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4. The potential for geostationary remote sensing of NO2 to improve weather prediction

Author

Liu, Xueling, Mizzi, Arthur P, Anderson, Jeffrey L, Fung, Inez, and Cohen, Ronald C

Subjects
Astronomical and Space Sciences, Atmospheric Sciences, Meteorology & Atmospheric Sciences
Abstract

Observations of winds in the planetary boundary layer remain sparse making it challenging to simulate and predict atmospheric conditions that are most important for describing and predicting urban air quality. Short-lived chemicals are observed as plumes whose location is affected by boundary layer winds and whose lifetime is affected by boundary layer height and mixing. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.

Published
2021

16. Self-administered intranasal etripamil using a symptom-prompted, repeat-dose regimen for atrioventricular-nodal-dependent supraventricular tachycardia (RAPID): a multicentre, randomised trial

Author

Ip, James E, Jones, Alonzo, Sr, Stambler, Bruce S, Schleinkofer, David E, Winters, Stephen, Lam, Wilson, Goldstein, Robert, Dor, Isaac Dor, Talwar, Sandeep, O'Neill, Padraig G, Koren, Michael J, Beinart, Sean C, Ambati, Srivani, Mazer, Sean P, Kinn, Robert M, Roy, Karine, Manshadi, Ramin, Kuk, Richard, Verma, Aditya, Phelan, Timothy, Stoian, Alexandru A, Ellenbogen, Kenneth, Jayachandran, J Vijay, Connelly, Terence P, Daccarett, Marcos, Gandhi, Gaurang, Mittal, Suneet, Abdel-Wahab, Amir, Augostini, Ralph, Ip, John H, Sorrentino, Denise, Roux, Jean-Francois, Brar, Ramandeep, Khoo, Clarence, Bennett, Matthew, Lo, Eric, Coutu, Benoit, Sterns, Laurence, Greer, Gerald S, Gujja, Pradeep, Gianfagna, Robert A, Swarup, Vijendra, Sogade, Felix, Wilton, Stephen, Ruisi, Christopher, Barbera, Saverio, Banchs, Javier E., Korley, Victoria, Schulze, Christopher, Damle, Roger, Healey, Jeffrey Sean, Pandey, A Shekhar, Anderson, Jeffrey L, Venkatachalam, K L, Noseworthy, Peter, Mondésert, Blandine, Verma, Atul, Friars, Douglas G, Kambur, Thomas R, Lockwood, Evan, Meininger, Glenn, Olsovsky, Greg, Jones Sr, Alonzo, Greer, Gerald, Mehta, Rohit, Akbar, Saleem, Salman, Malik, Rama, Bhola, Arora, Ramesh, Shah, Dhirenkumar, Shah, Rakesh, Cammarata, Michael, Owens, Andrew, Ludington, Katherine, Wharton, Marcus, Bagheri, Michael, Khan, Mohammed, Carr, Kenneth Warren, Siddique, Sultan, Parvathaneni, Sunthosh, Devabhaktuni, Subodh, Mouhaffel, Assad, Mark, George, Di Biase, Luigi, Rangappa, Sunil, Morton, Jared, Yarmohammadi, Hirad, Karim, Amin, Martinez Fleites, Aldo, Nero, Thomas, Shafiq, Qaiser, Pandey, Shekhar, Khaykin, Yaariv, Joza, Jacqueline, Gosselin, Gilbert, Skanes, Allen, Vyselaar, John, Savard, Daniel, Heidbuchel, Hein, Xhaet, Olivier, Catez, Emmanuel, Vijgen, Johan, Godart, Pascal, Casado, Ruben, Mairesse, Georges, Rossenbacker, Tom, Haemers, Peter, Hoffer, Etienne, Hausman, Pierre, Vertes, Andras, Duray, Gabor, Szolnoki, Erzsebet, Csanadi, Zoltan, Lakatos, Ferenc, Alings, Marco, Dorman, Reinhart, Tuininga, Y S, Jansen, W, Velthuis, Sebastiaan, van Bemmel, B, Bech, Gerhard Jan Willem, Pisters, Ron, Valk, Suzanne, Romer, Tjeerd, Luermans, Justin, Beelen, Driek, van Eck, Martijn, Houtgraaf, Jaco, Vet, Thijs, Shellings, Dirk, Oosterhof, T, Miekus, Pawel, Bebenek, Waldemar, Bielecka-Dabrowa, Agata, Gniot, Jacek, Zmuda, Witold, Kasprzak, Michal, Balak, Wojciech, Ptaszynski, Pawel, Nowak, Jacek, Prokopczuk, Janusz, Przybylski, Andrzej, Wozniak-Skowerska, Iwona, Derejko, Pawel, Czarnecka, Danuta, Janas, Adam, Merino Llorens, Jose Luis, Tercedor, Luis, Iniguez Romo, Andres, Ruiz Granell, Ricardo, Gonzalez Juanatey, Jose Ramon, Pascual-Figal, Domingo, Martinez-Selles, Manuel, Anguera Camos, Ignacio, Ibanez Criado, Alicia, Matrinez Basterra, Javier, Izquierdo, Alvaro, Rivas Gandara, Nuria, Alegret Colome, Josep M, Perez Diez, Diego, Martínez Hervás, Miguel Ángel, Medina, Maria, Segura Saint-Gerons, Jose María, Pedrote Leal, Alonso, Fernandez Lozano, Ignacio, Quesada, Aurelio, Guerra Ramos, Jose, Sarrias, Axel, Ramos Maqueda, Javier, Mansourati, Jacques, Defaye, Pascal, Guedon-Moreau, Laurence, Milhem, Antoine, De Guillebon, Maxime, Chevalier, Philippe, Badoz, Marc, Kriatselis, Charalampos, Simonis, Gregor, Lewalter, Thorsten, Zarse, Markus, Wilke, Andreas, Kraemer, Fabian, Al-Zoebi, Ayham, Camm, A John, Dorian, Paul, Kowey, Peter R, Merino, Jose L, Piccini, Jonathan P, Pokorney, Sean D, Sager, Philip T, Wharton, J Marcus, Bharucha, David B, Plat, Francis, Shardonofsky, Silvia, and Chen, Michael

Published
2023
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21. Contemporary Predictors of Major Adverse Cardiovascular Events following Percutaneous Coronary Intervention: A Nationally Representative US Sample

Author

Horne, Benjamin D., primary, Atreja, Nipun, additional, Venditto, John, additional, Wilson, Thomas, additional, Muhlestein, Joseph B., additional, St. Clair, Joshua R. St., additional, Knowlton, Kirk U., additional, Khan, Naeem D., additional, Bhalla, Narinder, additional, and Anderson, Jeffrey L., additional

Published
2024
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33. First Randomized, Multicenter, Placebo-Controlled Study of Self-Administered Intranasal Etripamil for Acute Conversion of Spontaneous Paroxysmal Supraventricular Tachycardia (NODE-301)

Author

Stambler, Bruce S., Plat, Francis, Sager, Philip T., Shardonofsky, Silvia, Wight, Douglas, Potvin, Diane, Pandey, A. Shekhar, Ip, James E., Coutu, Benoit, Mondésert, Blandine, Sterns, Laurence D., Bennett, Matthew, Anderson, Jeffrey L., Damle, Roger, Haberman, Ronald, and Camm, A. John

Published
2022
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36. Abstract 12122: Five-Year Serial Measurement of the Intermountain Mortality Risk Score for Population Health Assessment Reveals Personalized Temporal Trajectories of Differential Longevity Over Two Decades

Author

Horne, Benjamin D, Bledsoe, Joseph R, Muhlestein, Joseph B, Woller, Scott C, Knowlton, Kirk U, Majercik, Sarah, Bair, Tami L, Snow, Gregory L, Lloyd, James F, Le, Viet T, Bennett, Sterling T, Ronnow, Brianna S, May, Heidi T, and Anderson, Jeffrey L

Published
2022
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44. Assimilation of satellite NO2 observations at high spatial resolution using OSSEs

Author

Liu, Xueling, Mizzi, Arthur P, Anderson, Jeffrey L, Fung, Inez Y, and Cohen, Ronald C

Subjects
Astronomical and Space Sciences, Atmospheric Sciences, Meteorology & Atmospheric Sciences
Abstract

Observations of trace gases from space-based instruments offer the opportunity to constrain chemical and weather forecast and reanalysis models using the tools of data assimilation. In this study, observing system simulation experiments (OSSEs) are performed to investigate the potential of high space- and time-resolution column measurements as constraints on urban NOx emissions. The regional chemistry-meteorology assimilation system where meteorology and chemical variables are simultaneously assimilated is comprised of a chemical transport model, WRF-Chem, the Data Assimilation Research Testbed, and a geostationary observation simulator. We design OSSEs to investigate the sensitivity of emission inversions to the accuracy and uncertainty of the wind analyses and the emission updating scheme. We describe the overall model framework and some initial experiments that point out the first steps toward an optimal configuration for improving our understanding of NOx emissions by combining space-based measurements and data assimilation. Among the findings we describe is the dependence of errors in the estimated NOx emissions on the wind forecast errors, showing that wind vectors with a RMSE below 1 m s-1 allow inference of NOx emissions with a RMSE of less than 30 mol/(km2 × h) at the 3 km scale of the model we use. We demonstrate that our inference of emissions is more accurate when we simultaneously update both NOx emissions and NOx concentrations instead of solely updating emissions. Furthermore, based on our analyses, we recommend carrying out meteorology assimilations to stabilize NO2 transport from the initial wind errors before starting the emission assimilation. We show that wind uncertainties (calculated as a spread around a mean wind) are not important for estimating NOx emissions when the wind uncertainties are reduced below 1.5 m s-1. Finally, we present results assessing the role of separate vs. simultaneous chemical and meteorological assimilation in a model framework without covariance between the meteorology and chemistry.

Published
2017

46. Nonlinear and Non‐Gaussian Ensemble Assimilation of MOPITT CO.

Author

Gaubert, Benjamin, Anderson, Jeffrey L., Trudeau, Michael, Smith, Nadia, McKain, Kathryn, Pétron, Gabrielle, Raeder, Kevin, Arellano, Avelino F., Granier, Claire, Emmons, Louisa K., Ortega, Ivan, Hannigan, James W., Tang, Wenfu, Worden, Helen M., Ziskin, Daniel, and Edwards, David P.

Subjects
KALMAN filtering, CARBON monoxide, ATMOSPHERIC chemistry, ATMOSPHERIC models, ATMOSPHERIC composition, ATMOSPHERIC boundary layer, CARBON cycle
Abstract

Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets. Plain Language Summary: The MOPITT instrument on the NASA/Terra satellite can detect carbon monoxide (CO) pollution in the lower and mid‐tropospheric atmosphere but cannot accurately differentiate small changes in the altitude of pollution plumes. Such satellite observations are assimilated in numerical model predictions to improve the spatial and temporal distribution of CO in the atmosphere and to estimate emission fluxes. We present a novel method that does not require assumptions about the model and the observations, leading to a more efficient and accurate assimilation of the satellite observations. Key Points: A novel non Gaussian and nonlinear ensemble data assimilation (DA) framework is applied to MOPITT joint state/flux optimizationThe new method performs better than the Ensemble Adjustment Kalman Filter in comparison to independent observationsMOPITT observations indicate that CAMS‐GLOB‐ANT_v5.3 emission fluxes are underestimated across the mid‐latitudes in May 2018 [ABSTRACT FROM AUTHOR]

Published
2024
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47. Coronary Calcium Is Elevated in Patients with Myocardial Infarction without Standard Modifiable Risk Factors.

Author

Anderson, Jeffrey L., Knight, Stacey, Dong, Li, May, Heidi T., Le, Viet T., Bair, Tami L., and Knowlton, Kirk U.

Subjects
*MYOCARDIAL infarction, *CORONARY artery calcification, *MAJOR adverse cardiovascular events
Abstract

Objectives: Recent reports have highlighted myocardial infarction (MI) patients without standard modifiable risk factors (SMRF), noting them to be surprisingly common and to have a substantial risk of adverse outcomes. The objective of this study was to address the challenge of identifying at-risk patients without SMRF and providing preventive therapy. Methods: Patients presenting between 2001 and 2021 to Intermountain Health catheterization laboratories with a diagnosis of MI were included if they also had a coronary artery calcium (CAC) scan by computed tomography within 2 years. SMRF were defined as a clinical diagnosis or treatment of hypertension, hyperlipidemia, diabetes, or smoking. The co-primary endpoints in SMRF-less patients were: (1) proportion of patients with an elevated (>50%ile) CAC score, and (2) an indication for statin therapy (i.e., CAC ≥ 100 AU or ≥75%ile). The 60-day and long-term major adverse cardiovascular events were determined. A comparison set included MI patients with SMRF. Results: We identified 429 MI patients with a concurrent CAC scan, of which 60 had no SMRF. SMRF status did not distinguish most risk factors or interventions. No-SMRF patients had a high CAC prevalence and percentile (82% ≥ 50%ile; median, 80%ile), and 77% met criteria for preventive therapy. As expected, patients with SMRF had high CAC scores and percentiles. Outcomes were more favorable for No-SMRF status and for lower CAC scores. Conclusions: Patients without SMRF presenting with an MI have a high prevalence and percentile of CAC. Wider application of CAC scans, including in those without SMRF, is promising as a method to identify an additional at-risk population for MI and to provide primary preventive therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Comparison of Three Atherosclerotic Cardiovascular Disease Risk Scores With and Without Coronary Calcium for Predicting Revascularization and Major Adverse Coronary Events in Symptomatic Patients Undergoing Positron Emission Tomography-Stress Testing

Author

Anderson, Jeffrey L., Le, Viet T., Min, David B., Biswas, Santanu, Minder, C. Michael, McCubrey, Raymond O., Knight, Stacey, Horne, Benjamin D., Mason, Steve, Lappe, Donald L., Muhlestein, Joseph B., and Knowlton, Kirk U.

Published
2020
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50. Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system

Author

Mizzi, Arthur P, Arellano, Avelino F, Edwards, David P, Anderson, Jeffrey L, and Pfister, Gabriele G

Subjects
Earth Sciences
Abstract

This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of compact phase space retrievals of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by g 1/4 g ¯35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.

Published
2016

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