Turbulence characteristics over tropical station Gadanki (13.5°N, 79.2°E) estimated using high-resolution GPS radiosonde data

2010 ◽  
Vol 115 (D7) ◽  
Author(s):  
Debashis Nath ◽  
M. Venkat Ratnam ◽  
A. K. Patra ◽  
B. V. Krishna Murthy ◽  
S. Vijaya Bhaskar Rao
Keyword(s):  
High Resolution ◽  
Radiosonde Data ◽  
Turbulence Characteristics ◽  
Tropical Station ◽  
Gps Radiosonde

Diurnal variation of ducts observed over a tropical station, Gadanki, using high-resolution GPS radiosonde observations

Radio Science ◽  
2016 ◽  
Vol 51 (4) ◽  
pp. 247-258 ◽  
Author(s):  
G. Manjula ◽  
M. Roja Raman ◽  
M. Venkat Ratnam ◽  
A. V. Chandrasekhar ◽  
S. Vijaya Bhaskara Rao
Keyword(s):  
High Resolution ◽  
Diurnal Variation ◽  
Tropical Station ◽  
Gps Radiosonde

scholarly journals Gravity wave characteristics observed over a tropical station using high-resolution GPS radiosonde soundings

2009 ◽  
Vol 114 (D6) ◽  
Author(s):  
Debashis Nath ◽  
M. Venkat Ratnam ◽  
V. V. M. Jagannadha Rao ◽  
B. V. Krishna Murthy ◽  
S. Vijaya Bhaskara Rao
Keyword(s):  
High Resolution ◽  
Gravity Wave ◽  
Wave Characteristics ◽  
Tropical Station ◽  
Gps Radiosonde

scholarly journals Anomalous propagation conditions observed over a tropical station using high-resolution GPS radiosonde observations

Radio Science ◽  
2013 ◽  
Vol 48 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Ghouse Basha ◽  
M. Venkat Ratnam ◽  
G. Manjula ◽  
A. V. Chandra Sekhar
Keyword(s):  
High Resolution ◽  
Anomalous Propagation ◽  
Tropical Station ◽  
Gps Radiosonde

scholarly journals A Comparison between Gravity Wave Momentum Fluxes in Observations and Climate Models

2013 ◽  
Vol 26 (17) ◽  
pp. 6383-6405 ◽  
Author(s):  
Marvin A. Geller ◽  
M. Joan Alexander ◽  
Peter T. Love ◽  
Julio Bacmeister ◽  
Manfred Ern ◽  
...  
Keyword(s):  
High Resolution ◽  
Gravity Wave ◽  
Gravity Waves ◽  
Climate Models ◽  
Radiosonde Data ◽  
Wave Source ◽  
Wide Range ◽  
Momentum Fluxes ◽  
High Resolution Models ◽  
Wave Momentum

Abstract For the first time, a formal comparison is made between gravity wave momentum fluxes in models and those derived from observations. Although gravity waves occur over a wide range of spatial and temporal scales, the focus of this paper is on scales that are being parameterized in present climate models, sub-1000-km scales. Only observational methods that permit derivation of gravity wave momentum fluxes over large geographical areas are discussed, and these are from satellite temperature measurements, constant-density long-duration balloons, and high-vertical-resolution radiosonde data. The models discussed include two high-resolution models in which gravity waves are explicitly modeled, Kanto and the Community Atmosphere Model, version 5 (CAM5), and three climate models containing gravity wave parameterizations, MAECHAM5, Hadley Centre Global Environmental Model 3 (HadGEM3), and the Goddard Institute for Space Studies (GISS) model. Measurements generally show similar flux magnitudes as in models, except that the fluxes derived from satellite measurements fall off more rapidly with height. This is likely due to limitations on the observable range of wavelengths, although other factors may contribute. When one accounts for this more rapid fall off, the geographical distribution of the fluxes from observations and models compare reasonably well, except for certain features that depend on the specification of the nonorographic gravity wave source functions in the climate models. For instance, both the observed fluxes and those in the high-resolution models are very small at summer high latitudes, but this is not the case for some of the climate models. This comparison between gravity wave fluxes from climate models, high-resolution models, and fluxes derived from observations indicates that such efforts offer a promising path toward improving specifications of gravity wave sources in climate models.

scholarly journals The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data

2014 ◽  
Vol 7 (2) ◽  
pp. 1393-1455
Author(s):  
X. J. Sun ◽  
R. W. Zhang ◽  
G. J. Marseille ◽  
A. Stoffelen ◽  
D. Donovan ◽  
...  
Keyword(s):  
Optical Properties ◽  
High Resolution ◽  
Relative Humidity ◽  
Weather Prediction ◽  
Error Variance ◽  
Radiosonde Data ◽  
Atmospheric Conditions ◽  
Wind Profiles ◽  
Height Assignment ◽  
Aerosol Backscatter

Abstract. The ESA Aeolus mission aims to measure wind profiles from space. In preparation for launch we aim to assess the expected bias in retrieved winds from the Mie and Rayleigh channel signals induced by atmospheric heterogeneity. Observation biases are known to be detrimental when gone undetected in Numerical Weather Prediction (NWP). Aeolus processing equipment should therefore be prepared to detect heterogeneous atmospheric scenes and take measures, e.g., reject or reduce the weight of observations when used in NWP. Radiosondes provide the wind vector at about 10 m resolution. We present a method to simulate co-located cloud and aerosol optical properties from radiosonde observations. We show that cloud layers can be detected along the radiosonde path from radiosonde measured relative humidity and temperature. A parameterization for aerosol backscatter and extinction along the radiosonde path is presented based on a climatological aerosol backscatter profile and radiosonde relative humidity. The resulting high-resolution database of atmospheric wind and optical properties serves as input for Aeolus wind simulations. It is shown that Aeolus wind error variance grows quadratically with bin size and the wind-shear over the bin. Strong scattering aerosol or cloud layers may cause biases exceeding 1ms−1 for typical tropospheric conditions and 1 km Mie channel bin size, i.e., substantially larger than the mission bias requirement of 0.4 ms−1. Advanced level-2 processing of Aeolus winds including estimation of atmosphere optical properties is needed to detect regions with large heterogeneity, potentially yielding biased winds. Besides applicable for Aeolus the radiosonde database of co-located high-resolution wind and cloud information can be used for the validation of atmospheric motion wind vectors (AMV) or to correct their height assignment errors.

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