UMBC GESTAR II

Permanent URI for this collectionhttp://hdl.handle.net/11603/24116

n December 2021, GESTAR II partnered with NASA Goddard Space Flight Center’s Earth Science Division to advance Earth science and Goddard’s leadership by providing a competitive environment to hire and retain high-quality scientists who are on track to be leaders at NASA, in academia and in industry. GESTAR II exemplifies the power of mentorship, embracing a career development strategy that only a university research center can provide. In GESTAR II, early-career researchers and students can build outstanding resumes, launching them to become the Earth science leaders of tomorrow.

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Widespread Expansion of Salt Marsh Pools Observed on Maine Marshes Since 2009
(WIley, 2025-02-25) DeWater, K.; Kochtitzky, W.; Ellis, R.; Merrill, P.; Pittsley, M.; Morgan, P.; Burns, C.; Campbell, Anthony D.; Adamowicz, S.
Salt marshes provide critical habitats, protect coastal infrastructure, and are increasingly exposed to sea level rise, with many having a history of agricultural use and ditching over the centuries. Pool area coverage can be considered an indicator of marsh health but is rarely quantified. In this study, we digitized marsh pools using aerial imagery to quantify changes in pool area and density on 12 salt marshes in Maine from 2009 to 2021 as a case study of marsh response to environmental changes. We categorized pools into three types: mega-pools, individual pools, and perimeter pools, based on morphology and examined whether pools remained singular, split, or combined. We found a 15.7% increase in pool area from 2009 to 2021 on all marshes, primarily driven by mega-pool expansion, whereas individual and perimeter pools remained relatively constant. The rate of pool expansion across all marshes was 49,000 m2 a?1 with mean mega-pool size 6,400 � 400 m2. There was an increase in pool count per km2 on all marshes except for the York River marsh, which still experienced area expansion. Pools primarily increase in cover through merging or being engulfed by mega-pools. Area cover change was not substantial when pools remained singular, split into many pools, or were only present in 2009 or 2021. Mega-pools were larger on lower marsh elevations and expanded at a greater rate when overlapping ditches, suggesting influence by sea level rise and historic agriculture. Marsh restoration projects that promote the drainage and re-vegetation of mega-pools may reverse this trend.
Evaluating and Comparing Dust Emission Schemes within the NASA GEOS Model
(NTRS, 2025-01-15) Joshi, Janak R.; Silva, Arlindo da; Colarco, Peter; Darmenov, Anton; Ginoux, Paul; Kok, Jasper; Collow, Allison; Nowottnick, Edward
Integrating the Interconnections Between Groundwater and Land Surface Processes Through the Coupled NASA Land Information System and ParFlow Environment
(AGU, 2025) Maina, Fadji; Rosen, Dan; Abbaszadeh, Peyman; Yang, Chen; Kumar, Sujay V.; Rodell, Matthew; Maxwell, Reed
Understanding the interactions between the atmosphere, the land, and the subsurface is fundamental to hydrology and is critical for a better assessment of the impacts of climate change and human management on hydrological systems. However, many land surface models simplify the subsurface hydrology and thereby these interactions. In this study, we couple the land surface model Noah-MP included in the NASA Land Information System (LIS) with the integrated hydrologic model ParFlow (ParFlow-LIS) using the Earth System Modeling Framework (ESMF) and the National United Operational Prediction Capability (NUOPC). This coupling improves the simulation of water and energy cycle processes by adding the three-dimensional variably saturated and heterogeneous flow in the subsurface using sophisticated and nonlinear physics-based equations as well as the advances in satellite remote sensing-based data assimilation of the land surface, thereby benefiting the integrated hydrologic modeling and data assimilation community. We use the High Plains aquifer, located in the central United States, as a testbed to evaluate the coupled ParFlow-LIS system. The new ParFlow-LIS system accounts for the effects of topographically driven flows on the land surface, producing more fine-scale patterns of land surface states and fluxes than standalone LIS. In addition, ParFlow-LIS enables the consideration of the effect of subsurface water storage on evapotranspiration. This is particularly important in areas and times with dry soils, such as during drought conditions or in the presence of a cone of depression due to pumping.
Quantifying Geomagnetic Activity's Contribution to the Global E-Region Electron Density's Day-To-Day Variability Using Spire Radio Occultation Observations
(AGU, 45691) Salinas, Cornelius Csar Jude; Wu, Dong L.; Qian, Liying
Using the unprecedented sampling of the Spire Radio Occultation (RO) data set, this paper statistically estimates geomagnetic dependencies of the global E-region Electron Density's (Ne) day-to-day variability. To assesses how much Spire RO-observed variabilities are consistent with known Physics, comparison is made with the Specified Dynamics–Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD-WACCM-X), a first principles Physics-based model. Results show that the largest geomagnetic dependency on Spire and SD-WACCM-X E-region Ne occurs at night over the auroral latitudes with coefficients of determination at around 49% and 80%, respectively. Their regression coefficients are both between +10%/Kp index to +16%/Kp index. On the other hand, Spire and SD-WACCM-X substantially disagree on the geomagnetic dependencies during day-time. These results suggest that Spire RO's observations of E-region Ne geomagnetic dependencies may only be substantially explained by known physics at night and not during the day.
Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System (I): Assimilation Framework
(AMS, 2025-01-31) Zhu, Y.; Arnold, N. P.; Yang, E.-G.; Ganeshan, M.; Salmun, H.; Palm, S.; Santanello, J.; McGrath-Spangler, E. L.; Lewis, Jasper; Molod, A.; Wu, D.; Lei, T.; Akkraoui, A. El; Sienkiewicz, M.
In this study, a strategy and framework are developed to build a global Planetary Boundary Layer (PBL) height (PBLH) analysis and monitoring capability from multiple observing systems in the NASA Global Earth Observing System (GEOS) data assimilation system. To facilitate this effort, PBLH are derived from radiosonde and Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity data. As PBLH can be sensitive to potentially disparate observables and retrieval algorithms, new model PBLH definitions consistent with each observation type are added to the forecast model for the calculation of first guess departures from observations (OmF). These model definitions are augmented to the control variable vector, interacting with other control variables through flow-dependent ensemble background error covariance component. Moreover, to capture capping inversions, methods are explored using PBLH data to improve background error covariance through inflation of ensemble spread and adjustment of vertical localization length scale for virtual temperature and relative humidity variables. Experiments are conducted to assess the separate and combined impacts of these methods and the correlation relationships between PBLH and other control variables in the background error covariance. Preliminary results show that these changes are beneficial to the assimilation of other observations to improve the PBL thermodynamic structure.
Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression
(IEEE, 2025-01-16) Zhang, Olivia; Grissom, Brianna; Pulido, Julian; Munoz-Ordaz, Kenia; He, Jonathan; Cham, Mostafa; Jing, Haotong; Qian, Weikang; Wen, Yixin; Wang, Jianwu
Accurate quantitative precipitation estimation (QPE) is essential for managing water resources, monitoring flash floods, creating hydrological models, and more. Traditional methods of obtaining precipitation data from rain gauges and radars have limitations such as sparse coverage and inaccurate estimates for different precipitation types and intensities. Symbolic regression, a machine learning method that generates mathematical equations fitting the data, presents a unique approach to estimating precipitation that is both accurate and interpretable. Using WSR-88D dual-polarimetric radar data from Oklahoma and Florida over three dates, we tested symbolic regression models involving genetic programming and deep learning, symbolic regression on separate clusters of the data, and the incorporation of knowledge-based loss terms into the loss function. We found that symbolic regression is both accurate in estimating rainfall and interpretable through learned equations. Accuracy and simplicity of the learned equations can be slightly improved by clustering the data based on select radar variables and by adjusting the loss function with knowledge-based loss terms. This research provides insights into improving QPE accuracy through interpretable symbolic regression methods
Describing seasonal mixtures of cloud regimes via “regimes of regimes”
(AMS, 2025-01-22) Cho, Nayeong; Oreopoulos, Lazaros; Lee, Dongmin; Tan, Jackson; Jin, Daeho
We propose a new type of cloud classification, relevant to monthly or longer time scales, but which inherently still encompasses cloud subgrid variability information at ~100 km scales. Our proposed classification partitions frequencies of occurrence over these scales of previously defined cloud regimes (CRs). We call the resulting distinct cloud entities regimes of regimes (RORs). While the CRs have been previously shown to successfully classify daily mesoscale subgrid variability via distributions of cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness, the RORs essentially represent the prevalent seasonal mixtures of these CRs. RORs thus embody the seasonal cloudiness of a mesoscale region. We show that each ROR can still be associated with more traditional cloud classifications via composites of coincident active (lidar and cloud radar) cloud views. In a first application that gauges the potential utility of RORs, we pair them with CERES EBAF radiative fluxes to gain insight into recent trends of the cloud radiative effect. The ROR corresponding to an environment of shallow convection stands out in this analysis largely because of its declining population. Our study demonstrates the potential of RORs to categorize globally mesoscale cloudiness at monthly/seasonal scales and to serve as proxies of different atmospheric states at these scales.
Advancing Ensemble Streamflow Prediction Through Satellite-based Precipitation Product and Model Parameter Uncertainty Quantification
(2025-1-18) Peng, Kaidi; Wright, Daniel Benjamin; Derin, Yagmur; Alexander, Gary Aaron; Pradhan, Ankita; Zoccatelli, Davide; Hartke, Samantha H.; Li, Zhe; Tan, Jackson
Satellite-based quantitative precipitation estimates (QPE), such as NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG), provide easily accessible continental-to-global precipitation forcings for flood prediction and other hydrologic applications. Nevertheless, when used in hydrologic prediction, uncertainty in satellite-based QPE often leads to significant bias. This forcing uncertainty is further blended with other error sources, including process representation, parameter values, and their interactions. The identification and decoupling of these uncertainties can enhance our understanding of their respective impacts, thereby improving hydrologic prediction. Addressing this issue worldwide is challenging, however, largely due to the scarcity of precipitation ground truth and complex uncertainty interactions. Therefore, we propose an efficient uncertainty quantification framework for ensemble streamflow prediction, which keeps different uncertainty sources separable through hierarchical Bayesian inference. Satellite-based QPE uncertainty is characterized by a novel near-realtime quasi-global satellite-only ensemble precipitation dataset (STREAM-Sat), which is completely independent of ground-based precipitation measurements. Model parameter uncertainty in a distributed physics-based hydrologic model is inferred by an Iterative Ensemble Smoother (IES). To illustrate the impact and limitations of precipitation uncertainty, we compared ensemble streamflow predictions driven by both model parameter and satellite precipitation uncertainties and ensemble streamflow predictions driven by model parameter uncertainty and deterministic QPE. We demonstrate that the quantification of satellite-based QPE uncertainty notably improves the accuracy and reliability of streamflow predictions. This study also lays a foundation for satellite-based streamflow prediction in ungauged regions.
A New Age of SAR: How Can Commercial Smallsat Constellations Contribute to NASA's Surface Deformation and Change Mission?
(AGU, 2025-01-21) Huang, Stacey; Osmanoğlu, Batuhan; Scheuchl, Bernd; Oveisgharan, Shadi; Sauber, Jeanne M.; Jo, MinJeong; Khazendar, Ala; Tymofyeyeva, Ekaterina; Wusk, Betsy; Albayrak, Arif
In response to the 2017 Decadal Survey, NASA conducted a five-year study on the Surface Deformation and Change (SDC) designated observable to study potential mission concepts. As part of the SDC mission study, the Commercial Synthetic Aperture Radar (ComSAR) subgroup was tasked with evaluating the current landscape of the SAR and interferometric SAR (InSAR) industry to assess whether NASA could leverage commercial smallsat products to meet the needs of the SDC science mission. The assessment found that although the commercial SAR industry is growing rapidly, off-the-shelf products can currently only make a small—albeit distinct—contribution to SDC mission goals. This gap is due to different design goals between current commercial systems (which prioritize targeted high-resolution, non-interferometric observations at short wavelengths with a daily or faster revisit) and a future SDC architecture (which focuses on broad, moderate-resolution, and interferometric observations at long wavelengths). Even by 2030, planned commercial constellations are expected to only cover 65% of the area needed to match NISAR coverage. Still, high-resolution and rapid-repeat capabilities can augment scientific findings from a future SDC mission, as demonstrated by recent contributions from commercial data to applied sciences, cryosphere, and volcanology. Future innovations on smallsat constellation concepts could further contribute to SDC science and applications. Although current constellation designs are not fully able to satisfy desired SDC science capabilities, initial positive feedback to a request for information indicates a potential future path for a customized SDC commercial architecture; more studies will be needed to determine the feasibility of these approaches.
Predicting the Frequency of Low Cloud Mesoscale Morphologies in Southern Ocean Extratropical Cyclones Using Cloud Controlling Factors
(2025-01-19) Tong, Shuoyun; Wood, Robert; Yuan, Tianle
Shortwave radiation biases over the Southern Ocean (SO) stem largely from a poor understanding of low clouds in the cold sectors of extratropical cyclones, where rapid transitions between low cloud mesoscale morphologies are frequent. Stratus dominates the poleward regime of the cyclones. It transitions into closed mesoscale cellular convection (MCC) downstream and then to open MCC in the cold sector of cyclones. Clustered and suppressed cumulus are often found in the warm sector. Principal component (PC) analysis is applied to a set of cloud controlling factors to characterize properties of the entire extratropical cyclone that are critical to low cloud mesoscale morphologies. The first two PCs are strongly related to cyclone intensity and sea surface temperature averaged over the cyclone domain, respectively. Daily average insolation at the top of the atmosphere, which has large seasonal and latitudinal variability over the SO, is used as an additional independent predictor. Closed and open MCC are negatively correlated with insolation, while disorganized MCC and clustered cumulus are positively correlated with insolation. In stronger cyclones, closed MCC, open MCC, and clustered cumulus tend to be more frequent, whereas stratus and suppressed cumulus tend to be less common. In cyclones over a colder sea surface, closed MCC and stratus are more abundant, and clustered cumulus and suppressed cumulus are less abundant. These results deepen the current understanding of low cloud processes and provide insights of transitions between morphologies, and thus changes in cloud radiative effects, over the SO in a changing climate.
Mind the leaf anatomy while taking ground truth with portable chlorophyll meters
(Springer Nature, 2025-01-13) Lhotáková, Zuzana; Neuwirthová, Eva; Potůčková, Markéta; Červená, Lucie; Hunt, Lena; Kupková, Lucie; Lukeš, Petr; Campbell, Petya; Albrechtová, Jana
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400–2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R² > 0.80, nRMSE < 15%), regardless of the variation in their specific internal structure (mesomorphic, scleromorphic, or scleromorphic with hypodermis), similarly to the performance of four reflectance indices (R² > 0.90, nRMSE < 16%). For grasses, the model to predict chlorophyll content across multiple species had low performance with CCM-300 (R² = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R² = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia
(AMS, 2023-09-19) Balashov, Nikolay V.; Huff, Amy K.; Thompson, Anne M.
The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.
Complementary Roles of LEO and GEO IR Sounders in NWP Evaluated using an OSSE Framework
(NTRS, 2024-12) Mcgrath-Spangler, Erica; Prive, Nikki; Karpowicz, Bryan M.; Kim, Min-Jeong; Heidinger, Andrew
The United States is currently preparing for the next-generation weather satellite program. Building off the multi-decadal successes of the Low-Earth Orbit (LEO) infrared (IR) sounder program, NOAA and NASA are planning for the Geostationary eXtended Observations (GeoXO) Sounder (GXS) that will join international counterparts to form a global ring from geosynchronous orbit (GEO). To address questions about the complementarities and roles of these two platform types in the context of numerical weather prediction (NWP) and forecast accuracy, NASA’s Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework was used to test their impact individually and in concert. Analyses, forecasts, and forecast sensitivity-based observations impact (FSOI) are examined. Overall, each has a role in forecast error reduction with the most beneficial impacts occurring when both LEO and GEO sounders are used together to address the needs of global skill and targeted regional weather phenomena.
Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields
(EGU, 2024-11-11) Maratt Satheesan, Swathi; Eichmann, Kai-Uwe; Burrows, John P.; Weber, Mark; Stauffer, Ryan; Thompson, Anne M.; Kollonige, Debra
We present the CHORA (Cloud Height Ozone Reference Algorithm) for retrieving tropospheric-ozone columns from S5P-TROPOMI (Sentinel-5 Precursor–TROPOspheric Monitoring Instrument). The method uses a local-cloud reference sector (CLC – CHORA Local Cloud) to determine the stratospheric (above-cloud) column, which is subtracted from the total column in clear-sky scenes in the same zonal band to retrieve the tropospheric column. The standard CCD (convective cloud differential) approach uses cloud data from the Pacific region (CPC – CHORA Pacific Cloud) instead. An important assumption for the standard method is the zonal invariance of stratospheric ozone. The local-cloud approach is the first step to diminish this constraint in order to extend the CCD method to mid-latitudes, where stratospheric-ozone variability is larger. An iterative approach has been developed for the automatic selection of an optimal local-cloud reference sector around each retrieval grid box varying latitudinally by ± 1° and longitudinally between ± 5 and ± 50°. The optimised CLCT (CHORA Local Cloud Theil–Sen) algorithm, a follow-up from the CLC, employs a homogeneity criterion for total ozone from the cloud reference sector in order to overcome the inhomogeneities in stratospheric ozone. It directly estimates the above-cloud column ozone for a common reference altitude of 270 hPa using the Theil–Sen regression. The latter allows for the combination of the CCD method with the cloud-slicing algorithm that retrieves upper-tropospheric ozone volume mixing ratios. Monthly averaged tropospheric-column ozone (TCO) using the Pacific cloud reference sector (CPC) and the local-cloud reference sector (CLC, CLCT) has been determined over the tropics and subtropics (26° S–22° N) using TROPOMI for the time period from 2018 to 2022. The accuracy of the various methods was investigated by means of comparisons with spatially collocated NASA/GSFC SHADOZ (Southern Hemisphere Additional Ozonesondes) measurements and the ESA TROPOMI level-2 tropospheric-ozone product. At eight out of nine tropical stations, tropospheric-ozone columns using the CLCT yield better agreement with ozonesondes than the CPC. In the tropical region (20° S–20° N), the CLCT shows a significantly lower overall mean bias and dispersion of 1 ± 7 %, outperforming both the CPC (12 ± 10 %) and CCD-ESA (22 ± 10 %). The CLCT surpasses the ESA operational product, providing more accurate tropospheric-ozone retrievals at eight out of nine stations in the tropics. For the Hilo station, with a larger stratospheric-ozone variability due to its proximity to the subtropics, the bias of +30 % (CPC) is effectively reduced to −5 % (CLCT). Similarly, in the subtropics (Reunion, Irene, Hanoi, and King's Park), the CLCT algorithm provides an overall bias and scatter of −11 ± 9 % with respect to sondes. The CLCT effectively reduces the impact of stratospheric-ozone inhomogeneity, typically at higher latitudes. These results demonstrate the advantage of the local-cloud reference sector in the subtropics. The algorithm is therefore an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like GEMS (Geostationary Environment Monitoring Spectrometer, Korea), ESA Sentinel-4, and NASA TEMPO (Tropospheric Emissions: Monitoring of POllution), predominantly covering the middle latitudes.
Impact of Nitrogen Dioxide (NO₂) Pollution on Asthma: The Case of Louisiana State (2005–2020)
(MDPI, 2024-12-10) Bhattarai, Keshav; Lamsal, Lok; Gyawali, Madhu; Neupane, Sujan; Gautam, Shiva P.; Bakshi, Arundhati; Yeager, John
This study explores the connection between tropospheric nitrogen dioxide (NO₂) vertical column density levels and asthma hospitalization cases in Louisiana from 2005 to 2020. Utilizing NO₂ data from NASA’s Ozone Measurement Instrument (OMI) aboard the Aura satellite, the research integrates these atmospheric measurements with socioeconomic data at the census tract level. This study employs a generalized linear mixed model (GLIMMIX) with a logit link and Beta distribution to analyze the relationship between seasonal NO₂ levels and asthma hospitalization cases during winter, fall, spring, and summer. By analyzing OMI data, this research quantifies seasonal variations in NO₂ levels and their corresponding impact on asthma hospitalizations. The findings reveal a relationship between NO₂ levels and asthma hospitalizations, particularly in communities with high Black and/or low-income populations, with the strongest effects observed during winter. Specifically, the analysis shows that, for each unit increase in NO₂ levels, the odds of asthma-related hospitalizations increase by approximately 26.3% (p < 0.0001), with a 95% confidence interval ranging from 23.3% to 29.5%. Assuming a causal link between NO₂ and asthma, these findings suggest that reducing NO₂ emissions could alleviate healthcare burdens associated with respiratory diseases such as asthma.
Dynamical drivers of free-tropospheric ozone increases over equatorial Southeast Asia
Stauffer, Ryan M.; Thompson, Anne M.; Kollonige, Debra E.; Komala, Ninong; Al-Ghazali, Habib Khirzin; Risdianto, Dian Yudha; Fairudz bin Jamaluddin, Ahmad; Sammathuria, Mohan Kumar; Zakaria, Norazura Binti; Johnson, Bryan J.; Cullis, Patrick D.
5 years of Sentinel-5P TROPOMI operational ozone profiling and geophysical validation using ozonesonde and lidar ground-based networks
(EGU, 2024-07-04) Keppens, Arno; Di Pede, Serena; Hubert, Daan; Lambert, Jean-Christopher; Veefkind, Pepijn; Sneep, Maarten; De Haan, Johan; ter Linden, Mark; Leblanc, Thierry; Compernolle, Steven; Verhoelst, Tijl; Granville, José; Nath, Oindrila; Fjæraa, Ann Mari; Boyd, Ian; Niemeijer, Sander; Van Malderen, Roeland; Smit, Herman G. J.; Duflot, Valentin; Godin-Beekmann, Sophie; Johnson, Bryan J.; Steinbrecht, Wolfgang; Tarasick, David W.; Kollonige, Debra E.; Stauffer, Ryan M.; Thompson, Anne M.; Dehn, Angelika; Zehner, Claus
The Sentinel-5 Precursor (S5P) satellite operated by the European Space Agency has carried the TROPOspheric Monitoring Instrument (TROPOMI) on a Sun-synchronous low-Earth orbit since 13 October 2017. The S5P mission has acquired more than 5 years of TROPOMI nadir ozone profile data retrieved from the level 0 to 1B processor version 2.0 and the level 1B to 2 optimal-estimation-based processor version 2.4.0. The latter is described in detail in this work, followed by the geophysical validation of the resulting ozone profiles for the period May 2018 to April 2023. Comparison of TROPOMI ozone profile data to co-located ozonesonde and lidar measurements used as references concludes to a median agreement better than 5 % to 10 % in the troposphere. The bias goes up to −15 % in the upper stratosphere (35–45 km) where it can exhibit vertical oscillations. The comparisons show a dispersion of about 30 % in the troposphere and 10 % to 20 % in the upper troposphere to lower stratosphere and in the middle stratosphere, which is close to mission requirements. Chi-square tests of the observed differences confirm on average the validity of the ex ante (prognostic) satellite and ground-based data uncertainty estimates in the middle stratosphere above about 20 km. Around the tropopause and below, the mean chi-square value increases up to about four, meaning that the ex ante TROPOMI uncertainty is underestimated. The information content of the ozone profile retrieval is characterised by about five to six vertical subcolumns of independent information and a vertical sensitivity (i.e. the fraction of the information that originates from the measurement) nearly equal to unity at altitudes from about 20 to 50 km, decreasing rapidly at altitudes above and below. The barycentre of the retrieved information is usually close to the nominal retrieval altitude in the 20–50 km altitude range, with positive and negative offsets of up to 10 km below and above this range, respectively. The effective vertical resolution of the profile retrieval usually ranges within 10–15 km, with a minimum close to 7 km in the middle stratosphere. Increased sensitivities and higher effective vertical resolutions are observed at higher solar zenith angles (above about 60°), as can be expected, and correlate with higher retrieved ozone concentrations. The vertical sensitivity of the TROPOMI tropospheric ozone retrieval is found to depend on the solar zenith angle, which translates into a seasonal and meridian dependence of the bias with respect to reference measurements. A similar although smaller effect can be seen for the viewing zenith angle. Additionally, the bias is negatively correlated with the surface albedo for the lowest three ozone subcolumns (0–18 km), despite the albedo's apparently slightly positive correlation with the retrieval degrees of freedom in the signal. For the 5 years of TROPOMI ozone profile data that are available now, an overall positive drift is detected for the same three subcolumns, while a negative drift is observed above (24–32 km), resulting in a negligible vertically integrated drift.
Validation of ACE-FTS version 5.2 ozone data with ozonesonde measurements
(EGU, 2024-12-12) Zou, Jiansheng; Walker, Kaley A.; Sheese, Patrick E.; Boone, Chris D.; Stauffer, Ryan M.; Thompson, Anne M.; Tarasick, David W.
Two decades of ACE-FTS, the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer, version 5.2 (v5.2) ozone data (2004–2023) are evaluated with ozonesonde data from across the globe. The biases between the ACE-FTS and ozonesonde measurements are first estimated by analyzing coincident data pairs. A second approach is taken for the validation by comparing the ACE-FTS and ozonesonde monthly mean time series, with the former generated by sampling the ACE-FTS data within latitude/longitude boxes (i.e., ± 5°/± 30°) surrounding the stations and calculating the monthly averages. The biases, correlations, variation patterns, and the mean states of the two time series are compared. The biases estimated in this way exhibit more consistent and smoother features than using the coincident pair method. The ACE-FTS and ozonesonde monthly mean time series are highly correlated and exhibit similar variation patterns in the lower stratosphere at all latitudes. The ACE-FTS instrument drifts for each station are assessed in terms of the long-term linear trends relative to ozonesondes, which, although highly stable, may have their own minor changes with time. The ACE-FTS ozone profiles exhibit in general high biases in the stratosphere for altitudes above ∼ 20 km, increasing with altitude up to ∼ 10 % at around 30 km. For altitudes between 20 km and the tropopause, biases of up to ± 10 % are found, depending on altitude and latitude with the largest biases found in the tropics and southern mid-latitudes. The ACE-FTS instrument drifts are generally non-significant overall in the stratosphere with high variation between the stations. Averaging the individual station instrument drifts within several latitude bands results in small non-significant drifts of within ± 1 %–2 % per decade in the northern mid-latitudes to high latitudes and the southern high latitudes. It also results in a positive but non-significant drift of up to 5 % per decade in the tropics and southern mid-latitudes, with overall uncertainties in this region ranging up to 5 %–10 % per decade (2σ level) in the low stratosphere. As part of this assessment, an analysis of ozonesonde measurement stability using ACE-FTS as a transfer standard is conducted and finds small step changes in ozonesonde response at some stations. These results are in general agreement with recent findings using other satellite data sources.
Updates on Tropical Shadoz Sonde Data Quality Assurance & Trends
(NTRS, 2023-11-03) Thompson, Anne M.; Stauffer, Ryan M.; Kollonige, Debra E.
Tropical tropospheric ozone distribution and trends from in situ and satellite data
(EGU, 2024-09-09) Gaudel, Audrey; Bourgeois, Ilann; Li, Meng; Chang, Kai-Lan; Ziemke, Jerald; Sauvage, Bastien; Stauffer, Ryan M.; Thompson, Anne M.; Kollonige, Debra E.; Smith, Nadia; Hubert, Daan; Keppens, Arno; Cuesta, Juan; Heue, Klaus-Peter; Veefkind, Pepijn; Aikin, Kenneth; Peischl, Jeff; Thompson, Chelsea R.; Ryerson, Thomas B.; Frost, Gregory J.; McDonald, Brian C.; Cooper, Owen R.
Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than midlatitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from midlatitude regions toward the Equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high-quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOspheric Monitoring Instrument (TROPOMI), Ozone Monitoring Instrument (OMI), OMI/Microwave Limb Sounder (MLS), Ozone Mapping Profiler Suite (OMPS)/Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI)/Global Ozone Monitoring Experiment 2 (GOME2). With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., western Africa, tropical South Atlantic, Southeast Asia, Malaysia and Indonesia), with present-day 95th percentile ozone values reaching 80 nmol mol⁻¹ in the lower free troposphere, comparable to midlatitude regions such as northeastern China and Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper-tropospheric increases above India, Southeast Asia, and Malaysia and Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol⁻¹ decade⁻¹), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol⁻¹ decade⁻¹ close to the surface (India and Malaysia–Indonesia, respectively). The longest continuous satellite records only span 2004–2019 but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol⁻¹ decade⁻¹ (OMI) and 1.69 ± 0.89 nmol mol⁻¹ decade⁻¹ (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004–2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high-frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, western Africa, and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia–Indonesia are regions with such strong increases in ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year timescale.

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