The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies

Kawanishi, Toneo; Sezai, Toshihiro; Ito, Yoshiyasu; Imaoka, Keiji; Takeshima, T.; Ishido, Yoshinari; Shibata, Akira; Miura, Minoru; Inahata, H.; Spencer, Roy W.

doi:10.1109/tgrs.2002.808331

Cited by 480 publications

(153 citation statements)

References 12 publications

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“…We use data products from the A-Train constellation (Stephens et al 2002;L'Ecuyer and Jiang 2010 Here, we use Column Water Vapor (CWV) from the AMSR-E sensor (Kawanishi et al 2003) derived from the version 7 Remote Sensing System algorithms Meissner 2000, 2007). The CWV has an expected precision of 1 kgm -2 .…”

Section: Characterizing Aggregation In Clouds and Their Environmentmentioning

confidence: 99%

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles

2017

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Data from several coincident satellite sensors are analyzed to determine the dependence of cloud and precipitation characteristics of tropical regions on the variance in the water vapor field. Increased vapor variance is associated with decreased high cloud fraction and an enhancement of low-level radiative cooling in dry regions of the domain. The result is found across a range of sea surface temperatures and rain rates. This suggests the possibility of an enhanced low-level circulation feeding the moist convecting areas when vapor variance is large. These findings are consistent with idealized models of self-aggregation, in which the aggregation of convection is maintained by a combination of low-level radiative cooling in dry regions and mid-to-upper-level radiative warming in cloudy regions.

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Section: Characterizing Aggregation In Clouds and Their Environmentmentioning

confidence: 99%

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles

2017

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“…In the past two decades, various studies have demonstrated that soil moisture can be retrieved by passive satellite missions, such as the Soil Moisture and Ocean Salinity (SMOS) mission [1,2], the Soil Moisture Active/Passive (SMAP) mission [3], the Special Sensor Microwave/Imager (SSM/I) mission, the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)and Advanced Microwave Scanning Radiometer 2 (AMSR2) mission [4][5][6][7][8]. Soil moisture data sets acquired from all of these passive missions are widely used.…”

Section: Introductionmentioning

confidence: 99%

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Zhang

Ping

et al. 2017

Remote Sensing

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Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product.

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“…In this radiative transfer scheme, horizontal and vertical polarization is considered only at the surface. Rigorous satellite orbit, sensor scanning, and associated antenna gain functions of the AMSR-E [Kawanishi et al, 2003] are incorporated to achieve a realistic representation of microwave sensor-observable Tb . Figure 4c shows the geolocation error of raining pixels.…”

Section: Aqua Amsr-e Microwave Tbmentioning

confidence: 99%

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Matsui

Santanello

Shi

et al. 2014

J. Geophys. Res. Atmos.

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Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

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The advanced microwave scanning radiometer for the earth observing system (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies

Cited by 480 publications

References 12 publications

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

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