A Lagrangian approach towards extracting signals of urban CO₂ emissions from satellite observations of atmospheric column CO₂ (XCO₂): X-Stochastic... by D. Wu, J. C. Lin, B. Fasoli, T. Oda, X. Ye, T. Lauvaux, E. G. Yang, E. A. Kort

“…Error estimates show that the 68 % confidence limit of XCO₂ uncertainties due to transport (horizontal wind plus vertical mixing) and emission uncertainties contribute to  ∼ 33 % and  ∼ 20 % of the mean latitudinally integrated urban signals, respectively, over the five overpasses, using meteorological fields from the Global Data Assimilation System (GDAS). In addition, a sizeable mean difference of −0.55 ppm in background derived from a previous study employing simple statistics (regional daily median) leads to a  ∼ 39 % higher mean observed urban signal and a larger posterior scaling factor. …”
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High-resolution observations in the western Mediterranean Sea: the REP14-MED experiment by R. Onken, H.-V. Fiekas, L. Beguery, I. Borrione, A. Funk, M. Hemming, M. Hemming, J. Hernandez-Lasheras, K. J. Heywood, J. Kaiser, M. Knoll, B. Mourre, P. Oddo, P.-M. Poulain, B. Y. Queste, A. Russo, K. Shitashima, M. Siderius, E. Thorp Küsel

“…The objective of this article is to provide an overview of the huge data set which has been utilised by various studies, focusing on (i) water masses and circulation, (ii) operational forecasting, (iii) data assimilation, (iv) variability of the ocean, and (v) new payloads for gliders.…”
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Gradients of column CO₂ across North America from the NOAA Global Greenhouse Gas Reference Network by X. Lan, X. Lan, P. Tans, C. Sweeney, C. Sweeney, A. Andrews, A. Jacobson, A. Jacobson, M. Crotwell, M. Crotwell, E. Dlugokencky, J. Kofler, J. Kofler, P. Lang, K. Thoning, S. Wolter, S. Wolter

“…The 11-year mean CO₂ dry mole fraction (XCO₂) in the column below  ∼  330 hPa ( ∼  8 km above sea level) from NOAA's CO₂ data assimilation model, CarbonTracker (CT2015), demonstrates good agreement with those calculated from calibrated measurements on aircraft and towers. …”
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Improving the joint estimation of CO₂ and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0) by Z. Liu, Z. Liu, N. Zeng, N. Zeng, N. Zeng, Y. Liu, Y. Liu, E. Kalnay, G. Asrar, B. Wu, Q. Cai, D. Liu, P. Han, P. Han

“…Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. …”
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Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine by Binfei Hao, Mingguo Ma, Shiwei Li, Qiuping Li, Dalei Hao, Jing Huang, Zhongxi Ge, Hong Yang, Xujun Han

“…The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. …”
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Contrasting terrestrial carbon cycle responses to the 1997/98 and 2015/16 extreme El Niño events by J. Wang, J. Wang, N. Zeng, N. Zeng, M. Wang, F. Jiang, H. Wang, Z. Jiang

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Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data by A. Jia, S. Liang, D. Wang, L. Ma, Z. Wang, S. Xu

“…Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. …”
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High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations by C.-L. Tsai, C.-L. Tsai, K. Kim, Y.-C. Liou, G. Lee

“…From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. …”
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The impact of multi-species surface chemical observation assimilation on air quality forecasts in China by Z. Peng, L. Lei, L. Lei, Z. Liu, J. Sun, J. Sun, A. Ding, A. Ding, J. Ban, D. Chen, X. Kou, K. Chu, K. Chu

“…<p>An ensemble Kalman filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM<span class="inline-formula">₁₀</span>, PM<span class="inline-formula">_2.5</span>, <span class="inline-formula">SO₂</span>, <span class="inline-formula">NO₂</span>, <span class="inline-formula">O₃</span>, and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). …”
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Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model by Meijun Wang, Meijun Wang, Gang Yin, Gang Yin, Min Mao, Min Mao, Hao Zhang, Hua Zhang, Zengyun Hu, Zengyun Hu, Zengyun Hu, Xi Chen, Xi Chen, Xi Chen

“…Six soil moisture datasets from the period 1981–2020 were employed, which included ERA5 (the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis 5), ESA-CCI (European Space Agency Climate Change Initiative), GLDAS (Global Land Data Assimilation System), MERRA-2 (The Modern-Era Retrospective Analysis for Research and Applications, Version 2), RSSSM (A Remote Sensing-based global 10-day resolution Surface Soil Moisture dataset), and SSM-Feng (Regional multimodal fusion of surface soil moisture data in China). …”
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The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis by J. Qiu, J. Qiu, J. Dong, W. T. Crow, X. Zhang, X. Zhang, R. H. Reichle, G. J. M. De Lannoy

“…Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without <span class="inline-formula"><i>T</i>_b</span> assimilation. …”
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Uncertainty Quantification in ocean state estimation by Kalmikov, Alexander G

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Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16 by P. C. Campbell, P. C. Campbell, Y. Tang, Y. Tang, P. Lee, P. Lee, B. Baker, D. Tong, D. Tong, R. Saylor, A. Stein, J. Huang, J. Huang, H.-C. Huang, H.-C. Huang, E. Strobach, E. Strobach, J. McQueen, L. Pan, L. Pan, I. Stajner, J. Sims, J. Tirado-Delgado, J. Tirado-Delgado, Y. Jung, F. Yang, T. L. Spero, R. C. Gilliam

“…NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. …”
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EuroGOOS roadmap for operational coastal downstream services by Ghada El Serafy, Ghada El Serafy, Lőrinc Mészáros, Lőrinc Mészáros, Vicente Fernández, Arthur Capet, Jun She, Marcos Garcia Sotillo, Angelique Melet, Sebastien Legrand, Baptiste Mourre, Francisco Campuzano, Ivan Federico, Antonio Guarnieri, Anna Rubio, Tomasz Dabrowski, Georg Umgiesser, Joanna Staneva, Laura Ursella, Ivane Pairaud, Antonello Bruschi, Helen Frigstad, Katrijn Baetens, Veronique Creach, Guillaume Charria, Enrique Alvarez Fanjul

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TRIMS LST: a daily 1&thinsp;km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022) by W. Tang, J. Zhou, J. Ma, Z. Wang, L. Ding, X. Zhang, X. Zhang, X. Zhang

“…A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. …”
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The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network by S. Crowell, D. Baker, A. Schuh, S. Basu, S. Basu, A. R. Jacobson, A. R. Jacobson, F. Chevallier, J. Liu, F. Deng, L. Feng, L. Feng, K. McKain, K. McKain, A. Chatterjee, A. Chatterjee, J. B. Miller, B. B. Stephens, A. Eldering, D. Crisp, D. Schimel, R. Nassar, C. W. O'Dell, T. Oda, T. Oda, C. Sweeney, P. I. Palmer, P. I. Palmer, D. B. A. Jones

“…We employ a large ensemble of atmospheric inversions utilizing different transport models, data assimilation techniques, and prior flux distributions in order to quantify the satellite-informed fluxes from OCO-2 Version 7r land observations and their uncertainties at continental scales. …”
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Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations by D. Goto, Y. Morino, T. Ohara, T. T. Sekiyama, J. Uchida, T. Nakajima

“…The LMVE is a classical and basic version of data assimilation, although the estimation itself is still useful for obtaining the best estimates by combining simulations and observations without a large amount of computer resources, even for high-resolution models. …”
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High-resolution quantification of atmospheric CO₂ mixing ratios in the Greater Toronto Area, Canada by S. C. Pugliese, J. G. Murphy, F. R. Vogel, F. R. Vogel, M. D. Moran, J. Zhang, Q. Zheng, C. A. Stroud, S. Ren, D. Worthy, G. Broquet

“…To assess the accuracy of the SOCE inventory, we developed an observation–model framework using the GEM-MACH chemistry–transport model run on a high-resolution grid with 2.5 km grid spacing coupled to the Fossil Fuel Data Assimilation System (FFDAS) v2 inventories for anthropogenic CO₂ emissions and the European Centre for Medium-Range Weather Forecasts (ECMWF) land carbon model C-TESSEL for biogenic fluxes. …”
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HYDROLOGIC AND CRYOSPHERIC PROCESSES OBSERVED FROM SPACE by M. Menenti, X. Li, J. Wang, H. Vereecken, J. Li, M. Mancini, Q. Liu, L. Jia, J. Li, C. Kuenzer, S. Huang, H. Yesou, J. Wen, Y. Kerr, X. Cheng, N. Gourmelen, C. Ke, R. Ludwig, H. Lin, M. Eineder, Y. Ma, Z. B. Su

“…<br><br> Highlights on hydrologic processes included: Global comparison of SMOS, ASCAT and ERA soil moisture data products; Relative deviations evaluated by climate zone; Soil moisture data products improved with ancillary data; Assimilation of FY - , TRMM and GPM precipitation data products in WRF; Improved algorithm and data products on fractional snow cover; Improvement of MODIS ET with assimilation of LST; TRMM data products evaluated in the Yangtze; Calibration of river basin models using LST; System to calibrate, correct and normalize (spatial, spectral) data collected by imaging spectral radiometers; Integration of data acquired by different sensors, e.g. …”
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A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan by A. McNally, A. McNally, A. McNally, J. Jacob, J. Jacob, K. Arsenault, K. Arsenault, K. Slinski, K. Slinski, D. P. Sarmiento, D. P. Sarmiento, A. Hoell, S. Pervez, J. Rowland, M. Budde, S. Kumar, C. Peters-Lidard, J. P. Verdin

“…The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. …”
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