Meteorologie
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2023

  • Barry, J., S. Meilinger, K. Pfeilsticker, A. Herman-Czezuch, N. Kimiaie, C. Schirrmeister, R. Yousif, T. Buchmann, J. Grabenstein, H. Deneke, J. Witthuhn, C. Emde, F. Gödde, B. Mayer, L. Scheck, M. Schroedter-Homscheidt, P. Hofbauer, M. Struck, 2023, Irradiance and cloud optical properties from solar photovoltaic systems, Atmos. Meas. Tech., in review.
  • Baur, F., L. Scheck, C. Stumpf, C. Köpken-Watts, R. Potthast, 2023, A neural network-based method for generating synthetic 1.6 μm near-infrared satellite images, AMT, in review.
  • Valmassoi, A., J. D. Keller, D. T. Kleist, S. English, B. Ahrens, I. Bašták Ďurán, E. Bauernschubert, M. G. Bosilovich, M. Fujiwara, H. Hersbach, L. Lei, U. Löhnert, N. Mamnun, C. R. Martin, A. Moore, D. Niermann, J. J. Ruiz, L. Scheck, 2023, Current Challenges and Future Directions in Data Assimilation and Reanalysis, Bull. Amer. Meteor. Soc., 104, E756–E767.

2022

  • Gleiter, T., T. Janjic, N. Chen, 2022, Ensemble Kalman Filter based Data Assimilation for Tropical Waves in the MJO Skeleton Model, QJR Meteorol Soc, in print, doi: 10.1002/qj.4245, (link).
  • Legler, S. and T. Janjic, 2022, Combining data assimilation and machine learning to estimate parameters of a convective-scale model, Q. J. R. Meteorol. Soc., 1-15, https://doi.org/10.1002/qj.4235, (link).
  • Zeng, Y., Y. Feng, A. de Lozar, K. Stephan, L. Scheck, K. Khosravianghadikolaei, U. Blahak, 2022, Evaluating Latent-Heat-Nudging Schemes and Radar forward Operator Settings for a Convective Summer Period over Germany Using the ICON-KENDA System, Remote Sens., 14, 5295.

2021

  • Feng, Y., T. Janjic, Y. Zeng, A.Seifert, J. Min, 2021, Representing Microphysical Uncertainty in Convective-scale Data Assimilation using Additive Noise, Journal of Advances in Modeling Earth Systems, 13, e2021MS002606, (link).
  • Geiss, S., L. Scheck, A. de Lozar, M. Weissmann, 2021, Understanding the model representation of clouds based on visible and infrared satellite observations, ACP, 21, 12273-12290.
  • Janjic, T. and Y. Zeng, 2021, Weakly constrained LETKF for estimation of hydrometeor variables in convective-scale data assimilation, Geophysical Research Letters, 48, e2021GL094962, DOI: 10.1029/2021GL094962, (link).
  • Janjic, T., Y. Ruckstuhl, P. L. Toint, 2021, A data assimilation algorithm for predicting rain, Quart. J. Roy. Meteor. Soc., 1949– 1963, (link).
  • Martin, A., M. Weissmann, O. Reitebuch, M. Rennie, A. Geiß, A. Cress , 2021, Validation of Aeolus winds using radiosonde observations and NWP model equivalents, Atmos. Meas. Tech. , 14, 2167–2183.
  • Ruckstuhl Y., T. Janjic, S. Rasp, 2021, Training a convolutional neural network to conserve mass in data assimilation, Nonlinear Processes in Geophysics, 28, 111–119, (link).
  • Scheck, L., 2021, A neural network based forward operator for visible satellite images and its adjoint, JQSRT, 274, 0022-4073.
  • Zeng, Y., A. de Lozar, T. Janjic, A. Seifert, 2021, Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation, Geoscientific Model Development, 14, 1295–1307, (link).
  • Zeng, Y., Janjic, T., Feng, Y., Blahak, U., de Lozar, A., Bauernschubert, E., Stephan, K., and Min, J., 2021, Interpreting estimated Observation Error Statistics of Weather Radar Measurements using the ICON-LAM-KENDA System, Atmos. Meas. Tech., 14, 5735–5756, (link).
  • Zeng, Y., T. Janjic, A. de Lozar, C. A. Welzbacher, U. Blahak, A. Seifert, 2021, Assimilating radar radial wind and reflectivity data in an idealized setup of the COSMO-KENDA system, Atmospheric Research, 249, 105282, (link).

2020

  • Bachmann, K., C. Keil, G.C. Craig, M. Weissmann, C. Welzbacher, 2020, Predictability of Deep Convection in Idealized and Operational Forecasts : Effects of Radar Data Assimilation , Orography and Synoptic Weather Regime, Monthly Weather Review, 148 , 63-81, (link).
  • Hutt, A., C. Schraff, H. Anlauf, L. Bach, M. Baldauf, E. Bauernschubert, A. Cress, R. Faulwetter, F. Fundel, C. Köpken-Watts, H. Reich, A. Schomburg, J. Schroettle, K. Stephan, O. Stiller, M. Weissmann, R. Potthast, 2020, Assimilation of SEVIRI water vapour channels with an ensemble Kalman filter on the convective scale, Frontiers in Earth Science, 8, 70 (link).
  • Necker, T., M. Weissmann, Y. Ruckstuhl, J. Anderson, T. Miyoshi , 2020, Sampling error correction evaluated using a convective-scale 1000-member ensemble, Mon. Wea. Rev., 148, 1229–1249 (link).
  • Necker, T., S. Geiss, M. Weissmann, J. Ruiz, T. Miyoshi, G.-Y. Lien, 2020, A convective-scale 1000-member ensemble simulation and potential applications, Q. J. R. Meteorol. Soc., 146, 1423-1442 (link).
  • Ruckstuhl, Y. and T. Janjić, 2020, Combined State-Parameter Estimation with the LETKF for Convective-Scale Weather Forecasting, Mon. Wea. Rev., 148, 1607–1628, (link).
  • Scheck, L., M. Weissmann, L. Bach, 2020, Assimilating visible satellite images for convective-scale numerical weather prediction: A case study, Q. J. R. Meteorol. Soc., 146, 3165–3186 (link).
  • Schindler, M., Weissmann, M., Schäfler, A., Radnoti, G., 2020, The impact of dropsonde and extra radiosonde observations during NAWDEX in autumn 2016, Mon. Wea. Rev., 148 , 809-824 (link).
  • Schroettle, J., M. Weissmann, L. Scheck, A. Hutt, 2020, Assimilating visible and thermal radiances in idealized simulations of deep convection, Mon. Wea. Rev., accepted, 148 , 4357 - 4375 (link).
  • Zeng, Y., T. Janjic, A. de Lozar, S. Rasp, U. Blahak, A. Seifert, G. C. Craig, 2020, Comparison of methods accounting for subgrid-scale model error in convective-scale data assimilation, Mon. Wea. Rev., 148, 2457-2477, (link).

2019

  • Bachmann, K., C. Keil, M. Weissmann, 2019, Impact of radar data assimilation and orography on predictability of deep convection, Q. J. R. Meteorol. Soc., , 145, 117-130 (link).
  • Camus C., P. Offermann, M. Weissmann, C. Buerhop, J. Hauch, C. J. Brabec, 2019, Site-Specific Assessment of Mechanical Loads on Photovoltaic Modules from Meteorological Reanalysis Data, Solar Energy, 188, 1134-1145 (link).
  • Keil, C., F. Baur, K. Bachmann, S. Rasp, L. Schneider, C. Barthlott, 2019, Relative contribution of soil moisture, boundary layer and microphysical perturbations on convective predictability in different weather regimes, Q. J. R. Meteorol. Soc., 145, 3102-3115, (link).
  • Zeng Y., T. Janjic, M. Sommer, A. de Lozar, U. Blahak, A. Seifert, 2019, Representation of model error in convective-scale data assimilation: additive noise based on model truncation error, J. Advances in Modelling Earth Systems, 11, 752-770, (link).

2018

  • Gustafsson, N., T. Janjic, C. Schraff, D. Leuenberger, M. Weissmann, H. Reich, P. Brousseau, T. Montmerle, E. Wattrelot, A. Bucanek, M. Mile, R. Hamdi, M. Lindskog, J. Barkmeijer, M. Dahlbom, B. Macpherson, S. Ballard, G. Inverarity, J. Carley, C. Alexander, D. Dowell, S. Liu, Y. Ikuta and T. Fujita, 2018, Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres., Q. J. R. Meteorol. Soc. , 144, 1218-1256 (link).
  • Janjic, T., N. Bormann, M. Bocquet, J. A. Carton, S.E. Cohn, S.L. Dance, S.N. Losa, N.K. Nichols, R. Potthast, J.A. Waller, P. Weston, 2018, On the representation error in data assimilation, accepted Q. J. R. Meteorol. Soc., 144:713, 1257-1278, (link).
  • Janjic, T., R. Potthast, P. J. Van Leeuwen , 2018, Editorial for Advances in Data Assimilation Methods, Q. J. R. Meteorol. Soc., 144:713, 1189-1190, (link).
  • Necker, T., M. Weissmann, M. Sommer, 2018, The importance of appropriate verification metrics for the assessment of observation impact in a convection-permitting modelling system, Q. J. R. Meteorol. Soc.,, 144, 1667-1680 (link).
  • Ruckstuhl Y. and T. Janjic, 2018, Parameter and state estimation with ensemble Kalman filter based approaches for convective scale data assimilation, Q. J. R. Meteorol. Soc., 144:712, 826--841, (link).
  • Scheck, L., Weissmann, M., Mayer, B., 2018, Efficient methods to account for cloud top inclination and cloud overlap in synthetic visible satellite images, JTECH, 35, 665 - 685 (link).
  • Sommer, M. and T. Janjic, 2018, A flexible additive inflation scheme for treating model error in Ensemble Kalman Filters, Q. J. R. Meteorol. Soc., 144, 2026--2037, (link).
  • Zeng. Y. T. Janjic, A. de Lozar, U. Blahak, H. Reich, C. Keil, A. Seifert, 2018, Representation of model error in convective-scale data assimilation: Additive noise, relaxation methods and combinations, J. Advances in Modelling Earth Systems, 10, 2889–2911, (link).

2017

  • Bley, S., H. Deneke, F. Senf, L. Scheck, 2017, Metrics for the evaluation of warm convective cloud fields in a large‐eddy simulation with Meteosat images, Q.J.R. Meteorol. Soc., 143, 2050-2060.
  • Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D. and Quaas, J., 2017, Large-eddy simulations over Germany using ICON: A comprehensive evaluation, Q.J.R. Meteorol. Soc., 143, 69-100.
  • Lange, H., G. C. Craig, T. Janjic, 2017, Characterizing Noise and Spurious Convection in Convective Data Assimilation, Q. J. R. Meteorol. Soc., 143:709, 3060–3069, doi:10.1002/qj.3162.
  • Zeng, Y., T. Janjic, Y. Ruckstuhl and M. Verlaan, 2017, Ensemble-Type Kalman Filter Algorithm conserving Mass, Total Energy and Enstrophy, Q. J. R. Meteorol. Soc., 143:708, 2902–2914, doi:10.1002/qj.3142.

2016

  • Folger, K. and M. Weissmann, 2016, Lidar-based height correction for the assimilation of atmospheric motion vectors, J. Appl. Meteor. Climatol., 55, 2211–2227 (link).
  • Harnisch, F., M. Weissmann, and A. Perianez, 2016, Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system, Q. J. R. Meteorol. Soc., 142, 1797–1808. (link).
  • Haslehner, M., T. Janjic, G. C. Craig, 2016, Testing particle filters on convective scale models. Part l: A stochastic cloud model, Q. J. R. Meteorol. Soc., 142:696, 1439-1452, doi = 10.1002/qj.2745.
  • Haslehner, M., T. Janjic, G. C. Craig, 2016, Testing particle filters on convective scale models. Part lI: A modified shallow water model, Q. J. R. Meteorol. Soc., 142:697, 1628-1646, doi = 10.1002/qj.2757.
  • Lange, H. and T. Janjic, 2016, Assimilation of Mode-S EHS Aircraft Observations in COSMO-KENDA, Monthly Weather Review, 144:5, 1697–1711, (link).
  • Scheck, L., P. Frerebeau, R. Buras-Schnell and B. Mayer, 2016, A fast radiative transfer method for the simulation of visible satellite imagery, Journal of Quantitative Spectroscopy and Radiative Transfer, 175, 54-67.
  • Simmer, C., G. Adrian, S. Jones, V. Wirth, M. Göber, C. Hohenegger, T. Janjic, J. Keller, C. Ohlwein, A. Seifert, S. Trömel, T. Ulbrich, K. Wapler, M. Weissmann, J. Keller, M. Masbou, S. Meilinger, N. Riss, A. Schomburg, C. Stein, A. Vormann, 2016, HErZ - The German Hans-Ertel Centre for Weather Research, Bull. Amer. Soc., 97, 1057-1068. (link).
  • Sommer, M. and M. Weissmann, 2016, Ensemble-based approximation of observation impact using an observation-based verification metric, Tellus A, 68, 27885 (link).
  • Y.Zeng and T. Janjic, 2016, Study of Conservation Laws with the Local Ensemble Transform Kalman Filter, Q. J. R. Meteorol. Soc., 142, 2359–2372, doi:10.1002/qj.2829.
  • Zeng, Y., U. Blahak, D. Jerger, 2016, An efficient modular volume‐scanning radar forward operator for NWP models: description and coupling to the COSMO model, Q. J. R. Meteorol. Soc., 142, 3234-3256.

2015

  • Harnisch, F. and C. Keil, 2015, Initial conditions for convective-scale ensemble forecasting provided by ensemble data assimilation, Mon. Wea. Rev., 143, 1583-1600, doi: 10.1175/MWR-D-14-00209.1.
  • Schäfler, A. and F. Harnisch, 2015, Impact of the inflow moisture on the evolution of a Warm Conveyor Belt, Q. J. R. Meteorol. Soc., 141, 299-310 (link).
  • Wapler, K., F. Harnisch, T. Pardowitz and F. Senf, 2015, Characterisation and predictability of a strong and a weak forcing severe convective event - a multi-data approach, Meteorologische Zeitschrift, Vol. 24 No. 4, p. 393-410. (link).

2014

  • Albertella A.,R. Savcenko,T. Janjić, R. Rummel, W. Bosch and J. Schröter, 2014, Earth on the Edge: Science for a Sustainable Planet, International Association of Geodesy Symposia, Springer Berlin Heidelberg, 139, 81-87.
  • Baker, W., R. Atlas, C. Cardinali, A. Clement, G. Emmitt, B. Gentry, M. Hardesty, E. Källén, M. Kavaya, R. Langland, M. Masutani, W. McCarty, B. Pierce, Z. Pu, L. P. Riishojgaard, J. Ryan, S. Tucker, M. Weissmann and J. Yoe, 2014, Lidar-Measured Wind Profiles – The Missing Link in the Global Observing System, Bull. Amer. Soc., 95, 543–564. (link).
  • Folger, K., and M. Weissmann, 2014, Height correction of atmospheric motion vectors using satellite lidar observations from CALIPSO, J. Appl. Meteor. Climatol., 53, 1809–1819 (link).
  • Janjic, T., D.McLaughlin, S.E. Cohn and M. Verlaan, 2014, Conservation of mass and preservation of positivity with ensemble-type Kalman filter algorithms, Monthly Weather Review, 142, 755-773.
  • Keil C, Heinlein F, Craig GC., 2014, The convective adjustment time-scale as indicator of predictability of convective precipitation., Q. J. R. Meteorol. Soc., 140, 480-490, DOI:10.1002/qj.2143, (link) .
  • Kostka, P. M., M. Weissmann, R. Buras, B. Mayer and O. Stiller , 2014, Observation Operator for Visible and Near-Infrared Satellite Reflectances, J. Atmos. Oceanic Technol., 31, 1216–1233. (link).
  • Kühnlein C., C. Keil, G. Craig G. and C. Gebhardt, 2014, The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation, Q.J.R. Meteorol.Soc., 140, 1552–1562 (link).
  • Lange, H. and G.C. Craig, 2014, The Impact of Data Assimilation Length Scales on Analysis and Prediction of Convective Storms, Monthly Weather Review, 142, 3781–3808 (link).
  • Loza, S., S. Danilov, J. Schröter, T. Janjic-Pfander, L. Nerger, and F. Janssen, 2014, Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics, Journal of Marine Systems, 129, 259-270.
  • Sommer, M. and M. Weissmann, 2014, Observation Impact in a Convective-Scale Localized Ensemble Transform Kalman Filter, Q. J. R. Meteorol. Soc., 140, 2672–2679. (link).
  • Weissmann, M., M. Göber, C. Hohenegger, T. Janjic, J. Keller, C. Ohlwein, A. Seifert, S. Trömel, T. Ulbrich, K. Wapler, C. Bollmeyer, H. Deneke, 2014, Initial phase of the Hans-Ertel Centre for Weather Research – A virtual centre at the interface of basic and applied weather and climate research, Meteorologische Zeitschrift, 23, 193 - 208. (link).
  • Würsch, M. and GC Craig, 2014, A simple dynamical model of cumulus convection for data assimilation research., Meteorol. Z., 23, 483-490 (link).

2013

  • Craig, G. C. and M. Würsch, 2013, The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model, Q. J. R. Meteorol. Soc., 139, 515-523. (link).
  • Grams, C. M. , S. C. Jones, C. Davis, P. Harr, M. Weissmann, 2013, The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part I: upper level ridgebuilding and modification of the jet, Quart. J. Roy. Meteor. Soc, 139, 2148–2164. (link).
  • Weissmann, M., K. Folger and H. Lange, 2013, Height correction of atmospheric motion vectors using airborne lidar observations, J. Appl. Meteor. Climatol., 52, 1868–1877. (link).

2012

  • Weissmann M., R. H. Langland, P. M. Pauley, S. Rahm, C. Cardinali, 2012, Influence of airborne Doppler wind lidar profiles on ECMWF and NOGAPS forecasts, Q. J. R. Meteorol. Soc., 138, 118-130. (link).