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So-Young Ha and Chris Snyder

Abstract

The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach was successfully performed in the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed (DART) system. The mesoscale cycling experiment for the continuous ensemble data assimilation was verified against independent surface mesonet observations and demonstrated the positive impact on short-range forecasts over the contiguous U.S. (CONUS) domain throughout the month-long period of June 2008. The EnKF assimilation of surface observations was found useful for systematically improving the simulation of the depth and the structure of the planetary boundary layer (PBL) and the reduction of surface bias errors. These benefits were extended above PBL and resulted in a better precipitation forecast for up to 12 h. With the careful specification of observation errors, not only the reliability of the ensemble system but also the quality of the following forecast was improved, especially in moisture. In this retrospective case study of a squall line, assimilation of surface observations produced analysis increments consistent with the structure and dynamics of the boundary layer. As a result, it enhanced the horizontal gradient of temperature and moisture across the frontal system to provide a favorable condition for the convective initiation and the following heavy rainfall prediction in the Oklahoma Panhandle. Even with the assimilation of upper-level observations, the analysis without the assimilation of surface observations simulated a surface cold front that was much weaker and slower than observed.

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Soyoung Ha, Judith Berner, and Chris Snyder

Abstract

Mesoscale forecasts are strongly influenced by physical processes that are either poorly resolved or must be parameterized in numerical models. In part because of errors in these parameterizations, mesoscale ensemble data assimilation systems generally suffer from underdispersiveness, which can limit the quality of analyses. Two explicit representations of model error for mesoscale ensemble data assimilation are explored: a multiphysics ensemble in which each member’s forecast is based on a distinct suite of physical parameterization, and stochastic kinetic energy backscatter in which small noise terms are included in the forecast model equations. These two model error techniques are compared with a baseline experiment that includes spatially and temporally adaptive covariance inflation, in a domain over the continental United States using the Weather Research and Forecasting (WRF) Model for mesoscale ensemble forecasts and the Data Assimilation Research Testbed (DART) for the ensemble Kalman filter. Verification against independent observations and Rapid Update Cycle (RUC) 13-km analyses for the month of June 2008 showed that including the model error representation improved not only the analysis ensemble, but also short-range forecasts initialized from these analyses. Explicitly accounting for model uncertainty led to a better-tuned ensemble spread, a more skillful ensemble mean, and higher probabilistic scores, as well as significantly reducing the need for inflation. In particular, the stochastic backscatter scheme consistently outperformed both the multiphysics approach and the control run with adaptive inflation over almost all levels of the atmosphere both deterministically and probabilistically.

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Joseph B. Klemp, William C. Skamarock, and Soyoung Ha

Abstract

Although the equations of motion for a compressible atmosphere accommodate acoustic waves, these modes typically play an insignificant role in atmospheric processes of physical interest. In numerically integrating the compressible equations, it is often beneficial to filter these acoustic modes to control acoustic noise and prevent its artificial growth. Here, a new technique is proposed for filtering the 3D divergence that may damp acoustic modes more effectively than filters previously implemented in numerical modes using horizontally explicit vertically implicit (HEVI) and split-explicit time integration schemes. With this approach, a divergence damping term is added as a final adjustment to the horizontal velocity at the new time level after completing the vertically implicit portion of the time step. In this manner, the divergence used in the filter term has exactly the same numerical form as that used in the discrete pressure equation. Analysis of the dispersion equation for this form of the filter documents its stability characteristics and confirms that it effectively damps acoustic modes with little artificial influence on the amplitude or propagation of the gravity wave modes that are of physical interest. Some specific aspects of the implementation of the filter in the Model for Prediction Across Scales (MPAS) are discussed, and results are presented to illustrate some of the beneficial aspects of suppressing acoustic noise.

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Zhiquan Liu, Craig S. Schwartz, Chris Snyder, and So-Young Ha

Abstract

The impact of assimilating radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on forecasts of several tropical cyclones (TCs) was studied using the Weather Research and Forecasting Model (WRF) and a limited-area ensemble Kalman filter (EnKF). Analysis/forecast cycling experiments with and without AMSU-A radiance assimilation were performed over the Atlantic Ocean for the period 11 August–13 September 2008, when five named storms formed. For convenience, the radiance forward operators and bias-correction coefficients, along with the majority of quality-control decisions, were computed by a separate, preexisting variational assimilation system. The bias-correction coefficients were obtained from 3-month offline statistics and fixed during the EnKF analysis cycles. The vertical location of each radiance observation, which is required for covariance localization in the EnKF, was taken to be the level at which the AMSU-A channels’ weighting functions peaked.

Deterministic 72-h WRF forecasts initialized from the ensemble-mean analyses were evaluated with a focus on TC prediction. Assimilating AMSU-A radiances produced better depictions of the environmental fields when compared to reanalyses and dropwindsonde observations. Radiance assimilation also resulted in substantial improvement of TC track and intensity forecasts with track-error reduction up to 16% for forecasts beyond 36 h. Additionally, assimilating both radiances and satellite winds gave markedly more benefit for TC track forecasts than solely assimilating radiances.

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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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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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William C. Skamarock, Michael G. Duda, Soyoung Ha, and Sang-Hun Park

Abstract

A regional configuration of the atmospheric component of the Model for Prediction Across Scales (MPAS-A) is described and evaluated. It employs horizontally unstructured spherical centroidal Voronoi meshes (nominally hexagonal), and lateral boundary conditions used in rectangular grid regional models are adapted to the MPAS-A Voronoi mesh discretization. Test results using a perfect-model assumption show that the lateral boundary conditions are stable and robust. As found in other regional modeling studies, configurations using larger regional domains generally have smaller solution errors compared to configurations employing smaller regional domains. MPAS-A supports variable-resolution meshes, and when regional domains are expanded to include a coarser outer mesh, the variable-resolution configurations recover most of the error reduction compared to a configuration using uniform high resolution, and at much-reduced cost. The wider relaxation-zone region of the variable-resolution mesh also helps reconcile differences near the lateral boundary that evolve between the regional model solution and the driving solution, and the configuration is more stable than one using a uniform high-resolution regional mesh. At convection-permitting resolution, solutions produced using global variable-resolution MPAS-A configurations have smaller solution errors than the regional configurations after about 48 h.

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