Assimilation of high-resolution Ocean Color Monitor (OCM) aerosol optical depth in WRF-Chem improves PM₂.₅ forecasts over the Indian region – Nature

Executive Summary

This report details an investigation into the assimilation of high-resolution (770 m) Aerosol Optical Depth (AOD) data from the Oceansat-3 Ocean Colour Monitor (OCM) sensor into the Weather Research and Forecasting model with Chemistry (WRF-Chem). The primary objective was to enhance the accuracy of fine particulate matter (PM₂.₅) forecasts across India, a critical step toward achieving key Sustainable Development Goals (SDGs). The evaluation, conducted from 01–15 November 2023, demonstrates that OCM AOD assimilation substantially improves forecast accuracy. This initiative directly supports several SDGs:

• SDG 3 (Good Health and Well-being): By providing more accurate air pollution forecasts, the system enables timely public health advisories, helping to mitigate the adverse health effects of poor air quality.
• SDG 11 (Sustainable Cities and Communities): Enhanced forecasting provides a crucial tool for urban air quality management, allowing policymakers to implement effective, evidence-based interventions to create healthier and more sustainable urban environments.
• SDG 17 (Partnerships for the Goals): The successful integration of data from a national satellite (Oceansat-3) highlights India’s technological capacity and commitment to building resilient environmental monitoring systems, ensuring data continuity as older international platforms like MODIS are phased out.

Key findings indicate that the assimilation process reduced PM₂.₅ forecast biases by 30–70% and lowered the root mean square error (RMSE) in critical regions, including Delhi, Punjab, and West Bengal. The forecast accuracy was highest on the first day (RMSE of 21.35 µg/m³, correlation of 0.75) and showed a gradual decline over a 72-hour period. The performance of OCM data assimilation was found to be comparable, and in some cases superior, to that of the established MODIS sensor, positioning OCM as a reliable and vital data source for India’s AIRWISE operational forecasting system.

Introduction: Aligning Air Quality Forecasting with Sustainable Development Goals

The Challenge of Air Pollution in India and its Impact on SDGs

Rapid industrialization and urbanization in India have led to a severe degradation of air quality, with widespread PM₂.₅ pollution posing significant risks to public health and the environment. This challenge directly impedes progress on several Sustainable Development Goals.

• SDG 3 (Good Health and Well-being): High levels of PM₂.₅ are linked to respiratory and cardiovascular diseases, contributing to premature mortality and undermining public health targets.
• SDG 11 (Sustainable Cities and Communities): Persistent air pollution makes cities less livable and sustainable, directly contradicting the goal of creating safe, resilient, and inclusive urban spaces (Target 11.6).

Accurate and timely air quality forecasts are therefore essential not only for environmental management but also as a public health tool to protect vulnerable populations and advance the 2030 Agenda for Sustainable Development.

The Role of Advanced Forecasting Systems in Sustainable Urban Management

To address this challenge, India’s Ministry of Earth Sciences (MoES) developed the Air Quality Warning and Integrated Decision Support System for Emissions (AIRWISE). This system integrates numerical modeling with satellite and ground-based observations to provide actionable forecasts. By enabling proactive measures such as the Graded Response Action Plan (GRAP) in Delhi-NCR, AIRWISE serves as a critical tool for urban governance, directly supporting the objectives of SDG 11 by helping authorities manage pollution sources and reduce the adverse environmental impact of cities.

Ensuring Data Continuity for Long-Term Sustainability

The AIRWISE system has historically relied on AOD data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). With the MODIS platform approaching its end-of-life, ensuring the continuity of high-quality satellite data is imperative for maintaining forecasting capabilities. The deployment of the Oceansat-3 OCM sensor by the Indian Space Research Organisation (ISRO) represents a significant advancement in national technological capacity. This study evaluates OCM as a viable alternative to MODIS, a crucial step for building resilient national infrastructure and fostering the self-reliance needed to achieve long-term environmental monitoring goals, in line with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 17 (Partnerships for the Goals).

Methodology: Integrating High-Resolution Satellite Data for SDG-Informed Forecasting

Experimental Design

The study was conducted for the period of 01–14 November 2023, a time of significant pollution events in India. Three distinct simulation experiments were performed to assess the impact of data assimilation:

1. CNTL (Control): A baseline simulation using the WRF-Chem model without any AOD data assimilation.
2. MODISDA (MODIS Data Assimilation): A simulation incorporating AOD data from the established NASA MODIS sensor.
3. OCMDA (OCM Data Assimilation): A simulation incorporating high-resolution AOD data from the new ISRO OCM sensor.

All experiments generated 72-hour forecasts daily, allowing for an evaluation of forecast performance over time.

Data Sources and Assimilation Framework

The research utilized a multi-source data approach within a 3DVAR data assimilation framework to improve the initial conditions of the WRF-Chem model. Key data sources included:

• Oceansat-3 OCM: High-resolution (770 m) AOD retrievals providing detailed spatial coverage over the Indian subcontinent. This dataset is central to building a sustainable, long-term national monitoring capability.
• MODIS: AOD retrievals from NASA’s Terra and Aqua satellites, used as a benchmark to evaluate the performance of the OCM data.
• Ground-based PM₂.₅ Measurements: Data from 258 monitoring stations across 12 Indian states, operated by the Central Pollution Control Board (CPCB) and other agencies, were used for validation. This validation against ground-truth data ensures the forecasts are reliable for public health applications (SDG 3).

Performance Evaluation Metrics

To quantitatively assess the improvements in forecast accuracy, the following statistical metrics were used to compare model outputs against ground-based observations:

• Mean Bias (MB): To determine the average overestimation or underestimation of the forecasts.
• Root Mean Square Error (RMSE): To measure the magnitude of forecast errors.
• Pearson’s Correlation Coefficient (R): To evaluate the linear relationship between forecasted and observed values.

Results: Enhanced Forecast Accuracy for Public Health and Urban Planning

Impact of OCM Data Assimilation on Aerosol Forecasts

The assimilation of OCM AOD data resulted in a dramatic improvement in the model’s representation of atmospheric aerosol loading. The control (CNTL) experiment significantly underestimated AOD, with values in the 0.1–0.4 range, whereas satellite observations showed values between 0.8–1.0. The OCMDA experiment successfully corrected this underestimation, increasing AOD levels by 1.5 to 2.0 times over the heavily polluted Indo-Gangetic Plain (IGP). This enhanced accuracy is fundamental for providing reliable information to support policies aimed at protecting public health (SDG 3) and managing urban air quality (SDG 11).

Improvements in Surface PM₂.₅ Concentration Forecasts

The correction in column AOD translated directly to improved forecasts of surface-level PM₂.₅, the pollutant of greatest concern for human health. On average, OCM data assimilation increased forecasted PM₂.₅ concentrations by 35–45 µg/m³ over the IGP, bringing them into much closer alignment with ground-based observations. Both the OCMDA and MODISDA experiments demonstrated a superior ability to capture the magnitude and timing of severe pollution events, such as the post-Diwali pollution spike on November 13, where observed PM₂.₅ levels exceeded 400 µg/m³. The CNTL run failed to capture this event, underscoring the critical role of data assimilation in creating a warning system that can effectively inform public response during acute pollution episodes, a key requirement for sustainable urban communities (SDG 11).

Regional Performance Analysis

The benefits of OCM data assimilation were observed across India, with particularly strong improvements in regions facing severe air quality challenges. This regional success demonstrates the system’s potential to support tailored, location-specific air quality management strategies.

• Bihar: On Day 1, OCMDA reduced the RMSE from 62.44 µg/m³ (CNTL) to 39.36 µg/m³.
• Gujarat: The Day 1 RMSE improved from 35.18 µg/m³ (CNTL) to 21.35 µg/m³ (OCMDA), while the correlation coefficient (R) rose from 0.51 to 0.75, indicating a much stronger agreement with observations.
• Delhi: Both assimilation experiments corrected the consistent underestimation of PM₂.₅ seen in the CNTL run, providing a more realistic forecast for the national capital region.

The analysis confirmed that forecast accuracy was highest on Day 1 and gradually decreased over the 72-hour forecast period, as model uncertainties began to outweigh the initial benefits of assimilation. This highlights the importance of daily assimilation cycles for maintaining high-quality operational forecasts.

Conclusion: Oceansat-3 OCM as a Key Enabler for Sustainable Development in India

Validation of OCM as a Viable Data Source

This study provides the first-ever validation of assimilating high-resolution AOD data from the Oceansat-3 OCM sensor for operational air quality forecasting in India. The results conclusively demonstrate that OCM data significantly improves the accuracy of PM₂.₅ forecasts. The performance of the OCMDA experiment was found to be on par with, and in some regions superior to, the established MODISDA benchmark. This confirms that OCM is a robust and reliable data source, capable of ensuring the continuity and enhancement of India’s air quality forecasting capabilities.

Contribution to Sustainable Development Goals

The successful integration of OCM data into the AIRWISE forecasting system represents a significant contribution to India’s progress toward several SDGs.

• SDG 3 (Good Health and Well-being): By delivering more accurate and reliable air pollution warnings, the system empowers individuals and authorities to take protective measures, reducing public exposure to harmful pollutants and mitigating negative health impacts.
• SDG 11 (Sustainable Cities and Communities): The enhanced system provides a scientifically robust tool for urban planners and environmental agencies to implement targeted pollution control strategies, thereby making Indian cities healthier, safer, and more sustainable.
• SDG 17 (Partnerships for the Goals): This work exemplifies a successful synergy between national scientific institutions (IITM, ISRO, MoES). By developing and utilizing indigenous satellite technology, India strengthens its capacity for environmental monitoring and reduces its reliance on foreign assets, contributing to global goals through national action.

Future Outlook

The findings of this report validate the OCM sensor as a cornerstone for the future of air quality forecasting in India. Future work should focus on leveraging the sensor’s high spatial resolution by assimilating OCM data into city-scale models. This will enable even more precise, localized forecasts, further strengthening the ability of municipal authorities to manage air quality at the urban level and accelerate progress towards achieving SDG 11. The continued development of such advanced, data-driven systems is critical for tackling complex environmental challenges and building a sustainable future.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 3: Good Health and Well-being

   The article directly connects to SDG 3 by focusing on the public health risks associated with air pollution. It explicitly mentions that widespread PM₂.₅ pollution poses “serious environmental and public health risks” and cites research on “premature mortality in India due to PM2.5 and ozone exposure.” The primary motivation for developing accurate air quality forecasts is to enable “timely advisories and interventions that reduce exposure during high-pollution episodes, particularly for vulnerable groups,” which is central to ensuring healthy lives and promoting well-being.

2. SDG 11: Sustainable Cities and Communities

   The research is framed within the context of India’s “rapid urbanization, industrialization, and rising energy demand,” which are worsening air quality in cities. The article highlights the need for “a reliable air quality forecasting is essential for Indian cities due to the severe and persistent air pollution challenges.” The development and implementation of the Air Quality Early Warning System (AQEWS) and the AIRWISE system for Delhi-NCR are direct efforts to make cities more sustainable and resilient by managing a critical environmental problem, aligning with the goal of improving urban living conditions.

3. SDG 9: Industry, Innovation, and Infrastructure

   This goal is addressed through the article’s focus on scientific research and technological innovation. The study describes a cutting-edge technological solution: assimilating high-resolution satellite data from a new sensor (Oceansat-3 OCM) into an advanced chemical-transport model (WRF-Chem). The entire AIRWISE framework is presented as “a cutting-edge tool that combines high-resolution numerical modeling based air quality forecasts with actionable source attribution insights for air quality management.” This represents an upgrade of technological capabilities and an investment in scientific infrastructure to solve a pressing national issue.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.

   The article’s core objective is to improve the forecasting of fine particulate matter (PM₂.₅), a major air pollutant responsible for significant mortality and morbidity. By creating a more accurate “Air Quality Early Warning System,” policymakers and the public can take measures to “reduce exposure during high-pollution episodes.” This directly contributes to the goal of reducing illnesses and deaths caused by air pollution.

2. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.

   The study is explicitly focused on improving the management of air quality in Indian urban centers, such as Delhi, which suffer from severe pollution. The article states that “a reliable air quality forecasting is essential for Indian cities due to the severe and persistent air pollution challenges driven by rapid urbanization, industrialization, and vehicular emissions.” The AIRWISE system helps policymakers implement plans like the Graded Response Action Plan (GRAP) in Delhi-NCR, which is a direct mechanism for reducing the adverse environmental impact of the city by managing its air quality.

3. Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries…

   The research presented in the article is a clear example of enhancing scientific research and upgrading technological capabilities in India. The study involves advanced techniques like “three-dimensional variational (3DVAR) data assimilation,” using data from a new, high-resolution Indian satellite sensor (Oceansat-3 OCM), and improving a complex forecasting model (WRF-Chem). This work, conducted by institutions like the Indian Institute of Tropical Meteorology and the Indian Space Research Organisation, directly supports the target of fostering innovation and building domestic scientific capacity.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Indicator for Targets 3.9 and 11.6: Annual mean levels of fine particulate matter (PM₂.₅).

   This is the central metric throughout the article. The official indicator for Target 11.6 is 11.6.2: “Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities.” The article is entirely dedicated to improving the forecast of “fine particulate matter (PM₂.₅).” It consistently uses PM₂.₅ concentrations, measured in micrograms per cubic meter (µg/m³), as the key variable for evaluating model performance. For example, it reports that assimilation reduces “PM₂.₅ biases by 30–70%” and improves “surface PM₂.₅ estimates by approximately 60 µg/m³.” This metric serves as a direct measure of air quality in cities (Target 11.6) and is the primary determinant of health impacts from air pollution (Target 3.9).

2. Indicator for Target 9.5: Development and enhancement of advanced air quality forecasting systems.

   While the article does not mention official indicators like R&D expenditure, it provides qualitative and performance-based evidence of progress in scientific and technological capabilities. The evolution of the system from “AQEWS” to “AIRWISE,” the successful integration of a new national satellite’s data (OCM), and the demonstrated improvement in forecast accuracy (e.g., reduction in RMSE and increase in correlation coefficient R) are tangible outcomes of enhanced research and innovation. The system itself, which provides “actionable source attribution insights for air quality management,” is an indicator of upgraded technological capacity for environmental management.

4. Table of SDGs, Targets, and Indicators

Source: nature.com