LAGRANGIAN ANALYSIS OF PRECIPITATION CELLS USING SATELLITE, RADAR, AND LIGHTNING OBSERVATIONS

The nowcasting of precipitation events, especially ones associated with intense thunderstorms, remains a challenging endeavor that suffers from relatively low skill and high false alarm rates. In order to further our understanding of severe weather systems, we have performed a Lagrangian analysis of precipitation cells over Germany combining geostationary satellite retrievals with radar and lightning observations. The radar and lightning signatures of tracked precipitation cells have been composited with cloud macroand microphysical retrievals from Meteosat-9 rapid scan data, provided by the EUMETSAT Climate Monitoring and Nowcasting/Very Short-Range Forecasting Science Application Facilities. The life cycles of radar reflectivity, cell size, cloud optical thickness, drop effective radius, cloud water path, and total lightning have been analyzed for a large number of tracks. The mean time evolution of precipitation systems has then been characterized by synchronizing individual events relative to various measures of peak intensity (maximum optical thickness, droplet size, lightning tendency, etc.). These diagnostics will later be used to evaluate operational severe weather forecasts at the German Weather Service and devise improved methods for the prediction of convective initiation. DATA AND METHODOLOGY Precipitation tracks have been obtained from the German Weather Service’s operational KONRAD (convective development in radar products) scheme. KONRAD detects and tracks a cell in 5-minute 2D radar data as long as a continuous area with reflectivity exceeding 46 dBZ of at least 12 km2 exists. For each radar cell location the corresponding cloud optical thickness, droplet effective radius, and liquid/ice water path have been determined by spatially averaging the 5-min SEVIRI rapid scan retrievals over a small domain centered on the precipitation object. Total lightning (TL) data from the LIghtning detection NETwork (LINET) have also been included in the composite dataset. The results presented below are based on those 55 days from April-August 2011 that had a reasonable number of tracks during daytime –when satellite microphysical retrievals are available– resulting in a total of ~1,700 precipitation tracks. As an example, KONRAD tracks with a duration exceeding 30 minutes are plotted for a summer day in Figure 1. The radar-based precipitation tracks are dominated by short-lived events, but in our analysis we have imposed a 30-minute minimum lifetime threshold to ensure a fair number of 5-minute satellite retrievals for lifecycle analysis. Note that most cells are advected by the prevailing winds from the southwest to the northeast, which is typical for our entire dataset. Color indicates changes in the size of the precipitation cell. Apparently, KONRAD detects rain events at different stages of development and usually does not capture the full evolution of a storm from initiation through mature stage to dissipation. Figure 1: KONRAD precipitation tracks with a lifetime > 30 minutes on 22 June 2011. Color indicates cell size from smaller (blue) to larger (red). The evolution of satellite-derived cloud properties along two particular KONRAD tracks is given in Figure 2. In trajectory #2108 (left column), the cell was detected at a mature stage with optical thickness > 100 and was tracked as its optical thickness decreased. Interestingly, cloud-top effective radius showed the opposite evolution and steadily increased; this behaviour, that is opposite trends in optical thickness and effective radius, was fairly common along KONRAD tracks. Ice water path, which is proportional to the product of optical thickness and effective radius, also decreased because it is usually dominated by the evolution of optical thickness. In trajectory #1965, on the other hand, the cell was detected at an early stage and was tracked as its optical thickness, effective radius, and ice water path generally increased. In both cases the precipitation cell was detected when cloud top had already glaciated –this was true for all tracks in our dataset. Earlier stages of storm development could be captured by extrapolating the radar tracks backward, as indicated by the magenta data points. Cloud optical thickness steadily decreased along both backtrajectories, although cell #2108 remained in the ice phase. Cell #1965, in contrast, showed a smooth transition to the liquid phase. We note that a single KONRAD track might contain several individual rain cells, as suggested by the cyclic evolution of cloud properties along trajectory #1965 (see the multiple maxima, especially in effective radius). This is a distinct possibility if short-lived, adjacent and consecutive rain cells within a large cloud system are lumped together as a single long-lived event. Later on this might necessitate segmenting a trajectory into individual subsystems, for the time being, however, our unit of analysis is a precipitation track as identified by KONRAD. Figure 2: Evolution of satellite-derived cloud properties along KONRAD track #2108 (left column) and #1965 (right column), from top to bottom: cloud optical thickness, drop effective radius, cloud water path, and thermodynamic phase. Here, times ≥ 0 correspond to the radar-detected cell locations, which are also colored according to cell size. Negative time values (colored magenta) refer to locations obtained by linearly extrapolating the track backwards, up to 45 minutes, using the radar-determined advection speed at the first cell location. As discussed above, precipitation radar tracks usually capture the parent clouds at different phases in their lifecycles. Therefore, tracks have to be time-synchronized to obtain a coherent composite picture of mean storm evolution. We have synchronized tracks relative to the occurrence of the peak value in a given observed parameter, the time of which is set to 0 to provide a common temporal reference point. Selecting different synchronization parameters can shed light on different aspects of storm development. In the following we show a couple of examples. COMPOSITE MEAN CLOUD PROPERTIES ALONG KONRAD TRACKS When tracks are synchronized relative to the time of peak effective radius (Re), the following mean picture emerges (see Figure 3). During the growing phase of Re prior to its maximum, cloud optical thickness, ice water path, and total lightning remain relatively constant with only small variations. When Re starts to drop after reaching its maximum, however, the other three quantities show a strong increase. On average, precipitation cell size tends to be larger in the decaying phase of Re, reaching its maximum extent when Re is minimum and optical thickness, ice water path, and flash count are maximum. Figure 3: Evolution of satellite-retrieved cloud properties and ground-based total lightning count averaged over ~1,700 KONRAD tracks and synchronized to the time of maximum effective radius. Color indicates mean cell size. Overall, we have found a strong positive correlation between total lightning and optical thickness (or ice water path) during both Re phases (Figure 4, left panel). This result is consistent with previous studies based on space-borne or ground-based radar measurements and conveys the straightforward message that larger, thicker, wetter clouds produce more lightning. The mean relationship between total lightning and effective radius, on the other hand, appears more complex (Figure 4, right panel). For a given effective radius, flash count tends to be considerably lower in the growing than the decaying phase. In addition, lightning is rather uncorrelated with Re in the growing phase but shows a strong negative correlation with Re in the decaying phase. At this stage, we can only speculate about the physical processes responsible for these observational results. The literature seems to agree on the crucial role large ice particles play in lightning generation. Some theories postulate that lightning activity increases when large ice drops, produced during the strong updraft phase of convection, start to fall from the upper cloud regions into the mixed-phase main charge separation region deeper in the cloud. On the face of it, such a physical mechanism would be consistent with our finding that flash count increases as cloud-top effective radius decreases. Figure 4: Mean total lightning as a function of mean cloud optical thickness (left) and mean cloud-top effective radius (right), as taken from Figure 3. Data before and after the maximum effective radius are plotted in blue and red, respectively. effective radius optical thickness total lightning ice water path TIME LAG BETWEEN PEAK LIGHTNING AND PEAK EFFECTIVE RADIUS To further investigate the relationship between lightning and cloud-top effective radius, we have calculated for each track the time difference between the peaks in lightning and effective radius, and binned these time differences as a function of maximum total lightning (see Figure 5). When maximum total lightning is relatively small (<50), total lightning and effective radius tends to peak simultaneously. For stronger storms with larger maximum total lightning, however, peak lightning increasingly lags peak effective radius. The lag time reaches 25-30 minutes for the most intense storms with a maximum total lightning of several hundred flashes. This finding gives further support to the mechanism coupling effective radius and lightning described in the previous paragraph. That is, the lag time probably corresponds to the time required for large cloud-top ice particles to fall deeper in the cloud and intensify charge separation. Figure 5: Mean time difference between peak lightning and peak effective radius binned according to peak lightning. Here, time is counted from the first cell position in each track; hence, positive time differences indicate peak lightning lagging peak effective radius. HAIL WARNING VERSUS LIGHTNING ACTIVIT