Evaluation of an algorithm for the detection of brightband signatures M. Franco ∗(1), M. Pfeifer (2), D. Sempere-Torres (1) (1) Grup de Recerca Aplicada en Hidrometeorologia Universitat Politecnica de Catalunya Barcelona, Spain (2) DLR Institut f¨ur Physik der Atmosph¨are Oberpfaffenhofen, Germany Report of the COST Short Term Scientific Mission of Monika Pfeifer to GRAHI, Barcelona 11th June 2007
∗
[email protected]
1. Introduction One of the main goals of radar observations is the determination of the amount of precipitation that is falling at the ground with a high spatial and temporal resolution. In order to provide this information to the endusers, a number of problems related to the observations with radar systems have to be solved. First of all, the observed reflectivities have to be translated to rain rates which is normally done applying a Z-R relationships. However, choosing an appropriate Z-R relationship for a given situation is difficult, as these relationships are directly correlated to the drop size distribution (DSD) of the precipitation. This DSD is known to be highly variable in time and space depending on the dynamical situation and the predominant microphysical processes (Lee and Zawadzki (2005)) which also determine the precipitation type and the dominating hydrometeor type. Therefore, different Z-R relationships have to be applied in stratiform or convective situations, in rain and in snow. Other problems in quantitative precipitation estimation (QPE) are due to the scanning geometry of a radar having the effect that the radar beam, with increasing distance from the radar, intersects the precipitation fields at higher altitudes. While the observations at the nearest range bins normally come from scattering in rain, at a given distance the radar beam reaches the melting layer and the snow. This phase change in the precipitating media is of utmost importance in radar meteorology because the dielectric properties of the particles change accordingly to the melting degree. The result of the phase change is a sudden increase in reflectivity due to the transition from snow to rain which is called the brightband (BB). This discontinuity in the profile of reflectivity makes it especially difficult to apply an appropriate Z-R relationship. Therefore, the detection and correction for the brightband is still one of the most challenging problems in radar meteorology and particularly in QPE, especially in cases when its signature is not easy to define. 1
For improved QPE, a method for the classification of the different precipitation regimes in convective and stratiform parts as well as regions affected by melting processes is needed. Such a classification scheme allows for an optimized choice of a Z-R relationship for the given precipitation type as well as the application of predefined vertical profiles of reflectivities (VPR) in order to correct for the observations at higher altitudes. Sanchez-Diezma et al. (2000) proposed an algorithm for the detection of brightband signatures based on conventional volumetric radar observations. This algorithm has been further developed by Franco et al. (2006) to also distinguish between convective and stratiform precipitation. Before its application, its performance has to be evaluated against observations that provide independent information on the microphysical properties of the precipitation. Such an evaluation can be done employing polarimetric radar observations. In contrast to conventional systems, a polarimetric radar is capable of controlling the polarization state of the transmitted and received electromagnetic wave. As most precipitating particles are not spherical in shape and, thus, appear differently in the two polarization planes, polarimetry gives additional information on the characteristics of the precipitation. Therefore, polarimetric radar systems observe reflectivity as conventional radars but also provide information about the microphysical properties of the precipitation in the volume of interest. Polarimetric signatures are among others dependent on the size, shape, thermodynamic phase, and falling behavior of the individual particles present in the observed volume. Combining the information content of the different polarimetric variables allows for the discrimination of the predominant hydrometeor type within the scanned volume which gives insights into the microphysics of the system (e. g. H¨oller et al. (1994), Vivekanandan et al. (1999), Zrnic et al. (2001)). Within this work, observations by the DLR polarimetric diversity Doppler radar POLDIRAD (Schroth et al. 1988) will be employed as a reference for the
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evaluation of the brightband detection algorithm. POLDIRAD operates at C band (5.5027 GHz, 5.45 cm) and in contrast to most polarimetric radar systems developed for routine measurements, it is fully polarized. This means that besides the standard linear polarization, it can also be driven at circular or elliptical polarizations. Furthermore, POLDIRAD measures, both, the polar as well as the crosspolar signals allowing for the observation of the linear depolarization ratio (LDR ) which normally is not routinely included and has to be substituted by ρHV (e. g. Straka et al. (2000)). The capabilities of POLDIRAD were demonstrated in a number of projects investigating the formation and life cycle of severe weather events including fronts and deep convective systems (e. g. Meischner et al. (1991), H¨oller et al. (1994), Dotzek et al. (2001)). Within this study, polarimetric radar observations will be employed for the evaluation of an algorithm for the detection of the brightband based on conventional volumetric radar data. This will be done applying the BB algorithm to the observations of reflectivity and comparing the results to the observations of LDR . This paper is organized as follows. First, the physics of the melting layer as well as the basic concepts of polarimetric radar observations will be revised in order to facilitate the discussion. Then, the brightband detection algorithm will be introduced. The paper finishes with a discussion of the results and an outlook regarding future work.
2. Polarimetric radar observations During operation, a polarimetric radar alternately transmits a horizontally and vertically polarized electromagnetic wave on a pulse-to-pulse basis while receiving the two polarization states for each pulse. Thus, in addition to reflectivity there are a number of additional parameters available. These are the intrinsic variables providing information about backscatter from hydrometeors in the resolution volume and the propagation variables providing information about hydrometeors between 3
the radar and the resolution volume. This work will focus on LDR as it is used later for the evaluation of the brightband algorithm. When non spherical particles are illuminated by the radar beam, a portion of the incident horizontally polarized wave is depolarized and scattered into the vertical direction. This depolarization can be measured by transmitting horizontally polarized radar signals and measuring both, horizontally and vertically polarized echoes. The linear depolarization ratio (LDR ) is then defined as the logarithm of the ratio of the cross-polar power (zHV ) to the copolar power (zHH ) received:
LDR [dB] = 10 log
zHV , zHH
(1)
In general, LDR depends on the asymmetry of the shapes of the scatterers and on the orientation of the symmetry axes relative to the direction of the incident beam. Depolarization is largest for particles that appear to be canted to the polarization basis and, therefore, LDR increases to maximum values for nonspherical particles oriented at canting angles near 45◦ . The factors determining the amount of depolarization are, thus, the mean shape of the particles and their mean canting angle but also the precipitation phase. Wet ice particles (melting or growing in wet mode) can lead to an increase of LDR due to the enhanced dielectric constant as compared to pure ice. Therefore, LDR is especially useful for the detection of graupel and hail above the melting level as they often consist of a small portion of water increasing the dielectric constant. Moreover, densely rimed ice hydrometeors exhibit a typical tumbling falling behavior, thus, increasing LDR . However, the highest values of LDR of about −15 dB are associated with melting snowflakes (Illingworth 2004) and, therefore, found within the region of the brightband.
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3. Physics of the melting layer Before the description of the algorithm, the most important physical properties of the brightband will be revised in order to facilitate the later discussion. The bright band results from the differences in the dielectric constant between water and ice. This is due to the dielectric constant of pure ice being almost five times smaller than the one for water which can be further reduced for very light ice particles such as snow (Straka et al. 2000). As ice hydrometeors normally do not consist of pure ice nor is the structure of the material homogeneous, the dielectric constant of hydrometeors is highly variable. These variations in dielectric constants result in significant differences in reflectivity for the same amount of precipitating water and cause prominent features such as the increased reflectivities in the brightband. Furthermore, high dielectric constants cause larger depolarizations while polarimetric signature are reduced for low density ice hydrometeors through the dielectric constant (Matrosov et al. 1996). Figure 1 shows measurements (Hagen et al. 1993) of a typical vertical cross section of polarimetric radar quantities throughout the melting level with large discontinuities in reflectivity, [Figure 1 about here.] LDR , and ZDR . Ice particles falling beneath the 0◦ C isotherm start to melt slowly. A water coat evolves around the ice crystal increasing the dielectric constant massively while the crystal size diminishes very slowly through melting. This gradient in reflectivity is further enhanced due to changes in the snow spectrum above the melting layer because of aggregation and below the melting layer because of the rapid outfall of the smaller and heavier rain drops. This results in higher precipitation fluxes in this zone below the melting layer which diminishes reflectivity. The vertical profile shows that the peak values of ZHH , LDR , and ZDR appear at different heights. Maximum ZHH is caused by the largest particles. Maximum LDR indicates heavy tumbling wet ice 5
particles whereas maximum ZDR is reached when oblate particles are orientated horizontally. The LDR peak is observed at higher altitudes than the ZDR peak. This indicates that melting particles are first tumbling and later fall horizontally aligned. The fall velocity is increasing through the melting layer until all particles have been melted and only drops are present.
4. An algorithm for the detection of the brightband employing volumetric observations of reflectivity Sanchez-Diezma et al. (2000) proposed an algorithm for the detection of the brightband based on volumetric observations of reflectivity. The goal of this algorithm is the detection of peaks of reflectivity related to the bright band phenomena in stratiform rain. This is done, analyzing a set of observed VPRs to obtain an approximate estimate of the mean bright band height for the whole radar domain. Then, a refinement of the first step is applied. For all observed VPRs, the values located around this assumed height of the brightband are examined searching for a local maximum of reflectivity whose intensity exceeds a certain threshold. Pixels that fulfill this condition are classified as stratiform. For distances where the lowest PPI is above the bright band, the observed local VPRs are not able to capture the bright band peak. In these regions, a gradient criterium is applied comparing the observed gradient of reflectivity to typical values for brightband situations. This algorithm has been further developed by Franco et al. (2006) to also distinguish between stratiform and convective precipitation. A new criterion is introduced based on the vertical structure of the profile of reflectivity. Convective precipitation usually consists of concentrated cores of high intensity that extend up to the tropopause as high columns or towers. This pattern contrasts strongly with the structure of stratiform precipitation which normally shows lower intensities at
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all heights and also in reflectivity, except for the melting layer where the bright band is produced due to melting snow. Franco et al. (2006) introduced the vertically averaged reflectivity above the apparent brightband as a new quantity for the distinction between stratiform and convective precipitation. This quantity is calculated as:
Z abb =
R hechotop
Z dh , hechotop − hbb hbb
(2)
where Z is the reflectivity factor expressed in mm6 /m3 , hechotop is the echotop height, and hbb is the apparent bright band top. Z abb discriminates stratiform and convective precipitation. However, it is not able to identify shallow convection which only extends to heights near the 0◦ C isotherm.
5. Evaluation of the BB algorithm The algorithm for the detection of the brightband will be tested for two case studies observed by the DLR polarimetric radar POLIDRAD. First, the focus will be on a stratiform precipitation event with a prominent brightband structure. In the second case, a squall line with enhanced reflectivities in the convection and a large trailing region with stratiform precipitation will be discussed. The discussion will concentrate on observations of reflectivity being the basis for the algorithm of brightband detection. Then, the classification of precipitation type and the brightband detection as proposed by Franco et al. (2006) will be evaluated. This algorithm differentiates between regions attributed to the stratiform precipitation, convection, regions affected by the brightband, and regions that can not be classified. The evaluation of the BB algorithm will be based on observations of LDR . In general, all regions with values of LDR larger than -25 dB can be related to wet, tumbling partices. However, the enhanced depolarization can be due to both, melting snow as well as
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ice particles growing in wet modes as graupel or hail. Therefore, for a distinction between convection and the melting layer, further criteria have to applied. This can be the horizontal structure of LDR or information about the vertical extent of both, LDR and reflectivity. During this study, for the further distinction between convective areas and the region of the brightband, the same criteria as in the algorithm by Franco et al. (2006) are employed. Up to now, the evaluation will be on a objective basis, but for future more systematic studies a statistical analyses should be developed.
5a. 3. August 2006
On August 3rd, 2006, a stratiform precipitation event persisted during several hours over southern Bavaria. The general weather situation was dominated by a low over the North Sea and is shown in Figure 2 at 18:45 UTC by the visible channel of Meteosat8.
[Figure 2 about here.] For the evaluation of the brightband algorithm, a volume scan at 18:47 UTC was chosen. At this time, the volume was scanned using 8 elevation angles from 1 to 14◦ with a maximum range of 120 km. [Figure 3 about here.] Figure 3 shows the 1◦ PPI scan of reflectivity (left) together with the corresponding observations of LDR (right). At this time, the precipitation was mainly stratiform distributed over a large area east of the radar. There are some regions with enhanced reflectivities reaching more than 35 dBZ south and north east of the radar. These regions with higher reflectivities are found at a fairly constant distance of approximately 60 km which could be a sign for brightband effects. However, the 8
patchiness of the reflectivity pattern can also be attributed to shallow convection. The observations of LDR show a well defined ring shaped pattern with values above -22 dB as expected in the brightband. As for reflectivity, possible signs for convection are found in LDR . These are the deviation from the ring shape north east of the radar and the very high values up to -15 dB south east of the radar. Furthermore, some enhanced values of LDR are found in the proximity of the radar. These can be clearly attributed to contamination of the observations as for example due to side lobe effects.
[Figure 4 about here.] In the 2◦ PPI scan (Figure 4), the typical ring shape of the brightband can be recognized easily. This is due to the radar beam intersecting the melting layer at a smaller distance from the radar and, hence, more structure in the observations because of less beam broadening. The observations of LDR show still enhanced values south east of the radar beam which, because of its vertical extent, should be attributed to shallow convection. However, northeast of the radar, the 2◦ PPI observations of LDR do not reproduce the deviations from the expected spherical shape of the brightband as in the 1◦ scan and, therefore, at least at this height, no evidence for convective activity is found. Figure 5 shows the 1◦ PPI scan of reflectivity together with a vertical cross section along the white line derived from the volumetric data. Looking at the vertical cross section of reflectivity, the brightband can be clearly defined at distances of 55 to 90 kilometers from the starting point, thus, east of the radar. Furthermore, the decision of attributing the enhanced value of reflectivity and LDR south east of the radar to shallow convection is confirmed. This region is intersected by the vertical cross section at a distance of 30 to 50 km from the initial point and clearly shows enhanced reflectivities from the lowest point of observations at 2.1 km to 3.4 km. Looking at the region 9
north east of the radar, the enhanced reflectivities seem to come from shallow convection reaching maximum heights of 2.9 km. This explains why they do not appear in the 2◦ PPI scan.
[Figure 5 about here.] The algorithm for the detection of the brightband was applied to the volume scan and the results are shown in Figure 6 for the 1◦ and 2◦ PPI together with a classification based on LDR from the polarimetric observations. [Figure 6 about here.] For the 1◦ PPI scan, both the classification from the BB algorithm as well as the one based on the polarimetric data define large regions to be affected by the melting zone. The principal pattern of the BB derived from the two algorithms is the same. Differences appear in the nearest distances from the radar, where the polarimetric algorithm gives BB signatures due to the enhanced values of LDR attributed to side lobe effects. Furthermore, larger differences appear in the region north east of the radar where shallow convection has been found. Here, the algorithm using only reflectivity overestimates the region of the melting layer clearly. Furthermore, there is a slight bias in the height of the melting layer between both algorithms where the one based on LDR defines a larger zone to be affected by melting effects which also extends to lower altitudes as compared to the one based on reflectivity. A possible explanation can be in the different altitude of the peak values of reflectivity and LDR in the melting zone as discussed in Figure 1 where the peak of LDR was found at lower altitudes than the one for reflectivity. In the 2 ◦ elevation scans, the typical shape of the brightband is better defined and the same results as in the 1◦ scans are found. Both algorithms agree in the altitude of the brightband with the algorithm based on LDR defining a larger area and hence a larger vertical extent of the atmosphere to be affected by melting effects. 10
5b. 12. August 2004
On August 12th, 2004 a cold front crossed Germany enforcing the development of severe thunderstorms with high intensities in southern Bavaria. The synoptic scale circulation was characterized by a south-westerly flow at upper levels and easterly winds near the surface. Thunderstorms developed near the Lake of Constance and propagated eastwards from 15:00 to 23:00 UTC. The front developed into a squall line with a sharply defined convective line producing hail and a trailing region of stratiform precipitation. Figure 7 shows the large scale features of the system as seen by the visible channel of METEOSAT8 at 19:00 UTC.
[Figure 7 about here.] The evaluation is based on a volume scan at 19:11 UTC. At this time, 7 elevations were scanned from 1 to 7◦ . Figure 8 shows the 1◦ and 2◦ PPI scans of reflectivity and LDR . [Figure 8 about here.] At this time, the convective line had already passed Munich and the system was already decaying. In the observations, the convective line is clearly visible at a distance of about 100 km east of the radar with reflectivities still reaching more than 45 dBZ. West of the convective line, the precipitation is mainly stratiform with intensities in the order of 20 dBZ. In the observations of LDR , the regions affected by the brightband as well as the convective cores of the squall line are clearly visible. Although the peak values of LDR are comparable in both, the convection and the melting layer, they can be distinguished by the horizontal extent of regions with values exceeding -20 dB. These regions are larger and more homogeneous in the brightband in contrast to the convection where these extreme values are only found in the cores of heavy precipitation containing 11
hail or densely rimed graupel. In the 1◦ PPI scan, the differentiation between the convection and the brightband results to be difficult because both features coincide in the observations. In the 2◦ PPI scans, however, the brightband signature appears at shorter distances from the radar and the distinction between the brightband with its typical ring shape and the convection is easy employing LDR . However, although in the observations of LDR the brightband can be clearly defined, it does not appear as clearly in the 2◦ PPI scan of reflectivity. [Figure 9 about here.] The classification of precipitation type is shown in Figure 9 where the detection of the convective zone has been derived for both schemes using the volumetric data of reflectivity according to the algorithm proposed by Franco et al. (2006). Both algorithms reproduce the same general structure of the brightband. However, the classification of the brightband gives a more patchy pattern and the overall structure is less clear than in the stratiform case. Comparing the algorithms, the algorithms proposed by Sanchez-Diezma et al. (2000) defines larger areas to be affected by the brightband as compared to the observations of LDR . This is especially true west of the radar where several cells at distances nearer to the radar as the expected brightband height are classified to be affected by the brightband. The small values of LDR in these regions confirm that the enhanced reflectivities can be attributed to shallow convection and are not produced by melting snow. In the 2◦ PPI scan, both algorithms agree very well apart from the fact that the algorithm based on LDR defines a larger vertical extent of the precipitation field to be affected by the brightband.
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6. Discussion and Outlook This paper summarizes the first results from an evaluation of an algorithm for the detection of brightband signatures from volumetric observations of reflectivity. The algorithm has been applied to volume scans from the DLR polarimetric diversity radar POLDIRAD. Then, the information from the polarimetric variable LDR has been used for an evaluation of the performance of the brightband detection algorithm. Up to now, the algorithm has been tested on two case studies concentrating on a stratiform and a convective event. In general, the algorithm has been able to detect the regions affected by the melting layer correctly as compared to the observations of LDR . Minor deviations have been found in regions with shallow convection. Furthermore, differences in the vertical extent and in the height of the brightband have been attributed to the different altitudes of the peak values of LDR and reflectivity. The two case studies showed that the algorithm for the detection of brightband signatures is able to reproduce a sensible classification of precipitation types in accordance to the results derived from the polarimetric radar quantities. However, a more systematic evaluation employing a larger data basis is needed to generalize these results. The BB algorithm has been developed for the Spanish radar network scanning 20 elevations per volume scan. The evaluation of the two cases showed that for a correct application of the BB algorithm more PPI scans than the 7-8 scans used by POLDIRAD for a volume scan are needed. A more systematic evaluation may become possible employing data from the COPS experiment taking place in summer 2007 in southwestern Germany where the DLR polarimetric radar POLDIRAD has been transferred to France. Here, it is planned to increase the number of elevations per volume scan in the POLDIRAD scan strategy. Using case studies from the COPS experiment, a systematic evaluation on a statistical basis of the BB 13
algorithm is planned.
Acknowledgement The results were obtained within the framework of a COST Short Term Scientific Mission founded by COST 731.
References Dotzek, N., H. H¨oller, C. Thery, and T. Fehr, 2001: Lightning evolution related to radar-derived microphysics in the 21 July 1998 EULINOX supercell storm. Atmospheric Research, 56, 335– 354. Franco, M., R. Sanchez-Diezma, and D. Sempere-Torres, 2006: Correction of the error realted to the vertical profile of reflectivity: previous partitioning of precipitation types. Proceedings of ERAD, 125–127. Hagen, M., J. Hubbert, C. Richter, V. N. Bringi, and P. Meischner, 1993: Bright band observations with radar and aircraft. 26th International Conference on Radar Meteorology, Norman, American Meteorological Society, 304–305. H¨oller, H., V. Bringi, J. Hubbert, M. Hagen, and P. F. Meischner, 1994: Life cycle and precipitation formation in a hybrid-type hailstorm revealed by polarimetric and Doppler radar measurements. Journal of the Atmospheric Sciences, 51, 2500–2522. Illingworth, A., 2004: Weather Radar, P. F. Meischner, chapter Polarimetric Measurements. 130 – 166.
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Lee, G. W. and I. Zawadzki, 2005: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation. Journal of Applied Meteorology, 44, 241–255. Matrosov, S. Y., R. F. Reinking, R. A. Kropfli, and B. W. Bartram, 1996: Estimation of ice hydrometeor types and shapes from radar polarization measurements. Journal of Atmospheric and Oceanic Technology, 13, 85–96. Meischner, P. F., V. N. Bringi, D. Heimann, and H. H¨oller, 1991: A squall line in southern Germany: Kinematics and precipitation formation as deduced by advanced polarimetric and Doppler radar measurements. Monthly Weather Review, 119, 678–701. Sanchez-Diezma, R., I. Zawadzki, and D. Sempere-Torres, 2000: Identification of the bright band through the analysis of volumetric radar data. Journal of Geophysical Research, 105, 2225– 2236. Schroth, A. C., M. S. Chandra, and P. F. Meischner, 1988: A C-band coherent polarimetric radar for propagation and cloud physics research. Journal of Atmospheric and Oceanic Technology, 5, 804–822. Straka, J. M., D. S. Zrnic, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. Journal of Applied Meteorology, 39. Vivekanandan, J., D. S. Zrnic, S. M. Ellis, R. Oye, A. V. Ryzhkov, and J. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bulletin of the American Meteorological Society, 80, 381–388. Zrnic, D. S., A. Ryzhkov, J. Straka, Y. Liu, and J. Vivekananadan, 2001: Testing a procedure for 15
automatic classification of hydrometeor types. Journal of Atmospheric and Oceanic Technology, 18, 892–913.
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List of Figures 1
Vertical profiles of ZHH [dBZ], LDR [dB], ZDR [dB], and terminal falling velocity Vcw [m s−1 ] measured by POLDIRAD together with particle images recorded by the PMS 2D cloud particle probe measurements (0.8 mm range) on board of the DLR Falcon aircraft (Hagen et al. (1993)). . . . . . . . . . . . . . . . . . . . . . . 18
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The general weather situation leading to strong precipitation over southern Bavaria on August 3, 2006 as seen by the visible channel of Meteosat8 at 18:45 UTC. . . . 19
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1◦ PPI scan of reflectivity [dBZ](left) and LDR [dB] (right) by the polarimetric Doppler radar POLDIRAD on August 3, 2006. . . . . . . . . . . . . . . . . . . . 20
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1◦ PPI scan of reflectivity [dBZ](left) and LDR [dB](right) by the polarimetric Doppler radar POLDIRAD on August 3, 2006. . . . . . . . . . . . . . . . . . . . 21
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1◦ PPI scan of reflectivity [dBZ] with a vertical cross section along the white line derived from the volumetric data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
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Classification of the precipitation in stratiform precipitation and brightband signatures based on volumetric reflectivity observations (left) and polarimetric quantities(right) for August 3, 2006. In the upper line, results from the 1◦ PPI scan and in the lower line for the 2◦ PPI scan. . . . . . . . . . . . . . . . . . . . . . . . . . 23
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METEOSAT8 visible channel at 19:00 UTC for August 12, 2004. . . . . . . . . . 24
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1◦ (left) and 2◦ (right) PPI scan of reflectivity (top) and LDR (bottom) by the polarimetric Doppler radar POLDIRAD on August 12, 2004. . . . . . . . . . . . . . 25
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Classification of the precipitation in stratiform precipitation and brightband signatures based on volumetric reflectivity observations (left) and polarimetric quantities for the 1◦ PPI scan for August 12, 2004. The upper row shows the results for the 1◦ PPI scan and the lower row for the 2◦ PPI scan. . . . . . . . . . . . . . . . . 26
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Figure 1: Vertical profiles of ZHH [dBZ], LDR [dB], ZDR [dB], and terminal falling velocity Vcw [m s−1 ] measured by POLDIRAD together with particle images recorded by the PMS 2D cloud particle probe measurements (0.8 mm range) on board of the DLR Falcon aircraft (Hagen et al. (1993)).
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Figure 2: The general weather situation leading to strong precipitation over southern Bavaria on August 3, 2006 as seen by the visible channel of Meteosat8 at 18:45 UTC.
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Figure 5: 1◦ PPI scan of reflectivity [dBZ] with a vertical cross section along the white line derived from the volumetric data.
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Figure 6: Classification of the precipitation in stratiform precipitation and brightband signatures based on volumetric reflectivity observations (left) and polarimetric quantities(right) for August 3, 2006. In the upper line, results from the 1◦ PPI scan and in the lower line for the 2◦ PPI scan.
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Figure 7: METEOSAT8 visible channel at 19:00 UTC for August 12, 2004.
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-100 -50 0 50 100 -100 -50 0 50 100 distancia (km) distancia (km) /tifo/dades/maria_dades/resultados/radar_DLR/graficas/200408121911/CL5e1.ps /tifo/dades/maria_dades/resultados/radar_DLR/graficas/200408121911/CL5pole1.ps r
Stratiform Bright Band
-50 Convective
-100
-100 -100
-50
0 distancia (km)
50
100
-100
-50
0 distancia (km)
50
100 r
Figure 9: Classification of the precipitation in stratiform precipitation and brightband signatures based on volumetric reflectivity observations (left) and polarimetric quantities for the 1◦ PPI scan for August 12, 2004. The upper row shows the results for the 1◦ PPI scan and the lower row for the 2◦ PPI scan.
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