Objectives: To develop a system for lightning/thunderstorm...
Transcript of Objectives: To develop a system for lightning/thunderstorm...
Modeling framework for thunderstorm/ lightning predictionThunderstorm/lightning Modeling Team, Monsoon Mission, IITM, Pune, India
Background
Proposed roadmap of
lightning/thunderstorm now-casting
Results: Case studies
Acknowledgements: Indian Institute of Tropical Meteorology, Pune is supported by the Ministry
of Earth Sciences, Govt. of India, New Delhi. Team sincerely thanks Director, IITM, Program
manager, Aaditya HPCS, WG-TP, MoES, IMD, NCMRWF.
RAC 2019
RAC meeting 2019: 05 - 06 February 2019, IITM, PUNE, INDIA
Thunderstorms (TS) are the source of lightning discharge, which is
the major cause of natural calamity (i.e. damage of public properties
and loss of life) across the globe. In India, the loss of human life
due to lightning strike is particularly high over different parts of the
sub-continent due to large occurrence of TS in pre-monsoon season
(March-May). Unfortunately, besides some purely empirical methods,
there is hardly any systematic mechanism involving dynamical model
and suits of observational inputs which provides a reliable forecast to
issue a warning prior to the occurrence of lightning.
A system of modeling framework for thunderstorm/lightning
prediction based on state-of the art dynamical model (e.g., WRF) as
well as hybrid (Model and Statistics) methods need to be setup due
to the high demand from the society.
Systematically evaluate the model performance in terms of model
skill and biases. Intensify the efforts to identify causes of biases in
the simulation of thunderstorm in numerical model (e.g., WRF) and
develop/improve the physical processes. The model simulated CG-
lightning flash counts results are compared with Maharashtra
Lightning Detection Network (MLDN) data.
Development towards Lightning simulation
Presently, there is hardly any mechanism which provides a
forecast to issue a warning prior to the occurrence of lightning
and the lightning activity is a typical phenomenon of severe
weather characterized by strong convection.
Conventional approach for thunderstorm prediction using
dynamical model obsevations are well documented and have
some limitations (Mukhopadhyay et al., 2003,2005; Chaudhari et al.,
2010, Ghosh et al., 2004, Rajeevan et al., 2010, Madhulata et al., 2013).
Dynamical Lightning parameterization and dynamical
lightning potential index should be implemented in the
dynamical model along with microphysics for the proper
feedback and coupling .
More and more observed data are now used for the
verification and improving modeling system and further model
development activity.
Dynamical Model set-up
Future research
Objectives: To develop a system for lightning/thunderstorm prediction using dynamical model
Convection Microphysics Lightning para.
M - I Yes Yes (2m & 1m) * Yes – PR92
M - II No Yes (2m & 1m) # Yes – PR94
M - III No Yes (2m & 1m) $ Yes - LPI
* Price, C., and D. Rind (1992) – Based on buoyancy & convective flux,
CAPE, cloud condensate# Price, C., and D. Rind (1994) - Based on dbz, CAPE, cloud condensate$ Yair et al., (2010) – Vertical velocity & cloud condensate
Lightning Parameterization
Lightning Potential Index (LPI)
Radar reflectivityCG-flash counts (#) TS: 27042017
TS: 13052017
Cherrapunje (Meghalaya); East Khasi hills districts of Meghalaya; Bangladesh) [Multiple cell,
max_reflectivity 49 dBz], isolated TS over Kolkata
Thunderstorm occurred over Gangetic WB and Odisha (source: IMD FDP Storm report)
East India region:
Maharashtra region:
TS 27042017-RADARTS 13052017-RADAR
Southern Peninsula (SP) region:
Rainfall
Event MP-06 MP-10 MP-16 MP-17 MP-18
29042017 GPM 0.251 0.294 0.257 0.301 0.305
TRMM 0.38 0.445 0.415 0.399 0.392
05052016 GPM 0.404 0.409 0.398 0.402 0.410
TRMM 0.437 0.468 0.451 0.476 0.501
15032017 GPM 0.313 0.318 0.321 0.318 0.311
TRMM 0.361 0.368 0.376 0.377 0.332
15 TS cases CG lightning flash
counts
Rain (TRMM 3B42) Rain (GPM)
Correlation
Coefficient
0.65
(varies: 0.55 – 0.75)
0.41
(varies: 0.35 – 0.55)
0.32
(varies: 0.25 – 0.45)
Events PSS TS ETS HR FAR SR
TS (15) 0.65 0.51 0.39 0.85 0.19 0.56
Skill Score
Verification & validation
Understanding cloud processes:
Das, S. K. et al., (2019)
Problem identified (DSD)
Strongly Electrified Weakly Electrified
Mudier et al., (2019)
Role of aerosols in severe storm (e.g., TS)
Lightning/Radar data assimilation.
Improvement of model physical parameterization.
New ‘Electric filed’ parameterization along with cloud-
aerosol interaction.
More lightning/Radar data all over the India are required.
Statistical method (e.g., PCA/LDA technique based on Ghosh
et al, 2004; Rajeevan et al., 2010) need to be tested.
First time in India, New approaches for dynamical
‘lightning parameterization’ or ‘lightning potential’
schemes are introduced in the dynamical model (e.g.,
WRF).
Now the present set-up of the regional climate model
(WRF) can simulate cloud-to-ground (CG) lightning
flashes directly (online).
The dynamical Lightning Potential Index (LPI) also
implemented in WRF first time to simulate thunderstorm.
The results are validated with IITM observed ‘lightning
flash count’ data. Results of Correlation and different
verification skill scores shows hope for lighting/TS now-
casting.
Basic research for understanding physical processes for
thunderstorm are carried out for further improvement.
Achievement
Greeshma, M. et al., (2019)
Gayatri, V. et al., (2019)
Hazra, A. et al., (2019)