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Robert A. Warren, Alain Protat, Steven T. Siems, Hamish A. Ramsay, Valentin Louf, Michael J. Manton, and Thomas A. Kane


Calibration error represents a significant source of uncertainty in quantitative applications of ground-based radar (GR) reflectivity data. Correcting it requires knowledge of the true reflectivity at well-defined locations and times during a volume scan. Previous work has demonstrated that observations from certain spaceborne radar (SR) platforms may be suitable for this purpose. Specifically, the Ku-band precipitation radars on board the Tropical Rainfall Measuring Mission (TRMM) satellite and its successor, the Global Precipitation Measurement (GPM) mission Core Observatory satellite together provide nearly two decades of well-calibrated reflectivity measurements over low-latitude regions (±35°). However, when comparing SR and GR reflectivities, great care must be taken to account for differences in instrument sensitivity and frequency, and to ensure that the observations are spatially and temporally coincident. Here, a volume-matching method, developed as part of the ground validation network for GPM, is adapted and used to quantify historical calibration errors for three S-band radars in the vicinity of Sydney, Australia. Volume-matched GR–SR sample pairs are identified over a 7-yr period and carefully filtered to isolate reflectivity differences associated with GR calibration error. These are then used in combination with radar engineering work records to derive a piecewise-constant time series of calibration error for each site. The efficacy of this approach is verified through comparisons between GR reflectivities in regions of overlapping coverage, with improved agreement when the estimated errors are removed.

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Vidhi Bharti, Eric Schulz, Christopher W. Fairall, Byron W. Blomquist, Yi Huang, Alain Protat, Steven T. Siems, and Michael J. Manton


Given the large uncertainties in surface heat fluxes over the Southern Ocean, an assessment of fluxes obtained by European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) product, the Australian Integrated Marine Observing System (IMOS) routine observations, and the Objectively Analyzed Air–Sea Heat Fluxes (OAFlux) project hybrid dataset is performed. The surface fluxes are calculated using the COARE 3.5 bulk algorithm with in situ data obtained from the NOAA Physical Sciences Division flux system during the Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean (CAPRICORN) experiment on board the R/V Investigator during a voyage (March–April 2016) in the Australian sector of the Southern Ocean (43°–53°S). ERA-Interim and OAFlux data are further compared with the Southern Ocean Flux Station (SOFS) air–sea flux moored surface float deployed for a year (March 2015–April 2016) at ~46.7°S, 142°E. The results indicate that ERA-Interim (3 hourly at 0.25°) and OAFlux (daily at 1°) estimate sensible heat flux H s accurately to within ±5 W m−2 and latent heat flux H l to within ±10 W m−2. ERA-Interim gives a positive bias in H s at low latitudes (<47°S) and in H l at high latitudes (>47°S), and OAFlux displays consistently positive bias in H l at all latitudes. No systematic bias with respect to wind or rain conditions was observed. Although some differences in the bulk flux algorithms are noted, these biases can be largely attributed to the uncertainties in the observations used to derive the flux products.

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