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  • Arctic Sea Ice Melt Onset Timing From Passive Microwave‐Based and Surface Air Temperature‐Based Methods
    Melt onset (MO) on Arctic sea ice has been monitored using satellite‐based passive microwave (PMW) observations since 1979. In this work, surface air temperatures from the International Arctic Buoy Programme/Polar Exchange at the Sea Surface and NASA's Atmospheric Infrared Sounder are used to derive MO date estimates using three threshold methods which are then compared with a record of PMW‐based MO dates. Results from the PMW data indicate a shift toward increasingly early MO timing, with significant trends as large as −9.45 days/decade in the E. Siberian Sea and −5.69 days/decade for the entire Arctic, consistent with other studies highlighting the overall decline of Arctic sea ice. Results indicate that the surface air temperature‐based MO date estimates produced using a −1 °C threshold are ~11 days later than the PMW‐based MO date estimates Arctic wide. A statistical comparison of the Polar Exchange at the Sea Surface and PMW‐based MO dates indicate that despite the ~11‐day bias, correlations between the MO date time series are generally good (≥0.6) for most of the Arctic Ocean while the Atmospheric Infrared Sounder and PMW‐based MO dates are generally better at capturing the statistically similar long‐term trends in MO dates for the Arctic and several Arctic subregions. Application of these results can contribute to the development of new methods to monitor the sea ice melting state. more
  • Convergence Issues in the Estimation of Interchannel Correlated Observation Errors in Infrared Radiance Data
    A posteriori consistency diagnostics have been used in recent years to estimate correlated observation error. These diagnostics provide an estimate of what the observation error covariances should be and could, in turn, be introduced in the assimilation to improve the statistical consistency between the error statistics used in the assimilation and those obtained from observation departures with respect to the background and the analysis. To estimate the observation error covariances, it is often assumed that the background error statistics are optimal, an assumption that is open to criticism. The consequence is that if the background error covariances are in error, then the estimated observation error statistics will adjust accordingly to fit the innovation error covariances. In this paper, the RTTOV radiative transfer model is used as the observation operator. Using controlled experiments, the background error is considered fixed, and it is shown that the iterative procedure to estimate the observation error may require more than one iteration. It is also shown that the underlying matrix equation being solved can be factorized, and the exact solution can be obtained. If the true background error covariances are used in the assimilation, the estimated observation error covariances are then obtained by subtracting the background error covariances from those of the innovations. This can be applied to the full set of assimilated observations. Using the Environment Canada assimilation system, the results for several types of observations indicate that the background error estimation would deserve additional attention. more
  • Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions
    We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals. more
  • Observed differences between near-surface air and skin temperatures using satellite and ground-based data, Theoretical and Applied Climatology
    Accurate estimates of long-term land surface temperature (Ts) and near-surface air temperature (Ta) at finer spatio-temporal resolutions are crucial for surface energy budget studies, for environmental applications, for land surface model data assimilation, and for climate change assessment and its associated impacts. The Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua satellite provide a unique opportunity to estimate both temperatures twice daily at the global scale. In this study, differences between Ta and Ts were assessed locally over regions of North America from 2009 to 2013 using ground-based observations covering a wide range of geographical, topographical, and land cover types. The differences between Ta and Ts during non-precipitating conditions are generally 2–3 times larger than precipitating conditions. However, these differences show noticeable diurnal and seasonal variations. The differences between Ta and Ts were also investigated at the global scale using the AIRS estimates under clear-sky conditions for the period 2003–2015. The tropical regions showed about 5–20 °C warmer Ts than Taduring the day-time, whereas opposite characteristics (about 2–5 °C cooler Ts than Ta) are found over most parts of the globe during the night-time. Additionally, Ts estimates from the AIRS and the MODIS sensors were inter-compared. Although large-scale features of Ts were essentially similar for both sensors, considerable differences in magnitudes were observed (> 6 °C over mountainous regions). Finally, Ta and Ts estimates from the AIRS and MODIS sensors were validated against ground-based observations for the period of 2009–2013. The error characteristics notably varied with ground stations and no clear evidence of their dependency on land cover types or elevation was detected. However, the MODIS-derived Ts estimates generally showed larger biases and higher errors compared to the AIRS-derived estimates. The biases and errors increased steadily when the spatial resolution of the MODIS estimates changed from finer to coarser. These results suggest that representativeness error should be properly accounted for when validating satellite-based temperature estimates with point observations. more