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|Ali, M. M., Bhat, G. S., Long, D. G., Bharadwaj, S., & Bourassa, M. A. (2013). Estimating Wind Stress at the Ocean Surface From Scatterometer Observations. IEEE Geosci. Remote Sensing Lett., 10(5), 1129–1132.|
|Ali, M. M., Bhowmick, S. A., Sharma, R., Chaudhury, A., Pezzullo, J. C., Bourassa, M. A., et al. (2015). An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 8(11), 5317–5323.|
|Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements. IEEE Geosci. Remote Sensing Lett., 13(6), 821–825.|
|Bentamy, A., Piollé, J. F., Grouazel, A., Danielson, R., Gulev, S., Paul, F., et al. (2017). Review and assessment of latent and sensible heat flux accuracy over the global oceans. Remote Sensing of Environment, 201, 196–218.|
Bhardwaj, A., & Misra, V. (2019). Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products. Remote Sensing, 11(9), 1080.
Abstract: We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions. View Full-Text
|Campagnolo, M. L., Sun, Q., Liu, Y., Schaaf, C., Wang, Z., & Román, M. O. (2016). Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS. Remote Sensing of Environment, 175, 52–64.|
|Daneshgar Asl, S., Dukhovskoy, D. S., Bourassa, M., & MacDonald, I. R. (2017). Hindcast modeling of oil slick persistence from natural seeps. Remote Sensing of Environment, 189, 96–107.|
Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., et al. (2020). FluxSat: Measuring the Ocean-Atmosphere Turbulent Exchange of Heat and Moisture from Space. Remote Sensing, 12(11), 1796.
Abstract: Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean-atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air-sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean-atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean-atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.
Keywords: air-sea interactions; mesoscale; fluxes
|Holbach, H. M., & Bourassa, M. A. (2017). Platform and Across-Swath Comparison of Vorticity Spectra From QuikSCAT, ASCAT-A, OSCAT, and ASCAT-B Scatterometers. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 10(5), 2205–2213.|
Morey, S., Wienders, N., Dukhovskoy, D., & Bourassa, M. (2018). Measurement Characteristics of Near-Surface Currents from Ultra-Thin Drifters, Drogued Drifters, and HF Radar. Remote Sensing, 10(10), 1633.
Abstract: Concurrent measurements by satellite tracked drifters of different hull and drogue configurations and coastal high-frequency radar reveal substantial differences in estimates of the near-surface velocity. These measurements are important for understanding and predicting material transport on the ocean surface as well as the vertical structure of the near-surface currents. These near-surface current observations were obtained during a field experiment in the northern Gulf of Mexico intended to test a new ultra-thin drifter design. During the experiment, thirty small cylindrical drifters with 5 cm height, twenty-eight similar drifters with 10 cm hull height, and fourteen drifters with 91 cm tall drogues centered at 100 cm depth were deployed within the footprint of coastal High-Frequency (HF) radar. Comparison of collocated velocity measurements reveals systematic differences in surface velocity estimates obtained from the different measurement techniques, as well as provides information on properties of the drifter behavior and near-surface shear. Results show that the HF radar velocity estimates had magnitudes significantly lower than the 5 cm and 10 cm drifter velocity of approximately 45% and 35%, respectively. The HF radar velocity magnitudes were similar to the drogued drifter velocity. Analysis of wave directional spectra measurements reveals that surface Stokes drift accounts for much of the velocity difference between the drogued drifters and the thin surface drifters except during times of wave breaking.
Keywords: surface drifters; surface currents; HF Radar