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Yuan Jiang
and
Qin Xu

Abstract

By fitting a parametric vortex model directly to aliased radar radial velocities scanned from a hurricane, the maximum tangential velocity and its radial distance from the hurricane vortex center can be estimated by the recently developed alias-robust vortex analysis. This vortex analysis can be refined to produce a suitable reference radial velocity field on each tilt of a radar scan for the reference check in the first main step of dealiasing. This paper presents the techniques developed for the refinements and shows how and to what extent the refined vortex analysis can improve the reference check and thus enhance the dealiased data coverage, especially over severely aliased data areas around the vortex core of a hurricane or typhoon. In addition, stringent threshold conditions are used in the reference check and the subsequent continuity check to ensure the accepted data are free of alias or almost so. The robustness and improved performance of the method are exemplified by the results tested with severely aliased radial velocities scanned by operational WSR-88D radars from hurricanes in the United States and by operational China New Generation Weather Radar (CINRAD) base data format SA radars from typhoons in China.

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Qin Xu
,
Yuan Jiang
, and
Liping Liu

Abstract

An alias-robust least squares method that produces less errors than established methods is developed to produce reference radial velocities for automatically correcting raw aliased Doppler velocities scanned from hurricanes. This method estimates the maximum tangential velocity V M and its radial distance R M from the hurricane vortex center by fitting a parametric vortex model directly to raw aliased velocities at and around each selected vertical level. In this method, aliasing-caused zigzag discontinuities in the relationship between the observed and true radial velocities are formulated into the cost function by applying an alias operator to the entire analysis-minus-observation term to ensure the cost function to be smooth and concave around the global minimum. Simulated radar velocity observations are used to examine the cost function geometry around the global minimum in the space of control parameters (V M , R M ). The results show that the global minimum point can estimate the true (V M , R M ) approximately if the hurricane vortex center location is approximately known and the hurricane core and vicinity areas are adequately covered by the radar scans, and the global minimum can be found accurately by an efficient descent algorithm as long as the initial guess is in the concave vicinity of the global minimum. The method is used with elaborated refinements for automated dealiasing, and this utility is highlighted by an example applied to severely aliased radial velocities scanned from a hurricane.

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Qin Jiang
and
Daniel T. Dawson II

Abstract

Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.

Significance Statement

Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.

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Xu Qin
,
Jiang-she Zhang
, and
Xiao-dong Yan

Abstract

In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

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Chang-Jiang Zhang
,
Jin-Fang Qian
,
Lei-Ming Ma
, and
Xiao-Qin Lu

Abstract

An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

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Huibin Gao
,
Qin Ju
,
Peng Jiang
,
Wenming Yan
,
Wei Wang
,
Xiaolei Fu
, and
Zhenchun Hao

Abstract

Shallow groundwater evaporation (Eg ) is a major component of the hydrological cycle, especially in semiarid and arid locations. Empirical methods are commonly used to estimate Eg . However, most of these methods can only weakly represent Eg variations along the soil depth and do not consider the energy driver. In this paper, a temperature coefficient was proposed and incorporated into two preferred empirical models to characterize the impacts of soil temperature and air temperature lags on Eg . The method was evaluated using in situ daily data obtained from nonweighing bare soil lysimeters. The results indicated that the models that considered the temperature gradient variable (T) conformed to the changes in the actual Eg values with depth more appropriately than the original models, accompanied by 4.3%–8.8% accuracy improvements overall. Shallow groundwater evaporation Eg was found to be influenced by the water table depth (H), T, and pan evaporation (E 0) in descending order, and strong interactions were found between H and T. Moreover, the impact of precipitation on Eg was investigated; measurements from dry days without precipitation revealed the actual Eg process, the relative errors in the cumulative Eg values derived at different depths demonstrated a positive relationship with infiltration recharge, and the errors related to precipitation induced 6.7%–8.3% Eg underestimations. These results contribute to a better understanding of evaporative losses from shallow groundwater and the typical Eg situation that occurs simultaneously with recharge, and they provide promising perspectives for corresponding integrated hydrologic modeling research.

Free access
Song Yang
,
Yundi Jiang
,
Dawei Zheng
,
R. Wayne Higgins
,
Qin Zhang
,
Vernon E. Kousky
, and
Min Wen

Abstract

Variations of U.S. regional precipitation in both observations and free-run experiments with the NCEP Climate Forecast System (CFS) are investigated. The seasonality of precipitation over the continental United States and the time–frequency characteristics of precipitation over the Southwest (SW) are the focus. The differences in precipitation variation among different model resolutions are also analyzed.

The spatial distribution of U.S. precipitation is characterized by high values over the East and the West Coasts, especially over the Gulf Coast and southeast states, and low values elsewhere except over the SW in summer. A large annual cycle of precipitation occurs over the SW, northern plains, and the West Coast. Overall, the CFS captures the above features reasonably well, except for the SW. However, it overestimates the precipitation over the western United States, except the SW in summer, and underestimates the precipitation over the central South, except in springtime. It also overestimates (underestimates) the precipitation seasonality over the intermountain area and Gulf Coast states (SW, West Coast, and northern Midwest). The model using T126 resolution captures the observed features more realistically than at the lower T62 resolution over a large part of the United States.

The variability of observed SW precipitation is characterized by a large annual cycle, followed by a semiannual cycle, and the oscillating signals on annual, semiannual, and interannual time scales account for 41% of the total precipitation variability. However, the CFS, at both T62 and T126 resolution, fails in capturing the above feature. The variability of SW precipitation in the CFS is much less periodic. The annual oscillation of model precipitation is much weaker than that observed and it is even much weaker than the simulated semiannual oscillation. The weakly simulated annual cycle is attributed by the unrealistic precipitation simulations of all seasons, especially spring and summer. On the annual time scale, the CFS fails in simulating the relationship between the SW precipitation and the basinwide sea surface temperature (SST) and the overlying atmospheric circulation. On the semiannual time scale, the model exaggerates the response of the regional precipitation to the variations of SST and atmospheric circulation over the tropics and western Atlantic, including the Gulf of Mexico. This study also demonstrates a challenge for the next-generation CFS, at T126 resolution, to predict the variability of North American monsoon climate.

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