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Ying-Hwa Kuo and Yong-Run Guo

Abstract

This paper presents results from a series of observing system simulation experiments (OSSEs), designed to test a dynamic initialization procedure for continuous assimilation of observations from a hypothetical network of profilers. A 40-km adiabatic mesoscale model was used to generate a set of simulated observations from a network of 77 wind profilers over the continental United States with a station separation of 360 km. These observations were then assimilated into an 80-km model during a 12-h preforecast integration through a Newtonian nudging technique.

We found that dynamic initialization by nudging can successfully assimilate the time-continuous wind profiler observations into the model. The profiler data assimilation is effective in recovering mesoscale circulations which are not properly resolved by the observing network (due to inadequate horizontal resolution), while at the same time controlling the error growth for large-scale circulations. The impact is particularly significant in the divergence field, which is crucial for an accurate precipitation forecast. The improved initial state leads to further improvement in the subsequent forecast, demonstrating the value of the time-continuous profiler observations on short-range numerical weather prediction.

Assimilation of the wind field is found to be considerably more effective than assimilation of the temperature field. Specifically, wind assimilation leads to improvement in both the temperature and the wind fields, while temperature assimilation produces little improvement in the wind field. The best results are obtained when both temperature and wind fields are assimilated.

The proposed demonstration network of 31 profiles is likely to have a positive impact on short-range numerical weather prediction, though mainly confined over the localized region covered by the profiler network. Further expansion of the profiler network to increase its spatial resolution and its areal coverage is needed if improved prediction is expected over a larger area.

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Ying-Hwa Kuo, Yong-Run Guo, and Ed R. Westwater

Abstract

Significant progress has been made over the past decade in the development of remote-sensing instruments to profile wind and temperature. However, the current technology of profiling water vapor remotely is still far from perfect. Although some promising optical research systems, such as the Raman lidar, can provide high vertical resolution profiles of water vapor, it may be years before they are generally available. Currently, there are several systems that can measure the vertically integrated water vapor (i.e., precipitable water) with a high degree of accuracy. In this paper we use a simple method to assimilate precipitable water measurements (possibly from a network of dual-channel ground-based microwave radiometers or a satellite-based system) into a mesoscale model. The basic idea is to relax the predicted precipitable water toward the observed value, while retaining the vertical structure of the model humidity field. We test this method with the special 3-h soundings available from the Severe Environmental Storms and Mesoscale Experiment. The results show that the assimilation of precipitable water into a mesoscale model recovers the vertical structure of water vapor with an accuracy much higher than that from statistical retrieval based on climatology. The improved analysis due to assimilation also leads to improved short-range precipitation forecasts.

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John R. Gyakum, Ying-Hwa Kuo, Zitian Guo, and Yong-Run Guo

Abstract

The rapid surface cyclogenesis of March 1984 is examined from an observational and modeling perspective, in terms of both potential vorticity (PV) and traditional quasigeostraphic reasoning, during its evolution from a mesoscale cyclone to a state in which it is identifiable as a large-scale extratropical cyclone. The first stage of the cyclonic development is characterized by a surface warm anomaly forming as a consequence of surface heat fluxes. Subsequently, a lower-tropospheric PV maximum develops in association with a mesoscale pattern of rainfall in excess of 10 mm h−1. The numerical forecasts replicated the evolution of both features, though more slowly than actually occurred. This organized rainfall occurs in response to a vigorous midtropospheric cyclonic vorticity maximum. Lower-tropospheric PV generation is found to be the unique feature of the rapid mesoscale cyclogenesis that is directly related to condensation heating, with both horizontal and vertical gradients of heating contributing. The former component of PV generation occurs only during the first hours of incipient cyclogenesis and is uniquely related to mesoscale precipitation pattern in a region of strong baroclinity and vertical wind shear.

The second stage of development occurs when high-PV stratospheric air arrives over the cyclone center, and induces further rapid spinup. The resulting rapid spinup is dependent not only on the existence of this reservoir of high-PV air, but also on its interaction with the lower-tropospheric PV maximum that was produced by condensation heating.

The rapid small-scale cyclogenesis may be explained by the following sequence of events. Strong surface heating produces a surrogate surface PV anomaly. The associated planetary boundary layer heating and moistening leads to moist convection that occurs in the midst of a strong lower-tropospheric baroclinic zone. Such convection and its consequent latent heating in the midst of strong vertical wind shear is responsible for the generation of a lower-tropospheric PV maximum and the incipient mesoscale cyclogenesis. The interaction of this mesoscale PV anomaly with a strong upper-level trough, or a strong PV anomaly that extends from the stratosphere down to 600 mb, products the second phase of rapid cyclogenesis in which the surface cyclone is transformed into a large-scale extratropical cyclone.

The rapid cyclogenesis depends crucially on the existence of the upper trough, the amplitude of boundary layer heating, the strength of condensation, and the interaction of these processes.

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So-Young Ha, Ying-Hwa Kuo, Yong-Run Guo, and Gyu-Ho Lim

Abstract

With the recent advance in Global Positioning System (GPS) atmospheric sensing technology, slant wet delay along each ray path can be measured with a few millimeters accuracy. In this study, the impact of slant wet delay is assessed on the short-range prediction of a squall line. Since the current GPS observation network in the central United States is not of high enough density to capture the mesoscale variation of moisture in time and space, a set of observing system simulation experiments is performed to assimilate slant wet delay data from a hypothetical network of ground-based GPS receivers using the four-dimensional variational data assimilation technique. In the assimilation of slant wet delay data, significant changes in moisture, temperature, and wind fields within the boundary layer were found. These changes lead to a stronger surface cold front and stronger convective instability ahead of the front. Consequently, the assimilation of slant wet delay produces a considerably improved 6-h forecast of a squall line in terms of rainfall prediction and mesoscale frontal structure.

Previous studies have shown that the assimilation of GPS-derived precipitable water data can improve moisture analysis and rainfall prediction. In order to assess the additional value of slant wet delay data assimilation, a parallel experiment is performed in which precipitable water data is assimilated. The assimilation of slant wet delay data is demonstrated to be superior in recovering water vapor information between receiver sites and in short-range precipitation forecast both in terms of rainfall distribution and intensity. As revealed by atmospheric soundings in the vicinity of the squall line, the assimilation of slant wet delay data more accurately retrieves the temperature and moisture structure in the convectively unstable region.

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Qingnong Xiao, Ying-Hwa Kuo, Juanzhen Sun, Wen-Chau Lee, Eunha Lim, Yong-Run Guo, and Dale M. Barker

Abstract

In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc) and rainwater (qr) is used in the 3DVAR background fields. The observation operator for Doppler radial velocity is developed and implemented within the 3DVAR system. A series of experiments, assimilating the Korean Jindo radar data for the 10 June 2002 heavy rainfall case, indicates that the scheme for Doppler velocity assimilation is stable and robust in a cycling mode making use of high-frequency radar data. The 3DVAR with assimilation of Doppler radial velocities is shown to improve the prediction of the rainband movement and intensity change. As a result, an improved skill for the short-range heavy rainfall forecast is obtained. The forecasts of other quantities, for example, winds, are also improved. Continuous assimilation with 3-h update cycles is important in producing an improved heavy rainfall forecast. Assimilation of Doppler radar radial velocities using the 3DVAR background fields from a cycling procedure produces skillful rainfall forecasts when verified against observations.

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Ling-Feng Hsiao, Chi-Sann Liou, Tien-Chiang Yeh, Yong-Run Guo, Der-Song Chen, Kang-Ning Huang, Chuen-Teyr Terng, and Jen-Her Chen

Abstract

This paper introduces a relocation scheme for tropical cyclone (TC) initialization in the Advanced Research Weather Research and Forecasting (ARW-WRF) model and demonstrates its application to 70 forecasts of Typhoons Sinlaku (2008), Jangmi (2008), and Linfa (2009) for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings. An efficient and dynamically consistent TC vortex relocation scheme for the WRF terrain-following mass coordinate has been developed to improve the first guess of the TC analysis, and hence improves the tropical cyclone initialization. The vortex relocation scheme separates the first-guess atmospheric flow into a TC circulation and environmental flow, relocates the TC circulation to its observed location, and adds the relocated TC circulation back to the environmental flow to obtain the updated first guess with a correct TC position. Analysis of these typhoon cases indicates that the relocation procedure moves the typhoon circulation to the observed typhoon position without generating discontinuities or sharp gradients in the first guess.

Numerical experiments with and without the vortex relocation procedure for Typhoons Sinlaku, Jangmi, and Linfa forecasts show that about 67% of the first-guess fields need a vortex relocation to correct typhoon position errors while eliminates the topographical effect. As the vortex relocation effectively removes the typhoon position errors in the analysis, the simulated typhoon tracks are considerably improved for all forecast times, especially in the early periods as large adjustments appeared without the vortex relocation. Comparison of the horizontal and vertical vortex structures shows that large errors in the first-guess fields due to an incorrect typhoon position are eliminated by the vortex relocation scheme and that the analyzed typhoon circulation is stronger and more symmetric without distortions, and better agrees with observations. The result suggests that the main difficulty of objective analysis methods [e.g., three-dimensional variational data assimilation (3DVAR)], in TC analysis comes from poor first-guess fields with incorrect TC positions rather than not enough model resolution or observations. In addition, by computing the eccentricity and correlation of the axes of the initial typhoon circulation, the distorted typhoon circulation caused by the position error without the vortex relocation scheme is demonstrated to be responsible for larger track errors. Therefore, by eliminating the typhoon position error in the first guess that avoids a distorted initial typhoon circulation, the vortex relocation scheme is able to improve the ARW-WRF typhoon initialization and forecasts particularly when using data assimilation update cycling.

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Ling-Feng Hsiao, Der-Song Chen, Ying-Hwa Kuo, Yong-Run Guo, Tien-Chiang Yeh, Jing-Shan Hong, Chin-Tzu Fong, and Cheng-Shang Lee

Abstract

In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of suppressing the growth of the systematic model error. A typhoon prediction model using the Advanced Research core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions.

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Dale Barker, Xiang-Yu Huang, Zhiquan Liu, Tom Auligné, Xin Zhang, Steven Rugg, Raji Ajjaji, Al Bourgeois, John Bray, Yongsheng Chen, Meral Demirtas, Yong-Run Guo, Tom Henderson, Wei Huang, Hui-Chuan Lin, John Michalakes, Syed Rizvi, and Xiaoyan Zhang

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

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