1 Effect of Scale-Aware Planetary Boundary Layer Schemes on Tropical 2 Cyclone Intensification and Structural Changes in the Gray Zone 3 Xiaomin Chen 1* , Ming Xue 2 , Bowen Zhou 1 , Juan Fang 1 , Jun A. Zhang 3, 4 , and Frank D. Marks 3 4 1 Key Laboratory for Mesoscale Severe Weather, Ministry of Education, and School of 5 Atmospheric Sciences, Nanjing University, Nanjing, China 6 2 Center for Analysis and Prediction of Storms, and School of Meteorology, University of 7 Oklahoma, Norman, Oklahoma 8 3 NOAA/AOML Hurricane Research Division, Miami, Florida 9 4 Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, Florida 10 11 12 13 Submitted to Monthly Weather Review 14 Revised by April 6, 2021 15 16 17 * Corresponding author: Dr. Xiaomin Chen. Current affiliation: NOAA/AOML Hurricane Research Division, Miami, Florida. Email: [email protected]. Manuscript (non-LaTeX) Click here to access/download;Manuscript (non- LaTeX);Earl_draft_2010_scale_aware_Final_submit_r3.docx
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Effect of Scale-Aware Planetary Boundary Layer Schemes on Tropical 2 Cyclone Intensification and Structural Changes in the Gray Zone 3
Xiaomin Chen1*, Ming Xue2, Bowen Zhou1, Juan Fang1, Jun A. Zhang3, 4, and Frank D. Marks3 4
1Key Laboratory for Mesoscale Severe Weather, Ministry of Education, and School of 5 Atmospheric Sciences, Nanjing University, Nanjing, China 6
2Center for Analysis and Prediction of Storms, and School of Meteorology, University of 7 Oklahoma, Norman, Oklahoma 8
3NOAA/AOML Hurricane Research Division, Miami, Florida 9 4Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, Florida 10
11
12
13
Submitted to Monthly Weather Review 14
Revised by April 6, 2021 15
16
17
* Corresponding author: Dr. Xiaomin Chen. Current affiliation: NOAA/AOML Hurricane Research Division, Miami, Florida. Email: [email protected].
Manuscript (non-LaTeX) Click here to access/download;Manuscript (non-LaTeX);Earl_draft_2010_scale_aware_Final_submit_r3.docx
including nonlocal terms, i.e., the YSU and Shin-Hong (SH) schemes, are used in these 454
simulations. SH includes the parameterization of scale dependency of the subgrid-scale (SGS) 455
turbulence flux in the gray zone and is considered a scale-aware variation of the more traditional 456
YSU. The essence of the scale-awareness in SH is to reduce the SGS vertical turbulence mixing 457
in the gray zone by multiplying a function of dimensionless grid spacing (i.e., the horizontal grid 458
spacing normalized by the boundary layer depth), since more turbulent fluxes can be explicitly 459
resolved at finer horizontal resolutions. 460
21
Results show that the experiments using SH and YSU (i.e., CTL-SH and CTL-YSU, 461
respectively) are capable of reproducing the rapid intensification (RI) of Earl at the gray-zone 462
resolutions. However, the CTL-SH TC undergoes a faster intensification during the RI period and 463
reaches a much stronger intensity after RI than the CTL-YSU TC. Additionally, the contraction of 464
the radius of maximum wind (RMW) in CTL-SH is faster preceding RI onset, and the inner-core 465
size remains smaller during the RI period. 466
Further analysis reveals that the scale-awareness starts to play a role as the diagnosed 467
boundary layer height increases to a scale comparable with the sub-kilometer horizontal grid 468
spacing. The scale-aware effect is most prominent through the early stage to the middle of RI, 469
when nonlocal turbulent fluxes are substantially reduced due to the effect of parameterized 470
convective rolls. In the late RI and subsequent eyewall replacement, the scale-aware effect 471
dwindles as the nonlocal scale-aware coefficients increase. This is mainly due to the rapid 472
increase in the surface frictional velocity during RI such that the large ratio of π’β/π€β (>0.65) 473
becomes unfavorable for the βconvective roll formationβ, as is parameterized in the scale-aware 474
coefficients for nonlocal turbulent fluxes in SH. Additionally, the higher boundary layer height in 475
the non-precipitation region ahead of the storm and radially outward of the convective rainband 476
contributes to the smaller scale-aware coefficient and thereby more notable reduction in the SGS 477
turbulent fluxes. 478
While both the scale awareness and different parameterization of the nonlocal turbulent heat 479
flux in SH reduce the vertical turbulent mixing, the scale awareness plays a dominant role in 480
reducing the TC inner core size and increasing the TC intensity. The reduced vertical mixing 481
induces stronger radial inflow and helps retain more water vapor in the lower boundary layer. The 482
resulting stronger moisture convergence and convective diabatic heating closer to the TC center 483
22
benefit faster RMW contraction before RI onset and higher intensification rates during RI. 484
Additional sensitivity experiments that switch the PBL scheme at RI onset confirm that SH tends 485
to produce a stronger TC with a smaller RMW during the RI period than YSU, while the vortex 486
structure at RI onset is the controlling factor in the intensification rate during RI. 487
To our knowledge, this study presents a first look into the effect of a scale-aware PBL 488
scheme on the TC intensity and structural evolution in the gray zone. As model grid spacings 489
keep decreasing, results in this study can provide guidance for physics development of global and 490
regional models for TC forecast purposes. Recognizing that the existing scale-aware PBL 491
schemes are generally developed in the context of non-TC conditions, we hope this study will 492
promote interests and attention toward the PBL scheme development for the TC boundary layer, 493
which is quite different from the traditional continental convective boundary layer due to its 494
predominance of shear-driven turbulence mechanisms in the lower-to-middle boundary layer 495
(Bryan et al. 2017) as well as the effect of strong rotation on the boundary layer dynamics 496
(Eliassen 1971; Kepert 2001) and turbulence characteristics (Cione et al. 2020). Last, we should 497
note that this study is based on a single case and limited model physics configurations; similar 498
comparisons should be performed with more cases and with different model configurations to test 499
the robustness of the results. This is a topic for future studies. 500
501
Acknowledgments: This study had been supported by the National Key R&D Program of China 502
under Grant 2017YFC1501601; the Natural Science Foundation of China Grants 41775056. The 503
authors want to acknowledge Drs. Gus Alaka and Xuejin Zhang for their suggestions to improve 504
the early version of this manuscript. The authors are also grateful for the helpful comments from 505
two anonymous reviewers. The first author, Xiaomin Chen, is currently supported by the NRC 506
23
Research Associateship Programs. Numerical simulations were performed at the High 507
Performance Computing Center (HPCC) of Nanjing University. 508
24
REFERENCES 509
Aksoy, A., S. Lorsolo, T. Vukicevic, K. J. Sellwood, S. D. Aberson, and F. Zhang, 2012: The 510 HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for high-resolution 511 data: The impact of airborne Doppler radar observations in an OSSE. Mon. Wea. Rev., 512 140, 1843-1862. 513
Arakawa, A., J. H. Jung, and C. M. Wu, 2011: Toward unification of the multiscale modeling of 514 the atmosphere. Atmos. Chem. Phys., 11, 3731-3742. 515
Biswas, M. K., and Coauthors, 2020: Evaluation of the GrellβFreitas convective scheme in the 516 Hurricane Weather Research and Forecasting (HWRF) model. Wea. Forcasting, 35, 1017-517 1033. 518
Boutle, I. A., J. E. J. Eyre, and A. P. Lock, 2014: Seamless stratocumulus simulation across the 519 turbulent gray zone. Mon. Wea. Rev., 142, 1655-1668. 520
Braun, S. A., and W.-K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob 521 (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128, 3941-3961. 522
Brown, B. R., M. M. Bell, and A. J. Frambach, 2016: Validation of simulated hurricane drop size 523 distributions using polarimetric radar. Geophys. Res. Lett., 43, 910-917. 524
Bryan, G. H., R. P. Worsnop, J. K. Lundquist, and J. A. Zhang, 2017: A simple method for 525 simulating wind profiles in the boundary layer of tropical cyclones. Bound.-Layer 526 Meteor., 162, 475-502. 527
Cangialosi, J. P., 2010: Tropical cyclone report: Hurricane Earl, 25 Augustβ4 September 2010. 528 NOAA/NHC Tech. Rep. , AL072010, 29 pp. [Available online at 529 https://www.nhc.noaa.gov/data/tcr/AL072010_Earl.pdf.]. 530
Carrasco, C. A., C. W. Landsea, and Y.-L. Lin, 2014: The influence of tropical cyclone size on its 531 intensification. Wea. Forecasting, 29, 582-590. 532
Chen, H., and S. G. Gopalakrishnan, 2015: A study on the asymmetric rapid intensification of 533 Hurricane Earl (2010) using the HWRF system. J. Atmos. Sci., 72, 531-550. 534
Chen, X., M. Xue, and J. Fang, 2018: Rapid intensification of Typhoon Mujigae (2015) under 535 different sea surface temperatures: Structural changes leading to rapid intensification. J. 536 Atmos. Sci., 75, 4313-4335. 537
Chen, X., J.-F. Gu, J. A. Zhang, F. D. Marks, R. F. Rogers, and J. J. Cione, 2021: Boundary layer 538 recovery and precipitation symmetrization preceding rapid intensification of tropical 539 cyclones under shear. J. Atmos. Sci, DOI. 10.1175/JAS-D-20-0252.1 540
Chen, X., J. A. Zhang, and F. D. Marks, 2019: A thermodynamic pathway leading to rapid 541 intensification of tropical cyclones in shear. Geophys. Res. Lett., 46, 9241-9251. 542
Ching, J., R. Rotunno, M. LeMone, A. Martilli, B. Kosovic, P. A. Jimenez, and J. Dudhia, 2014: 543 Convectively induced secondary circulations in fine-grid mesoscale numerical weather 544 prediction models. Mon. Wea. Rev., 142, 3284-3302. 545
Choi, H.-J., and J.-Y. Han, 2020: Effect of scale-aware nonlocal planetary boundary layer scheme 546 on lake-effect precipitation at gray-zone resolutions. Mon. Wea. Rev., 148, 2761-2776. 547
Cione, J. J., and Coauthors, 2020: Eye of the storm: Observing hurricanes with a small unmanned 548 aircraft system. Bull. Am. Meteorol. Soc., 101, E186-E205. 549
Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane 550 WRF model. Mon. Wea. Rev., 136, 1990-2005. 551
Donelan, M. A., and Coauthors, 2004: On the limiting aerodynamic roughness of the ocean in 552 very strong winds. Geophys. Res. Lett., 31. 553
Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment 554 using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077-3107. 555
Eliassen, A., 1971: On the ekman layer in a circular vortex. Journal of the Meteorological Society 556 of Japan. Ser. II, 49A, 784-789. 557
Foster, R. C., 2005: Why rolls are prevalent in the hurricane boundary layer. J. Atmos. Sci., 62, 558 2647-2661. 559
ββ, 2009: Boundary-layer similarity under an axisymmetric, gradient wind vortex. Bound.-560 Layer Meteor., 131, 321-344. 561
Franklin, J. L., S. J. Lord, S. E. Feuer, and F. D. Marks, Jr., 1993: The kinematic structure of 562 Hurricane Gloria (1985) determined from nested analyses of dropwindsonde and doppler 563 radar data. Mon. Wea. Rev., 121, 2433-2451. 564
Gopalakrishnan, S. G., F. Marks, J. A. Zhang, X. Zhang, J.-W. Bao, and V. Tallapragada, 2013: A 565 study of the impacts of vertical diffusion on the structure and intensity of the tropical 566 cyclones using the high-resolution HWRF system. J. Atmos. Sci., 70, 524-541. 567
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit 568 treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341. 569
Honnert, R., V. Masson, and F. Couvreux, 2011: A diagnostic for evaluating the representation of 570 turbulence in atmospheric models at the kilometric scale. J. Atmos. Sci., 68, 3112-3131. 571
Hu, X.-M., M. Xue, and X. Li, 2019: The use of high-resolution sounding data to evaluate and 572 optimize non-local PBL schemes for simulating the slightly stable upper convective 573 boundary layer. Mon. Wea. Rev., 147, 2825-2841 574
Ito, J., T. Oizumi, and H. Niino, 2017: Near-surface coherent structures explored by large eddy 575 simulation of entire tropical cyclones. Scientific Reports, 7, 3798. 576
Ito, J., H. Niino, M. Nakanishi, and C.-H. Moeng, 2015: An extension of the MellorβYamada 577 model to the terra incognita zone for dry convective mixed layers in the free convection 578 regime. Bound.-Layer Meteor., 157, 23-43. 579
Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The 580 Kain-Fritsch scheme. The representation of cumulus convection in numerical models, 581 Springer, 165-170. 582
Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical 583 cyclones in the North Atlantic basin. Wea. Forcasting, 18, 1093-1108. 584
Kepert, J., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: 585 Linear theory. J. Atmos. Sci., 58, 2469-2484. 586
Kepert, J., and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone 587 core. Part II: Nonlinear enhancement. J. Atmos. Sci., 58, 2485-2501. 588
Kepert, J. D., 2010: Slab- and height-resolving models of the tropical cyclone boundary layer. 589 Part I: Comparing the simulations. Quart. J. Roy. Meteor. Soc., 136, 1686-1699. 590
ββ, 2012: Choosing a boundary layer parameterization for tropical cyclone modeling. Mon. 591 Wea. Rev., 140, 1427-1445. 592
LeMone, M. A., and Coauthors, 2010: Simulating the IHOP_2002 fair-weather CBL with the 593 WRF-ARWβNoah modeling system. Part I: Surface fluxes and cbl structure and evolution 594 along the eastern track. Mon. Wea. Rev., 138, 722-744. 595
Lilly, D. K., 1966: On the instability of Ekman boundary flow. J. Atmos. Sci., 23, 481-494. 596
26
Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud 597 microphysics scheme with prognostic cloud condensation nuclei (ccn) for weather and 598 climate models. Mon. Wea. Rev., 138, 1587-1612. 599
Marks, F. D., Jr., 1985: Evolution of the structure of precipitation in Hurricane Allen (1980). 600 Mon. Wea. Rev., 113, 909-930. 601
Miyamoto, Y., Y. Kajikawa, R. Yoshida, T. Yamaura, H. Yashiro, and H. Tomita, 2013: Deep 602 moist atmospheric convection in a subkilometer global simulation. Geophys. Res. Lett., 603 40, 4922-4926. 604
Miyamoto, Y., and D. S. Nolan, 2018: Structural changes preceding rapid intensification in 605 tropical cyclones as shown in a large ensemble of idealized simulations. J. Atmos. Sci., 75, 606 555-569. 607
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative 608 transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the 609 longwave. J. Geophys. Res., 102, 16663-16682. 610
Moeng, C.-H., and P. P. Sullivan, 1994: A comparison of shear- and buoyancy-driven planetary 611 boundary layer flows. J. Atmos. Sci., 51, 999-1022. 612
Molinari, J., J. Frank, and D. Vollaro, 2013: Convective bursts, downdraft cooling, and boundary 613 layer recovery in a sheared tropical storm. Mon. Wea. Rev., 141, 1048-1060. 614
Montgomery, M. T., J. A. Zhang, and R. K. Smith, 2014: An analysis of the observed low-level 615 structure of rapidly intensifying and mature Hurricane Earl (2010). Quart. J. Roy. Meteor. 616 Soc., 140, 2132-2146. 617
Morrison, I., S. Businger, F. Marks, P. Dodge, and J. A. Businger, 2005: An observational case for 618 the prevalence of roll vortices in the hurricane boundary layer*. J. Atmos. Sci., 62, 2662-619 2673. 620
Nolan, D. S., J. A. Zhang, and D. P. Stern, 2009: Evaluation of planetary boundary layer 621 parameterizations in tropical cyclones by comparison of in situ observations and high-622 resolution simulations of Hurricane Isabel (2003). Part I: Initialization, maximum winds, 623 and the outer-core boundary layer. Mon. Wea. Rev., 137, 3651-3674. 624
Powell, M. D., P. J. Vickery, and T. A. Reinhold, 2003: Reduced drag coefficient for high wind 625 speeds in tropical cyclones. Nature, 422, 279-283. 626
Qin, N., and D.-L. Zhang, 2018: On the extraordinary intensification of Hurricane Patricia 627 (2015). Part I: Numerical experiments. Wea. Forcasting, 33, 1205-1224. 628
Riemer, M., M. T. Montgomery, and M. E. Nicholls, 2010: A new paradigm for intensity 629 modification of tropical cyclones: Thermodynamic impact of vertical wind shear on the 630 inflow layer. Atmos. Chem. Phys., 10, 3163-3188. 631
Rogers, R. F., P. D. Reasor, and J. A. Zhang, 2015: Multiscale structure and evolution of 632 Hurricane Earl (2010) during rapid intensification. Mon. Wea. Rev., 143, 536-562. 633
Reasor, P. D., M. T. Montgomery, F. D. Marks, Jr., and J. F. Gamache, 2000: Low-wavenumber 634 structure and evolution of the hurricane inner core observed by airborne dual-Doppler 635 radar. Mon. Wea. Rev., 128, 1653-1680. 636
Ren, Y., J. A. Zhang, S. R. Guimond, and X. Wang, 2019: Hurricane boundary layer height 637 relative to storm motion from gps dropsonde composites. Atmosphere, 339. 638
Shapiro, L. J., and H. E. Willoughby, 1982: The response of balanced hurricanes to local sources 639 of heat and momentum. J. Atmos. Sci., 39, 378-394. 640
27
Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in 641 convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250-271. 642
Shin, H. H., and J. Dudhia, 2016: Evaluation of pbl parameterizations in WRF at subkilometer 643 grid spacings: Turbulence statistics in the dry convective boundary layer. Mon. Wea. Rev., 644 144, 1161-1177. 645
Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for 646 weather research and forecasting applications. Journal of Computational Physics, 227, 647 3465-3485. 648
Smith, R. K., and M. T. Montgomery, 2016: The efficiency of diabatic heating and tropical 649 cyclone intensification. Quart. J. Roy. Meteor. Soc., 142, 2081-2086. 650
Smith, R. K., M. T. Montgomery, and N. Van Sang, 2009: Tropical cyclone spin-up revisited. 651 Quart. J. Roy. Meteor. Soc., 135, 1321-1335. 652
Smith, R. K., and G. L. Thomsen, 2010: Dependence of tropical-cyclone intensification on the 653 boundary-layer representation in a numerical model. Quart. J. Roy. Meteor. Soc., 136, 1671-654 1685. 655
Susca-Lopata, G., J. Zawislak, E. J. Zipser, and R. F. Rogers, 2015: The role of observed 656 environmental conditions and precipitation evolution in the rapid intensification of 657 Hurricane Earl (2010). Mon. Wea. Rev., 143, 2207-2223. 658
Sykes, R. I., and D. S. Henn, 1988: Large-eddy simulation of turbulent sheared convection. J. 659 Atmos. Sci., 46, 1106-1118. 660
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter 661 precipitation using an improved bulk microphysics scheme. Part II: Implementation of a 662 new snow parameterization. Mon. Wea. Rev., 136, 5095-5115. 663
Wu D., F. Zhang, X. Chen, A. Ryzhkov, K. Zhao, M. Kumjian, X. Chen, and P. Chan, 2021: 664 Evaluation of microphysics schemes in tropical cyclones using polarimetric radar 665 observations: Convective precipitation in an outer rainband, Mon. Wea. Rev., 666 https://doi.org/10.1175/MWR-D-19-0378.1 667
Wyngaard, J. C., 2004: Toward numerical modeling in the βterra incognitaβ. J. Atmos. Sci., 61, 668 1816-1826. 669
Xu, H., Y. Wang, and M. Wang, 2018: The performance of a scale-aware nonlocal PBL scheme 670 for the subkilometer simulation of a deep CBL over the Taklimakan Desert. Advances in 671 Meteorology, 2018, 8759594. 672
Xu, J., and Y. Wang, 2018: Effect of the initial vortex structure on intensification of a numerically 673 simulated tropical cyclone. Journal of the Meteorological Society of Japan. Ser. II, 96, 674 111-126. 675
Zhang, J. A., and R. F. Rogers, 2018: Effects of parameterized boundary layer structure on 676 hurricane rapid intensification in shear. Mon. Wea. Rev., 147, 853-871. 677
Zhang, J. A., W. M. Drennan, P. G. Black, and J. R. French, 2009: Turbulence structure of the 678 hurricane boundary layer between the outer rainbands. J. Atmos. Sci., 66, 2455-2467. 679
Zhang, J. A., F. D. Marks, M. T. Montgomery, and S. Lorsolo, 2011a: An estimation of turbulent 680 characteristics in the low-level region of intense Hurricanes Allen (1980) and Hugo 681 (1989). Mon. Wea. Rev., 139, 1447-1462. 682
Zhang, J. A., R. F. Rogers, D. S. Nolan, and F. D. Marks, 2011b: On the characteristic height 683 scales of the hurricane boundary layer. Mon. Wea. Rev., 139, 2523-2535. 684
28
Zhang, J. A., D. S. Nolan, R. F. Rogers, and V. Tallapragada, 2015: Evaluating the impact of 685 improvements in the boundary layer parameterization on hurricane intensity and structure 686 forecasts in HWRF. Mon. Wea. Rev., 143, 3136-3155. 687
Zhou, B., M. Xue, and K. Zhu, 2017: A grid-refinement-based approach for modeling the 688 convective boundary layer in the gray zone: A pilot study. J. Atmos. Sci., 74, 3497-3513. 689
Zhou, B., S. Sun, K. Yao, and K. Zhu, 2018: Reexamining the gradient and countergradient 690 representation of the local and nonlocal heat fluxes in the convective boundary layer. J. 691 Atmos. Sci., 75, 2317-2336. 692
693
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Table 1. Numerical experiments design. 694
Experiment Description
CTL-YSU YSU PBL scheme
CTL-SH Shin-Hong PBL scheme
SH-NoSA As in CTL-SH, but the scale-aware effect is turned off (i.e., πππΏ =
ππΏ = 1).
SH2YSU As in CTL-SH, the PBL scheme is switched to YSU after 1800
UTC 29 August, 2010.
YSU2SH As in CTL-YSU, the PBL scheme is switched to Shin-Hong after
1800 UTC 29 August, 2010.
lateR1-YSU As in CTL-YSU, but the simulations start 12-h later than CTL-
YSU
lateR1-SH As in CTL-SH, but the simulations start 12-h later than CTL-SH
lateR2-YSU As in CTL-YSU, but the simulations start 24-h later than CTL-
YSU
lateR2-SH As in CTL-SH, but the simulations start 24-h later than CTL-SH
695
696
30
Figure captions 697
Fig. 1. (a) Stability dependency function Ccs in the Shin-Hong PBL scheme; (b) Nonlocal scale-698 aware coefficient for momentum (black) and potential temperature (red) flux when Ccs = 1 699 (solid) or Ccs = 2 (dashed). 700
Fig. 2. Quadruple-nested domains for the simulation of Hurricane Earl (2010). The shading 701 denotes the sea surface temperature (Β°C) at 1800 UTC 26 August 2010. 702
Fig. 3. Verification of the simulated (a) track, (b) minimum SLP (hPa), and (c) 10-m maximum 703 wind speed (m sβ1). The gray, black, and red lines in each panel represent the best track data 704 from the National Hurricane Center, CTL-YSU, and CTL-SH experiment, respectively. 705
Fig. 4. Evolution of (a) maximum 10-m axisymmetric tangential wind (m sβ1), (b) 10-m radius of 706 maximum wind (km), (c) mean boundary layer height (m) in the CTL-YSU (black), CTL-707 SH (red), and SH-NoSA (green) experiments. Evolution of (d) mean local (black) and 708 nonlocal (red) scale-aware coefficients for momentum (dashed) and ΞΈ (solid), (e) mean u* 709 (m sβ1, black) and w* (m sβ1, red), and (f) mean u*/w* (red) and Ccs (black) in the CTL-SH 710 experiment. The results in (c)-(f) are averaged within r=200 km. The local scale-aware 711 coefficients in (d) are averaged within the lowest 300 m. The gray dashed line in each panel 712 denotes the time when the TC intensity of the two experiments begins to diverge. The black 713 arrow in (a) denotes the RI onset time. 714
Fig. 5. Plan view of (a) radar reflectivity averaged within the lowest 500 m and (b) boundary 715 layer height (m) at 0700 UTC 28 August 2010 for CTL-YSU experiment. (c)-(d) As in (a)-716 (b), but for CTL-SH experiment. (e)-(f) Plan view of nonlocal and local scale aware 717 coefficients for wind, respectively. The local coefficient is averaged within the lowest 300 718 m. Gray and black arrows denote the direction of storm motion and 200-850 hPa vertical 719 wind shear. Dash lines in (e)-(f) delimit the downshear and upshear semicircles. The thick 720 red circle denotes the RMW and the thin black circles denote the rings every 50 km. 721
Fig. 6. The composite radial profile of azimuthal-mean boundary layer height (left, m) and scale-722 aware coefficients (right) over 1200 UTC 27 Augustβ0600 UTC 28 August, 0600 UTC 28 723 Augustβ0000 UTC 29 August, and 0000 UTC 29 Augustβ1800 UTC 29 August, 724 respectively. In left panels, the shading represents the Β± 1 standard deviation of the 725 boundary layer height, and the black (red) line denotes CTL-YSU (CTL-SH). In right 726 panels, the PL for momentum (black) and potential temperature (red) is averaged within the 727 lowest 300 m. The dashed and solid lines denote PNL and PL, respectively. 728
Fig. 7. Radial profile of composite (a)-(c) tangential wind, (d)-(f) radial wind (m sβ1), and (g)-(i) 729 divergence (10β3 sβ1) averaged within the lowest 300 m over the same 3 periods as in Fig. 6. 730 The legend for these plots is shown in panel (c). Note the y-axis is different between (g) and 731 (h)-(i). 732
Fig. 8. Hovmoller diagram of the azimuthal-mean diabatic heating at z = 2 km (shading, K hβ1) 733 and horizontal convergence (black contours with values of β2, β1, β0.5, β0.1Γ10β3 sβ1) at z 734 = 0.25 km for (a) CTL-YSU and (b) CTL-SH. The thick white line in each panel denotes the 735 RMW at z = 0.25 km. The white dash lines delimit the three periods before RI onset. 736
Fig. 9. Vertical profile of azimuthal-mean (a) radial wind (m sβ1), (b) specific humidity (g kgβ1), 737 and (c) potential temperature (K) averaged within r =200 km over the period from 1200 738 UTC 27 August to 0600 UTC 28 August 2010. The legend for these plots is shown in (a). 739
31
Fig. 10. Evolution of the simulated (a) minimum SLP (hPa), (b) 10-m maximum wind speed (m 740 sβ1) from 1800 UTC 26 August to 1800 UTC 31 August; evolution of (c) RMW (km) and (d) 741 radius of 17 m sβ1 tangential wind (km) from 1800 UTC 29 August to 1800 UTC 31 August. 742 The legend for these plots is shown in (a). The gray shading in (a)-(b) denotes the analysis 743 period in (c)-(d). 744
Fig. 11. Evolution of (a) minimum SLP (hPa), (b) 10-m maximum wind speed (m sβ1), and 10-m 745 RMW from lateR2-YSU (black) and lateR2-SH (red) experiment. The gray line in (a)-(b) 746 denotes the best track data from the National Hurricane Center. 747
Fig. 12. Schematic of the effect of scale-awareness on TC intensification and structural changes 748 based on the comparison of simulations with the SH and YSU PBL schemes. Vmax represents 749 the maximum TC intensity, |Vr| denotes the inflow strength, and qv denotes the specific 750 humidity. 751
752 753 754
32
755
Fig. 1. (a) Stability dependency function πΆππ in the Shin-Hong PBL scheme; (b) Nonlocal scale-756 aware coefficient for momentum (black) and potential temperature (red) flux when πΆππ = 1 757 (solid) or πΆππ = 2 (dashed). 758 759
33
760
Fig. 2. Quadruple-nested domains for the simulation of Hurricane Earl (2010). The shading 761 denotes the sea surface temperature (Β°C) at 1800 UTC 26 August 2010. 762 763
34
764
Fig. 3. Verification of the simulated (a) track, (b) minimum SLP (hPa), and (c) 10-m maximum 765 wind speed (m sβ1). The gray, black, and red lines in each panel represent the best track data from 766 the National Hurricane Center, CTL-YSU, and CTL-SH experiment, respectively. 767 768
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Fig. 4. Evolution of (a) maximum 10-m axisymmetric tangential wind (m sβ1), (b) 10-m radius of 770 maximum wind (km), (c) mean boundary layer height (m) in the CTL-YSU (black), CTL-SH 771 (red), and SH-NoSA (green) experiments. Evolution of (d) mean local (black) and nonlocal (red) 772 scale-aware coefficients for momentum (dashed) and π (solid), (e) mean π’β (m sβ1, black) and π€β 773 (m sβ1, red), and (f) mean π’β/π€β (red) and πΆππ (black) in the CTL-SH experiment. The results in 774 (c)-(f) are averaged within r=200 km. The local scale-aware coefficients in (d) are averaged 775 within the lowest 300 m. The gray dashed line in each panel denotes the time when the TC 776 intensity of the two experiments begins to diverge. The black arrow in (a) denotes the RI onset 777 time. 778
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779
Fig. 5. Plan view of (a) radar reflectivity averaged within the lowest 500 m and (b) boundary 780 layer height (m) at 0700 UTC 28 August 2010 for CTL-YSU experiment. (c)-(d) As in (a)-(b), 781 but for CTL-SH experiment. (e)-(f) Plan view of nonlocal and local scale aware coefficients for 782 wind, respectively. The local coefficient is averaged within the lowest 300 m. Gray and black 783 arrows denote the direction of storm motion and 200-850 hPa vertical wind shear. Dash lines in 784 (e)-(f) delimit the downshear and upshear semicircles. The thick red circle denotes the RMW and 785 the thin black circles denote the rings every 50 km. 786 787
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788 Fig. 6. The composite radial profile of azimuthal-mean boundary layer height (left, m) and scale-789 aware coefficients (right) over 1200 UTC 27 Augustβ0600 UTC 28 August, 0600 UTC 28 790 Augustβ0000 UTC 29 August, and 0000 UTC 29 Augustβ1800 UTC 29 August, respectively. In 791 left panels, the shading represents the Β± 1 standard deviation of the boundary layer height, and 792 the black (red) line denotes CTL-YSU (CTL-SH). In right panels, the ππΏ for momentum (black) 793 and potential temperature (red) is averaged within the lowest 300 m. The dashed and solid lines 794 denote πππΏ and ππΏ, respectively. 795
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796
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Fig. 7. Radial profile of composite (a)-(c) tangential wind, (d)-(f) radial wind (m sβ1), and (g)-(i) 798 divergence (10β3 sβ1) averaged within the lowest 300 m over the same 3 periods as in Fig. 6. The 799 legend for these plots is shown in panel (c). Note the y-axis is different between (g) and (h)-(i). 800 801
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802
Fig. 8. Hovmoller diagram of the azimuthal-mean diabatic heating at z = 2 km (shading, K hβ1) 803 and horizontal convergence (black contours with values of β2, β1, β0.5, β0.1Γ10β3 sβ1) at z = 804 0.25 km for (a) CTL-YSU and (b) CTL-SH. The thick white line in each panel denotes the RMW 805 at z = 0.25 km. The white dash lines delimit the three periods before RI onset. 806 807
808
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809
Fig. 9. Vertical profile of azimuthal-mean (a) radial wind (m sβ1), (b) specific humidity (g kgβ1), 810 and (c) potential temperature (K) averaged within r =200 km over the period from 1200 UTC 27 811 August to 0600 UTC 28 August 2010. The legend for these plots is shown in (a). 812 813
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814
Fig. 10. Evolution of the simulated (a) minimum SLP (hPa), (b) 10-m maximum wind speed (m 815 sβ1) from 1800 UTC 26 August to 1800 UTC 31 August; evolution of (c) RMW (km) and (d) 816 radius of 17 m sβ1 tangential wind (km) from 1800 UTC 29 August to 1800 UTC 31 August. The 817 legend for these plots is shown in (a). The gray shading in (a)-(b) denotes the analysis period in 818 (c)-(d). 819 820 821
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822
Fig. 11. Evolution of (a) minimum SLP (hPa), (b) 10-m maximum wind speed (m sβ1), and 10-m 823 RMW from lateR2-YSU (black) and lateR2-SH (red) experiment. The gray line in (a)-(b) denotes 824 the best track data from the National Hurricane Center. 825 826
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828
Fig. 12. Schematic of the effect of scale-awareness on TC intensification and structural changes 829 based on the comparison of simulations with the SH and YSU PBL schemes. Vmax represents the 830 maximum TC intensity, |Vr| denotes the inflow strength, and qv denotes the specific humidity. 831 832