Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing

Bobylev, Leonid; Zabolotskikh, E. V.; Mitnik, L.M.; Mitnik, M.L.

doi:10.1109/tgrs.2009.2028018

Cited by 54 publications

(41 citation statements)

References 49 publications

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“…We validated this algorithm using a database of collocated in time and space AMSR-E and buoy wind speed measurement data. The validation results showed that the total retrieval error was 1.2 m/s for the algorithm using a new geophysical model and 2.7 m/s for the algorithm using an old one [21].…”

Section: Resultsmentioning

confidence: 99%

“…The Neural Networks algorithms for integrated atmospheric water parameter retrievals from SSM/I and AMSR-E measurement data were developed on simulated brightness temperatures. They were extensively validated against in situ data and led to significant (40%) retrieval accuracy increase [21]. This model will be referenced as Ros92, though actually it is the combination of several empirical wind induced emissivity models.…”

Section: Ocean Emission Modelmentioning

confidence: 99%

“…The methodology for the algorithm development is described in detail in [21]. First, we carried out the simulations of the brightness temperatures for the input data sets of atmospheric meteorological parameter profiles and ocean surface parameter data (sea surface temperature, salinity and wind speed).…”

Section: Model Applicationmentioning

confidence: 99%

“…The algorithm details are beyond the scope of this paper but we summarize here the validation results. The algorithm was validated using Japan Aerospace Exploration Agency (JAXA) database of more than 15,700 collocated AMSR-E and buoy wind speed measurements with the total retrieval error of about 1.2 m/s for the algorithm using a new geophysical model and about 2.7 m/s for the algorithm using an old one, described in [21]. The data were spatially collocated within 50 km distance from the buoy locations.…”

Section: Model Applicationmentioning

confidence: 99%

“…The algorithm development, based on simulated brightness temperatures, can be successfully performed using the selected geophysical model and brightness temperature corrections for handling model/calibration inconsistencies. In this way, we developed a new Neural Network algorithm for sea surface wind speed retrievals from AMSR-E data, using the same approach as described in [21] and suggested geophysical model. The new geophysical model resulted in new possibilities associated with the adequate performance of the new algorithm under high wind conditions.…”

Section: Introductionmentioning

confidence: 99%

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An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

2014

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Abstract:In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments-for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data.

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Section: Resultsmentioning

confidence: 99%

Section: Ocean Emission Modelmentioning

confidence: 99%

Section: Model Applicationmentioning

confidence: 99%

Section: Model Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

2014

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Polar low climatology over the Nordic and Barents seas based on satellite passive microwave data

Smirnova

Golubkin

Bobylev

et al. 2015

Geophysical Research Letters

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A new climatology of polar lows over the Nordic and Barents seas for 14 seasons (1995/1996–2008/2009) is presented. For the first time in climatological studies of polar lows an approach based on satellite passive microwave data was adopted for polar low identification. A total of 637 polar lows were found in 14 extended winter seasons by combining total atmospheric water vapor content and sea surface wind speed fields retrieved from Special Sensor Microwave/Imager data. As derived, the polar low activity in the Norwegian and Barents Seas is found to be almost equal, and the main polar low genesis area is located northeastward of the North Cape. For the Barents Sea, a significant correlation is found between the number of polar lows and mean sea ice extent. Individual indicative polar low characteristics (i.e., diameter, lifetime, distance traveled, translation speed, and maximum wind speed) are also presented.

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The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts

et al. 2016

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Atmospheric humidity, clouds, precipitation, and evapotranspiration are essential components of the Arctic climate system. During recent decades, specific humidity and precipitation have generally increased in the Arctic, but changes in evapotranspiration are poorly known. Trends in clouds vary depending on the region and season. Climate model experiments suggest that increases in precipitation are related to global warming. In turn, feedbacks associated with the increase in atmospheric moisture and decrease in sea ice and snow cover have contributed to the Arctic amplification of global warming. Climate models have captured the overall wetting trend but have limited success in reproducing regional details. For the rest of the 21st century, climate models project strong warming and increasing precipitation, but different models yield different results for changes in cloud cover. The model differences are largest in months of minimum sea ice cover. Evapotranspiration is projected to increase in winter but in summer to decrease over the oceans and increase over land. Increasing net precipitation increases river discharge to the Arctic Ocean. Over sea ice in summer, projected increase in rain and decrease in snowfall decrease the surface albedo and, hence, further amplify snow/ice surface melt. With reducing sea ice, wind forcing on the Arctic Ocean increases with impacts on ocean currents and freshwater transport out of the Arctic. Improvements in observations, process understanding, and modeling capabilities are needed to better quantify the atmospheric role in the Arctic water cycle and its changes.

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Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing

Cited by 54 publications

References 49 publications

An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

Polar low climatology over the Nordic and Barents seas based on satellite passive microwave data

The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts

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