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Daniel Hodyss, Jeffrey L. Anderson, Nancy Collins, William F. Campbell, and Patrick A. Reinecke

Abstract

It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.

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Ting-Chi Wu, Hui Liu, Sharanya J. Majumdar, Christopher S. Velden, and Jeffrey L. Anderson

Abstract

The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

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Soyoung Ha, Chris Snyder, William C. Skamarock, Jeffrey Anderson, and Nancy Collins

Abstract

A global atmospheric analysis and forecast system is constructed based on the atmospheric component of the Model for Prediction Across Scales (MPAS-A) and the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The system is constructed using the unstructured MPAS-A Voronoi (nominally hexagonal) mesh and thus facilitates multiscale analysis and forecasting without the need for developing new covariance models at different scales. Cycling experiments with the assimilation of real observations show that the global ensemble system is robust and reliable throughout a one-month period for both quasi-uniform and variable-resolution meshes. The variable-mesh assimilation system consistently provides higher-quality analyses than those from the coarse uniform mesh, in addition to the benefits of the higher-resolution forecasts, which leads to substantial improvements in 5-day forecasts. Using the fractions skill score, the spatial scale for skillful precipitation forecasts is evaluated over the high-resolution area of the variable-resolution mesh. Skill decreases more rapidly at smaller scales, but the variable mesh consistently outperforms the coarse uniform mesh in precipitation forecasts at all times and thresholds. Use of incremental analysis updates (IAU) greatly decreases high-frequency noise overall and improves the quality of EnKF analyses, particularly in the tropics. Important aspects of the system design related to the unstructured Voronoi mesh are also investigated, including algorithms for handling the C-grid staggered horizontal velocities.

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Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

Abstract

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

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David M. Schultz, Altuğ Aksoy, Jeffrey Anderson, Tommaso Benacchio, Kristen L. Corbosiero, Matthew D. Eastin, Clark Evans, Jidong Gao, Almut Gassman, Joshua P. Hacker, Daniel Hodyss, Matthew R. Kumjian, Ron McTaggart-Cowan, Glen Romine, Paul Roundy, Angela Rowe, Elizabeth Satterfield, Russ S. Schumacher, Stan Trier, Christopher Weiss, Henry P. Huntington, and Gary M. Lackmann
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