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the cirrus research community regarding the contribution of the small particle mode to cirrus bulk properties with that argument being derived principally from data collected in situ. Following Mitchell et al. (2010) we examine remote sensing data to shed light on this issue except that we use a ground-based dataset consisting of extinction derived from Raman lidar combined with coincident Doppler velocity and radar reflectivity measured by Ka-band Doppler radar. We assume, as did Ivanova et al
the cirrus research community regarding the contribution of the small particle mode to cirrus bulk properties with that argument being derived principally from data collected in situ. Following Mitchell et al. (2010) we examine remote sensing data to shed light on this issue except that we use a ground-based dataset consisting of extinction derived from Raman lidar combined with coincident Doppler velocity and radar reflectivity measured by Ka-band Doppler radar. We assume, as did Ivanova et al
both MOD13A2 and MOD13C1/C2 and can assume values from 0 (0000: best quality) to 15 (1111: not useful). 4. Results and discussion Remote sensing of the Amazon forests at solar wavelengths is complicated due to persistent cloud and aerosols presence. Atmospheric corruption of surface reflectance data, from which the vegetation indices are evaluated, depends on prevailing cloud and aerosol optical thicknesses, among other factors ( Vermote and Kotchenova 2008 ; Tanre et al. 1992 ). The two data
both MOD13A2 and MOD13C1/C2 and can assume values from 0 (0000: best quality) to 15 (1111: not useful). 4. Results and discussion Remote sensing of the Amazon forests at solar wavelengths is complicated due to persistent cloud and aerosols presence. Atmospheric corruption of surface reflectance data, from which the vegetation indices are evaluated, depends on prevailing cloud and aerosol optical thicknesses, among other factors ( Vermote and Kotchenova 2008 ; Tanre et al. 1992 ). The two data
conditions. However, our experience with climate modeling shows that phenology is a critical aspect in global climate models. Phenology models are problematic because of the large uncertainties in modeling. So far, only a few studies assess phenology models with satellite observation data (e.g., Lüdeke et al. 1996 ; Randerson et al. 2009 ), and the reason for this clearly lies in the difficulty of determining comparable phenological events from different datasets, models, and remote sensing
conditions. However, our experience with climate modeling shows that phenology is a critical aspect in global climate models. Phenology models are problematic because of the large uncertainties in modeling. So far, only a few studies assess phenology models with satellite observation data (e.g., Lüdeke et al. 1996 ; Randerson et al. 2009 ), and the reason for this clearly lies in the difficulty of determining comparable phenological events from different datasets, models, and remote sensing
Variability of Water Vapor in Central Mexico from Two Remote Sensing Techniques: FTIR Spectroscopy and GPSthe study of water vapor have been performed in, for example, Zugspitze ( Sussmann and Borsdorff 2009 ), Kiruna ( Buehler et al. 2012 ), Izaña ( Schneider et al. 2010b ), and Mauna Loa, Hawaii ( Ortega et al. 2019 ). Other studies have already focused on the intercomparison between different remote sensing techniques, as that of Zugspitze ( Vogelmann et al. 2011 , 2015 ), Saint Petersburg ( Semenov et al. 2015 ), and in Addis Ababa ( Tsidu et al. 2015 ), this latter study also with GPS. From the
the study of water vapor have been performed in, for example, Zugspitze ( Sussmann and Borsdorff 2009 ), Kiruna ( Buehler et al. 2012 ), Izaña ( Schneider et al. 2010b ), and Mauna Loa, Hawaii ( Ortega et al. 2019 ). Other studies have already focused on the intercomparison between different remote sensing techniques, as that of Zugspitze ( Vogelmann et al. 2011 , 2015 ), Saint Petersburg ( Semenov et al. 2015 ), and in Addis Ababa ( Tsidu et al. 2015 ), this latter study also with GPS. From the
many mechanisms that constitute land–atmosphere interaction (coupling) is viewed by the World Climate Research Programme (WCRP) Global Energy and Water Cycle Experiment (GEWEX) as a key step toward improving short-to-medium-range weather forecast skill. A multiyear global categorization of coupling is unprecedented and only now is feasible with remote sensing. In the past, coupling research has largely focused on the analysis of large-scale model outputs. Betts (2004) developed many of the
many mechanisms that constitute land–atmosphere interaction (coupling) is viewed by the World Climate Research Programme (WCRP) Global Energy and Water Cycle Experiment (GEWEX) as a key step toward improving short-to-medium-range weather forecast skill. A multiyear global categorization of coupling is unprecedented and only now is feasible with remote sensing. In the past, coupling research has largely focused on the analysis of large-scale model outputs. Betts (2004) developed many of the
Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats1. Introduction Satellite-based radiometry missions of the ocean require validation of data products by in situ measurements to assess and improve, if necessary, the accuracy of satellite-derived quantities ( Mueller et al. 2003b ). The most fundamental of the quantities estimated by satellites are water-leaving radiance and remote sensing reflectance (the ratio of upwelling radiance to downwelling irradiance at the ocean surface). These are used to determine optical and biological
1. Introduction Satellite-based radiometry missions of the ocean require validation of data products by in situ measurements to assess and improve, if necessary, the accuracy of satellite-derived quantities ( Mueller et al. 2003b ). The most fundamental of the quantities estimated by satellites are water-leaving radiance and remote sensing reflectance (the ratio of upwelling radiance to downwelling irradiance at the ocean surface). These are used to determine optical and biological
models and remote sensing observations, Charlson et al. (2007) had noted that “current estimates of aerosol climate forcing rely on the conventional expression for albedo … that ‘direct’ and ‘indirect’ aerosol effects are separately calculated for the clear and cloudy portions of Earth. This approach would be appropriate if the atmosphere consisted entirely of nearly clear and overcast regions …. The existence of partly cloudy regions and the fact that the clear-cloudy distinction is ambiguous
models and remote sensing observations, Charlson et al. (2007) had noted that “current estimates of aerosol climate forcing rely on the conventional expression for albedo … that ‘direct’ and ‘indirect’ aerosol effects are separately calculated for the clear and cloudy portions of Earth. This approach would be appropriate if the atmosphere consisted entirely of nearly clear and overcast regions …. The existence of partly cloudy regions and the fact that the clear-cloudy distinction is ambiguous
remote sensing evaporative stress index (ESI), representing temporal anomalies in the ratio of actual evapotranspiration (ET) to potential ET (PET). In contrast with precipitation-based indices, the ESI algorithm requires no information about antecedent precipitation or subsurface soil characteristics. In this modeling approach, time-differential land surface temperature (LST) measurements derived from satellite imagery collected by the Geostationary Operational Environmental Satellites (GOES) in the
remote sensing evaporative stress index (ESI), representing temporal anomalies in the ratio of actual evapotranspiration (ET) to potential ET (PET). In contrast with precipitation-based indices, the ESI algorithm requires no information about antecedent precipitation or subsurface soil characteristics. In this modeling approach, time-differential land surface temperature (LST) measurements derived from satellite imagery collected by the Geostationary Operational Environmental Satellites (GOES) in the
1. Introduction A major goal of remote sensing of ice in clouds is the measurement of cloud optical properties because ice-forming clouds can influence Earth’s radiative properties ( Fig. 10-1 ). Figure 10-1a indicates that the magnitude of the solar radiative cooling of mixed-phase clouds strongly depends on the ice content. The more ice is in the cloud, the less the solar cooling effect. This is mostly a result of the decreasing optical thickness of the cloud when the ice content increases
1. Introduction A major goal of remote sensing of ice in clouds is the measurement of cloud optical properties because ice-forming clouds can influence Earth’s radiative properties ( Fig. 10-1 ). Figure 10-1a indicates that the magnitude of the solar radiative cooling of mixed-phase clouds strongly depends on the ice content. The more ice is in the cloud, the less the solar cooling effect. This is mostly a result of the decreasing optical thickness of the cloud when the ice content increases
dataset used. In this study, we will focus on soil moisture dataset dependency, because our analysis does not rely on precipitation observations (see below). Many studies infer soil moisture–precipitation feedback from a single source of soil moisture information, whether in situ measurement, remote sensing observation, or model simulation. Many studies have evaluated the differences between these soil moisture datasets (e.g., Albergel et al. 2012 ; Su et al. 2013 ; Tuttle and Salvucci 2014
dataset used. In this study, we will focus on soil moisture dataset dependency, because our analysis does not rely on precipitation observations (see below). Many studies infer soil moisture–precipitation feedback from a single source of soil moisture information, whether in situ measurement, remote sensing observation, or model simulation. Many studies have evaluated the differences between these soil moisture datasets (e.g., Albergel et al. 2012 ; Su et al. 2013 ; Tuttle and Salvucci 2014
