1,285
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometre-scale ensemble data assimilation system

ORCID Icon, &
Pages 1-14 | Received 06 Dec 2019, Accepted 21 Apr 2020, Published online: 19 May 2020

References

  • Ancell, B. C., Kashawlic, E. and Schroeder, J. L. 2015. Evaluation of wind forecasts and observation impacts from variational and ensemble data assimilation for wind energy applications. Mon. Weather Rev. 143, 3230–3245. doi:10.1175/MWR-D-15-0001.1
  • Anderson, J. L. and Anderson, S. L. 1999. A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Weather Rev. 127, 2741–2758. doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2
  • Baas, P. and Bosveld, F. 2010. Assimilation of Cabauw boundary layer observations in an atmospheric single-column model using an ensemble-Kalman filter. Technical report, Royal Netherlands Meteorological Institute, Netherlands.
  • Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M. and co-authors. 2011. Operational convective-scale numerical weather prediction with the COSMO model: description and Sensitivities. Mon. Weather Rev. 139, 3887–3905. doi:10.1175/MWR-D-10-05013.1
  • Bédard, J., Laroche, S. and Gauthier, P. 2015. A geo-statistical observation operator for the assimilation of near-surface wind data. QJR Meteorol. Soc. 141, 2857–2868. doi:10.1002/qj.2569
  • Benjamin, S. G., Schwartz, B. E., Koch, S. E. and Szoke, E. J. 2004. The value of wind profiler data in US weather forecasting. Bull. Amer. Meteor. Soc. 85, 1871–1886. doi:10.1175/BAMS-85-12-1871
  • Boutle, I., Finnenkoetter, A., Lock, A. and Wells, H. 2016. The London Model: forecasting fog at 333 m resolution. QJR Meteorol. Soc. 142, 360–371. doi:10.1002/qj.2656
  • Brümmer, B., Lange, I. and Konow, H. 2012. Atmospheric boundary layer measurements at the 280 m high Hamburg weather mast 1995-2011: mean annual and diurnal cycles. Metz. 21, 319–335. doi:10.1127/0941-2948/2012/0338
  • Bundesanstalt fuer Geowissenschaften und Rohstoffe. 2016. Bodenuebersichtskarte.
  • Buzzi, M. 2008. Challenges in operational numerical weather prediction at high resolution in complex terrain. Doctoral thesis. ETH Zurich.
  • Buzzi, M., Rotach, M. W., Holtslag, M. and Holtslag, A. A. 2011. Evaluation of the COSMO-SC turbulence scheme in a shear-driven stable boundary layer. Metz. 20, 335–350. doi:10.1127/0941-2948/2011/0050
  • Cerenzia, I. 2017. Challenges and critical aspects in stable boundary layer representation in numerical weather prediction modeling: diagnostic analyses and proposals for improvement. PhD thesis. Alma Mater Studiorum Università di Bologna.
  • Declair, S., Stephan, K. and Potthast, R. 2015. On the improvement of numerical weather prediction by assimilation of hub height wind information in convection-resulted models. In: EGU General Assembly Conference Abstracts, Vol. 17, Vienna, Austria, 9451.
  • Desroziers, G., Berre, L., Chapnik, B. and Poli, P. 2005. Diagnosis of observation, background and analysis-error statistics in observation space. QJR Meteorol. Soc. 131, 3385–3396. doi:10.1256/qj.05.108
  • Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R. and co-authors. 2007. The shuttle radar topography mission. Rev. Geophys. 45, RG2004, doi:10.1029/2005RG000183
  • Gaspari, G. and Cohn, S. E. 1999. Construction of correlation functions in two and three dimensions. QJR Meteorol Soc. 125, 723–757. doi:10.1002/qj.49712555417
  • Harlim, J. and Hunt, B. R. 2007. Four-dimensional local ensemble transform Kalman filter: numerical experiments with a global circulation model. Tellus A 59, 731–748. doi:10.1111/j.1600-0870.2007.00255.x
  • Hasager, C. B., Stein, D., Courtney, M., Peña, A., Mikkelsen, T. and co-authors. 2013. Hub height ocean winds over the north sea observed by the NORSEWInD Lidar Array: measuring techniques, quality control and data management. Remote Sens. 5, 4280–4303. doi:10.3390/rs5094280
  • Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O. and co-authors. 2017. Large-eddy simulations over Germany using ICON: a comprehensive evaluation. QJR Meteorol. Soc. 143, 69–100. doi:10.1002/qj.2947
  • Herzog, H.-J., Vogel, G. and Schubert, U. 2002. LLM – a nonhydrostatic model applied to high-resolving simulations of turbulent fluxes over heterogeneous terrain. Theor. Appl. Climatol. 73, 67–86. doi:10.1007/s00704-002-0694-4
  • Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M. and co-authors. 2005. Atmospheric data assimilation with an ensemble Kalman filter: results with real observations. Mon. Weather Rev. 133, 604–620. doi:10.1175/MWR-2864.1
  • Houtekamer, P. L. and Zhang, F. 2016. Review of the ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 144, 4489–4532. doi:10.1175/MWR-D-15-0440.1
  • Hu, H., Sun, J. and Zhang, Q. 2017. Assessing the impact of surface and wind profiler data on fog forecasting using WRF 3DVAR: an OSSE study on a dense fog event over North China. J. Appl. Meteorol. Climatol. 56, 1059–1081. doi:10.1175/JAMC-D-16-0246.1
  • Hunt, B., Kalnay, E., Kostelich, E., Ott, E., Patil, D. and co-authors. 2004. Four-dimensional ensemble Kalman filtering. Tellus A 56, 273–277. doi:10.3402/tellusa.v56i4.14424
  • Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230, 112–126. doi:10.1016/j.physd.2006.11.008
  • Ingleby, B. 2015. Global assimilation of air temperature, humidity, wind and pressure from surface stations. QJR Meteorol. Soc. 141, 504–517. doi:10.1002/qj.2372
  • Keil, M., Bock, M. and Esch, T. ( 2011. ). CORINE Land Cover 2006 - Europaweit harmonisierte Aktualisierung der Landbedeckungsdaten für Deutschland. Technical report, Umweltbundesamt.
  • Kleczek, M. A., Steeneveld, G.-J. and Holtslag, A. A. M. 2014. Evaluation of the weather research and forecasting mesoscale model for GABLS3: impact of boundary-layer schemes. Boundary-Layer Meteorol. 152, 213–243. doi:10.1007/s10546-014-9925-3
  • Miyoshi, T. 2005. Ensemble Kalman filter experiments with a primitive-equation global model. PhD thesis, University of Maryland.
  • Mylonas, M., Barbouchi, S., Herrmann, H. and Nastos, P. 2018. Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea. Renewable Energy 120, 446–456. doi:10.1016/j.renene.2017.12.088
  • NASA JPL. 2013. NASA Shuttle Radar Topography Mission Global 3 arc second [Data set].
  • Park, S.-Y., Lee, H.-W., Lee, S.-H. and Kim, D.-H. 2010. Impact of wind profiler data assimilation on wind field assessment over coastal areas. Asian J. Atmos. Environ. 4, 198–210. doi:10.5572/ajae.2010.4.3.198
  • Rostkier-Edelstein, D. and Hacker, J. P. 2013. Impact of flow dependence, column covariance, and forecast model type on surface-observation assimilation for probabilistic PBL profile nowcasts. Wea. Forecasting 28, 29–54. doi:10.1175/WAF-D-12-00043.1
  • Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K. and co-authors. 2016. Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). QJR Meteorol. Soc. 142, 1453–1472. doi:10.1002/qj.2748
  • Schrodin, R. and Heise, E. 2001. The Multi-Layer Version of the DWD Soil Model TERRA-LM. Technical Report 2, Deutscher Wetterdienst.
  • St-James, J. S. and Laroche, S. 2005. Assimilation of wind profiler data in the Canadian Meteorological Centre Analysis Systems. J. Atmos. Oceanic Technol. 22, 1181–1194. doi:10.1175/JTECH1765.1
  • Steiner, A., Köhler, C., V. and Schumann, J. 2014. EWeLiNE and ORKA: improving model physics for renewable energy. COSMO User Seminar 2014, Offenbach, 15.
  • Stull, R. B., eds. 1988. An Introduction to Boundary Layer Meteorology. Atmospheric and Oceanographic Sciences Library. Springer, Dordrecht.
  • van Stratum, B. J. H. and Stevens, B. 2015. The influence of misrepresenting the nocturnal boundary layer on idealized daytime convection in large-eddy simulation. J. Adv. Model. Earth Syst. 7, 423–436. doi:10.1002/2014MS000370
  • Wagner, R., Jørgensen, H. E., Paulsen, U. S., Larsen, T. J., Antoniou, I. and co-authors. 2008. Remote sensing used for power curves. IOP Conf. Ser: Earth Environ. Sci. 1, 012059. doi:10.1088/1755-1315/1/1/012059
  • Winkler, J., Denhard, M., Frank, H., Rhodin, A., Anlauf, H. and co-authors. 2018. ICON-EPS: the operational global ensemble forecasting system of DWD. In: EGU General Assembly Conference Abstracts, Vol. 20, Vienna, Austria, 13813.
  • Yang, S.-C., Kalnay, E., Hunt, B. and Bowler, N. E. 2009. Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter. QJR Meteorol. Soc. 135, 251–262. doi:10.1002/qj.353