Novel Method for the Estimation of Vertical Temperature Profiles Using a Coastal Acoustic Tomography System by Yeongbin Park, Yeongbin Park, Chanhyung Jeon, Hajin Song, Youngseok Choi, Youngseok Choi, Jeong-Yeob Chae, Eun-Joo Lee, Jin Sung Kim, Jae-Hun Park

“…The VTPE method was validated using data-assimilated and tide-included high-resolution ocean model outputs, including tide data, by comparing the estimated and simulated temperatures. …”
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Inverse-model estimates of the ocean's coupled phosphorus, silicon, and iron cycles by B. Pasquier, M. Holzer, M. Holzer

“…<br><br> Here we build a steady-state model of the ocean's coupled phosphorus, silicon, and iron cycles embedded in a data-assimilated steady-state global ocean circulation. …”
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Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data by J. Xie, R. P. Raj, R. P. Raj, L. Bertino, L. Bertino, A. Samuelsen, A. Samuelsen, T. Wakamatsu, T. Wakamatsu

“…The uncertainties of these two SSS products are quantified during the period of 2011–2013 against other SSS products: one data assimilative regional reanalysis; one data-driven reprocessing in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS); two climatologies – the 2013 World Ocean Atlas (WOA) and the Polar science center Hydrographic Climatology (PHC); and in situ datasets, both assimilated and independent. …”
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Evaluation of MOPITT Version 7 joint TIR–NIR X_CO retrievals with TCCON by J. K. Hedelius, T.-L. He, D. B. A. Jones, B. C. Baier, B. C. Baier, R. R. Buchholz, M. De Mazière, N. M. Deutscher, M. K. Dubey, D. G. Feist, D. G. Feist, D. G. Feist, D. W. T. Griffith, F. Hase, L. T. Iraci, P. Jeseck, M. Kiel, M. Kiel, R. Kivi, C. Liu, I. Morino, J. Notholt, Y.-S. Oh, H. Ohyama, D. F. Pollard, M. Rettinger, S. Roche, C. M. Roehl, M. Schneider, K. Shiomi, K. Strong, R. Sussmann, C. Sweeney, C. Sweeney, Y. Té, O. Uchino, V. A. Velazco, V. A. Velazco, W. Wang, T. Warneke, P. O. Wennberg, P. O. Wennberg, H. M. Worden, D. Wunch

“…When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5&thinsp;%. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. …”
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Lagrangian matches between observations from aircraft, lidar and radar in a warm conveyor belt crossing orography by M. Boettcher, A. Schäfler, M. Sprenger, H. Sodemann, S. Kaufmann, C. Voigt, C. Voigt, H. Schlager, D. Summa, D. Summa, P. Di Girolamo, D. Nerini, U. Germann, H. Wernli

“…To this aim, an ensemble of wind fields from the global analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble of Data Assimilations (EDA) system were used, which allowed for a probabilistic quantification of the WCB occurrence and the Lagrangian matches. …”
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Path planning and adaptive sampling in the coastal ocean by Lolla, Sri Venkata Tapovan

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Prediction, analysis, and learning of advective transport in dynamic fluid flows by Kulkarni, Chinmay Sameer.

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The Reprocessed Suomi NPP Satellite Observations by Cheng-Zhi Zou, Lihang Zhou, Lin Lin, Ninghai Sun, Yong Chen, Lawrence E. Flynn, Bin Zhang, Changyong Cao, Flavio Iturbide-Sanchez, Trevor Beck, Banghua Yan, Satya Kalluri, Yan Bai, Slawomir Blonski, Taeyoung Choi, Murty Divakarla, Yalong Gu, Xianjun Hao, Wei Li, Ding Liang, Jianguo Niu, Xi Shao, Larrabee Strow, David C. Tobin, Denis Tremblay, Sirish Uprety, Wenhui Wang, Hui Xu, Hu Yang, Mitchell D. Goldberg

“…Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.…”
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Assessment of a global contrail modeling method and operational strategies for contrail mitigation by Klima, Kelly

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Dense Water Formation in the North–Central Aegean Sea during Winter 2021–2022 by Manos Potiris, Ioannis G. Mamoutos, Elina Tragou, Vassilis Zervakis, Dimitris Kassis, Dionysios Ballas

“…The evolution and drivers of dense water formation (DWF) in the North–Central Aegean Sea (NCAeg) during winter 2021–2022 are studied using observations from two Argo floats and the output of an operational data-assimilating model. Dense water with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>σ</mi><mi>θ</mi></msub><mo>></mo><mn>29.1</mn><mrow><mo> </mo><msup><mrow><mrow><mi>kg</mi><mi mathvariant="normal">m</mi></mrow></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></mrow></semantics></math></inline-formula> was produced over most of the NCAeg, except for the northeastern part covered by Black Sea water (BSW), where the maximum surface density was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo><</mo><mn>29</mn><mo> </mo><msup><mrow><mrow><mi>kg</mi><mi mathvariant="normal">m</mi></mrow></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning by V. Balamurugan, J. Chen, A. Wenzel, F. N. Keutsch, F. N. Keutsch

“…</p> <p>Our <span class="inline-formula">NO₂</span> GBT model outperforms the CAMS model, a data-assimilated CTM but slightly underperforms for <span class="inline-formula">O₃</span>. …”
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High-order Discontinuous Galerkin Methods and Deep Reinforcement Learning with Application to Multiscale Ocean Modeling by Foucart, Corbin

“…Nested within such data-assimilative hydrostatic simulations in the Alboran Sea, we provide a demonstration of our new model’s ability to capture both hydrostatic and nonhydrostatic dynamics that arise in the presence of wind-forced instabilities in the upper ocean layers. …”
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Probabilistic regional ocean predictions : stochastic fields and optimal planning by Narayanan Subramani, Deepak

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