Ground-based microwave radiometer profilers in the 20-60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecast-ing during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events (∼235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied "quasi-operationally," i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 g/m 3 for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 kg/m 2 , with small bias (−0.1 kg/m 2 ) and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within ∼0.60 km.
[1] In addition to a proper radar calibration, quantitative estimation of precipitation from radars also requires the extraction of the precipitation signal out of the Doppler spectra. It also requires the proper conversion of this precipitation signal into reflectivity factor. This study shows how the measurement of rainfall and vertical air velocities can be performed using only observations from a radar operating at the VHF band (i.e., meter wavelengths). We verify the assumption that the dielectric factor jKj 2 = 0.93 is valid for rain observations in the VHF band. We then derive, analytically and numerically, a more general version of the radar equation valid for vertically pointing radars with targets within a few kilometers range but still within the antenna far-field region. Following this, we describe a new algorithm for extraction of rain signal out of VHF Doppler spectra. To validate our methods, we made colocated measurements of VHF Doppler spectra aloft and raindrop sizes at the ground. The analytical version of our radar equation compares well with similar equations available in current literature, and this validates the particular case of our numerically derived radar equation. We combine our numerical version of the radar equation and our algorithm for extracting precipitation signal. This combination allows us to obtain reflectivity factors (from rain signals) and vertical velocities (from air signals), these being simultaneous observations within the same sampling volume. From the data set collected, we found good agreement (linear correlation coefficient around 0.8) between the rain signals derived from VHF observations aloft and from drop sizes at ground level. Hence we are able to measure rainfall amounts and vertical air velocities in a simpler and more efficient way, using only observations from a VHF wind profiler. This represents a promising step toward the analysis of precipitation from large sets of radar data.
Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. The financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements towards the areas that are most beneficial to power systems operations.
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