Abstract. Global satellite observations of lidar backscatter measurements acquired by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and collocated sea surface wind speed data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), are used to investigate the relation between wind driven wave slope variance and sea surface wind speed. The new slope variance -wind speed relation established from this study is similar to the linear relation from Cox-Munk (1954) and the log-linear relation from Wu (1990) for wind speed larger than 7 m/s and 13.3 m/s, respectively. For wind speed less than 7 m/s, the slope variance is proportional to the square root of the wind speed, assuming a two dimensional isotropic Gaussian wave slope distribution. This slope variance -wind speed relation becomes linear if a one dimensional Gaussian wave slope distribution and linear slope variance -wind speed relation are assumed. Contributions from whitecaps and subsurface backscattering are effectively removed by using 532 nm lidar depolarization measurements. This new slope variance -wind speed relation is used to derive sea surface wind speed from CALIPSO single shot lidar measurements (70 m spot size), after correcting for atmospheric attenuation. The CALIPSO wind speed result agrees with the collocated AMSR-E wind speed, with 1.2 m/s rms error. Ocean surface with lowest atmospheric loading and moderate wind speed (7-9 m/s) is used as target for lidar calibration correction.Correspondence to: Y. Hu (yongxiang.hu-1@nasa.gov)
Abstract. Global satellite observations of lidar backscatter measurements acquired by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and collocated sea surface wind speed data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), are used to investigate the relation between wind driven wave slope variance and sea surface wind speed. The new slope variance – wind speed relation established from this study is similar to the linear relation from Cox-Munk (1954) and the log-linear relation from Wu (1972, 1990) for wind speed larger than 7 m/s and 13.3 m/s, respectively. For wind speed less than 7 m/s, the slope variance is proportional to the square root of the wind speed, assuming a two dimensional isotropic Gaussian wave slope distribution. This slope variance – wind speed relation becomes linear if a one dimensional Gaussian wave slope distribution is assumed. Contributions from whitecaps and subsurface backscattering are effectively removed by using 532 nm lidar depolarization measurements. This new slope variance – wind speed relation is used to derive sea surface wind speed from CALIPSO single shot lidar measurements (70 m spot size), after correcting for atmospheric attenuation. The CALIPSO wind speed result agrees with the collocated AMSR-E wind speed, with 1.2 m/s rms error.
Las áreas rururbanas, por su condición de borde, están sometidas a transformaciones provocadas por el crecimiento urbano y sufren los impactos asociados al proceso de globalización. Ambos factores imponen formas de apropiación y utilización de los recursos que obligan a los residentes a cambiar sus rutinas cotidianas y adoptar nuevas estrategias. En este artículo se caracterizan las funciones, el uso del suelo y las prácticas socioespaciales de los residentes del rururbano de Bahía Blanca, así como la incidencia de las presiones globales-urbanas y del sentido de pertenencia en la predisposición a cambiar de modo de vida y destino de las tierras.
<p>Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.</p>
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