A study of the error covariance matrix of radar rainfall estimates in stratiform rain

The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground is quantified toward deriving and understanding the error covariance matrix of these estimates. The focus here is on stratiform precipitation at a resolution of 15 km, which is most relevant for data assimilation onto mesoscale numerical models. In the characterization of the error structure, the following contributions are considered: (i) the individual effect of the range-dependent error (associated with beam broadening and increasing height of radar measurements with range), (ii) the error associated with the transformation from reflectivity to rain rate due to the variability of drop size distributions, and (iii) the interaction of the first two, that is, the term resulting from the cross correlation between the effects of the range-dependent error and the uncertainty related to the variability of drop size distributions (DSDs). For this purpose a large database of S-band radar observations at short range (where reflectivity near the ground is measured and the beam is narrow) is used to characterize the range-dependent error within a simulation framework, and disdrometric measurements collocated with the radar data are used to assess the impact of the variability of DSDs. It is noted that these two sources of error are well correlated in the vicinity of the melting layer as result of the physical processes that determine the density of snow (e.g., riming), which affect both the DSD variability and the vertical profile of reflectivity.Postprint (published version

Scale analysis of the diurnal cycle of precipitation over Continental United States

Rainfall initiation is related to diurnal and semidiurnal radiation forcing (e.g. Wallace 1975, Carbone et al. 2002, Surcel et al. 2010). Much of the observed warm season rainfall results from a thermodynamic response to strong diurnal cycle of land surface temperature. Therefore, over some continental regions deep convection tends to peak around local afternoon and early evening hours. However, there is regional uniqueness in the precipitation pattern that implies a connection between regional characteristics and the behaviour of the precipitation field (Wallace 1975, Carbone et al. 2002, Lee et al. 2007). Over western US the diurnal precipitation pattern becomes well organized with a late afternoon maximum along the eastern slopes of the Rocky Mountains (Carbone et al. 2002, Ahijevych et al. 2004, among others). This mountain-initiated convection tends to propagate away, leading to the local evening maximum over the adjacent plains (Lee et al. 2007). The daily occurrence of propagating systems has a high impact on the continental diurnal cycle of precipitation. Parker and Ahijevych (2007) found that approximately 90% of the episodes identified in the east-central US were due to propagating systems from the west. A consequence of these systems result on the transport of the diurnal cycle from west to east (Surcel et al. 2010). The objective of this work is to study the scale dependence of the diurnal cycle and the variability of the rainfall field with the location and time of the day, with special focus on the role of the different spatial scales in such variability.Postprint (published version

Scale characterization and correction of diurnal cycle errors in MAPLE

The most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumptionmade on its formulation, such as the fact that the motion vectors do not change and, evenmore important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle inMAPLEprecipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.Peer ReviewedPostprint (published version

A methodology for attenuation correction based on the minimization of a cost function

Postprint (published version

The diurnal cycle of precipitation from continental radar mosaics and numerical weather prediction models. Part II: intercomparison among numerical models and with nowcasting

This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting. The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic,which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar. Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill. The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 1038–858W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.Postprint (published version

Improving radar rainfall measurement stability using mountain returns in real time.

Postprint (published version