Weihs, R., & Bourassa, M. A. (2012). A comparison of modeled diurnally varying sea surface temperatures to geostationary satellite data. In
IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 5764–5766).
Weissman, D. E., & Bourassa, M. A. (2009). The combined effect of surface rain and wind on scatterometer observations of surface roughness. In
2009 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Cape Town, South Africa (I-pp. 108– I-111).
Weissman, D. E., & Bourassa, M. A. (2008). Measurements of the Effect of Rain-Induced Sea Surface Roughness on the QuikSCAT Scatterometer Radar Cross Section.
IEEE Trans. Geosci. Remote Sensing, 46(10), 2882–2894.
Weissman, D. E., & Bourassa, M. A. (2011). The Influence of Rainfall on Scatterometer Backscatter Within Tropical Cyclone Environments-Implications on Parameterization of Sea-Surface Stress. In
IEEE Transactions on Geoscience and Remote Sensing (Vol. 49, pp. 4805–4814).
Weissman, D. E., & Bourassa, M. A. (2011). The effect of rain on ASCAT observations of the sea surface radar cross section using simultaneous 3-d NEXRAD rain measurements. In
IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 1171–1174).
Weissman, D. E., Bourassa, M. A., O'Brien, J. J., & Tongue, J. S. (2003). Calibrating the quikscat/seawinds radar for measuring rainrate over the oceans.
IEEE Trans. Geosci. Remote Sensing, 41(12), 2814–2820.
Weissman, D. E., Morey, S., & Bourassa, M. (2017). Studies of the effects of rain on the performance of the SMAP radiometer surface salinity estimates and applications to remote sensing of river plumes. In
IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 1491–1494).
Weissman, D. E., H. Winterbottom, and M. A. Bourassa. (2010). Studies of the influence of rainfall upon scatterometer estimates for sea surface stress: applications to boundary layer parameterization and drag coefficient models within tropical cyclone environments. In
2010 IEEE International Geoscience and Remote Sensing Symposium (pp. 4154–4157).
Wentz, F. J., Ricciardulli, L., Rodriguez, E., Stiles, B. W., Bourassa, M. A., Long, D. G., et al. (2017). Evaluating and Extending the Ocean Wind Climate Data Record.
IEEE J Sel Top Appl Earth Obs Remote Sens, 10(5), 2165–2185.
Abstract: Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 sigmao measurements include 1) direct Ku-band sigmao intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.
Zou, M., Xiong, X., Wu, Z., Li, S., Zhang, Y., & Chen, L. (2019). Increase of Atmospheric Methane Observed from Space-Borne and Ground-Based Measurements.
Remote Sensing, 11(8).
Abstract: It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid-upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (X-CH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid-upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009-2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009-2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid-high northern latitude regions is larger than those in the tropics and southern hemisphere.