Changes between Version 287 and Version 288 of PublicationsandPresentations


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Jan 4, 2025, 5:43:55 PM (2 weeks ago)
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  • PublicationsandPresentations

    v287 v288  
    3535=== Accepted ===
    3636
    37 Bunsen, F., J. Hauck, L. Nerger, S. Torres-Valdés. (2024) Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model. Ocean Science, [https://doi.org/10.5194/egusphere-2024-1750 Preprint]
     37//Bunsen, F.//, J. Hauck, //L. Nerger//, S. Torres-Valdés. (2024) Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model. Ocean Science, [https://doi.org/10.5194/egusphere-2024-1750 Preprint]
    3838
    3939Guo, Y. ,Y. Yu , J. Liu (2024) Employment of an Arctic sea-ice data assimilation scheme in the coupled climate system model FGOALS-f3-L and its preliminary results, Atmospheric and Oceanic Science Letters (2024), [https://doi.org/10.1016/j.aosl.2024.100553 doi:10.1016/j.aosl.2024.100553]
    … …  
    7171Song, R., L. Mu, S. N. Loza, F. Kauker, X. Chen (2024) Assimilating Summer Sea-Ice Thickness Observations Improves Arctic Sea-Ice Forecast. Geophys. Rec. Lett., 51, e2024GL110405 [https://doi.org/10.1029/2024GL110405 doi:10.1029/2024GL110405]
    7272
    73 Döll, P., Hasan, H. M. M., Schulze, K., Gerdener, H., Börger, L., Shadkam, S., Ackermann, S., Hosseini-Moghari, S.-M., Müller Schmied, H., Güntner, A. Kusche, J. (2024) Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin. Hydrology and Earth System Sciences. 28, 2259-2295 [https://doi.org/10.5194/hess-28-2259-2024 doi:10.5194/hess-28-2259-2024]
     73Döll, P., Hasan, H. M. M., Schulze, K., Gerdener, H., Börger, L., Shadkam, S., Ackermann, S., Hosseini-Moghari, S.-M., Müller Schmied, H., Güntner, A., Kusche, J. (2024) Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin. Hydrology and Earth System Sciences. 28, 2259-2295 [https://doi.org/10.5194/hess-28-2259-2024 doi:10.5194/hess-28-2259-2024]
    7474
    7575Stramska, M., Jakacki, J. (2024) Variability of chlorophyll a concentration in surface waters of the open Baltic Sea. Oceanologica, 66, 365-380 [https://doi.org/10.1016/j.oceano.2024.02.003 doi:10.1016/j.oceano.2024.02.003]
    … …  
    7777Düsterhus, A., S. Brune (2024) Decadal Predictability of Seasonal Temperature Distributions, Geophysical Research Letters, 51, e2023GL107838, [https://doi.org/10.1029/2023GL107838 10.1029/2023GL107838]
    7878
    79 Shao, C. and //Nerger, L//. (2024) WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework, Geoscientific Model Development, 17, 4433–4445, [https://doi.org/10.5194/gmd-17-4433-2024 doi:10.5194/gmd-17-4433-2024]
     79//Shao, C.// and //Nerger, L//. (2024) WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework, Geoscientific Model Development, 17, 4433–4445, [https://doi.org/10.5194/gmd-17-4433-2024 doi:10.5194/gmd-17-4433-2024]
    8080
    8181Tang, Q., H. Delottier, W. Kurtz, //L. Nerger//, O. S. Schilling, P. Brunner (2024) HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model. Geoscientific Model Development, 17, 3559-3578 [https://doi.org/10.5194/gmd-17-3559-2024 doi:10.5194/gmd-17-3559-2024]
    … …  
    8787Strebel, L., H. Bogena, H. Vereecken, M. Andreasen, S. Aranda-Barranco, H.-J. Hendricks Franssen (2024) Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data. Hydrology andd Earth System Sciences, 28, 1001-1026 [https://doi.org/10.5194/hess-28-1001-2024 doi:10.5194/hess-28-1001-2024]
    8888
    89 Shao, C. & //L. Nerger// (2024) The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case. Remote Sensing, 16, 430 [https://doi.org/10.3390/rs16020430 doi:10.3390/rs16020430]
     89//Shao, C.// & //L. Nerger// (2024) The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case. Remote Sensing, 16, 430 [https://doi.org/10.3390/rs16020430 doi:10.3390/rs16020430]
    9090
    9191Li, Y., Z. Cong, D. Yang (2024) The ecohydrological response to soil moisture based on the distributed hydrological assimilation model in the mountain region. Ecohydrology, 17, e2606 [https://doi.org/10.1002/eco.2606 doi:10.1002/eco.2606]
    … …  
    104104Luo, H., Q. Yang, M. Mazloff, //L. Nerger//, and D. Chen (2023) The impacts of optimizing model-dependent parameters on the Antarctic sea ice data assimilation. Geophysical Research Letters, 50, e2023GL105690, [http://dx.doi.org/10.1029/2023GL105690  doi:10.1029/2023GL105690]
    105105
    106 Min, C., Q. Yang, H. Luo, D. Chen, T. Krumpen, //N. Mamnun//, X. Liu, and //L. Nerger// (2023). Improving Arctic sea-ice thickness estimates with the assimilation of !CryoSat-2 summer observations. Ocean-Land-Atmosphere Research, 6, 0025 [https://doi.org/10.34133/olar.0025 doi:10.34133/olar.0025]
     106//Min, C.//, Q. Yang, H. Luo, D. Chen, T. Krumpen, //N. Mamnun//, X. Liu, and //L. Nerger// (2023). Improving Arctic sea-ice thickness estimates with the assimilation of !CryoSat-2 summer observations. Ocean-Land-Atmosphere Research, 6, 0025 [https://doi.org/10.34133/olar.0025 doi:10.34133/olar.0025]
    107107
    108108Cook, S., F. Gillet-Chaulet, J. Fuerst. Robust reconstruction of glacier beds using transient 2D assimilation with Stokes. Journal of Glaciology. 69, 1393-1402, [https://doi.org/10.1017/jog.2023.26 doi:10.1017/jog.2023.26]
    … …  
    122122Brandhorst, N., I. Neuweiler. (2023) Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model. Hydrology and Earth System Sciences (HESS), 27, 1301-1323 [https://doi.org/10.5194/hess-27-1301-2023 doi:10.5194/hess-27-1301-2023]
    123123
    124 Pohlmann, H., S. Brune, K. Fröhlich, J. H. Jungclaus, C. Sgoff , J. Baehr. (2023) Impact of ocean data assimilation on climate predictions with ICON-ESM. Climate Dynamics, 61, 357–373 [https://doi.org/10.1007/s00382-022-06558-w]
     124Pohlmann, H., S. Brune, K. Fröhlich, J. H. Jungclaus, C. Sgoff, J. Baehr. (2023) Impact of ocean data assimilation on climate predictions with ICON-ESM. Climate Dynamics, 61, 357–373 [https://doi.org/10.1007/s00382-022-06558-w]
    125125
    126126Williams, N., N. Byrne, D. Feltham, P. J. van Leeuwen, R. Bannister, D. Schroeder, A. Ridout, //L. Nerger// (2023) The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system.  The Cryosphere, 17, 2509–2532 [https://doi.org/10.5194/tc-17-2509-2023 10.5194/tc-17-2509-2023]
    … …  
    135135Tian, Z., X Liang, J. Zhang, H. Bi, F. Zhao, C. Li (2022) Thermodynamical and Dynamical Impacts of an Intense Cyclone on Arctic Sea Ice, Journal of Geophysical Research Oceans, 127, e2022JC018436. [https://doi.org/10.1029/2022JC018436 doi:10.1029/2022JC018436]
    136136
    137 Mu, L., L. Nerger, J. Streffing, Q. Tang, B. Niraula, L. Zampieri, S. N. Loza, H. F. Goessling. (2022) Sea-ice forecasts with an upgraded AWI Coupled
     137Mu, L., //L. Nerger//, J. Streffing, //Q. Tang//, B. Niraula, L. Zampieri, S. N. Loza, H. F. Goessling. (2022) Sea-ice forecasts with an upgraded AWI Coupled
    138138Prediction System, Journal of Advances in Modeling Earth Systems, 14, e2022MS003176, [https://doi.org/10.1029/2022MS003176 doi:10.1029/2022MS003176]
    139139
    … …  
    142142Corbin, A, J. Kusche. (2022) Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model. Earth, Planets and Space, 74, 183. [https://doi.org/10.1186/s40623-022-01733-z doi:10.1186/s40623-022-01733-z]
    143143
    144 Mamnun, N., C. Voelker, M. Vrekoussis, L. Nerger. (2022) Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model. Frontiers in Marine Science, 9, 984236. [https://doi.org/10.3389/fmars.2022.984236 doi:10.3389/fmars.2022.984236]
     144//Mamnun, N.//, C. Voelker, M. Vrekoussis, //L. Nerger.// (2022) Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model. Frontiers in Marine Science, 9, 984236. [https://doi.org/10.3389/fmars.2022.984236 doi:10.3389/fmars.2022.984236]
    145145
    146146Hövel, L., S. Brune, J. Baehr. (2022) Decadal prediction of Marine HEatwaves in MPI_ESM. Geophysical Research Letters, 49, e2022GL099347. [https://doi.org/10.1029/2022GL099347 doi:10.1029/2022GL099347]
    … …  
    154154Ben Ali, M.Y., O. Léon, D. Donjat, H. Bézard, E. Laroche, V. Mons, F. Champagnat (2022) Data assimilation for aerothermal mean flow reconstruction using aero-optical observations: a synthetic investigation, 56th 3AF International Conference on Applied Aerodynamics 28 — 30 March 2022, Toulouse – France [https://www.researchgate.net/publication/359619309]
    155155
    156 Nerger, L. (2022) Data assimilation for nonlinear systems with a hybrid nonlinear-Kalman ensemble transform filter. Q. J. Meteorol. Soc., 148, 620-640 [https://doi.org/10.1002/qj.4221 doi:10.1002/qj.4221]
     156//Nerger, L.// (2022) Data assimilation for nonlinear systems with a hybrid nonlinear-Kalman ensemble transform filter. Q. J. Meteorol. Soc., 148, 620-640 [https://doi.org/10.1002/qj.4221 doi:10.1002/qj.4221]
    157157
    158158Schachtschneider, R., J. Saynisch-Wagner, V. Klemann, M. Bagge, M. Thomas (2022) An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model. Nonlinear Processes in Geophysics 29, 53-75 [https://doi.org/10.5194/npg-29-53-2022 doi:10.5194/npg-29-53-2022]
    … …  
    174174Models Intercomparison eXperiment Phase 2 (ITMIX2), Frontiers in  Earth Science 8, 571923. [https://doi.org/10.3389/feart.2020.571923 doi:10.3389/feart.2020.571923]
    175175
    176 Tang, Q., L. Mu, H. F. Goessling, T. Semmler, L. Nerger (2021) Strongly coupled data assimilation of ocean observations into an ocean-atmosphere model, Geophys. Res. Lett, 48, e2021GL094941 [https://doi.org/10.1029/2021GL094941 doi:10.1029/2021GL094941]
     176//Tang, Q.//, L. Mu, H. F. Goessling, T. Semmler, //L. Nerger// (2021) Strongly coupled data assimilation of ocean observations into an ocean-atmosphere model, Geophys. Res. Lett, 48, e2021GL094941 [https://doi.org/10.1029/2021GL094941 doi:10.1029/2021GL094941]
    177177
    178178Shu, Q., F. Qiao, J. Liu, Z. Song, Z. Chen, J. Zhao, X. Yin, Y. Song. (2021) Arctic sea ice concentration and thickness data assimilation in
    179179the FIO-ESM climate forecast system, Acta Oceanol. Sin., 40, 65–75 [https://doi.org/10.1007/s13131-021-1768-4 doi:10.1007/s13131-021-1768-4]
    180180
    181 Luo, H., Q. Yang, L. Mu, X. Tian-Kunze, L. Nerger, M. Mazloff, L. Kaleschke, D. Chen. (2021) DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations, Journal of Glaciology, 67, 1235-1240 [https://doi.org/10.1017/jog.2021.57 doi:10.1017/jog.2021.57]
     181Luo, H., Q. Yang, L. Mu, X. Tian-Kunze, //L. Nerger//, M. Mazloff, L. Kaleschke, D. Chen. (2021) DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations, Journal of Glaciology, 67, 1235-1240 [https://doi.org/10.1017/jog.2021.57 doi:10.1017/jog.2021.57]
    182182
    183183von Schuckmann K. et al (2021) Copernicus Marine Service Ocean State Report, Issue 5. J. Oper. Oce. 14:sup1, 1-185 [https://doi.org/10.1080/1755876X.2021.1946240 doi:10.1080/1755876X.2021.1946240]
    … …  
    195195Friedemann, S., Raffin, B.  An elastic framework for ensemble-based large-scale data assimilation. [Research Report] RR-9377, Inria Grenoble Rhône-Alpes, Université de Grenoble. 2020. hal-03017033v2 [https://hal.archives-ouvertes.fr/hal-03017033v2]
    196196
    197 Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., Nerger, L. (2020) Improving the ocean and atmosphere in a coupled ocean‐atmosphere model by assimilating satellite sea surface temperature and subsurface profile data. Q. J. Royal Metorol. Soc., 146, 4014-4029 [https://doi.org/10.1002/qj.3885 doi:10.1002/qj.3885]
     197//Tang, Q.//, Mu, L., Sidorenko, D., Goessling, H., Semmler, T., //Nerger, L.// (2020) Improving the ocean and atmosphere in a coupled ocean‐atmosphere model by assimilating satellite sea surface temperature and subsurface profile data. Q. J. Royal Metorol. Soc., 146, 4014-4029 [https://doi.org/10.1002/qj.3885 doi:10.1002/qj.3885]
    198198
    199199Zheng, Y., Albergel, C., Munier, S., Bonan, B., Calvet, J.-C. (2020) An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution. Geoscientific Model Development. 13, 3607-3625 [http://doi.org/10.5194/gmd-13-3607-2020 doi:10.5194/gmd-13-3607-2020]
    … …  
    207207Brune, S., Baehr, J. (2020) Preserving the coupled atmosphere-ocean feedback in initializations of decadal climate predictios. Wiley Interdisciplinary Reviews - Climate Change, e637 [https://doi.org/10.1002/wcc.637 doi:10.1002/wcc.637]
    208208
    209 Mu, L., Nerger, L., Tang, Q., Losa, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., Goessling, H. F. (2020) Towards a data assimilation system for seamless sea ice prediction based o the AWI climate model. Journal of Advances in Modeling Earth Systems, 12, e2019MS001937 [https://doi.org/10.1029/2019MS001937 doi:10.1029/2019MS001937]
     209Mu, L., //Nerger, L.//, //Tang, Q.//, Losa, S. N., Sidorenko, D., Wang, Q., Semmler, T., Zampieri, L., Losch, M., Goessling, H. F. (2020) Towards a data assimilation system for seamless sea ice prediction based o the AWI climate model. Journal of Advances in Modeling Earth Systems, 12, e2019MS001937 [https://doi.org/10.1029/2019MS001937 doi:10.1029/2019MS001937]
    210210
    211211Liang, X., Zhao, F., Li, C., Zhang, L., Li, B. (2020) Evaluation of ArcIOPS sea ice forecasting products during the ninth CHINARE-Arctic in summer 2018. Adv. Polar Science, 31, 14-25 [https://doi.org/10.13679/j.advps.2019.0019 doi:10.13679/j.advps.2019.0019]
    … …  
    213213Naz, B. S., Kollet, S., Hendricks-Franssen, H.-J., Montzka,C. Kurtz, W. (2020) A 3 km spatially and temporally consustent European daily soil moisture reanalysis from 2000 to 2015. Scientific Data 7, 111 [https://doi.org/10.1038/s41597-020-0450-6 doi:10.1038/s41597-020-0450-6]
    214214
    215 Pradhan, H.K., Voelker, C., Losa, S.N., Bracher, A., Nerger, L. (2020) Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets. Journal of Geophysical Research Oceans, 125, e2019JC015586 [https://doi.org/10.1029/2019JC015586 doi:10.1029/2019JC015586]
     215//Pradhan, H.K.//, Voelker, C., Losa, S.N., Bracher, A., //Nerger, L.// (2020) Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets. Journal of Geophysical Research Oceans, 125, e2019JC015586 [https://doi.org/10.1029/2019JC015586 doi:10.1029/2019JC015586]
    216216
    217217Sanchez, S., Wicht, J., Bärenzung, J. (2020) Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations. Earth, Planets and Space, Vol. 72, 157 [https://doi.org/10.1186/s40623-020-01279-y]