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An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI–open ocean–atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014–2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements. [ABSTRACT FROM AUTHOR]

A time-series-based methodology has been used for the estimation of soil moisture from AMSR-E 10.7-GHz H-polarized brightness temperature over India. The estimated soil moisture has been validated with in situ soil moisture and compared with the NSIDC AMSR-E soil moisture product. The soil moisture estimated using the present algorithm shows improvement over NSIDC soil moisture products over India. The present algorithm shows an root-mean-squared error of 8.8% with in situ soil moisture, which is 12.9% for the NSIDC products over Indian agromet stations. The sensitivity in the soil moisture is captured well from the present algorithm; however, it is limited to regions with low to moderate vegetation cover having low temporal variability. The algorithm is location specific that is tuned over India to provide better estimates than achievable using the NSIDC global algorithm. This will help in improving the mesoscale monsoon prediction of rainfall over India. [ABSTRACT FROM PUBLISHER]

This paper presents a conceptual conical-scanning radiometer/scatterometer (RadScat) instrument design for the purpose of improving satellite ocean vector wind retrievals under rain-free conditions. This technique combines the wind vector signature in the passive linearly polarized ocean brightness temperatures with the anisotropic signature of multiazimuthal radar cross-sectional measurements to retrieve oceanic surface wind vectors. The performance of the RadScat is evaluated using a Monte Carlo simulation based on actual measurements from the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer onboard the Advanced Earth Observing Satellite II. The results demonstrate significant improvements in wind vector retrievals, particularly in the near-subtrack swath, where the performance of conical-scanning scatterometers degrades. [ABSTRACT FROM AUTHOR]

Part of the Earth Observing System Aqua Advanced Microwave Scanning Radiometer (AMSR-E) Arctic sea ice validation campaign in March 2003 was dedicated to the validation of snow depth on sea ice and ice temperature products. The difficulty with validating these two variables is that neither can currently be measured other than in situ. For this reason, two aircraft flights on March 13 and 19, 2003, were dedicated to these products, and flight lines were coordinated with in situ measurements of snow and sea ice physical properties. One flight was in the vicinity of Barrow, AK, covering Elson Lagoon and the adjacent Chukchi and Beaufort Seas. The other flight was farther north in the Beaufort Sea (about 730 N, 147.5° W) and was coordinated with a Navy ice camp. The results confirm the AMSR-E snow depth algorithm and its coefficients for first-year ice when it is relatively smooth. For rough first-year ice and for multiyear ice, there is still a relationship between the spectral gradient ratio of 19 and 37 GHz, but a different set of algorithm coefficients is necessary. Comparisons using other AMSR-E channels did not provide a clear signature of sea ice characteristics and, hence, could not provide guidance for the choice of algorithm coefficients. The limited comparison of in situ snow-ice interface and surface temperatures with 6-GHz brightness temperatures, which are used for the retrieval of ice temperature, shows that the 6-GHz temperature is correlated with the snow-ice interface temperature to only a limited extent. For strong temperature gradients within the snow layer, it is clear that the 6-GHz temperature is a weighted average of the entire snow layer. [ABSTRACT FROM AUTHOR]

Accurate knowledge of snow-depth distribution over sea ice is critical for polar climate studies. Current snow-depth- over-sea-ice retrieval algorithms do not sufficiently account for variations in snow and ice physical properties that can affect the accuracy of retrievals. For this reason, airborne microwave observations were coordinated with ground-based measurements of snow depth and snow properties in the vicinity of Barrow, AK, in March 2003. In this paper, the effects of snowpack properties and ice conditions on microwave signatures are examined using detailed surface-based measurements and airborne observations in conjunction with a thermal microwave-emission model. A comparison of the Microwave Emission Model of Layered Snowpacks (MEMLS) simulations with detailed snowpack and ice data from stakes along the Elson Lagoon and the Beaufort Sea and radiometer data taken from low-level flights using a Polarimetric Scanning Radiometer (PSR-A) shows that MEMLS can be used to simulate snow on sea ice and is a useful tool for understanding the limitations of the snow-depth algorithm. Analysis of radiance data taken over the Elson Lagoon and the Beaufort Sea using MEMLS suggests that the radiometric differences between the two locations are due to the differences in sea-ice emissivity. Furthermore, measured brightness temperatures suggest that the current snow-depth retrieval algorithm is sufficient for areas of smooth first-year sea ice, whereas new algorithm coefficients are needed for rough first-year sea ice. Snowpack grain size and density remain an unresolved issue for snow-depth retrievals using passive-microwave radiances. [ABSTRACT FROM AUTHOR]

The multispectral sensing capabilities afforded by the 36-channel Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Earth Observing System (EOS) Terra and Aqua satellites have the potential to improve satellite-derived cloud and precipitation products. Included in this channel suite are spectral bands having particular sensitivity to both cloud vertical distribution and near cloud-top microphysics. EOS Aqua, the local afternoon crossing satellite, carries in addition to MODIS the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), a conically scanning passive microwave (PMW) instrument with 12 channels between 6.9 and 89 GHz. While limited by revisit time from low-earth orbit, Aqua provides an ideal test bed for investigating high refresh-rate, geostationary-based blended PMW/optical-spectrum techniques related to improved cloud characterization and precipitation. As the need for improved subdaily quantitative precipitation analysis has grown in recent years, blended-satellite techniques have taken on added relevance. In this paper, we present an application of the MODIS/AMSR-E sensor combination related to potential improvements to the Naval Research Laboratory (NRL)-developed blended-satellite precipitation technique, involving the characterization of cirrus clouds. The presence of cirrus clouds above and nearby to both convective and stratiform precipitation imposes a limit to the utilization of longwave (>10 μm wavelength) thermal infrared channels for precipitation techniques. The established "split window" technique involving the 11-12-μm brightness temperature difference (BTD) perform well for thin cirrus. We demonstrate that the 1.38-μm channel (and the 3.7-11-μm BTD at night) on MODIS, when combined with additional channels, is capable of decoupling thin surrounding cirrus from thicker ice clouds. This information may be useful for screening thin cirrus that is often falsely interpreted as light precipitation. Radiative transfer simulations are used to demonstrate the theoretical basis for the selected multispectral channel combinations, and examples involving daytime and night-time Aqua overpasses are presented. [ABSTRACT FROM AUTHOR]

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RI?! at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing. [ABSTRACT FROM AUTHOR]