Capability demonstration of a JEDI-based system for TEMPO assimilation: system description and evaluation

TL;DR

This paper presents the first implementation of a TEMPO NO2 data assimilation system using JEDI, demonstrating its ability to integrate high-frequency geostationary observations into atmospheric models and improve air quality predictions.

Contribution

The study introduces a novel JEDI-based system for assimilating TEMPO NO2 data with 4DEnVar and EDA, enabling real-time, high-resolution air quality monitoring.

Findings

Successfully integrated TEMPO NO2 data into GEOS-CF model.

Improved model performance in column NO2 and diurnal variability.

Systematic reductions in surface NO2 levels observed.

Abstract

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO2 retrievals from TEMPO alongside polar-orbiting TROPOMI data into NASA's GEOS Composition Forecast (GEOS-CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft-based observations from AEROMMA and STAQS field campaigns. Results show that the assimilation system successfully integrates geostationary NO2 observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in surface NO2 levels, improving agreement with some datasets (e.g., Pandora, AEROMMA) but degrading comparisons with others (e.g., AirNow). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual-concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI-based model emulators offer a promising path toward scalable, real-time assimilation of geostationary atmospheric composition data.