在天气预报业务中，发生在西太平洋副热带高压控制下的短时强降水容易出现漏报。为加深对西太平洋副热带高压控制下湖南短时强降水的认识，探究其成因和触发机制，本文利用地面自动站、多普勒天气雷达观测资料及FY-2F云顶亮温、NCEP再分析资料等，针对2018年9月6日一次西太平洋副热带高压控制下的湖南短时强降水成因开展研究。结果表明：在强盛的西太平洋副热带高压脊区内，丰沛的水汽、较强的不稳定能量及一定的抬升条件可触发短时强降水天气。正午前，受弱冷空气侵入影响，低层切变配合地面中尺度辐合线引起近地面动力抬升，从而触发对流性降水;午后，受太阳辐射影响，地面气温达到对流触发温度，从而触发热对流。正涡度区及低层辐合区在降水发生后都向上延伸，有利于垂直上升运动的维持，但较典型汛期强降水过程的动力条件明显偏弱。环境风及其垂直风切变小，且雷暴单体移动缓慢，有利于强降水在同一地区长时间维持。

基于多模式降水格点预报资料、青海省气象站实况资料及多源融合降水格点分析产品,针对青海省2020年7—8月强降水天气个例,采用TS(threat score)评分等传统检验方法和FSS(fraction skill score)评分及MODE(method of object-based diagnostic evaluation)空间检验方法,对比检验各模式在青海强降水中的预报性能。结果表明:(1)小雨及以上量级,欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)全球模式、中国气象局全球同化预报系统(CMA-GFS)及GRAPES区域中尺度数值预报系统(GRAPES-Meso)的传统TS评分均较高且预报能力相差不大,但不同检验方法下评分最高的模式略有不同;(2)中雨及以上量级,各模式预报较观测普遍偏西,且传统TS评分差异较为明显,但不同检验方法下模式评分优劣表现较为一致;(3)大雨及以上量级,各模式预报较观测普遍偏北,且预报能力较差,传统TS评分为0,FSS评分有效提高了模式差异性的评估能力,MODE方法则给出了预报和观测对象属性的具体表现,但对检验参数的选取较为敏感。

大气边界层作为连接下垫面和自由大气的重要桥梁，不仅影响局地的各种天气过程的发展和演变，而且在区域和全球的天气和气候变化中也扮演着关键角色。鉴于大气边界层自身的复杂性，对其数值模拟一直以来都是大气数值模拟研究中的热点和难点。通过归纳近几十年来大气边界层数值模式发展经历的3个阶段，梳理了干旱半干旱区、青藏高原地区和城市复杂下垫面3个陆地气候关键区边界层过程，以及海洋上特殊的台风边界层数值模拟研究取得的重要进展；总结出当前大气边界层数值模拟研究所面临的5个亟待解决的关键科学问题：云与边界层相互作用、边界层参数化、模式分辨率、边界层资料同化以及边界层发展机制。并明确了该领域未来需要在加强不同类型大气边界层过程的认识、边界层底和顶界面交换过程的理解、特殊地区边界层发展机制的解释、边界层参数化方案的改进、大涡模拟在边界层模拟中优势的充分发挥等5个方面开展重点研究，以期能为今后更系统地开展大气边界层数值模拟及相关研究提供参考依据。

利用MM5模式的4种边界层方案(ETA方案\, MRF方案\, Blackadar方案\, Gayno\|Seman方案)， 对2008年9月22~27日发生在四川盆地先是对流性, 后来是稳定性的暴雨过程进行了该4种不同边界层方案的数值模拟比较试验。整体而言， ETA方案对雨带的预报能力较差， 但对对流性降水有一定的预报能力； MRF方案对雨带(特别是稳定性降水)的预报能力相对最强； Blackadar方案对后24 h强降水最具能力， 且对对流性和稳定性降水的预报能力没有太大差别； Gayno-Seman方案对后24 h强降水预报能力较差。边界层对物理量场的影响随时间增大。在预报积分的前10 h以内， 各方案的涡度、 相对湿度、 垂直速度等物理量预报几乎无异; 积分10~24 h， 它们的量值间出现差异; 24 h后， 不同方案的预报不仅量值上有差异， 甚至变化趋势都不尽相同。高度场受边界层的影响最小， 受边界层方案影响最大的是U场， V场受边界层的影响呈高度和时间的分段函数， 湿度场受到的影响有明显的时间滞后性特征。对于温度场而言， 600 hPa似乎是温度场受边界层影响的一个拐点， 边界层影响在通过600 hPa后改变了影响规律。

This study evaluates the influence of planetary boundary layer parameterization on short-range (0–15 h) convection initiation (CI) forecasts within convection-allowing ensembles that utilize subsynoptic-scale observations collected during the Mesoscale Predictability Experiment. Three cases, 19–20 May, 31 May–1 June, and 8–9 June 2013, are considered, each characterized by a different large-scale flow pattern. An object-based method is used to verify and analyze CI forecasts. Local mixing parameterizations have, relative to nonlocal mixing parameterizations, higher probabilities of detection but also higher false alarm ratios, such that the ensemble mean forecast skill only subtly varied between parameterizations considered. Temporal error distributions associated with matched events are approximately normal around a zero mean, suggesting little systematic timing bias. Spatial error distributions are skewed, with average mean (median) distance errors of approximately 44 km (28 km). Matched event cumulative distribution functions suggest limited forecast skill increases beyond temporal and spatial thresholds of 1 h and 100 km, respectively. Forecast skill variation is greatest between cases with smaller variation between PBL parameterizations or between individual ensemble members for a given case, implying greatest control on CI forecast skill by larger-scale features than PBL parameterization. In agreement with previous studies, local mixing parameterizations tend to produce simulated boundary layers that are too shallow, cool, and moist, while nonlocal mixing parameterizations tend to be deeper, warmer, and drier. Forecasts poorly resolve strong capping inversions across all parameterizations, which is hypothesized to result primarily from implicit numerical diffusion associated with the default finite-differencing formulation for vertical advection used herein.

The representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.

In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.

This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).

Convection initiation (CI) and the subsequent upscale convective growth (UCG) at the coast of South China in a warm-sector heavy rainfall event are shown to be closely linked to a varying marine boundary layer jet (MBLJ) over the northern South China Sea (NSCS). To elucidate the dynamic and thermodynamic roles of the MBLJ in CI and UCG, we conducted and analyzed convection-permitting numerical simulations and observations. Compared to radar observations, the simulations captured CI locations and the following southwest–northeast-oriented convection development. The nocturnal MBLJ peaks at 950 hPa and significantly intensifies with turning from southwesterly to nearly southerly by inertial oscillation. The strengthened MBLJ promotes mesoscale ascent on its northwestern edge and terminus where enhanced convergence zones occur. Located directly downstream of the MBLJ, the coastal CI and UCG are dynamically supported by mesoscale ascent. From a thermodynamic perspective, a warm moist tongue over the NSCS is strengthened by the MBLJ-driven mesoscale ascent as well as by a high sea surface temperature. The warm moist tongue farther extends northeastward by horizontal transport and arrives at the coast where CI and UCG occur. Near the CI location, rapid development of a low-level saturated layer is mainly attributed to the mesoscale ascent and low-level moistening associated with the MBLJ. In addition, subsequent CI happens on either side of the original CI along the coast due to the delay of low-level moistening, which partly contributes to linear convective growth. Furthermore, ensemble simulations confirmed that a stronger MBLJ is more favorable to CI and UCG near the coast.

This paper proposes a revised vertical diffusion package with a nonlocal turbulent mixing coefficient in the planetary boundary layer (PBL). Based on the study of Noh et al. and accumulated results of the behavior of the Hong and Pan algorithm, a revised vertical diffusion algorithm that is suitable for weather forecasting and climate prediction models is developed. The major ingredient of the revision is the inclusion of an explicit treatment of entrainment processes at the top of the PBL. The new diffusion package is called the Yonsei University PBL (YSU PBL). In a one-dimensional offline test framework, the revised scheme is found to improve several features compared with the Hong and Pan implementation. The YSU PBL increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime, which alleviates the well-known problems in the Medium-Range Forecast (MRF) PBL. Excessive mixing in the mixed layer in the presence of strong winds is resolved. Overly rapid growth of the PBL in the case of the Hong and Pan is also rectified. The scheme has been successfully implemented in the Weather Research and Forecast model producing a more realistic structure of the PBL and its development. In a case study of a frontal tornado outbreak, it is found that some systematic biases of the large-scale features such as an afternoon cold bias at 850 hPa in the MRF PBL are resolved. Consequently, the new scheme does a better job in reproducing the convective inhibition. Because the convective inhibition is accurately predicted, widespread light precipitation ahead of a front, in the case of the MRF PBL, is reduced. In the frontal region, the YSU PBL scheme improves some characteristics, such as a double line of intense convection. This is because the boundary layer from the YSU PBL scheme remains less diluted by entrainment leaving more fuel for severe convection when the front triggers it.

Accurate depiction of meteorological conditions, especially within the planetary boundary layer (PBL), is important for air pollution modeling, and PBL parameterization schemes play a critical role in simulating the boundary layer. This study examines the sensitivity of the performance of the Weather Research and Forecast (WRF) model to the use of three different PBL schemes [Mellor–Yamada–Janjic (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2)]. Comparison of surface and boundary layer observations with 92 sets of daily, 36-h high-resolution WRF simulations with different schemes over Texas in July–September 2005 shows that the simulations with the YSU and ACM2 schemes give much less bias than with the MYJ scheme. Simulations with the MYJ scheme, the only local closure scheme of the three, produced the coldest and moistest biases in the PBL. The differences among the schemes are found to be due predominantly to differences in vertical mixing strength and entrainment of air from above the PBL. A sensitivity experiment with the ACM2 scheme confirms this diagnosis.

Meteorological model errors caused by imperfect parameterizations generally cannot be overcome simply by optimizing initial and boundary conditions. However, advanced data assimilation methods are capable of extracting significant information about parameterization behavior from the observations, and thus can be used to estimate model parameters while they adjust the model state. Such parameters should be identifiable, meaning that they must have a detectible impact on observable aspects of the model behavior, their individual impacts should be a monotonic function of the parameter values, and the various impacts should be clearly distinguishable from each other.

The modeling of the atmospheric boundary layer during convective conditions has long been a major source of uncertainty in the numerical modeling of meteorological conditions and air quality. Much of the difficulty stems from the large range of turbulent scales that are effective in the convective boundary layer (CBL). Both small-scale turbulence that is subgrid in most mesoscale grid models and large-scale turbulence extending to the depth of the CBL are important for the vertical transport of atmospheric properties and chemical species. Eddy diffusion schemes assume that all of the turbulence is subgrid and therefore cannot realistically simulate convective conditions. Simple nonlocal closure PBL models, such as the Blackadar convective model that has been a mainstay PBL option in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) for many years and the original asymmetric convective model (ACM), also an option in MM5, represent large-scale transport driven by convective plumes but neglect small-scale, subgrid turbulent mixing. A new version of the ACM (ACM2) has been developed that includes the nonlocal scheme of the original ACM combined with an eddy diffusion scheme. Thus, the ACM2 is able to represent both the supergrid- and subgrid-scale components of turbulent transport in the convective boundary layer. Testing the ACM2 in one-dimensional form and comparing it with large-eddy simulations and field data from the 1999 Cooperative Atmosphere–Surface Exchange Study demonstrates that the new scheme accurately simulates PBL heights, profiles of fluxes and mean quantities, and surface-level values. The ACM2 performs equally well for both meteorological parameters (e.g., potential temperature, moisture variables, and winds) and trace chemical concentrations, which is an advantage over eddy diffusion models that include a nonlocal term in the form of a gradient adjustment.

The development of NWP models with grid spacing down to ∼1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.

. The role played by planetary boundary layer (PBL) in the development and evolution of a severe convective storm is studied by means of meso-scale modeling and surface and upper air observations. The severe convective precipitation event that occurred on 14 September 1999 in the northeast of the Iberian Peninsula was simulated by means of the mesoscale model MM5 (version 3) using three different PBL schemes. The numerical results show a large impact of the PBL schemes on the precipitation fields associated to the convective storm. The schemes are based on different physical assumptions: the nonlocal first order Medium-Range Forecast (MRF) and Blackadar (BLA) scheme and the local, one-and-a-half order ETA scheme. Surface and radar observations are used to validate the model results. The comparison focuses on three aspects: the evolution, the spatial distribution and the 24-h accumulated precipitation. The comparison with rain gauge observations shows that the MRF, BLA and ETA schemes predicted most of the precipitation during the morning, whereas the rain gauge stations recorded rainfall during the evening. The evaluation performed with the radar data shows that all three numerical simulations produced a realistic spatial accumulated precipitation distribution. According to the quantity distribution, all three numerical simulations were able to predict precipitation quantities comparable to the rain gauge measurements. The MRF scheme predicted the largest average accumulated precipitation and the largest average precipitation rate, whereas the ETA scheme predicted the smallest accumulated precipitation and average precipitation rate. However, the ETA scheme yielded the highest extreme precipitation rates. The performance of the three schemes is analyzed in terms of the vertical distribution of potential temperature, specific humidity and conserved variables, like equivalent potential temperature and total water content. The MRF scheme showed more evidence of enhanced mixing than did the other schemes. Due to this process, more moisture was more efficiently transported to the free atmosphere. Consequently, the MRF scheme predicts more widespread precipitation. Furthermore, the enhanced mixing led to a less sharp capping inversion. However, the stronger inversion resulting from suppressed mixing processes in the case of the ETA scheme yielded higher values of convective available potential energy (CAPE) than did the other two schemes. Consequently, the more extreme precipitation rates are simulated by MM5 when the ETA scheme is used.\n