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Qingnong Xiao, Xiaoyan Zhang, Christopher Davis, John Tuttle, Greg Holland, and Patrick J. Fitzpatrick

Abstract

Initialization of the hurricane vortex in weather prediction models is vital to intensity forecasts out to at least 48 h. Airborne Doppler radar (ADR) data have sufficiently high horizontal and vertical resolution to resolve the hurricane vortex and its imbedded structures but have not been extensively used in hurricane initialization. Using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation (3DVAR) system, the ADR data are assimilated to recover the hurricane vortex dynamic and thermodynamic structures at the WRF model initial time. The impact of the ADR data on three hurricanes, Jeanne (2004), Katrina (2005) and Rita (2005), are examined during their rapid intensification and subsequent weakening periods before landfall.

With the ADR wind data assimilated, the three-dimensional winds in the hurricane vortex become stronger and the maximum 10-m winds agree better with independent estimates from best-track data than without ADR data assimilation. Through the multivariate incremental structure in WRF 3DVAR analysis, the central sea level pressures (CSLPs) for the three hurricanes are lower in response to the stronger vortex at initialization. The size and inner-core structure of each vortex are adjusted closer to observations of these attributes. Addition of reflectivity data in assimilation produces cloud water and rainwater analyses in the initial vortex. The temperature and moisture are also better represented in the hurricane initialization.

Forty-eight-hour forecasts are conducted to evaluate the impact of ADR data using the Advanced Research Hurricane WRF (AHW), a derivative of the Advanced Research WRF (ARW) model. Assimilation of ADR data improves the hurricane-intensity forecasts. Vortex asymmetries, size, and rainbands are also simulated better. Hurricane initialization with ADR data is quite promising toward reducing intensity forecast errors at modest computational expense.

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

Abstract

A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

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Haldun Karan, Patrick J. Fitzpatrick, Christopher M. Hill, Yongzuo Li, Qingnong Xiao, and Eunha Lim

Abstract

A detailed observational and Weather Research and Forecasting (WRF) model analysis utilizing Weather Surveillance Radar-1988 Doppler (WSR-88D), surface, and upper-air observations, as well as Geostationary Operational Environmental Satellite (GOES) images, shows a chain of events that leads to the formation of two prefrontal squall lines along the western Gulf coast on 29–30 April 2005. An approaching surface cold front (CF) generated an atmospheric bore that propagated along an inversion layer and excited high-frequency, low-level tropospheric gravity waves, initiating a squall line 60 km east of the cold front. This sequence of events manifested itself as low-level convergence ahead of the CF, which was detected by nearby WSR-88D radars. Two WRF model experiments were conducted in which one assimilated conventional observations (CTRL), and another included radar radial winds from nine WSR-88D locations (denoted as RADAR). Better representation of the low-level kinematics in RADAR yielded a distinct convergence line associated with the primary squall line.

The RADAR experiment, as well as observations (such as an 0600 UTC Slidell, Louisiana, sounding), show that the secondary squall line formed ahead of the primary squall line due to high water vapor and warm temperature advection from the Gulf of Mexico that, when combined with a deep dry layer above the atmospheric boundary layer (ABL), destabilized the atmosphere. Concurrently, a lower-tropospheric trough, propagating faster than the surface front, enhanced lifting in the region and instigated the formation of new convection. RADAR forecasted the secondary convection not only in the right place but also at about the right time, while the CTRL experiment completely missed this secondary convection.

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Soichiro Sugimoto, N. Andrew Crook, Juanzhen Sun, Qingnong Xiao, and Dale M. Barker

Abstract

The purpose of this study is to investigate the performance of 3DVAR radar data assimilation in terms of the retrievals of convective fields and their impact on subsequent quantitative precipitation forecasts (QPFs). An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains. Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component. The reflectivity factor assimilation is also beneficial especially for strong precipitation. It is demonstrated that the improved initial conditions through the 3DVAR analysis lead to improved skills on QPF.

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Qingnong Xiao, X. Zou, M. Pondeca, M. A. Shapiro, and C. Velden

Abstract

The impact of satellite-derived wind observations on the prediction of a mid–Pacific Ocean cyclone during the North Pacific Experiment (NORPEX, 14 Jan–27 Feb 1998) is assessed using a four-dimensional variational (4DVAR) approach in which a nonhydrostatic version of the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) serves as a strong constraint. The satellite-derived wind observations are retrieved through an automated tracking algorithm using water vapor visible, and infrared imagery from the operational Geostationary Meteorological Satellite-5 (GMS-5) and Geostationary Operational Environmental Satellite-9 (GOES-9) over the North Pacific basin. For the case studied, it is found that the amount of satellite wind data is much greater in the upper troposphere than in the lower troposphere.

The 4DVAR assimilation of the satellite wind observations is carried out on a single domain with 90-km horizontal resolution. Incorporation of satellite wind observations was found to increase the cyclonic zonal wind shear and the cross-front temperature gradient associated with the simulated cyclone. However, the improvement in the intensity of the simulated cyclone measured by the central sea level pressure is marginal using the same assimilation model. Increasing the forecast model resolution by nesting a 30-km resolution domain yields a more significant impact of the satellite-derived wind data on the cyclone intensity prediction. The GMS-5 satellite winds (upstream data) are found to have more influence on the quality of the cyclone development than the GOES-9 satellite winds (downstream data). An adjoint sensitivity study confirms that the most sensitive region is located upstream of the cyclone, and that the cyclone is more sensitive to the lower rather than the upper atmosphere. Therefore, it is anticipated that larger impacts on cyclone prediction in the mid–Pacific Ocean will occur when a greater or equal amount of satellite wind observations are made available for the lower troposphere as are available for the upper levels.

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Haldun Karan, Patrick J. Fitzpatrick, Christopher M. Hill, Yongzuo Li, Qingnong Xiao, and Eunha Lim
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Juanzhen Sun, Stanley B. Trier, Qingnong Xiao, Morris L. Weisman, Hongli Wang, Zhuming Ying, Mei Xu, and Ying Zhang

Abstract

Sensitivity of 0–12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8–9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough.

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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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Qingnong Xiao, Ying-Hwa Kuo, Zaizhong Ma, Wei Huang, Xiang-Yu Huang, Xiaoyan Zhang, Dale M. Barker, John Michalakes, and Jimy Dudhia

Abstract

The tangent linear and adjoint of an adiabatic version of the Weather Research and Forecasting (WRF) Model with its Advanced Research WRF (ARW) dynamic core have been developed. The source-to-source automatic differentiation tool [i.e., the Transformation of Algorithm (TAF) in FORTRAN] was used in the development. Tangent linear and adjoint checks of the developed adiabatic WRF adjoint modeling system (WAMS) were conducted, and all necessary correctness verification procedures were passed. As the first application, the adiabatic WAMS was used to study the adjoint sensitivity of a severe windstorm in Antarctica. Linearity tests indicated that an adjoint-based sensitivity study with the Antarctic Mesoscale Prediction System (AMPS) 90-km domain configuration for the windstorm is valid up to 24 h. The adjoint-based sensitivity calculation with adiabatic WAMS identified sensitive regions for the improvement of the 24-h forecast of the windstorm. It is indicated that the windstorm forecast largely relies on the model initial conditions in the area from the south part of the Trans-Antarctic Mountains to West Antarctica and between the Ross Ice Shelf and the South Pole. Based on the sensitivity analysis, the southerly or southeasterly wind at lower levels in the sensitivity region should be larger, the cyclone should be stronger, and the atmospheric stratification should be more stable over the north slope of the Trans-Antarctic Mountain to the Ross Ice Shelf, than the AMPS analyses. By constructing pseudo-observations in the sensitivity region using the gradient information of forecast windstorm intensity around McMurdo, the model initial conditions are revised with the WRF three-dimensional variational data assimilation, which leads to significant improvement in the prediction of the windstorm. An adjoint sensitivity study is an efficient way to identify sensitivity regions in order to collect more observations in the region for better forecasts in a specific aspect of interest.

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Christopher Davis, Wei Wang, Shuyi S. Chen, Yongsheng Chen, Kristen Corbosiero, Mark DeMaria, Jimy Dudhia, Greg Holland, Joe Klemp, John Michalakes, Heather Reeves, Richard Rotunno, Chris Snyder, and Qingnong Xiao

Abstract

Real-time forecasts of five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) (ARW) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and occasionally superior to, other operational forecasts for storm position and intensity. Recurring errors include 1) excessive intensification prior to landfall, 2) insufficient momentum exchange with the surface, and 3) inability to capture rapid intensification when observed. To address these errors several augmentations of the basic community model have been designed and tested as part of what is termed the Advanced Hurricane WRF (AHW) model. Based on sensitivity simulations of Katrina, the inner-core structure, particularly the size of the eye, was found to be sensitive to model resolution and surface momentum exchange. The forecast of rapid intensification and the structure of convective bands in Katrina were not significantly improved until the grid spacing approached 1 km. Coupling the atmospheric model to a columnar, mixed layer ocean model eliminated much of the erroneous intensification of Katrina prior to landfall noted in the real-time forecast.

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