Zhaoxia Pu

Sensitivity of Numerical Simulation of Early Rapid Intensification of Hurricane Emily (2005) to Cloud Microphysical and Planetary Boundary Layer Parameterizations

Abstract An advanced research version of the Weather Research and Forecasting (ARW) Model is used to simulate the early rapid intensification of Hurricane Emily (2005) using grids nested to high resolution (3 km). A series of numerical simulations is conducted to examine the sensitivity of the simulation to available cloud microphysical (CM) and planetary boundary layer (PBL) parameterization schemes. Results indicate that the numerical simulations of the early rapid intensification of Hurricane Emily are very sensitive to the choice of CM and PBL schemes in the ARW model. Specifically, with different CM schemes, the simulated minimum central sea level pressure (MSLP) varies by up to 29 hPa, and the use of various PBL schemes has resulted in differences in the simulated MSLP of up to 19 hPa during the 30-h forecast period. Physical processes associated with the above sensitivities are investigated. It is found that the magnitude of the environmental vertical wind shear is not well correlated with simulate...

Evaluation of bogus vortex techniques with four-dimensional variational data assimilation

Abstract The effectiveness of a four-dimensional variational data assimilation (4DVAR) technique for creating “bogus” vortices in numerical simulations of hurricanes is evaluated in this study. A series of numerical experiments is conducted to generate initial vortices for Hurricane Georges and Bonnie (1998) in the Atlantic Ocean by assimilating bogus sea level pressure and wind information into a mesoscale numerical model (MM5). Several different strategies are tested for investigating the sensitivity of the initial vortex representation to the type of bogus information. While some of the results in this study confirm conclusions made in previous studies, some significant differences are obtained regarding the role of bogus wind data in creating a realistic bogus vortex. In contrast with previous studies in which the bogus wind data had only a marginal impact on creating a realistic hurricane, this study concludes that the wind information is very important because 1) with assimilation of only bogus sea ...

The MATERHORN: Unraveling the Intricacies of Mountain Weather

AbstractEmerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multi-institutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platfor...

Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

AbstractThe performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the models inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terr...

LIDAR-MEASURED WIND PROFILES The Missing Link in the Global Observing System

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues. Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone. This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that ...

Impact of Airborne Doppler Radar Data Assimilation on the Numerical Simulation of Intensity Changes of Hurricane Dennis near a Landfall

Abstract Accurate forecasting of a hurricane’s intensity changes near its landfall is of great importance in making an effective hurricane warning. This study uses airborne Doppler radar data collected during the NASA Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 to examine the impact of airborne radar observations on the short-range numerical simulation of hurricane track and intensity changes. A series of numerical experiments is conducted for Hurricane Dennis (2005) to study its intensity changes near a landfall. Both radar reflectivity and radial velocity–derived wind fields are assimilated into the Weather Research and Forecasting (WRF) model with its three-dimensional variational data assimilation (3DVAR) system. Numerical results indicate that the radar data assimilation has greatly improved the simulated structure and intensity changes of Hurricane Dennis. Specifically, the assimilation of radar reflectivity data shows a notable influence on the thermal and hydrometeor ...

The Impact of Aircraft Dropsonde and Satellite Wind Data on Numerical Simulations of Two Landfalling Tropical Storms during the Tropical Cloud Systems and Processes Experiment

Abstract Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study. The results presented herein indicate the following. 1) Assimilation of dropwi...

Application of the Quasi-Inverse Method to Data Assimilation

Four-dimensional variational data assimilation (4D-Var) seeks to find an optimal initial field that minimizes a cost function defined as the squared distance between model solutions and observations within an assimilation window. For a perfect linear model, Lorenc showed that the 4D-Var forecast at the end of the window coincides with a Kalman filter analysis if two conditions are fulfilled: (a) addition to the cost function of a term that measures the distance to the background at the beginning of the assimilation window, and (b) use of the Kalman filter background error covariance in this term. The standard 4D-Var requires minimization algorithms along with adjoint models to compute gradient information needed for the minimization. In this study, an alternative method is suggested based on the use of the quasi-inverse model that, for certain applications, may help accelerate the solution of problems close to 4D-Var. The quasi-inverse approach for the forecast sensitivity problem is introduced, and then a closely related variational assimilation problem using the quasi-inverse model is formulated (i.e., the model is integrated backward but changing the sign of the dissipation terms). It is shown that if the cost function has no background term, and has a complete set of observations (as assumed in many classical 4D-Var papers), the new method solves the 4D-Var-minimization problem efficiently, and is in fact equivalent to the Newton algorithm but without having to compute a Hessian. If the background term is included but computed at the end of the interval, allowing the use of observations that are not complete, the minimization can still be carried out very efficiently. In this case, however, the method is much closer to a 3D-Var formulation in which the analysis is attained through a model integration. For this reason, the method is called ‘‘inverse 3D-Var’’ (I3D-Var). The I3D-Var method was applied to simple models (viscous Burgers’ equation and Lorenz model), and it was found that when the background term is ignored and complete fields of noisy observations are available at multiple times, the inverse 3D-Var method minimizes the same cost function as 4D-Var but converges much faster. Tests with the Advanced Regional Prediction System (ARPS) indicate that I3D-Var is about twice as fast as the adjoint Newton method and many times faster than the quasi-Newton LBFGS algorithm, which uses the adjoint model. Potential problems (including the growth of random errors during the integration back in time) and possible applications to preconditioning, and to problems such as storm-scale data assimilation and reanalysis are also discussed.

Variations Associated with Cores and Gaps of a Pacific Narrow Cold Frontal Rainband

Abstract A NOAA P-3 instrumented aircraft observed an intense, fast-moving narrow cold frontal rainband (NCFR) as it approached the California coast on 19 February 2001 during the Pacific Coastal Jets Experiment. Airborne Doppler radar data obtained while the frontal system was well offshore indicated that a narrow ribbon of very high radar reflectivity convective cores characterized the rainband at low levels with echo tops to ∼4–5 km, and pseudo-dual-Doppler analyses showed the low-level convergence of the prefrontal air. The NCFR consisted of gaps of weaker reflectivity and cores of stronger reflectivity along its length, perhaps as a result of hydrodynamic instability along its advancing leading edge. In contrast to some earlier studies of cold frontal rainbands, density-current theory described well the motion of the overall front. The character of the updraft structure along the NCFR varied systematically along the length of the precipitation cores and in the gap regions. The vertical shear of the c...

Ensemble Kalman filter assimilation of near-surface observations over complex terrain: comparison with 3DVAR for short-range forecasts

ABSTRACT Surface observations are the main conventional observations for weather forecasts. However, in modern numerical weather prediction, the use of surface observations, especially those data over complex terrain, remains a unique challenge. There are fundamental difficulties in assimilating surface observations with three-dimensional variational data assimilation (3DVAR). In this study, a series of observing system simulation experiments is performed with the ensemble Kalman filter (EnKF), an advanced data assimilation method to compare its ability to assimilate surface observations with 3DVAR. Using the advanced research version of the Weather Research and Forecasting (WRF) model, results from the assimilation of observations at a single observation station demonstrate that the EnKF can overcome some fundamental limitations that 3DVAR has in assimilating surface observations over complex terrain. Specifically, through its flow-dependent background error term, the EnKF produces more realistic analysis increments over complex terrain in general. More comprehensive comparisons are conducted in a short-range weather forecast using a synoptic case with two severe weather systems: a frontal system over complex terrain in the western US and a low-level jet system over the Great Plains. The EnKF is better than 3DVAR for the analysis and forecast of the low-level jet system over flat terrain. However, over complex terrain, the EnKF clearly performs better than 3DVAR, because it is more capable of handling surface data in the presence of terrain misrepresentation. In addition, results also suggest that caution is needed when dealing with errors due to model terrain representation. Data rejection may cause degraded forecasts because data are sparse over complex terrain. Owing to the use of limited ensemble sizes, the EnKF analysis is sensitive to the choice of horizontal and vertical localisation scales.