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Segregation of Forecast Errors in the Planetary Boundary Layer Parameterization Over the State of Odisha and Neighboring Regions in India During Summer Monsoon Season

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Abstract

Planetary boundary layer parametrization (PBL) is a key factor influencing weather forecast skills. This article provides a quantitative illustration of the segregation of forecast errors and their growth arising from different PBLs of the Weather Research and Forecasting (WRF) model. The systematic (root mean square) components of forecast errors arising from four different PBLs of the WRF over the state of Odisha (India) and its surrounding regions are elucidated. Error characterizations are carried out for the forecast lead time up to 96 h (day-4) for two contrasting monsoon seasons, i.e., 2013 (normal) and 2014 (deficit). A total of 1112 simulations are carried out for each initial condition, i.e., May 15 to September 29 for both monsoon seasons using four PBL schemes, i.e., Yonsei University (YSU), Mellor–Yamada–Nakanishi-Niino (MYNN), Asymmetric Convective Model version 2 (ACM2), and medium-range forecast (MRF). The overall results suggest the errors in thermodynamical variables (i.e., temperature and relative humidity) are large compared to the dynamical variable (i.e., wind). For the normal monsoon year (i.e., 2013), the MRF and MYNN exhibit the lowest root mean square error (RMSE) of temperature compared to ACM2 and YSU, whereas MRF (MYNN) shows the lowest (highest) error growth at 24–48 h (72–96 h). The deficit year (2014) has a higher temperature error compared to that of the normal monsoon year (2013), which might be due to frequent monsoon break periods. The spatial distribution of wind exhibits the lowest systematic error for MRF with lead time up to 48 h. The subsequent decrease (increase) of convergence (divergence) of error flux over northern Odisha and increase (decrease) of the same over southern Odisha suggests that the error propagation occurs from north to south. In general, both the convergence and divergence of error energy are found to be weak in MRF and MYNN, attributable to the lower error growth rate and hence the smaller systematic errors compared to ACM2 and YSU for both these monsoon seasons. It is also found that the systematic component of the linear (nonlinear) error growth rate is contributed by the physics (dynamics) components of the model. These findings will provide guidance for the model community and operational agencies to make an optimal choice of PBL parameterizations, particularly for the monsoon forecast.

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References

  • Berrisford, P., Dee, D., Fielding, K., Fuentes, M., Kallberg, P., Kobayashi, S., Uppala, S. (2011). The ERA-Interim archive version 2.0. ERA Technical Report Series. 1, 2(Tech. Rep.), 23. Retrieved from http://www.ecmwf.int/publications/library/do/references/list/782009

  • Bessac, J., Monahan, A. H., Christensen, H. M., & Weitzel, N. (2019). Stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes. Monthly Weather Review, 147(5), 1447–1469. https://doi.org/10.1175/MWR-D-18-0384.1.

    Article  Google Scholar 

  • Boer, G. J. (1993). Systematic and random error in an extended-range forecasting experiment. Monthly Weather Review. https://doi.org/10.1175/1520-0493(1993)121%3c0173:SAREIA%3e2.0.CO;2.

    Article  Google Scholar 

  • Collins, W. D., et al. (2004). Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN–464+STR. p 214.

  • Craig, G. C., Cohen, B. G. (2006). Fluctuations in an equilibrium convective ensemble. Part I: Theoretical formulation. Journal of the Atmospheric Sciences, 63(8), 1996–2004. https://doi.org/https://doi.org/10.1175/JAS3709.1

  • De, S. (2010). Role of non-linear scale interactions in limiting dynamical prediction of lower tropospheric boreal summer intraseasonal oscillations. Journal of Geophysical Research Atmospheres, 115(21), 1–18. https://doi.org/10.1029/2010JD013955.

    Article  Google Scholar 

  • Foroutan, H., Young, J., Napelenok, S., Ran, L., Appel, K., Gilliam, R., & Pleim, J. (2017). Journal of Advances in Modeling Earth Systems. Journal of Advances in Modeling Earth Systems, 9, 585–606. https://doi.org/10.1002/2013MS000282.Received.

    Article  Google Scholar 

  • Gadgil, S., Gadgil, S. (2006). The Indian monsoon, GDP and agriculture. Economic & Political Weekly, 41(November 25), 4887–4895. https://doi.org/https://doi.org/10.2307/4418949

  • Garratt, J. R. (1994). Review: the atmospheric boundary layer. Earth Science Reviews, 37(1–2), 89–134. https://doi.org/10.1016/0012-8252(94)90026-4.

    Article  Google Scholar 

  • Gentine, P., Betts, A. K., Lintner, B. R., Findell, K. L., Van Heerwaarden, C. C., Tzella, A., D’Andrea, F. (2013). A probabilistic bulk model of coupled mixed layer and convection. Part I: Clear-sky case. Journal of the Atmospheric Sciences, 70(6), 1543–1556. https://doi.org/https://doi.org/10.1175/JAS-D-12-0145.1

  • Hawkins, E., & Sutton, R. (2009). Decadal predictability of the Atlantic Ocean in a coupled GCM: Forecast skill and optimal perturbations using linear inverse modeling. Journal of Climate, 22(14), 3960–3978. https://doi.org/10.1175/2009JCLI2720.1.

    Article  Google Scholar 

  • Hazra, V., & Pattnaik, S. (2019). Systematic errors in the WRF model planetary boundary layer schemes for two contrasting monsoon seasons over the state of Odisha and its neighborhood region. Theoretical and Applied Climatology, 139(3–4), 1079–1096. https://doi.org/10.1007/s00704-019-03023-3.

    Article  Google Scholar 

  • Heckley, W. A. (1982). On the performance of the ECMWF model in the tropics, Workshop on Intercomparison of Large-scale Models used for Extended Range Forecasts, 30 June - 2 July 1982. https://www.ecmwf.int/node/9819

  • Hong, S. Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134(9), 2318–2341. https://doi.org/10.1175/MWR3199.1.

    Article  Google Scholar 

  • Hong, S. Y., & Pan, H. L. (1996). Non-local boundary layer vertical diffusion in a medium-range forecast model. Monthly Weather Review. https://doi.org/10.1175/1520-0493(1996)124%3c2322:NBLVDI%3e2.0.CO;2.

    Article  Google Scholar 

  • Hong, S. Y., & Jang, J. H. (2018). Impacts of shallow convection processes on a simulated Boreal summer climatology in a global atmospheric model. Asia-Pacific Journal of Atmospheric Sciences, 54(Suppl 1), 361.

    Article  Google Scholar 

  • Hong, S. Y., & Lim, J. O. J. (2006). The WRF single–moment 6–class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129–151.

    Google Scholar 

  • Hu, X. M., Nielsen-Gammon, J. W., & Zhang, F. (2010). Evaluation of three planetary boundary layer schemes in the WRF model. Journal of Applied Meteorology and Climatology, 49(9), 1831–1844. https://doi.org/10.1175/2010JAMC2432.1.

    Article  Google Scholar 

  • Huang, X., Zhou, T., Turner, A., Dai, A., Chen, X., Clark, R., & Zou, L. (2020). The recent decline and recovery of Indian summer monsoon rainfall: Relative roles of external forcing and internal variability. Journal of Climate, 33(12), 5035–5060. https://doi.org/10.1175/jcli-d-19-0833.1.

    Article  Google Scholar 

  • Kain, J. S. (2004). The Kain-Fritsch convective parameterization: An update. Journal of Applied Meteorology, 43, 170–181.

    Article  Google Scholar 

  • Kanamitsu, M. (1985). A Study of the Predictability of the ECMWF Operational Forecast Model in the Tropics. Journal of the Meteorological Society of Japan. Ser. II, 63(5), 779–804. https://doi.org/https://doi.org/10.2151/jmsj1965.63.5_779

  • Khouider, B., Biello, J., & Majda, A. J. (2010). A stochastic multicloud model for tropical convection. Communications in Mathematical Sciences, 8(1), 187–216. https://doi.org/10.4310/cms.2010.v8.n1.a10.

    Article  Google Scholar 

  • Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., & Wood, E. F. (2014). The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4), 585–601. https://doi.org/10.1175/BAMS-D-12-00050.1.

    Article  Google Scholar 

  • Krishnamurti, T. N., Molinari, J., & Pan, H. L. (1976). Numerical simulation of the Somali jet. Journal of the Atmospheric Sciences. https://doi.org/10.1175/1520-0469(1976)033%3c2350:NSOTSJ%3e2.0.CO;2.

    Article  Google Scholar 

  • Krishnamurti, T. N., Pasch, R., Kitade, T. (1983). WGNE forecast comparison experiments. WCRP Rept. No. 6, WMO.

  • Leutbecher, M., Lock, S. J., Ollinaho, P., Lang, S. T. K., Balsamo, G., Bechtold, P., & Weisheimer, A. (2017). Stochastic representations of model uncertainties at ECMWF: State of the art and future vision. Quarterly Journal of the Royal Meteorological Society, 143(707), 2315–2339. https://doi.org/10.1002/qj.3094.

    Article  Google Scholar 

  • Li, C. H., Berner, J., Hong, J. S., Fong, C. T., & Kuo, Y. H. (2020). The Taiwan WRF ensemble prediction system: Scientific description, model-error representation and performance results. Asia-Pacific Journal of Atmospheric Sciences, 56(1), 1–15. https://doi.org/10.1007/s13143-019-00127-8.

    Article  Google Scholar 

  • Lorenz, E. N. (1969). The predictability of a flow which possesses many scales of motion. Tellus, 21(3), 289–307. https://doi.org/10.3402/tellusa.v21i3.10086.

    Article  Google Scholar 

  • Lorenz, E. N. (1969). Three approaches to atmospheric predictability. Bulletin American Meteorological Society. https://doi.org/10.1175/1520-0469(1969)26%3c636:APARBN%3e2.0.CO;2.

    Article  Google Scholar 

  • Mishra, V., Smoliak, B. V., Lettenmaier, D. P., & Wallace, J. M. (2012). A prominent pattern of year-to-year variability in Indian Summer Monsoon Rainfall. Proceedings of the National Academy of Sciences of the United States of America, 109(19), 7213–7217. https://doi.org/10.1073/pnas.1119150109.

    Article  Google Scholar 

  • Nakanishi, M., & Niino, H. (2009). Development of an improved turbulence closure model for the atmospheric boundary layer. Journal of the Meteorological Society of Japan, 87(5), 895–912. https://doi.org/10.2151/jmsj.87.895.

    Article  Google Scholar 

  • NCEP (2000). National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2000, updated daily. NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research. Computational and Information Systems Laboratory. https://doi.org/10.5065/D6M043C6.

  • Nie, J., & Kuang, Z. (2012). Responses of shallow cumulus convection to large-scale temperature and moisture perturbations: A comparison of large-eddy simulations and a convective parameterization based on stochastically entraining parcels. Journal of the Atmospheric Sciences, 69(6), 1936–1956. https://doi.org/10.1175/JAS-D-11-0279.1.

    Article  Google Scholar 

  • Ollinaho, P., Lock, S. J., Leutbecher, M., Bechtold, P., Beljaars, A., Bozzo, A., & Sandu, I. (2017). Towards process-level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble. Quarterly Journal of the Royal Meteorological Society, 143(702), 408–422. https://doi.org/10.1002/qj.2931.

    Article  Google Scholar 

  • Paulson, C. A. (1970). The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology, 9, 857–861.

    Article  Google Scholar 

  • Plant, R. S., & Craig, G. C. (2008). A stochastic parameterization for deep convection based on equilibrium statistics. Journal of the Atmospheric Sciences, 65(1), 87–105. https://doi.org/10.1175/2007JAS2263.1.

    Article  Google Scholar 

  • Pleim, J. E. (2007). A combined local and non-local closure model for the atmospheric boundary layer. Part I: Model description and testing. Journal of Applied Meteorology and Climatology, 46(9), 1383–1395. https://doi.org/https://doi.org/10.1175/JAM2539.1

  • Romps, D. M., & Kuang, Z. (2010). Nature versus nurture in shallow convection. Journal of the Atmospheric Sciences, 67(5), 1655–1666. https://doi.org/10.1175/2009JAS3307.1.

    Article  Google Scholar 

  • Rougier, J., Sexton, D. M. H., Murphy, J. M., & Stainforth, D. (2009). Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. Journal of Climate, 22(13), 3540–3557. https://doi.org/10.1175/2008JCLI2533.1.

    Article  Google Scholar 

  • Samanta, D., Dash, M. K., Goswami, B. N., & Pandey, P. C. (2016). Extratropical anticyclonic Rossby wave breaking and Indian summer monsoon failure. Climate Dynamics, 46(5–6), 1547–1562. https://doi.org/10.1007/s00382-015-2661-7.

    Article  Google Scholar 

  • Singh, D., Ghosh, S., Roxy, M. K., & McDermid, S. (2019). Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings. Wiley Interdisciplinary Reviews: Climate Change, 10(2), 1–35. https://doi.org/10.1002/wcc.571.

    Article  Google Scholar 

  • Skamarock, W. C., Klemp, J. B., Dudhia, J. B., Gill, D. O., Barker, D. M., Duda, M. G., Powers, J. G. (2008). A description of the Advanced Research WRF Version 3, NCAR Technical Note TN-475+STR. Technical Report, (June), 113. https://doi.org/https://doi.org/10.5065/D68S4MVH

  • Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., & Allen, M. R. (2005). Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433(7024), 403–406. https://doi.org/10.1038/nature03301.

    Article  Google Scholar 

  • Stull, R. (1988). An introduction to boundary layer meteorology. London: Kluwer Acad. Publ., Boston.

    Book  Google Scholar 

  • Sugi, M., Kanamitsu, M. (1982). A Study of a Subtropical Upper Level Cyclone Using JMA Operational Forecast Model. Journal of the Meteorological Society of Japan. Ser. II, 60(4), 932–946. https://doi.org/https://doi.org/10.2151/jmsj1965.60.4_932

  • Sušelj, K., Hogan, T. F., & Teixeira, J. (2014). Implementation of a stochastic eddy-diffusivity/mass-flux parameterization into the Navy Global environmental model. Weather and Forecasting, 29(6), 1374–1390. https://doi.org/10.1175/WAF-D-14-00043.1.

    Article  Google Scholar 

  • Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., & Cuenca, R. H. (2004). Implementation and verification of the unified NOAH land surface model in the WRF model. In 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, pp. 11–15

  • Turner, A. G., & Annamalai, H. (2012). Climate change and the South Asian summer monsoon. Nature Climate Change, 2(8), 587–595. https://doi.org/10.1038/nclimate1495.

    Article  Google Scholar 

  • Wang, P. X., Wang, B., Cheng, H., Fasullo, J., Guo, Z. T., Kiefer, T., Liu, Z. Y. (2017). The global monsoon across time scales: Mechanisms and outstanding issues. Earth-Science Reviews, 174(July 2016), 84–121. https://doi.org/https://doi.org/10.1016/j.earscirev.2017.07.006

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Acknowledgments

The authors would like to thank the Indian Institute of Tropical Meteorology Pune under the Ministry of Earth System Sciences, Govt. of India, and Indian Institute of Technology Bhubaneswar for providing us all the research facilities and helpful assistance required for this purpose. We also thankfully acknowledge the Indian Space Research Organization (ISRO), Department of Science and Technology, Government of India, and the Scientific and Engineering Research Board (SERB) for providing financial aid in terms of research grants (RP-063, RP-132, RP-193) for carrying out this work. The authors thankfully acknowledge the visualization tool GrADS (Grid Analysis and Display System), which is a free software by the Linux community, the National Centre for Environment Prediction (NCEP-FNL), and the European Centre for Medium-Range Weather Forecasting (ECMWF) for providing us all those data sets required for the initialization and validation of the WRF model.

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Hazra, V., Pattnaik, S., De, S. et al. Segregation of Forecast Errors in the Planetary Boundary Layer Parameterization Over the State of Odisha and Neighboring Regions in India During Summer Monsoon Season. Pure Appl. Geophys. 178, 583–601 (2021). https://doi.org/10.1007/s00024-020-02651-5

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