Simulations of X-59 low-booms propagated through measured atmospheric profiles in Galveston, Texas

2020 ◽  
Vol 148 (4) ◽  
pp. 2524-2524
Author(s):  
William Doebler
Keyword(s):  
Atmospheric Profiles

Modelling collocation uncertainty of 3D atmospheric profiles

2014 ◽  
Vol 29 (2) ◽  
pp. 417-429 ◽  
Author(s):  
Rosaria Ignaccolo ◽  
Maria Franco-Villoria ◽  
Alessandro Fassò
Keyword(s):  
Atmospheric Profiles

scholarly journals Ozone profile smoothness as a priori information in the inversion of limb measurements

2004 ◽  
Vol 22 (10) ◽  
pp. 3411-3420 ◽  
Author(s):  
V. F. Sofieva ◽  
J. Tamminen ◽  
H. Haario ◽  
E. Kyrölä ◽  
M. Lehtinen
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
A Priori Information ◽  
Ozone Profile ◽  
Stellar Occultation ◽  
Atmospheric Profiles ◽  
Priori Information ◽  
Typical Measurement

Abstract. In this work we discuss inclusion of a priori information about the smoothness of atmospheric profiles in inversion algorithms. The smoothness requirement can be formulated in the form of Tikhonov-type regularization, where the smoothness of atmospheric profiles is considered as a constraint or in the form of Bayesian optimal estimation (maximum a posteriori method, MAP), where the smoothness of profiles can be included as a priori information. We develop further two recently proposed retrieval methods. One of them - Tikhonov-type regularization according to the target resolution - develops the classical Tikhonov regularization. The second method - maximum a posteriori method with smoothness a priori - effectively combines the ideas of the classical MAP method and Tikhonov-type regularization. We discuss a grid-independent formulation for the proposed inversion methods, thus isolating the choice of calculation grid from the question of how strong the smoothing should be. The discussed approaches are applied to the problem of ozone profile retrieval from stellar occultation measurements by the GOMOS instrument on board the Envisat satellite. Realistic simulations for the typical measurement conditions with smoothness a priori information created from 10-years analysis of ozone sounding at Sodankylä and analysis of the total retrieval error illustrate the advantages of the proposed methods. The proposed methods are equally applicable to other profile retrieval problems from remote sensing measurements.

Improved Site-Specific Numerical Prediction of Fog and Low Clouds: A Feasibility Study

2005 ◽  
Vol 20 (4) ◽  
pp. 627-646 ◽  
Author(s):  
Thierry Bergot ◽  
Dominique Carrer ◽  
Joël Noilhan ◽  
Philippe Bougeault
Keyword(s):  
Winter Season ◽  
Integrated Approach ◽  
Atmospheric Measurements ◽  
Forecast System ◽  
Short Term ◽  
Low Clouds ◽  
Atmospheric Profiles ◽  
Low Cloud ◽  
Accurate Forecast ◽  
1D Model

Abstract Accurate short-term forecasts of low ceiling and visibility are vital to air traffic operation, in order to maximize the use of an airport. The research presented here uses specific local observations and a detailed numerical 1D model in an integrated approach. The goal of the proposed methodology is to improve the local prediction of poor visibility and low clouds at Paris’s Charles de Gaulle International Airport. In addition to the development of an integrated observations and model-based forecasting system, this study will try to assess whether or not the increased local observing network yields improvements in short-term forecasts of low ceiling and poor visibility. Tests have been performed in a systematic manner during 5 months (the 2002/03 winter season). Encouraging results show that the inclusion of dedicated observations into the local 1D forecast system provides significant improvement to the forecast. Inspection of events indicates that the improvement in very short-term forecasts is a consequence of the ability of the forecast system to more accurately characterize the boundary layer processes, especially during night. Accurate forecast of low cloud seems more difficult since it strongly depends on the 3D mesoscale flow. This study also demonstrates that the use of a 1D model to forecast fogs and low clouds could only be beneficial if it is associated with local measurements and with a local assimilation scheme. The assimilation procedure used in this study is based on different steps: in the first step the atmospheric profiles are estimated in a one-dimensional variational data assimilation (1DVAR) framework, in the second step these atmospheric profiles are modified when fog and/or low clouds are detected, and in the third step the soil profiles are estimated in order to keep the consistency between the soil state and atmospheric measurements.

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