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Capture of a strong elevated ducting event, especially its maintenance and sudden change, is of great value to airborne radar to achieve its beyond-the-line-of-sight detection. However, the knowledge is not easily accessible over the open ocean and hence very rare. During the Air–Sea Interaction Survey (ASIS) over the western North Pacific (WNP) in May 2016, a strong elevated ducting event with a long-life period and sudden change in its evolution was observed. Measurements from the ASIS, images from the Himawari-8 satellite, reanalysis data from the ECMWF, and Weather Research and Forecasting (WRF) model, were used to analyze the maintenance and sudden change of this strong ducting event, together with the model performance on simulating it. The results showed that the maintenance of strong elevated ducts, with their tops ranging from 750 to 1050 m and average strength of approximately 38 M units, was caused by a strong dry air mass capping over the wet marine atmospheric boundary layer (MABL), together with the subsidence inversion associated with high pressure. The WRF model performs well in simulating them. However, a sudden increase in duct height with a slight decrease of strength was recorded by the subsequent GPS radiosonde, which was finally contributed to the mechanical turbulent inversion and hydrolapse associated with the marine low-level jet (MLLJ). The height of the maximum horizontal wind speed (Umh) of the MLLJ corresponds well with the bottom of the trapping layer. However, these jet-relevant ducts are generally weak and it is difficult to accurately simulate them by using the mesoscale numerical model, since the wind-shear produced eddies are too small to be properly parameterized.

In Part II of this study, the influence of an oceanic cold-core eddy on the atmospheric boundary layer structures of Typhoon Francisco (2013) is investigated, as well as a comparison with the cold wake effect. Results show that the eddy induces shallower mixed-layer depth and forms stable boundary layer above and near it. The changes of these features shift from northwest to southeast across the storm eye, following the translation of Francisco over the eddy. Nonetheless, the decrease in mixed-layer depth and formation of stable boundary layer caused by the cold wake are located at right rear of the storm. The sensible heat fluxes at the lowest atmospheric model level are mostly downward over the sea surface cooling region. Due to their different relative locations with Francisco, the diabatic heating in the northwest quadrant of the storm can be more effectively inhibited by the cold-core eddy than by the cold wake. The asymmetric characteristics of surface tangential wind are less sensitive to sea surface cooling than those of surface radial wind, implying a change in surface inflow angle. Different from previous studies, the surface inflow angle is found to be reduced especially above the cold-core eddy and cold wake region. An analysis of radial wind tendency budget indicates that the decrease in radial pressure gradient is dominant in changing the acceleration rate of surface radial wind, rather than the decrease in the Coriolis and centrifugal forces, and therefore more outward surface flow is induced by both the cold-core eddy and cold wake.

Simulations using a convection-permitting numerical model (The Weather Research and Forecasting, WRF) are conducted to investigate the mechanisms responsible for the maintenance and organization of long-lived moist convection into a Mei-yu rainband (091800–101,800 UTC June 2008). This rainband was composed of multiple two-dimensional precipitation bandings, being elongated from the southwest to the northeast with the narrow width. In addition to establishment of the Mei-yu frontal zone, the northwesterly flow behind the mid-tropospheric trough also brought about upper-level dry air. The downward transport of the dry air had a high value of potential vorticity and just overrode the low-level moist southwesterly component, inducing the conditional instability (CI) and frontogenesis at the coherent regions. This behavior combined with the Mei-yu frontogenesis, leading to successive convection to maintain the rainband, whereas the latent heat was not directly linked to the rainband longevity. The releases of CI, conditionally symmetric and inertial instabilities were simultaneously present in the bandings, as an indication of the mechanisms for the formation of banded structures. Therefore, the mesoscale circulations in banded convection developed in the form of coexisted upward and slantwise motions. Our attempt to integrate the dry intrusion and multiple instabilities could be a vital significance in discussing the Mei-yu rainband development.

In operational data assimilation systems, observation-error covariance matrices are commonly assumed to be diagonal. However, inter-channel and spatial observation-error correlations are inevitable for satellite radiances. The observation errors of the Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder (MWHS) onboard the FengYun-3A (FY-3A) and FY-3B satellites are empirically assigned and considered to be uncorrelated when they are assimilated into the WRF model’s Community Variational Data Assimilation System (WRFDA). To assimilate MWTS and MWHS measurements optimally, a good characterization of their observation errors is necessary. In this study, background and analysis residuals were used to diagnose the correlated observation-error characteristics of the MWTS and MWHS. It was found that the error standard deviations of the MWTS and MWHS were less than the values used in the WRFDA. MWTS had small inter-channel errors, while MWHS had significant inter-channel errors. The horizontal correlation length scales of MWTS and MWHS were about 120 and 60 km, respectively. A comparison between the diagnosis for instruments onboard the two satellites showed that the observation-error characteristics of the MWTS or MWHS were different when they were onboard different satellites. In addition, it was found that the error statistics were dependent on latitude and scan positions. The forecast experiments showed that using a modified thinning scheme based on diagnosed statistics can improve forecast accuracy.

This paper presents an evaluation of the observational impacts on blended sea surface winds from a two-dimensional variational data assimilation (2D-Var) scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal (DFS), and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two methods, a priori and a posteriori, are used to estimate the DFS of the zonal (u) and meridional (v) components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is reduced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.

Ocean waves generally mix with wind waves and swell, of which only wind waves depend on the local wind field. Using 15 years of hourly observational wind-wave data from eight buoys off the northwest coast of the United States (US), the authors develop a new significant wave height (Hs) and dominant wave period (Tp) scheme (termed as WHP) for open oceans. This scheme relies only on the properties that a single atmospheric model can provide: H s = 0.0143 U 10 2 + 0.9626 , H * = 0.0628 T * 3 / 2 where U10 is the wind speed at a reference height of 10 m, H* is the dimensionless wave height, and T* is the dimensionless wave period. This relation is fitted to U10 values between 4 and 25 m s−1. Comparison results show that the WHP scheme gives an almost best performance in predicting Hs and Tp for the open oceans across different regions. In addition to wind speed, considering the influence of wind direction and stability of the air/sea boundary layer is necessary for wind-wave prediction in coastal areas. Data from the Coupled Boundary Layers and Air–Sea Transfer experiment in LOW winds (CBLAST-LOW) and coastal buoys demonstrate that the effects of the wind direction and stability on Hs and Tp are significant for shallow water; with an increase in water depth, such impacts gradually decrease. Parameterizations for coastal wind waves are also investigated in this paper in Eqs. (19) , (20) , (21) , (22) , (23) , (24) , (25) , (26) , (27) , (28) , (29) , (30) , (31) .

Official forecasts of tropical cyclone (TC) tracks issued by the China Meteorological Administration (CMA); the Regional Specialized Meteorological Centre in Tokyo, Japan; and the Joint Typhoon Warning Center (JTWC) were used to evaluate the accuracies, biases, and trends of TC track forecasts during 2005–14 over the western North Pacific. Overall, the JTWC demonstrated the best forecasting performance. However, the CMA showed the most significant rate of improvement. Two main zones were discovered in the regional distribution of forecast errors: a low-latitude zone that comprises the South China Sea and the sea region east of the Philippines, and a midlatitude zone comprising the southern Sea of Japan and the sea region east of Japan. When TCs moved into the former zone, there were both translational speed and direction biases in the forecast tracks, whereas slow biases were predominated in the latter zone. Twelve synoptic flow patterns of TCs with the largest error have been identified based on the steering flow theory. Among them, the most two common pattern are the pattern with the combination of cyclonic circulations, subtropical ridges, and midlatitude troughs (CRT, 26 TCs), and the pattern of the TCs’ track that cannot be explained by steering flow (NSF, 6 TCs). In the CRT pattern, TCs move northwestward forced by the cyclonic circulations and the subtropical ridges and then turn poleward and eastward under the influence of the midlatitude troughs. In the NSF pattern, storms embedded in the southwest flow by the cyclonic circulation and the steering flow suggest TCs should turn to the right and move northeastward but instead TCs persisted in moving northwestward.

The impact of mesoscale ocean eddies on tropical cyclone intensities is investigated based on a combination of observations and atmosphere–ocean coupling simulations. A statistical analysis reveals that the tropical cyclone–eddy interactions occur at very high frequencies; over 90% of the recorded tropical cyclones over the western North Pacific have encountered ocean eddies from 2002 to 2011. The chances of confronting a cold core eddy (CCE) are slightly larger than confronting a warm core eddy (WCE). The observational sea surface temperature data have statistically evidenced that CCEs tend to promote the sea surface temperature decrease caused by tropical cyclones while WCEs tend to restrain such ocean responses. The roles of CCEs are statistically more significant than those of WCEs in modulating the sea surface temperature response. It is therefore proposed that CCEs should be paid no less attention than WCEs during the TC–ocean interaction process. The CCE-induced changes in sea surface temperature decreases are observed to be more remarkable for more intense and slower-moving tropical cyclones and for thinner depth of mixed layers. A set of numerical experiments reveal that the effects of ocean eddies are positively related to their strengths and storm intensities, and the eddy feedback is less pronounced when the eddy is located at one side of storm tracks than right below the tropical cyclone center. The eddy-induced moisture disequilibrium sooner vanishes after the departure of tropical cyclones. The intensity recoveries last for 1–2 days because of the dependence of surface enthalpy fluxes on surface winds.

An observational study focusing on the locations of tropical cyclogenesis induced by tropical cyclone energy dispersion (TCED) in the western North Pacific is conducted. Statistical results indicate that the cyclogenesis cases associated with TCED widely occur around preexisting tropical cyclones (TCs). In addition to the typical scenario of cyclogenesis to the southeast of a preexisting TC, new TCs can even form in the east or southwest directions at distances ranging from 1000 km to 3500 km. Further analyses reveal that the locations of cyclogenesis are mainly governed by large-scale environments via regulating the patterns of Rossby wave trains. The observational relationships between wave train regimes and the structures of environmental flows are revealed, which are broadly consistent with previous idealized numerical simulations. The results in this study provide a valuable reference for the prediction of cyclogenesis considering the TCED mechanism.

Dust particles in the atmosphere play an important role in air pollution, climate change, and biogeochemical cycles. Some of the dominant sources of dust in mid-latitude regions are in Asia. An intense dust storm engulfed Northern China at the beginning of May 2017, and PM10 mass concentrations of 1500&ndash, 2000 &mu, g m&minus, 3 were measured near the dust source region. We combined numerical simulations, air quality monitoring data, and satellite retrievals to investigate dust emission and transport during this event. We found that the event was closely related to cold front activity, characterized by increased wind speed, which increased dust emission. We improved the dust scheme using a local dust size distribution to better simulate the dust emission flux. We found that accurate parametrization of the dust size distribution was important to effectively simulate both dust emission and ambient particle concentration. We showed that using a local dust size distribution substantially improved the accuracy of the simulation, allowing both the spatial distribution of pollution caused by the dust storm and temporal variability in the pollution to be captured.

This study explored the influence of choosing a nonhydrostatic dynamical core or a hydrostatic dynamical core in the weather research and forecasting (WRF) model on the intensity and structure of simulated tropical cyclones (TCs). A comparison of cloud-resolving simulations using each core revealed significant differences in the TC simulations. In comparison with the nonhydrostatic simulation, the hydrostatic simulation produced a stronger and larger TC, associated with stronger convective activity. A budget analysis of the vertical momentum equation was conducted to investigate the underlying mechanisms. Although the hydrostatic dynamical core was used, the vertical motion was not in strict hydrostatic balance because of the existence of the vertical perturbation pressure gradient force, local buoyancy force, water loading, and sum of the Coriolis and diffusion effects. The contribution of the enhanced vertical perturbation pressure gradient force was found to be more important for stronger upward acceleration in the eyewall in the hydrostatic simulation than in the nonhydrostatic simulation. This is because it leads to intensified convection in the eyewall that releases more latent heat, which induces a larger low-level radial pressure gradient and inflow motion, and eventually leads to a stronger storm.

This study investigates tropical cyclone energy dispersion under horizontally sheared flows using a nonlinear barotropic model. In addition to common patterns, unusual features of Rossby wave trains are also found in flows with constant vorticity and vorticity gradients. In terms of the direction of the energy dispersion, the wave train can rotate clockwise and elongate southwestward under anticyclonic circulation (ASH), which contributes to the reenhancement of the tropical cyclone (TC). The wave train even splits into two obvious wavelike trains in flows with a southward vorticity gradient (WSH). Energy dispersed from TCs varies over time, and variations in the intensity of the wave train components typically occur in two stages. Wave-activity flux diagnosis and ray tracing calculations are extended to the frame that moves along with the TC to reveal the concrete progress of wave propagation. The direction of the wave-activity flux is primarily determined by the combination of the basic flow and the TC velocity. Along the flux, the distribution of pseudomomentum effectively illustrates the development of wave trains, particularly the rotation and split of wave propagation. Ray tracing involves the quantitative tracing of wave features along rays, which effectively coincide with the wave train regimes. Flows of a constant shear (parabolic meridional variation) produce linear (nonlinear) wave number variations. For the split wave trains, the real and complex wave number waves move along divergent trajectories and are responsible for different energy dispersion ducts.

Ding, J.; Fei, J.; Huang, X.; Cheng, X.; He, H.; Yuan, B., and Liang, Z., 2020. Validation of evaporation duct model in coastal regions with ASIT data. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 270–281. Coconut Creek (Florida), ISSN 0749-0208.This study aims to further verify the evaporation duct (ED) model in a coastal region as well as the need for model localization based on the measurements from the Air-Sea Interaction Tower (ASIT). The ducts tended to be more frequent and pronounced in coastal offshore winds than in onshore winds, especially in case of combining unstable conditions (100% and 9.5 m/8.7 m). They occurred with the lowest frequency (56.4%) and thickness (4.9 m/3.5 m) in stable onshore winds. The ED model with ψ functions of Edson04 (or Fairall03) and SHEBA07 delivered the best overall performance in terms of predicting local evaporation ducts. Its considerable advantages over the COARE 3.0 model and BYC model underscore the importance of localization to a given region. The overall performance of the localized ED model was encouraging but it encountered problems in case of extreme ducts under stable offshore conditions, when the available improvements to it failed. The reinforcement of a stable marine inner-boundary layer (MIBL) to a preexisting evaporation duct was considered the main reason for this and, hence, the future solution.

Based on the ARPS (Advanced Regional Prediction System) at 2 km grid spacing, the convective initiation (CI) of cells that evolved into a squall line on the southern flank of a Meiyu front in East China is investigated. The initiations of four convective cells, denoted CI-A through CI-D, are reasonably captured in the simulation. For CI-A and CI-B, locally enhanced convergence bands associated with boundary layer horizontal convective rolls (HCRs) play a crucial role in determining the exact locations of CIs, whereas a cold outflow boundary from earlier frontal precipitation contributes to additional convergence forcing. For CI-C, initiation occurs directly over the gust front, with the frontal convergence providing the main forcing. CI-D occurs south of and sufficiently far from the gust front, and is mainly forced by the HCR circulations. With surface heat flux turned off in the model, CI is much delayed. Therefore, surface heating increases the convective instability of air south of the front and causes the development of HCRs; it also enhances the gust front convergence by mixing higher southwesterly momentum toward the surface. When the condensation process is turned off, HCRs and/or gust frontal forcing are still able to lift the low-level air to super-saturation where CI is expected.