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Abstract
A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Ze –S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (∼3–4 dBZe ), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZe . The average conditional global snowfall rate is calculated to be about 0.28 mm h−1, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized. The potential of future multifrequency spaceborne radars is evaluated using proxy 35–13.6-GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement dual-frequency precipitation radar (DPR). It is estimated that because of its higher detectability threshold, only about 7%–1% of the near-surface radar reflectivity values and about 17%–4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.
Abstract
A dataset consisting of one year of CloudSat Cloud Profiling Radar (CPR) near-surface radar reflectivity Z associated with dry snowfall is examined in this study. The CPR observations are converted to snowfall rates S using derived Ze –S relationships, which were created from backscatter cross sections of various nonspherical and spherical ice particle models. The CPR reflectivity histograms show that the dominant mode of global near-surface dry snowfall has extremely light reflectivity values (∼3–4 dBZe ), and an estimated 94% of all CPR dry snowfall observations are less than 10 dBZe . The average conditional global snowfall rate is calculated to be about 0.28 mm h−1, but is regionally highly variable as well as strongly sensitive to the ice particle model chosen. Further, ground clutter contamination is found in regions of complex terrain even when a vertical reflectivity continuity threshold is utilized. The potential of future multifrequency spaceborne radars is evaluated using proxy 35–13.6-GHz reflectivities and sensor specifications of the proposed Global Precipitation Measurement dual-frequency precipitation radar (DPR). It is estimated that because of its higher detectability threshold, only about 7%–1% of the near-surface radar reflectivity values and about 17%–4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument, but these results are very sensitive to the chosen ice particle model. These potential detection shortcomings can be partially mitigated by using snowfall-rate distributions derived by the CPR or other similar high-frequency active sensors.
Abstract
The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.
Abstract
The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.
