Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation for the COSMO model (v5.07)
Meteorologisches Institut, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
Alberto de Lozar
Deutscher Wetterdienst, Offenbach, Germany
Tijana Janjic
Meteorologisches Institut, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
Axel Seifert
Deutscher Wetterdienst, Offenbach, Germany
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Cited articles
Aksoy, A., Dowell, D. C., and Snyder, C.: A multiscale comparative assessment
of the ensemble Kalman filter for assimilation of radar observations. Part
I: Storm-scale analyses, Mon. Weather Rev., 137, 1805–1824, 2009. a
Ancell, B. C.: Examination of analysis and forecast errors of high-resolution
assimilation, bias removal, and digital filter initialization with an
ensemble Kalman filter, Mon. Weather Rev., 140, 3992–4004, 2012. a
Baldlauf, M., Seifert, A., Förstner, J., Majewski, D. M., R., and
Reinhardt, T.: Operational convective-scale numerical weather prediciton
with the COSMO model: description and sensitivities, Mon. Weather Rev., 139,
3887–3905, 2011. a
Bloom, S. C., Takacs, L. L., Da Silva, A. M., and Ledvina, D.: Data
assimilation using incremental analysis updates, Mon. Weather Rev., 124,
1256–1271, 1996. a
Böing, S., Jonker, H. J. J., Sievesma, A. P., and Grabowski, W. W.: Influence
of the Subcloud Layer on the Denvelopment of a Deep Cinvective
Eensemble, J. Atmos. Sci., 69, 2682–2698, 2012. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and
three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Gauthier, P. and Thepaut, J. N.: Impact of the digital filter as a weak
constraint in the pre-operational 4DVAR assimilation system of Meteo-France,
Mon. Weather Rev., 129, 2089–2102, 2001. a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissman, M.,
Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bucanek, A., Mile,
M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B.,
Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S.,
Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for
convective-scale numerical weather prediction at operational centres, Q.
J. Roy. Meteor. Soc., 144, 1218–1256, 2018. a
He, H., Lei, L., Whitaker, J. S., and Tan, Z. M.: Impacts of Assimilation
Frequency on Ensemble Kalman Filter Data Assimilation and
Imbalances, J. Adv. Model. Earth Syst., 12, e2020MS002187, https://doi.org/10.1029/2020MS002187, 2020. a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter, Physica
D, 230, 112–126, 2007. a
Lange, H., Craig, G. C., and Janjić, T.: Characterizing noise and spurious
convection in convective data assimilation, Q. J. Roy. Meteor. Soc., 143,
3060–3069, 2017. a
Lei, L. and Whitaker, J. S.: A four-dimensional incremental analysis update for
the ensemble Kalman filter, Mon. Weather Rev., 144, 2605–2621, 2016. a
Lin, Y., Farley, R. D., and Orville, H. D.: Bulk parameterization of the snow
field in a cloud model, J. Clim. Appl. Meteor., 22, 1065–1092, 1983. a
Lynch, P. and Huang, X. Y.: Initialization of the HIRLAM model using a digital
filter, Mon. Weather Rev., 120, 1019–1034, 1992. a
Milan, M., Macpherson, B., Tubbs, R., Dow, G., Inverarity, G., Mittermaier, M.,
Halloran, G., Kelly, G., Li, D., Maycock, A., Payne, T., Piccolo, C.,
Stewart, L., and Wlasak, M.: Hourly 4D-Var in the Met Office UKV operational
forecast model, Q. J. Roy. Meteor. Soc., 146, 1281–1301, 2020. a
Reinhardt, T. and Seifert, A.: A three-category ice scheme for LMK, COSMO
News Letter, 6, 115–120, 2006. a
Rhodin, A., Lange, H., Potthast, R., and Janjic, T.: Documentation of the DWD
Data Assimilation System, Technical Report, Deutscher Wetterdienst (DWD), Offenbach, Germany, 305 pp., 2013. a
Robert, N. and Lean, H.: Scale-selective verification of rainfall accumulations
from high-resolution forecasts of convective events, Mon. Weather. Rev., 136,
78–96, 2008. a
Ruckstuhl, Y. M. and Janjić, T.: Parameter and state estimation with
ensemble Kalman filter based algorithms for convective-scale applications,
Q. J. Roy. Meteor. Soc., 144, 826–841, 2018. a
Seifert, A. and Heus, T.: Large-eddy simulation of organized precipitating trade wind cumulus clouds, Atmos. Chem. Phys., 13, 5631–5645, https://doi.org/10.5194/acp-13-5631-2013, 2013. a
Simmer, C., Adrian, G., Jones, S., Wirth, V., Göber, M., Hohenegger, C.,
Janjić, T., Keller, J., Ohlwein, C., Seifert, A., Trömel, S., Ulbrich,
T., Wapler, K., Weissmann, M., Keller, J., Masbou, M., Meilinger, S.,
Riß, N., Schomburg, A., Stein, C., Vormann, A., and Weingärtner, C.: HErZ – The
German Hans-Ertel Centre for Weather Research, B. Am. Meteorol.
Soc., 97, 1057–1068, 2016. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in
large-scale models, Mon. Weather Rev., 117, 1779–1799, 1989. a
Vetra-Carvalho, S., Dixon, M., Migliorini, S., Nichols, N. K., and Ballard,
S. P.: Breakdown of hydrostatic balance at convective scales in the forecast
errors in the Met Office Unified Model, Q. J. Roy. Meteor. Soc., 138,
1709–1720, 2012. a
Weisman, M. L. and Klemp, J. B.: The dependence of numerically simulated
convective storms on vertical wind shear and buoyancy, Mon. Weather Rev.,
110, 504–520, 1982. a
Weissmann, M., Göber, M., Hohenegger, C., Janjić, T., Keller, J., Ohlwein,
C., Seifert, A., Trömel, S., Ulbrich, T., Wapler, K., Bollmeyer, C., and
Deneke, H.: Initial phase of the Hans-Ertel Centre for Weather
Research – A virtual centre at the interface of basic and applied weather
and climate research, Meteor. Z., 23, 193–208, 2014. a
Wicker, L. J., Kain, J., Weiss, S., and Bright, D.: A brief description of the
supercell detection index, NOAA/SPC, Technical Report, 10 pp.,
available at:
https://www.spc.noaa.gov/exper/Spring_2005/SDI-docs.pdf (last access: 5 March 2021), 2005. a
Würsch, M. and Craig, G. C.: A simple dynamical model of cumulus
convection for data assimilation research, Meteor. Z., 23, 483–490, 2014. a
Yussouf, N., Gao, J., Stensrud, D. J., and Ge, G.: The impact of mesoscale
environmental uncertainty on the prediction of a tornadic supercell storm
using ensemble data assimilation approach, Adv. Meteor., 2013, 731647, https://doi.org/10.1155/2013/731647, 2013. a
Zeng, Y.: Applying a new integrated mass-flux adjustment filter in rapid update cycling of convective-scale data assimilation, Zenodo, https://doi.org/10.5281/zenodo.4023988, 2020. a
Zeng, Y. and Janjić, T.: Study of conservation laws with the local
ensemble transform kalman filter, Q. J. Roy. Meteor. Soc., 699,
2359–2372, 2016. a
Zeng, Y., Blahak, U., Neuper, M., and Jerger, D.: Radar Beam Tracing Methods
Based on Atmospheric Refractive Index, J. Atmos. Ocean. Tech., 31,
2650–2670, 2014. a
Zeng, Y., Blahak, U., and Jerger, D.: An efficient modular volume-scanning
radar forward operator for NWP models: description and coupling to the COSMO
model, Q. J. Roy. Meteor. Soc., 142, 3234–3256, 2016. a
Zeng, Y., Janjić, T., de Lozar, A., Blahak, U., Reich, H., Keil, C., and
Seifert, A.: Representation of model error in convective-scale data
assimilation: additive noise, relaxation methods and combinations, J. Adv.
Model. Earth Syst., 10, 2889–2911, 2018. a
Zeng, Y., Janjić, T., Sommer, M., de Lozar, A., Blahak, U., and Seifert,
A.: Representation of model error in convective-scale data assimilation:
additive noise based on model truncation error, J. Adv. Model. Earth Syst.,
11, 752–770, 2019. a
Zeng, Y., Janjić, T., de Lozar, A., Rasp, S., Blahak, U., Seifert, A., and
Craig, G. C.: Comparison of methods accounting for subgrid-scale model error
in convective-scale data assimilation, Mon. Weather Rev., 148, 2457–2477, 2020. a
Zeng, Y., Janjić, T., de Lozar, A., Welzbacher, C. A., Blahak, U., and
Seifert, A.: Assimilating radar radial wind and reflectivity data in an
idealized setup of the COSMO-KENDA system, Atmos. Res., 249, 105282, https://doi.org/10.1016/j.atmosres.2020.105282, 2021. a, b, c
Zhang, F., Snyder, C., and Sun, J.: Impacts of initial estimate and
observation availability on convective-scale data assimilation with an
ensemble Kalman filter, Mon. Weather Rev., 132, 1238–1253, 2004. a
Short summary
A new integrated mass-flux adjustment filter is introduced and examined with an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it successfully diminishes the imbalance in the analysis considerably and improves the forecasts.
A new integrated mass-flux adjustment filter is introduced and examined with an idealized setup...