Life cycle analysis of convective cells for nowcasting ...KIT –The Research University in the...
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KIT – The Research University in the Helmholtz Association C-band radar data KONRAD-2D data Cell statistics • Historic cell sample (2011-2017) • Filtering • Evolution of cell properties (area, intensity, track direction) COSMO model analyses • nonhydrostatic • nudging analysis • Δ = 1 ℎ • Δ = 2.8 or Δ = 7 Convective parameters Environment statistics • Historic fields (2011-2017) • Mean values and variances (locally and spatially) discrimination? Life cycle analysis of convective cells for nowcasting purposes considering atmospheric environment conditions Jannik Wilhelm 1 ([email protected]), Ulrich Blahak 2 , Kathrin Wapler 2 , Roland Potthast 2,3 , Michael Kunz 1,4 1 Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; 2 German Weather Service (DWD), Offenbach, Germany; 3 Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom; 4 Center for Disaster Management and Risk Reduction Technology (CEDIM), KIT Overview Kunz, M., J. Wandel, E. Fluck, S. Baumstark, S. Mohr, S. Schemm, 2018: Ambient conditions prevailing during hail events estimated from a combination of radar data and observations in central Europe. To be submitted to Quart. J. R. Met. Soc. Pucik, T., P. Groenemeijer,D. Ryva and M. Kolar, 2015: Proximity Soundings of Severe and Nonsevere Thunderstorms in Central Europe. Mon. Wea. Rev., vol. 143, p. 4805-4821 Sherburn, K. D., M. D. Parker, J. R. King and G. M. Lackmann, 2016: Composite Environments of Severe and Nonsevere High-Shear, Low-CAPE Convective Events. Wea. Forecasting, vol. 31, p. 1899-1927 Wapler, K., 2017: The Life Cycle of Hail Storms – Lightning, radar reflectivity and rotation characteristics. Atmospheric Research, vol. 193, p. 60-72 Wapler, K., L. M. Banon Peregrin, M. Buzzi, D. Heizenreder, A. Kann, I. Meirold-Mautner, A. Simon, Y. Wang, 2017: Conference Report 2 nd European Nowcasting Conference. Meteorologische Zeitschrift, vol. 27(1), p. 81-84 The representation of the life cycle of convective cells in state-of-the-art nowcasting procedures has not reached a satisfying state yet. Spatially and temporally accurate predictions of cell intensity, area, track, and their future tendency as well as associated potential threats, desirable to know with a preferably long lead time from a warning and precaution management viewpoint, are still lacking at present (see, e.g., Wapler et al. 2017). We plan to include information from statistical analyses of historical convec- tive cells into nowcasting methods of DWD by combining data from the cell detection and tracking algorithm KONRAD with high-resolution model fields from operational NWP (COSMO) analyses. The objective is to develop a novel method to estimate the life cycle ‘state’ of convective cells as well as a ‘forecast function’ considering the pre- vious cell evolution and atmospheric environmental conditions. Therefore, cell and model parameters with predictive skill have to be identified. On this basis, the method might have the potential to facilitate an improvement of on-line probabilistic predictions of cell track, intensity evolution and potential threat associated with the cells. Cell statistics We have developed reasonable cell sample filters on the basis of a short-term convectively active time period (27 May 2016 – 26 June 2016) using KONRAD cell detections over Germany and parts of its neighboring countries. Cells were more or less evenly distributed over the radar-covered area (not shown). The filters include inter alia thresholds for a minimum lifetime, unphysically tracked paths and a neighborhood criterion so as to avoid multicellular contributions – in such cases it is hardly possible to follow individual convective cores. Cell area evolution – reflected by the number of contiguous radar pixels with a reflectivity factor above 46 dBZ (figure 1; 3-month time period: 01 May 2016 – 31 July 2016) – seems to vary significantly with the maximum age of the cells. Inversely, it would be hard to foresee how long a cell will exactly live given a certain cell area at a certain time instance. This uncertainty could be transformed into a probabilistic estimation for the evolution of the cell area. Convective parameters As unavoidably the life cycle module shall have to deal well with potentially destructive cells, (thermo)dynamic parameters clearly separating between severe and nonsevere cells (e.g. with respect to hail size and its associated threats) will be of special interest (e.g., Pucik et al. 2015, Sherburn et al. 2016). Already existent and newly defined COSMO fields, including convective quantities and parameters shown to possibly have predictive skill, will provide a wide variety of model data useful for the future statistical analyses. As an example, Kunz et al. (2018), in particular, show that Storm Relative Helicity (0-3 km) is a good proxy for distinguishing between different track lengths L (~ lifetime) and hail size (diameter D) regimes for hail producing convective cells over Germany, France and BeNeLux (figure 2). The following issues for the statistical analyses will be focused on next: 1) Generation of a filtered multi-year repre- sentative convective cell sample. 2)Statistics of suitable convective COSMO model parameters during the convective events. 3) Development of a mathematical procedure linking information about atmospheric environment conditions and about the previous evolution of the cells, in order to ‚predict‘ single historic cell life cycles best. 4) Identification of relative importance of the cell and environment parameters applied. Figure 1: Time evolution of cell area for a 3-month sample. Each line is an average over all cells of a specific lifetime (counts given in numbers). Lines are only drawn for every second maximum cell age. Note that the purple line with largest cell extent represents an isolated hail-producing supercell (locally hail diameter 2-4 cm). N = 3960 Figure 2: Composite mean Storm Relative Helicity (0-3 km) fields relative to the midpoint of the hail storm track (10-year sample). (Kunz et al. 2018) Project plan Information about atmospheric environmental conditions Information about previous evolution of the cells INPUT Recent radar and KONRAD cell detection data INPUT Recent COSMO model forecast convective parameter fields Application of a suitable mathematical procedure to gain a „forecast function“ depending on a distinct set of parameters (Multivariate Linear Regression, Principal Component Analysis, Neural Network / Machine Learning?) 1) Estimation of the life cycle state of the cells 2) Application of the „forecast function“ OUTPUT Future life cycle states of the cells Cell (object) assimilation into NWP model cycles Additional informa- tion in nowcasting algorithms (e.g. NowCastMix [Wapler et al. 2017]) and warning management system Including life cycle information in DWD nowcasting process Statistics Identification of model parameters with predictive skill Future objectives: Once a ‚machinery‘ as sketched in the box above has been developed, plans for expansion include: 1) the use of multi-sensor data. 2) the transfer of the machinery to a possible 3D approach. 3) the use of ensemble model fields. References Outlook D < 3 cm L < 50 km D < 3 cm L > 100 km D > 5 cm L < 50 km km D < 3 cm L > 100 km N = 985 -400 -200 0 200 400 -400 -200 0 200 400 km D > 5 cm L > 100 km -400 -200 0 200 400 -400 -200 0 200 400
Transcript of Life cycle analysis of convective cells for nowcasting ...KIT –The Research University in the...
KIT – The Research University in the Helmholtz Association
C-band radardata
KONRAD-2D data
Cell statistics
• Historic cellsample (2011-2017)
• Filtering
• Evolution of cellproperties(area, intensity, track direction)
COSMO modelanalyses
• nonhydrostatic
• nudginganalysis
• Δ𝑡 = 1 ℎ
• Δ𝑥 = 2.8 𝑘𝑚 orΔ𝑥 = 7 𝑘𝑚
Convectiveparameters
Environment statistics
• Historic fields(2011-2017)
• Mean valuesand variances(locally andspatially) discrimination?
Life cycle analysis of convective cells for nowcasting purposes considering atmospheric environment conditions
Jannik Wilhelm1 ([email protected]), Ulrich Blahak2, Kathrin Wapler2, Roland Potthast2,3, Michael Kunz1,4
1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; 2German Weather Service (DWD), Offenbach, Germany; 3Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom; 4Center for Disaster Management and Risk Reduction Technology (CEDIM), KIT
Overview
Kunz, M., J. Wandel, E. Fluck, S. Baumstark,S. Mohr, S. Schemm, 2018: Ambient
conditions prevailing during hail events estimated from a combination ofradar data and observations in central Europe. To be submitted to Quart. J.R. Met. Soc.Pucik, T., P. Groenemeijer, D. Ryva and M. Kolar, 2015: Proximity Soundingsof Severe and Nonsevere Thunderstorms in Central Europe. Mon. Wea.Rev., vol. 143, p. 4805-4821Sherburn, K. D., M. D. Parker, J. R. King and G. M. Lackmann, 2016:Composite Environments of Severe and Nonsevere High-Shear, Low-CAPEConvective Events. Wea. Forecasting, vol. 31, p. 1899-1927Wapler, K., 2017: The Life Cycle of Hail Storms – Lightning, radarreflectivity and rotation characteristics. Atmospheric Research, vol. 193, p.60-72Wapler, K., L. M. Banon Peregrin, M. Buzzi, D. Heizenreder, A. Kann, I.Meirold-Mautner, A. Simon, Y. Wang, 2017: Conference Report 2nd
European Nowcasting Conference. Meteorologische Zeitschrift, vol. 27(1),p. 81-84
The representation ofthe life cycle of convective cells instate-of-the-art nowcasting procedureshas not reached a satisfying state yet.Spatially and temporally accuratepredictions of cell intensity, area, track,and their future tendency as well asassociated potential threats, desirableto know with a preferably long leadtime from a warning and precautionmanagement viewpoint, are stilllacking at present (see, e.g., Wapler etal. 2017).
We plan to include information fromstatistical analyses of historical convec-tive cells into nowcasting methods ofDWD by combining data from the celldetection and tracking algorithmKONRAD with high-resolution modelfields from operational NWP (COSMO)analyses. The objective is to develop anovel method to estimate the life cycle‘state’ of convective cells as well as a‘forecast function’ considering the pre-vious cell evolution and atmosphericenvironmental conditions. Therefore,cell and model parameters withpredictive skill have to be identified.On this basis, the method might havethe potential to facilitate animprovement of on-line probabilisticpredictions of cell track, intensityevolution and potential threatassociated with the cells.
Cell statistics
We have developed reasonable cell sample filters on the basis of a short-termconvectively active time period (27 May 2016 – 26 June 2016) using KONRADcell detections over Germany and parts of its neighboring countries. Cells weremore or less evenly distributed over the radar-covered area (not shown).
The filters include inter alia thresholds for a minimum lifetime, unphysicallytracked paths and a neighborhood criterion so as to avoid multicellularcontributions – in such cases it is hardly possible to follow individual convectivecores.
Cell area evolution – reflected by the number of contiguous radar pixels with areflectivity factor above 46 dBZ (figure 1; 3-month time period: 01 May 2016 –31 July 2016) – seems to vary significantly with the maximum age of the cells.
Inversely, it would be hard to foresee how long a cell will exactly live given acertain cell area at a certain time instance. This uncertainty could betransformed into a probabilistic estimation for the evolution of the cell area.
Convective parameters
As unavoidably the life cycle module shall have to deal well with potentiallydestructive cells, (thermo)dynamic parameters clearly separating betweensevere and nonsevere cells (e.g. with respect to hail size and its associatedthreats) will be of special interest (e.g., Pucik et al. 2015, Sherburn et al. 2016).
Already existent and newly defined COSMO fields, including convectivequantities and parameters shown to possibly have predictive skill, will provide awide variety of model data useful for the future statistical analyses.
As an example, Kunz et al. (2018), in particular, show that Storm RelativeHelicity (0-3 km) is a good proxy for distinguishing between different tracklengths L (~ lifetime) and hail size (diameter D) regimes for hail producingconvective cells over Germany, France and BeNeLux (figure 2).
The following issues for the statistical analyseswill be focused on next:
1) Generation of a filtered multi-year repre-sentative convective cell sample.
2) Statistics of suitable convective COSMO modelparameters during the convective events.
3) Development of a mathematical procedurelinking information about atmosphericenvironment conditions and about theprevious evolution of the cells, in order to‚predict‘ single historic cell life cycles best.
4) Identification of relative importance of thecell and environment parameters applied.
Figure 1: Time evolution of cell area for a 3-month sample. Each line is an averageover all cells of a specific lifetime (counts given in numbers). Lines are only drawnfor every second maximum cell age. Note that the purple line with largest cell extentrepresents an isolated hail-producing supercell (locally hail diameter 2-4 cm).
N = 3960
Figure 2: Composite mean Storm Relative Helicity (0-3 km) fields relative to themidpoint of the hail storm track (10-year sample). (Kunz et al. 2018)
Project plan
Information aboutatmospheric
environmental conditions
Information aboutprevious evolution of the
cells
INPUTRecent radar and
KONRAD cell detection data
INPUTRecent COSMO model
forecast convectiveparameter fields
Application of a suitablemathematical procedure to gaina „forecast function“ dependingon a distinct set of parameters
(Multivariate Linear Regression, Principal Component Analysis,
Neural Network / Machine Learning?)
1) Estimation
of the life cycle
state of the cells
2) Application of
the „forecast
function“
OUTPUTFuture life cycle
states of the cells
Cell (object) assimilation into
NWP model cycles
Additional informa-tion in nowcasting
algorithms(e.g. NowCastMix
[Wapler et al. 2017]) and warning
management system
Including life cycle information in DWD nowcasting process
Statistics
Identification of model parameters with predictive skill
Future objectives:Once a ‚machinery‘ as sketched in the boxabove has been developed, plans for expansioninclude:
1) the use of multi-sensor data.2) the transfer of the machinery to a possible
3D approach.3) the use of ensemble model fields.
References
Outlook
D < 3 cm
L < 50 kmD < 3 cm
L > 100 km
D > 5 cm
L < 50 km
km
D < 3 cm
L > 100 km
N = 985
-400 -200 0 200 400 -400 -200 0 200 400 km
D > 5 cm
L > 100 km
-40
0 -
20
0 0
20
0 4
00
-40
0 -
20
0 0
20
0 4
00
