Waves to Weather - DFG Collaborative Research Center 165
Development of a predictability index for heavy precipitation Transfer project T1 Federico Grazzini (LMU München, Arpae-Simc Bologna) George C. Craig (LMU München) Volkmar Wirth (JGU,Institut für Physik der Atmosphäre, Mainz)
Project rationale Working hypotesis: Heavy precipitation events (HPe) predictability is increased by synoptic-scale ascent in large scale dynamical processes such as incoming Rossby Wave Packets (RWPs) or boundary forcing, as orography, sea contrasts, soil characteristics etc. At the same time predictability is reduced by fast processes with rapid error grotwh, such as cumulus convection.
Upper lavel precursos of heavy precip over the Alps have better predictability (in terms of Z500 scores) than normal conditions. This is mostly attributable to RWPs forcing (Grazzini F. 2007). Higher predictabilty of RWPs has been subsequently confirmed also at hemispheric scale (Grazzini & Vitart 2015)
Schematic illustrating the main contrasting factors regulating predictability. Large-scale predictability forcing could be modulated by the presence of RWPs. However upscale error growth could be large and destroy part of the signal. We belive this is particularly true in case when convection is predominant respect to large-scale forcing. This could be detected using the convective time scale framework as described by Keil et al. 2014
HPe with different predictability Large-scale precursors of Piedmont flood Nov. 2016
Data and work programme
200hPa wind V-component [30N,60N] – red/blu isolines [m/s] Total column water vapour [30N,60N] – green shading [mm] Orography [30N,60N] – brown shading [m]
Target area [46N,5E,43N,11E] TCWV_med = 18.6 mm Tau_m_of_max = 1.5 h Cape_m_of_max = 554 J/kg Prec_int_max = 50 mm/h 2211 12UTC - 2511 12 UTC
Regional observational network part of the ArCIS dataset
Upper air fields dataset ERA5 reanalysis (1979-present) 31km globally Including EPS reforecast 20m ENS For RWPs computation and others upper level fields
RWP
Work packages: WP 1 - Identify a large set HPe over a test area (ArCIS gridded dataset, N-Italy)
Large-scale precursors of Braunsbach flood June 2016 Target area [50N,5E,47N,11E] TCWV_med = 24.2 mm Tau_m_of_max = 4.3 h Cape_m_of_max = 1140 J/kg Prec_int_max = 90 mm/h
P W R
0,9 0,8 0,7
R²
0,6 0,5 0,4
TP12 Braunsbach TP12 Piemonte TCWV Braunsbach TCWV Piemonte
0,3 0,2 0,1 0 12
24
36
48
60
72
84 96 Forecast step
108
WP 2 - Compute potential predictability (PP) for days with HPe; for a variable linked to large-scale (i.e. IVT) and for surface daily precipitation. - Stratify HPe events in classes of PP and check if PP could be explained by characteristics of incoming RWPs (like duration) or control of synoptic-scale over local thermodinamic instability (convective time scale).
Potential Predictability
1
- Identify RWPs associated with HPe using tracking methods devoloped in W2W framework (i.e LWA from Uni Mainz)
120
132
144
156
168
WP 3 - Discriminatory analysis to determine which of the above factors are mostly affecting predictability. Test results based on PP and check if those are reproduced in terms of deterministic predictive skill - Attempt to define an index, that based on the above ingredients, provides, for each lead time, the predictable scales for precipitation.
Potential predictability computation according to Lavers et al. 2014
Contact: Federico Grazzini, LMU/Arpae-Simc, Theresienstr.37, 80333 München, Germany
[email protected] 
