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  • Author or Editor: Ge Gao x
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Guoqing Ge, Jidong Gao, and Ming Xue

Abstract

A diagnostic pressure equation is incorporated into a storm-scale three-dimensional variational data assimilation (3DVAR) system in the form of a weak constraint in addition to a mass continuity equation constraint (MCEC). The goal of this diagnostic pressure equation constraint (DPEC) is to couple different model variables to help build a more dynamic consistent analysis, and therefore improve the data assimilation results and subsequent forecasts. Observational System Simulation Experiments (OSSEs) are first performed to examine the impact of the pressure equation constraint on storm-scale radar data assimilation using an idealized tornadic thunderstorm simulation. The impact of MCEC is also investigated relative to that of DPEC. It is shown that DPEC can improve the data assimilation results slightly after a given period of data assimilation. Including both DPEC and MCEC yields the best data assimilation results. Sensitivity tests show that MCEC is not very sensitive to the choice of its weighting coefficients in the cost function, while DPEC is more sensitive and its weight should be carefully chosen. The updated 3DVAR system with DPEC is further applied to the 5 May 2007 Greensburg, Kansas, tornadic supercell storm case assimilating real radar data. It is shown that the use of DPEC can speed up the spinup of precipitation during the intermittent data assimilation process and also improve the follow-on forecast in terms of the general evolution of storm cells and mesocyclone rotation near the time of observed tornado.

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Xuan Wang, Romain Husson, Haoyu Jiang, Ge Chen, and Guoping Gao

Abstract

Wave measurements retrieved by Sentinel-1A level-2 ocean (OCN) products are sensitive to swells other than wind seas, and are considered to provide a finer resolution of ocean swells. To assess the capability of swell retrieval globally, OCN products are validated against WAVEWATCH III (WW3) wave spectra for two available incidence angles [“wave mode” (WV); WV1: 23°; WV2: 36°], focused on the integral wave parameters and most energetic wave system of Sentinel-1A. The wave parameter difference between Sentinel-1A and WW3 along antenna look angles for WV1 demonstrates the obvious impact of the nonlinearity influence in the azimuth direction, resulting in an unrealistically high wave height at the low wave frequency, and the spurious split of wave systems in the range direction, due to the vanishing of velocity bunching modulation. WV2 is less pronounced in these two aspects, but tends to shift wave energy to a higher wave frequency in the range direction. The inside discrepancy of wave energy has two noticeable features: the difference in peak wavelengths in the wave spectrum is positively clustered in the azimuth direction and negatively clustered in the range direction; some of the most energetic partitions derived from Sentinel-1A are difficult to assign to any wave systems in WW3. This phenomenon could be related to wind-wave coupling as the azimuth cutoff/WW3 peak wavelength is confined to a ratio below 0.5 for the negative difference between Sentinel-1A and WW3 peak wavelengths and the spectral distance of most energetic wave system in Sentinel-1A highly resembles “swell pools.”

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Guoqing Ge, Jidong Gao, Keith Brewster, and Ming Xue

Abstract

The radar ray path and beam broadening equations are important for assimilation of radar data into numerical weather prediction (NWP) models. They can be used to determine the physical location of each radar measurement and to properly map the atmospheric state variables from the model grid to the radar measurement space as part of the forward observation operators. Historically, different degrees of approximations have been made with these equations; however, no systematic evaluation of their impact exists, at least in the context of variational data assimilation. This study examines the effects of simplifying ray path and ray broadening calculations on the radar data assimilation in a 3D variational data assimilation (3DVAR) system. Several groups of Observational System Simulation Experiments (OSSEs) are performed to test the impact of these equations to radar data assimilation with an idealized tornadic thunderstorm case. This study shows that the errors caused by simplifications vary with the distance between the analyzed storm and the radar. For single time level wind analysis, as the surface range increases, the impact of beam broadening on analyzed wind field becomes evident and can cause relatively large error for distances beyond 150 km. The impact of the earth’s curvature is more significant, even for distances beyond 60 km, because it places the data at the wrong vertical location. The impact of refractive index gradient is also tested. It is shown that the variations of refractive index gradient have a very small impact on the wind analysis results.

Two time series of 1-h-long data assimilation experiments are further conducted to illustrate the impact of the beam broadening and earth curvature on all retrieved model variables. It is shown that all model variables can be retrieved to some degrees in all data assimilation experiments. Similar to the wind analysis experiments, the impacts of both factors are not obvious when radars are relatively close to the storm. When the radars are far from the storm (especially beyond 150 km), overlooking beam broadening degrades the accuracy of assimilation results slightly, whereas ignoring the earth’s curvature leads to significant errors.

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