate is available for B. biswasi, but the known habitat is tall Mesua ferrea jungles, often located on hill slopes. 
• IUCN Red List Status: Critically Endangered.
• Threats: Threatened by habitat loss and degradation caused by shifting agriculture, extraction of non-timber forest products, etc.
• About Flying Squirrels: All flying squirrels are nocturnal, possibly because their gliding membrane hinders their movement, and thus darkness gives them extra protection from predatory birds
  • The membrane does allow an effective escape from flightless tree predators. 

Must Read: Rising Human Animal Conflict: Reasons And Consequences

News Source: TH

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Context: This article is based on the news “India Skills Report finds Kerala the most preferred State to work” which was published in the Hindu. The talent assessment agency Wheebox recently unveiled India Skills Report 2024: “Impact of AI on the Future of Work, Skilling & Mobility”.

India Skills Report 2024: Key Highlights

• State of Employability in India: It has improved with 51.25% of the assessed youths found to be employable with the required skills.
• State with Maximum Employability in India Skills Report 2024: Telangana has the highest concentration of employable talent in the age group of 18-21 where 85.45% were found employable.
• Status of Young Employability in India Skills Report 2024:
  • Age Range of 18-21 years: Haryana stands out as a top performer in youth employability development, with an impressive 76.47% of test takers scoring above 60% on their WNET tests.
  • Haryana is followed by Maharashtra, Andhra Pradesh, Uttar Pradesh, Kerala, and Telangana have the highest concentration of highly employable youth. 
  • Age range of 22 to 25 years:  Uttar Pradesh stands out with the highest talent concentration at 74.77%, followed closely by Maharashtra at 71.97%. 
• Computer Skills: Thiruvananthapuram claimed the first position among cities for nurturing computer skills while Kerala came third overall among States.
• Skill Penetration: India stands at first position in AI skill penetration and talent concentration.
• Increase in Female LFPR:  There is a consistent increase in female labor force participation(LFPR), fostering healthy competition and gradually correcting gender imbalances across societal and cultural realms. 
  • LFPR is defined as the percentage of persons in the labour force (i.e. working or seeking or available for work)in the population.

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Skills Distribution Across States and Cities in India

• English as a Second Language: Karnataka emerged as a leader with 73.33% proficiency, reflecting the state’s emphasis on language skills. 
  • It is followed by Uttar Pradesh and Maharashtra showcasing a diverse geographic spread of English proficiency. 
• Numerical Skills : Telangana witnessed numerical skills at 78.68%, emphasizing the state’s focus on quantitative abilities. 
  • Uttar Pradesh and Bihar excelling in numerical skills exhibit diverse proficiency levels in other domains. 
  • Bengaluru and Vijayawada lead in cities, indicating strong numerical capabilities in major urban hubs. 
  • Proficiency in numerical skills is vital for roles in finance, data analytics, and STEM fields. 
• Critical Thinking : Telangana tops in critical thinking with 37.70%, showcasing a culture valuing analytical thinking. 
  • Maharashtra and Andhra Pradesh closely follow, indicating a trend of developing critical thinking skills. 
• Computer Skills : Maharashtra, Uttar Pradesh, and Kerala lead in computer skills, underscoring their commitment to technological advancements. 
  • Bengaluru emerged as a technology hub, leading among cities.

Transformative Impact of Artificial Intelligence (AI) on the India’s Workforce

• AI Skills:  With an installed talent base of 416K AI professionals as of August 2023, the nation is well positioned to meet the current demand of approximately 629K. 
  • This demand is projected to escalate to 1 million by the year 2026.
  • The Indian AI industry is projected to reach USD 28.8 billion by 2025, with a CAGR of 45%. 
  • This is propelled by the widespread adoption of AI across diverse sectors such as healthcare, finance, retail agriculture, and manufacturing.

• Escalating demand for AI professionals in India: There is a 14-fold increase in AI-skilled individuals within the country from January 2016 to June 2023.
  • India stands among the top five nations witnessing a significant rise in AI talent, alongside Singapore, Finland, Ireland, and Canada.
• Skill Penetration: India has a relative AI skill penetration three times the global average.
• AI Skill Gap: India currently has a 60% to 73% gap between demand and supply in crucial roles like ML engineer, data scientist, DevOps engineer, and data architect.

Challenges with Skill Development in India

• Skill Gap: In India, there exists an enormous skill gap between the demand from industries and the skillset of the young people acquired through education and training. 
  • Around 31% is illiterate, only 13% had a primary education, and only 6% were college graduates. Further, only about 2% of the workforce had formal vocational training, and only 9% had non-formal, vocational training.

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• Skills Mismatch: There is lack of industry-faculty interaction because of which the skill sets provided by the educational and training institutes suit the employers’ requirements. 
  • As a result, though the people may be skilled but they do not get employment. 
• Informal Workforce: Over 90% of India’s workforce is in the informal sector. 
  • According to researchers at the NCAER, India is trapped in a vicious cycle where informality of the workforce leads to lower incentives to acquire new skills. Majority of Indians who work in agriculture do not have the skills to take up industrial or services sector jobs.
  • National Council of Applied Economic Research (NCAER) is India’s oldest and largest independent, non-profit, economic policy research think tank.

• Poor Quality of Skill Training: The Government skill programs suffer from no training centres to poor training, no certification, no placement or forced placement, lack of trainers, and no assurance of a decent wage for those trained.
  • As per the government data,  between 2015 and March 2023, of the 10 million (1,37,24,226) people trained under the Pradhan Mantri Kaushal Vikas Yojana, less than 20 percent were “reported as placed”.
• Poor Industrial Linkage: It results in a lack of understanding of the current demands and trends. This gap prevents the alignment of curriculum with the practical needs of the job market.
• Lack of Curriculum Standardization: There is no standardization of the course curriculum making  it difficult to compare courses across different training institutes creating ambiguity among the students about skills which will be imparted.

Skilling Efforts by India

• Pradhan Mantri Kaushal Vikas Yojana (PMKVY): It aims to enable Indian youth to take up industry-relevant skill training that will help them secure a better livelihood.
• Skill India Mission: It aims to provide skill training to millions of youth nationwide. It focuses on sectors such as manufacturing, construction, healthcare, tourism, and information technology. 
• National Skill Development Corporation (NSDC):  It plays a role in fostering skill development initiatives  by collaborating with training partners, industry bodies, and vocational training providers to enhance skilling efforts. 
• National Skill Development Mission (NSDM): It aims to create an end-to-end implementation framework that provides opportunities for quality short and long-term Skill Development (SD), leading to productive employment and career progression that meets the aspirations of trainees.
• Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP): It aims to strengthen institutional mechanisms for skill development and increase access to quality and market-relevant training for youth across the country.
• Sector Skill Councils (SSC): These were set up as interfacing organizations between the industry and the Government. 
• National Migration Policy (NMP): The government is working to develop a NMP to protect the rights of migrant workers and facilitate their mobility as well as access to resources.
  • The number of Indian workers migrating to other countries for work has increased by 20% in the past five years.

Must Read: Periodic Labour Force Survey (PLFS) Annual Report 2022

Way Forward

• Upskilling and Reskilling the workforce: There should be an emphasis on upskilling the existing workforce to adapt to the changing technological landscape.
  • The emergence of new-age technologies like AI, automation, robotics and analytics has created a high demand for professionals with emerging skills. 
  • Equipping students with the necessary skills for the future job market by focusing on practical experiences, industry collaboration, and leveraging technology.
• Government, Academia, and Corporate Sector Collaboration: It will help design and implement comprehensive skill development programs, promote employee skill development and create a talent pool per the industries’ requirements.
  • These initiatives should focus on traditional engineering skills and cutting-edge digital technologies, enabling the workforce to meet the demands of a rapidly evolving industry.
• Promoting Vocational Education and Apprenticeship Programmes: It will create a steady pipeline of skilled manpower. 
  • These initiatives can align the curriculum with the specific needs of the infrastructure sector by engaging with educational institutions and industry experts.
• Promoting Consultancy Sector:  It can address the demand for specialized expertise. Engaging experienced professionals in consulting roles will enhance project efficiency and quality.
• Career Counselling: Setting up career counseling and guidance in secondary schools to provide the necessary exposure to students to vocational education at an early age.
• Awareness Creation: Raising awareness and engaging students in emerging technologies such as automation, Artificial Intelligence, data analytics, robotics etc.
• Integration of distance and regular education: It will provide flexibility and wider access to learners from diverse backgrounds based on their preferences and requirements.

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Conclusion:

The India Skills Report 2024 highlights improvements in employability, the transformative impact of AI, and challenges in skill development, emphasizing the need for comprehensive collaboration and proactive measures to bridge the skill gap and prepare the workforce for the evolving job market.

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Context: At the recent COP28 of UNFCCC, NASA and IBM announced that an Artificial Intelligence (AI) tool called watsonx.ai would be available on the open-source AI platform Hugging Space. 

Watsonx.AI: An AI tool for Weather forecasting

• Watsonx.ai is a collaborative AI tool developed by IBM and NASA has the potential to enhance the ability to predict hurricanes, droughts, and other severe weather events with increased precision.
• Utilising NASA’s store of valuable of data and IBM’s AI technology, the model can help scientists estimate the past and future extent of wildfires, floods, and urban heat maps.
• Watsonx.ai will help users monitor the Earth from space, measuring environmental changes that have already happened while also making predictions.
• Effort aims to widen access to NASA earth science data for geospatial intelligence and accelerate climate-related discoveries.
• The Indian government is testing AI to build climate models to improve weather forecasting as torrential rains, floods, and droughts proliferate across the country. 

Must Read: Global Partnership On Artificial Intelligence – GPAI

About Weather Forecasting

• Definition of Weather: It simply refers to the state of the atmosphere at a particular place and time as regards heat, cloudiness, dryness, sunshine, wind, rain, etc. 
• Definition of Weather Forecasting: The prediction of the weather through application of the principles of physics, supplemented by a variety of statistical and empirical techniques. 
  • It is a continuous, data-intensive, multidimensional, dynamic and chaotic process. 
• Meteorology: Meteorology is the science of weather. Knowledge of meteorology forms the basis of scientific weather forecasting, often made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve in future. 
• Types of Weather Predictions: There are two types of weather predictions, viz., short-range and long-range forecasts. 
  • Short-range forecasts range from 1 to 14 days. 
  • Long-range forecasts are predictions over 14 days. 
  • As predictive models attempt to forecast farther into the future, the predictions become less accurate.
• Forecasts in India: The India Meteorological Department (IMD) provides forecasts based on mathematical models using supercomputers. Using AI with an expanded observation network could help generate higher-quality forecast data at lower cost.

Significance of Using AI for Weather Forecasts

• Improve Accuracy: AI software can make the weather prediction process more effective as one of the key strengths of AI is its ability to work with large sets of data. 
  • This can be attributed to AI machine learning capabilities. AI can be programmed to analyze multiple data sets of weather information to provide more accurate forecast maps.
• Improved Efficiency: Recently, Nvidia, Google DeepMind, and Huawei have introduced machine-learning methods that can predict the weather at least as accurately as conventional methods and much more quickly. 
  • Researchers at DeepMind have unveiled a cutting-edge weather prediction program named “GraphCast,” leveraging machine learning to forecast weather variables up to 10 days in advance, all within a one-minute timeframe.
  • Predictions for extreme weather events such as thunderstorms can be more complicated to predict compared to cyclones since these events develop over a very short period of time and also dissipate equally fast. AI and machine learning have the potential to speed up these predictions. 
• Combating Climate Change: Changing climate conditions have increased the frequency of severe storms, floods, heat waves, and more enormous wildfires. As a result, scientists are using AI techniques for more accurate forecasts that help to minimize damage and save lives.
  • For example, ClimateAi use deep learning, a form of inductive reasoning, for seasonal forecasting.

• Continuous Adaptation Process: In the case of weather prediction, AI programs don’t need additional input from human operators to make predictions, they can continuously observe data without rest. This infinite data analysis allows the models to learn more about patterns and how to predict them more precisely than a human could.
  • In addition, if radar or satellite observes sudden changes in weather patterns, AI programs can adapt more quickly to the new data than traditional computer systems.
• Preparedness: With increased accuracy and efficiency of predictions, AI tool can predict when and where a natural disaster might occur. Such predictions would give populated areas in the path of a disaster enough time to take precautions, saving countless lives.
  • For example, Huawei’s Pangu-Weather Model, is said to be capable of predicting global weather a week in advance which would allow meteorologists to better study weather patterns in real-time.

What are the challenges associated with weather forecasts?

• Lack of Quality Data: Access to that data is an issue as climate data sets are massive and take significant time to collect. 
  • Further, AI models require large amounts of high-quality historical data for training which may be scarce or incomplete for certain regions, hindering the effectiveness of AI models.
• Complex Atmospheric Processes: Weather systems are highly complex and small changes in initial conditions can lead to significant variations in outcomes, making it challenging to develop accurate predictive models. While current weather forecasting systems are quite advanced, they are not good at adapting to sudden changes.
• Local Variations: AI models need to account for microclimates and local geographical features that can significantly influence weather conditions. Accurate predictions at a local level may be challenging due to the diversity of terrain and local atmospheric conditions. 
  • Without having high-resolution data in space and time, no AI model for location-specific magnification of existing model forecasts is feasible.
  • For example, in a tropical environment like the Florida Keys, the weather doesn’t change much from day to day, so researchers had to manually look at variations in the atmosphere that the algorithms don’t always take into account.
• Lack of Interpretability: Some advanced AI models, particularly deep learning models, are often considered black boxes, meaning it can be challenging to understand how they arrive at specific predictions. 
  • This can be a barrier to gaining trust in the predictions and may limit the model’s usefulness in critical applications.
• Accessibility: Training and running sophisticated AI models for weather forecasting require access to high-performance computing infrastructure which may be limited, particularly in developed regions.

Way Forward

• Improving Data Quality and Quantity: There is a need to explore alternative data sources, such as satellite imagery, remote sensing, and crowd-sourced data, to supplement traditional weather station data.
• Enhanced Computational Resources: Investing in high-performance computing infrastructure to support the training and deployment of sophisticated AI models. 
  • Further exploring cloud computing solutions can provide scalable and flexible computational resources.
• Real-time Adaptability: Design AI models that can adapt quickly to changing conditions by incorporating real-time data assimilation techniques while continuously updating data on new observations can improve their performance over time.
• Integration with Traditional Methods: Foster collaboration between meteorologists and data scientists to develop hybrid forecasting approaches that leverage the strengths of both AI and traditional methods which can improve their accuracy.

Conclusion:

Watsonx.ai, a joint effort by IBM and NASA, uses AI to improve weather forecasting, offering better accuracy and preparedness. 

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