Articles | Volume 28, issue 4
https://doi.org/10.5194/npg-28-615-2021
© 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/npg-28-615-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2021
Research article |
| 01 Nov 2021
| 01 Nov 2021
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
RIKEN Center for Computational Science, Kobe, Japan
Center for Oceanic and Atmospheric Research (CIMA-UBA/CONICET); Atmospheric and Oceanographic Science Department, FCEyN, University of Buenos Aires; French-Argentinean Institute for the Study of Climate and its Impacts (UMI-IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina
Guo-Yuan Lien
Research and Development Center, Central Weather Bureau, Taipei, Taiwan
Keiichi Kondo
Department of Observation and Data Assimilation Research, Meteorological Research Institute, Tsukuba, Japan
Shigenori Otsuka
RIKEN Center for Computational Science, Kobe, Japan
RIKEN Cluster for Pioneering Research, Kobe, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Takemasa Miyoshi
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, Japan
RIKEN Cluster for Pioneering Research, Kobe, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Atmospheric and Oceanic Science Department, University of Maryland, College Park, Maryland, USA
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021, https://doi.org/10.5194/npg-28-23-2021, 2021
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To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020, https://doi.org/10.5194/npg-27-187-2020, 2020
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A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
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Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019, https://doi.org/10.5194/npg-26-381-2019, 2019
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Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
Cited articles
Aksoy, A., Dowell, D. C., and Snyder, C.: A Multicase Comparative Assessment of
the Ensemble Kalman Filter for Assimilation of Radar Observations. Part
I: Storm-Scale Analyses, Mon. Weather Rev., 137, 1805–1824,
https://doi.org/10.1175/2008MWR2691.1, 2009. a
Beljaars, A. C. M. and Holtslag, A. A. M.: Flux Parameterization over Land
Surfaces for Atmospheric Models, J. Appl. Meteorol., 30,
327–341, https://doi.org/10.1175/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2, 1991. a
Bick, T., Simmer, C., Trömel, S., Wapler, K., Hendricks Franssen, H.-J.,
Stephan, K., Blahak, U., Schraff, C., Reich, H., Zeng, Y., and Potthast, R.:
Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the
convective scale, Q. J. Roy. Meteor. Soc., 142,
1490–1504, https://doi.org/10.1002/qj.2751, 2016. a
Evensen, G.: The ensemble Kalman filter for combined state and parameter
estimation, IEEE Contr. Syst. Mag., 29, 83–104,
https://doi.org/10.1109/MCS.2009.932223, 2009. a, b
Honda, T., Miyoshi, T., Lien, G.-Y., Nishizawa, S., Yoshida, R., Adachi, S. A.,
Terasaki, K., Okamoto, K., Tomita, H., and Bessho, K.: Assimilating All-Sky
Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015),
Mon. Weather Rev., 146, 213–229, https://doi.org/10.1175/MWR-D-16-0357.1, 2018. a
Honda, T., Lien, G.-Y., Amemiya, A., and Yashiro, H.: SCALE-LETKF version for npg-2021-15 manuscript (npg-2021-15), Zenodo [code], https://doi.org/10.5281/zenodo.5540380, 2021. a
Houtekamer, P. L. and Zhang, F.: Review of the Ensemble Kalman Filter for
Atmospheric Data Assimilation, Mon. Weather Rev., 144, 4489–4532,
https://doi.org/10.1175/MWR-D-15-0440.1, 2016. a
Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E., Patil, D. J.,
Sauer, T., Szunyogh, I., Yorke, J. A., and Zimin, A. V.:
Four-dimensional ensemble Kalman filtering, Tellus A, 56, 273–277, https://doi.org/10.3402/tellusa.v56i4.14424, 2004. 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,
https://doi.org/10.1016/j.physd.2006.11.008, 2007. a
Jacques, D. and Zawadzki, I.: The Impacts of Representing the Correlation of
Errors in Radar Data Assimilation. Part I: Experiments with Simulated
Background and Observation Estimates, Mon. Weather Rev., 142, 3998–4016, https://doi.org/10.1175/MWR-D-14-00104.1, 2014. a
Kawabata, T. and Ueno, G.: Non-Gaussian Probability Densities of Convection
Initiation and Development Investigated Using a Particle Filter with a
Storm-Scale Numerical Weather Prediction Model, Mon. Weather Rev., 148,
3–20, https://doi.org/10.1175/MWR-D-18-0367.1, 2020. a, b
Kondo, K. and Miyoshi, T.: Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model, Nonlin. Processes Geophys., 26, 211–225, https://doi.org/10.5194/npg-26-211-2019, 2019. a, b, c, d
Kullback, S. and Leibler, R. A.: On Information and Sufficiency, Ann. Math.
Statist., 22, 79–86, https://doi.org/10.1214/aoms/1177729694, 1951. a
Lange, H. and Craig, G. C.: The Impact of Data Assimilation Length Scales on
Analysis and Prediction of Convective Storms, Mon. Weather Rev., 142,
3781–3808, https://doi.org/10.1175/MWR-D-13-00304.1, 2014. a
Lei, J., Bickel, P., and Snyder, C.: Comparison of Ensemble Kalman Filters
under Non-Gaussianity, Mon. Weather Rev., 138, 1293–1306,
https://doi.org/10.1175/2009MWR3133.1, 2010. a
Lien, G.-Y., Miyoshi, T., Nishizawa, S., Yoshida, R., Yashiro, H., Adachi,
S. A., Yamaura, T., and Tomita, H.: The Near-Real-Time SCALE-LETKF System: A
Case of the September 2015 Kanto-Tohoku Heavy Rainfall, SOLA, 13, 1–6,
https://doi.org/10.2151/sola.2017-001, 2017. a, b, c
Maejima, Y. and Miyoshi, T.: Impact of the Window Length of
Four-Dimensional Local Ensemble Transform Kalman Filter: A Case of Convective
Rain Event, SOLA, 16, 37–42, https://doi.org/10.2151/sola.2020-007, 2020. a, b
Maldonado, P., Ruiz, J., and Saulo, C.: Sensitivity to Initial and Boundary
Perturbations in Convective-Scale Ensemble-Based Data Assimilation:
Imperfect-Model OSSEs, SOLA, 17, 96–102, https://doi.org/10.2151/sola.2021-015, 2021. a
Miyoshi, T., Kondo, K., and Imamura, T.: The 10,240-member ensemble Kalman
filtering with an intermediate AGCM, Geophys. Res. Lett., 41,
5264–5271, https://doi.org/10.1002/2014GL060863, 2014. a
Miyoshi, T., Kondo, K., and Terasaki, K.: Big Ensemble Data Assimilation
in Numerical Weather Prediction, Computer, 48, 15–21,
https://doi.org/10.1109/MC.2015.332, 2015. a
Miyoshi, T., Kunii, M., Ruiz, J., Lien, G.-Y., Satoh, S., Ushio,
T., Bessho, K., Seko, H., Tomita, H., and Ishikawa, Y.: “Big Data
Assimilation” Revolutionizing Severe Weather Prediction, B.
Am. Meteorol. Soc., 97, 1347–1354,
https://doi.org/10.1175/BAMS-D-15-00144.1, 2016a. a, b
Miyoshi, T., Lien, G., Satoh, S., Ushio, T., Bessho, K., Tomita,
H., Nishizawa, S., Yoshida, R., Adachi, S. A., Liao, J., Gerofi,
B., Ishikawa, Y., Kunii, M., Ruiz, J., Maejima, Y., Otsuka, S.,
Otsuka, M., Okamoto, K., and Seko, H.: “Big Data Assimilation”
Toward Post-Petascale Severe Weather Prediction: An Overview and Progress,
Proceedings of the IEEE, 104, 2155–2179, https://doi.org/10.1109/JPROC.2016.2602560,
2016b. a
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model with
Condensation Physics: Its Design and Verification, Bound.-Lay.
Meteorol., 112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98,
2004. a
National Institute of Information and Communications Technology: Phased Array Weather Radar Real-time observation data, PAWR [data set], available at: https://pawr.nict.go.jp/index_en.html, last access: 18 October 2021. a
Necker, T., Geiss, S., Weissmann, M., Ruiz, J., Miyoshi, T., and
Lien, G.-Y.: A convective-scale 1,000-member ensemble simulation and
potential applications, Q. J. Roy. Meteor.
Soc., 146, 1423–1442, https://doi.org/10.1002/qj.3744, 2020a. a, b
Necker, T., Weissmann, M., Ruckstuhl, Y., Anderson, J., and Miyoshi,
T.: Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member
Ensemble, Mon. Weather Rev., 148, 1229–1249,
https://doi.org/10.1175/MWR-D-19-0154.1, 2020b. a
Nishizawa, S., Yashiro, H., Sato, Y., Miyamoto, Y., and Tomita, H.: Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations, Geosci. Model Dev., 8, 3393–3419, https://doi.org/10.5194/gmd-8-3393-2015, 2015. a
Nishizawa, S., Yashiro, H., Yamaura, T., Adachi, S. A., Yoshida R., Sato, Y., Sueki, K., Matsushima, T., Kawai, Y., and Tomita, H.: SCALE (5.4.4), Zenodo [code], https://doi.org/10.5281/zenodo.5146864, 2021. a
Ruiz, J. J., Miyoshi, T., Satoh, S., and Ushio, T.: A Quality Control Algorithm
for the Osaka Phased Array Weather Radar, SOLA, 11, 48–52,
https://doi.org/10.2151/sola.2015-011, 2015. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B.
Am. Meteorol. Soc., 91, 1015–1058,
https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Sekiguchi, M. and Nakajima, T.: A k-distribution-based radiation code and its
computational optimization for an atmospheric general circulation model,
J. Quant. Spectrosc. Ra., 109, 2779–2793, https://doi.org/10.1016/j.jqsrt.2008.07.013, 2008. a
Stensrud, D. J., Wicker, L. J., Xue, M., Dawson, D. T., Yussouf, N., Wheatley,
D. M., Thompson, T. E., Snook, N. A., Smith, T. M., Schenkman, A. D., Potvin,
C. K., Mansell, E. R., Lei, T., Kuhlman, K. M., Jung, Y., Jones, T. A., Gao,
J., Coniglio, M. C., Brooks, H. E., and Brewster, K. A.: Progress and
challenges with Warn-on-Forecast, 6th European Conference
on Severe Storms 2011, Palma de Mallorca, Spain, 3–7 October 2011, Atmos. Res., 123, 2–16,
https://doi.org/10.1016/j.atmosres.2012.04.004, 2013. a
Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P.,
Onvlee-Hooimeyer, J., Joe, P., Barker, D. M., Li, P.-W., Golding, B., Xu, M.,
and Pinto, J.: Use of NWP for Nowcasting Convective Precipitation: Recent
Progress and Challenges, B. Am. Meteorol. Soc., 95,
409–426, https://doi.org/10.1175/BAMS-D-11-00263.1, 2014. a
Tomita, H.: New Microphysical Schemes with Five and Six Categories by
Diagnostic Generation of Cloud Ice, J. Meteorol. Soc.
Jpn. Ser. II, 86A, 121–142, https://doi.org/10.2151/jmsj.86A.121, 2008. a
Whitaker, J. S. and Hamill, T. M.: Evaluating Methods to Account for System
Errors in Ensemble Data Assimilation, Mon. Weather Rev., 140,
3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
a
Yoshikawa, E., Ushio, T., Kawasaki, Z., Yoshida, S., Morimoto, T.,
Mizutani, F., and Wada, M.: MMSE Beam Forming on Fast-Scanning Phased
Array Weather Radar, IEEE T. Geosci. Remote, 51,
3077–3088, https://doi.org/10.1109/TGRS.2012.2211607, 2013. a
Short summary
Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Effective use of observations with numerical weather prediction models, also known as data...
