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Johnny C. L. Chan and Xudong Liang

Abstract

This study investigates the physical processes associated with changes in the convective structure of a tropical cyclone (TC) during landfall using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, version 3 (MM5). The land surface is moved toward a spunup vortex at a constant zonal speed on an f plane. Four experiments are carried out with the following fluxes modified over land: turning off sensible heat flux, turning off moisture flux, setting a higher surface roughness, and combining the last two processes.

The results suggest that sensible heat flux appears to show no appreciable effect while moisture supply is the dominant factor in modifying the convective structure. Prior to landfall, maximum precipitation is found to the front and left quadrants of the TC but to the front and right quadrants after landfall when moisture is turned off and surface roughness increased.

To understand the physical processes involved, a conceptual experiment is carried out in which moisture supply only occurs over the ocean and at the lowest level of the atmosphere, and such supply is transported around by the averaged circulation of the TC. It is shown that the dry air over land is being advected up and around so that at some locations the stability of the atmosphere is reduced. Analyses of the data from the more realistic numerical experiments demonstrate that convective instability is indeed largest just upstream of where the maximum rainfall occurs. In other words, the effect of the change in moisture supply on the convection distribution during TC landfall is through the modification of the moist static stability of the atmosphere.

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Feng Chen, Xudong Liang, and Hao Ma

Abstract

An improved Doppler radar radial velocity assimilation observation operator is proposed based on the integrating velocity–azimuth process (IVAP) method. This improved operator can ingest both radial wind and its spatial distribution characteristics to deduce the two components of the mean wind within a given area. With this operator, the system can be used to assimilate information from tangential wind and radial wind. On the other hand, because the improved observation operator is defined within a given area, which can be uniformly chosen in both the observation and analysis coordinate systems, it has a thinning function. The traditional observation operator and the improved observation operator, along with their corresponding data processing modules, were implemented in the community Gridpoint Statistical Interpolation analysis system (GSI) to demonstrate the superiority of the improved operator. The results of single analysis unit experiments revealed that the two operators are comparable when the analysis unit is small. When the analysis unit becomes larger, the analysis results of the improved operator are better than those of the traditional operator because the former can ingest more wind information than the latter. The results of a typhoon case study indicated that both operators effectively ingested radial wind information and produced more reasonable typhoon structures than those in the background fields. The tangential velocity relative to the radar was retrieved by the improved operator through ingesting tangential wind information from the spatial distribution characteristics of radial wind. Because of the improved vortex intensity and structure, obvious improvements were seen in both track and intensity predictions when the improved operator was used.

Open access
Yi Luo, Xudong Liang, Gang Wang, and Zheng Cao

Abstract

In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.

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Xudong Liang, Yanxin Xie, Jinfang Yin, Yi Luo, Dan Yao, and Feng Li

Abstract

Dealiasing is a common procedure in radar radial velocity quality control. Generally, there are two dealiasing steps: a continuity check and a reference check. In this paper, a modified version that uses azimuthal variance of radial velocity is introduced based on the integrating velocity–azimuth process (IVAP) method, referred to as the V-IVAP method. The new method can retrieve the averaged winds within a local area instead of averaged wind within a full range circle by the velocity–azimuth display (VAD) or the modified VAD method. The V-IVAP method is insensitive to the alias of the velocity, and provides a better way to produce reference velocities for a reference check. Instead of a continuity check, we use the IVAP method for a fine reference check because of its high-frequency filtering function. Then a dealiasing procedure with two steps of reference check is developed. The performance of the automatic dealiasing procedure is demonstrated by retrieving the wind field of a tornado. Using the dealiased radar velocities, the retrieved winds reveal a clear mesoscale vortex. A test based on radar network observations also has shown that the two-step dealiasing procedure based on V-IVAP and IVAP methods is reliable.

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Wei Peng, Xudong Liang, Xin Zhang, Xiangyu Huang, Bing Lu, and Qiao Fu

Abstract

Generally, the results of data assimilation are not well balanced dynamically due to errors in background, observations, or the model itself. So, initialization methods have been introduced to remove spurious gravity waves from the analysis. One of the initialization methods is digital filter initialization (DFI), which has been used in operational forecast systems, though its physical meaning is not well understood. Other methods eliminate high-frequency noise in optimized initial conditions by introducing physical constraints, such as the model constraint scheme, which minimizes the time tendency of model variables. In this study, a physical filter initialization (PFI) scheme, based on the model constraint scheme, is implemented in the four-dimensional variational data assimilation (4DVar) system of the Weather Research and Forecasting (WRF) Model. The impacts of the PFI scheme are examined by both single-observation and real-data experiments. The results indicate that the PFI scheme can eliminate high-frequency noise effectively, obtain flow-dependent analysis increments, and shorten forecast spinup time. Consequently, the precipitation forecast is improved to a certain extent, especially during the first few hours thanks to the shorter spinup time.

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Paolo M. Ruti, Oksana Tarasova, Julia H. Keller, Greg Carmichael, Øystein Hov, Sarah C. Jones, Deon Terblanche, Cheryl Anderson-Lefale, Ana P. Barros, Peter Bauer, Véronique Bouchet, Guy Brasseur, Gilbert Brunet, Phil DeCola, Victor Dike, Mariane Diop Kane, Christopher Gan, Kevin R. Gurney, Steven Hamburg, Wilco Hazeleger, Michel Jean, David Johnston, Alastair Lewis, Peter Li, Xudong Liang, Valerio Lucarini, Amanda Lynch, Elena Manaenkova, Nam Jae-Cheol, Satoru Ohtake, Nadia Pinardi, Jan Polcher, Elizabeth Ritchie, Andi Eka Sakya, Celeste Saulo, Amith Singhee, Ardhasena Sopaheluwakan, Andrea Steiner, Alan Thorpe, and Moeka Yamaji

Abstract

Whether on an urban or planetary scale, covering time scales of a few minutes or a few decades, the societal need for more accurate weather, climate, water, and environmental information has led to a more seamless thinking across disciplines and communities. This challenge, at the intersection of scientific research and society’s need, is among the most important scientific and technological challenges of our time. The “Science Summit on Seamless Research for Weather, Climate, Water, and Environment” organized by the World Meteorological Organization (WMO) in 2017, has brought together researchers from a variety of institutions for a cross-disciplinary exchange of knowledge and ideas relating to seamless Earth system science. The outcomes of the Science Summit, and the interactions it sparked, highlight the benefit of a seamless Earth system science approach. Such an approach has the potential to break down artificial barriers that may exist due to different observing systems, models, time and space scales, and compartments of the Earth system. In this context, the main future challenges for research infrastructures have been identified. A value cycle approach has been proposed to guide innovation in seamless Earth system prediction. The engagement of researchers, users, and stakeholders will be crucial for the successful development of a seamless Earth system science that meets the needs of society.

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Paolo Ruti, Oksana Tarasova, Julia Keller, Greg Carmichael, Øystein Hov, Sarah Jones, Deon Terblanche, Cheryl Anderson-Lefale, Ana Barros, Peter Bauer, Véronique Bouchet, Guy Brasseur, Gilbert Brunet, Phil DeCola, Victor Dike, Mariane Diop Kane, Christopher Gan, Kevin Gurney, Steven Hamburg, Wilco Hazeleger, Michel Jean, David Johnston, Alastair Lewis, Peter Li, Xudong Liang, Valerio Lucarini, Amanda Lynch, Elena Manaenkova, Nam Jae-Cheol, Satoru Ohtake, Nadia Pinardi, Jan Polcher, Elizabeth Ritchie, Andi Eka Sakya, Celeste Saulo, Amith Singhee, Ardhasena Sopaheluwakan, Andrea Steiner, Alan Thorpe, and Moeka Yamaji
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