Case, Jonathan L.

Gatlin, Patrick N.

McCaul, Eugene W., Jr.

Priftis, George

Srikishen, Jayanthi

NASA Technical Reports Server

Ensemble Lightning Forecasts and Validation over the 
Hindu Kush Himalayan Region
Jonathan L. Case*1, Patrick N. Gatlin2, Jayanthi Srikishen3, 
Georgios Priftis4, and Eugene W. McCaul Jr.3
1ENSCO, Inc; 2NASA/Marshall Space Flight Center;
3Universities Space Research Association;  4University of Alabama – Huntsville
9 January 2019
Ninth Conf. on Meteorological Application of Lightning Data; Phoenix, AZ Talk 4.2
https://ntrs.nasa.gov/search.jsp?R=20190000058 2019-08-30T05:00:49+00:00Z
Why Thunderstorms and 
Why the Hindu Kush Himalayan (HKH) Region?
Cecil and Blankenship (2012)
Straight-line wind damage 
to coconut crop in 
Bangladesh
Flash Floods in Nepal
Large Hail in Nepal
Tornado damage in 
Bangladesh
Lightning in 
Kathmandu
**Numerous Lightning Casualties
Nested mode grid domain over South Asia
– WRF community model
– 12-km outer grid 4-km nested grid (picture at right)
– Daily 48-hour forecasts with 1800 UTC initialization
– Strategy: Create sufficient spread in ensemble system by 
varying both initial/boundary conditions and 
physics parameterizations
2018 Spring Demonstration (Mar to May):
Ensemble Model Configuration
Run 12-member ensemble 
of model runs with varying 
initial conditions & physics 12 km
4 km
Table at left shows initial 
condition and physics 
variations for the 12 
ensemble runs.
MP →
PBL ↓
Godd Lin WSM6 Morr
YSU HKH1: GFS
HKH2: 
GEFS 03
HKH3: 
GEFS 05
HKH4: 
GEFS 07
MYJ HKH5: GEFS 09
HKH6: 
GEFS 11
HKH7: 
GEFS 13
HKH8: 
GEFS 15
MYNN2 HKH9: GEFS 17
HKH10: 
GEFS 19
HKH11: 
GEFS 02
HKH12:
GEFS 04
**Chose different GEFS 
members to initialize each 
ensemble member
**Varied Planetary Boundary 
Layer (PBL) and Microphysics 
(MP) schemes, as these are 
important related to convection 
and lightning
Internal Project Web PagePost-processing to generate model products
– Hourly output frequency
– Following NOAA SPC/NSSL/WPC (Kain et al. 2010),
use model fields for thunderstorm hazard proxies:
• Convective intensity: Composite reflectivity
• Lightning: Lightning Forecast Algorithm (McCaul et al. 2009)
• Straight-line winds: Max output interval 10m wind speed
• Hail threat: Max output interval total column graupel
• Mesocyclone/tornado: Max output interval updraft helicity
• Flooding rainfall: Accumulated precip thresholds (esp. 3 hours)
Generate visualization products
– GrADS/python for deterministic (HKH1) output
– Python scripts for ensemble web products (right)
– Netcdf/GDAL for NASA/SERVIR Web Mapping Service
Post-Processing and Product Generation
WRF Lightning Forecast Algorithm
Lightning Forecast Algorithm (LFA; McCaul et al. 2009]
– Weighted combination of graupel flux at -15deg C (threat 1) and vertically-integrated ice (threat 2):
• 𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑘𝑘1 𝑤𝑤𝑞𝑞𝑔𝑔 |𝑇𝑇=−15𝐶𝐶 ;   𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑘𝑘2 ∫ 𝜌𝜌 𝑞𝑞𝑔𝑔 + 𝑞𝑞𝑠𝑠 + 𝑞𝑞𝑖𝑖 𝑑𝑑𝑑𝑑
• LFA = 0.95*THR1 + 0.05*THR2
• Coefficients k1, k2 empirically determined through calibration
against northern Alabama total lightning flash rate observations
• Requirement: WRF model must run in convection-permitting mode;
i.e., sufficiently fine horizontal grid spacing (~5km or less), 
with convective parameterization scheme de-activated, and 
microphysics scheme with graupel.
– LFA represents both in-cloud and
cloud-to-ground lightning activity
– Units are total flashes km-2 (5 min)-1
– LFA set to zero below 0.07 flashes
km-2 (5 min)-1 (~1 flash per hour)
WRF Lightning Forecast Algorithm
Lightning Forecast Algorithm (LFA; McCaul et al. 2009]
– Weighted combination of graupel flux at -15deg C (threat 1) and vertically-integrated ice (threat 2):
• 𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑘𝑘1 𝑤𝑤𝑞𝑞𝑔𝑔 |𝑇𝑇=−15𝐶𝐶 ;   𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑘𝑘2 ∫ 𝜌𝜌 𝑞𝑞𝑔𝑔 + 𝑞𝑞𝑠𝑠 + 𝑞𝑞𝑖𝑖 𝑑𝑑𝑑𝑑
• LFA = 0.95*THR1 + 0.05*THR2
• Coefficients k1, k2 empirically determined through calibration
against northern Alabama total lightning flash rate observations
• Requirement: WRF model must run in convection-permitting mode;
i.e., sufficiently fine horizontal grid spacing (~5km or less), 
with convective parameterization scheme de-activated, and 
microphysics scheme with graupel.
– LFA represents both in-cloud and
cloud-to-ground lightning activity
– Units are total flashes km-2 (5 min)-1
– LFA set to zero below 0.07 flashes
km-2 (5 min)-1 (~1 flash per hour)
Ensemble Products for Spring 2018 Experiment
Summarize 12-member ensemble into meaningful fields for severe 
thunderstorm forecast guidance (Schwartz et al. 2015; Ebert 2001; Clark 2017)
• Postage Stamps
• Ensemble Statistics
– Mean
– Minimum / Maximum
– Spread
• “Paintball” plots
• Probability products
– Grid point probability
– Neighborhood probability
– Probability Matched Mean (PMM)
• Daily Summaries for Day-1 / Day-2
– Maximum hazard maps and 
probabilities for 1-24h and 25-48h
Event Location Lightning Wind Hail
29-Mar-18 Bhutan X
NE India X X
30-Mar-18 Bangladesh X X X
Nepal X
N India X
NE India X X
11-Apr-18 NW India X X
17-Apr-18 N. India X X
21-Apr-18 NE India X
22-Apr-18 Bangladesh X X
29-Apr-18 Bangladesh X
N. India X
30-Apr-18 Bangladesh X X
2-May-18 N. India X X
6-May-18 Bangladesh X
NE India X X
7-May-18 NE India X
9-May-18 Bangladesh X
NE India X X
10-May-18 Bangladesh X X
NE India X
11-May-18 Bangladesh X X X
NE India X X
13-May-18 N. India X X
15-May-18 Bangladesh X
*Totals:
• 17 lightning events with fatalities
− Over 100 total lightning fatalities
during Spring Campaign
• 16 damaging wind events
• 7 damaging hail events
• 30 March 2018 event highlighted
(in forthcoming slides)
*Not an exhaustive list; sourced from local news 
media outlets available online
Pre-monsoon 2018 Storm Reports
30 March 2018 Event:
Southeast Nepal/northern & central Bangladesh/NE India 
Deadly Lightning, Damaging Winds and Large Hail
30 Mar 2018 Severe Event
Japan Meteorological 
Agency Himawari-8 
True Color RGB 
ENTLN Total Lightning Observations
• Earth Networks Total Lightning Network
– Can detect both cloud-to-ground strokes and 
intra-cloud pulses at long range
– Technology: Wideband E-field at 1Hz to 12 
MHz; TOA
– Detection efficiency: 28% in W. Hemisphere 
(Rudlosky et al. 2014)...likely less in S. Asia
Daily Summary of ENTLN Observations:
30 March 2018 
Stroke Density (Strokes/km2)
ENTLN Stations
30 Mar 2018 Severe Event
Deterministic Forecast Fields from ensemble member#1
Initialized at 1800 UTC 29 March 2018
WRF Model 200 hPa Wind Speeds
30 Mar 2018 Severe Event
Deterministic 4-km WRF model radar reflectivity & LFA Lightning Flash Rates: First 24 hours
30 Mar 2018 Severe Event
Ensemble Lightning Forecast Products
Initialized at 1800 UTC 29 March 2018
30 Mar 2018 Severe Event
Day-1 Ensemble Summary Fields
(18z 29 March to 18z 30 March)
• Highest lightning probabilities over central Bangladesh and SE Nepal into northeastern India
• Higher threshold probability (> 1 flash/min) focused over central and northern Bangladesh
Postage Stamp: Max lightning flash rates in first 24h Probability of any lightning flashes within 20 
km of a point during first 24 hours
Bangl.
India
Nepal
Bhutan
ENTLN Total Lightning Observations
Daily Summary of ENTLN Observations:
30 March 2018 
(Observed ENTLN daily 
lightning summary shown 
again at right for comparison 
to ensemble model 
probability patterns on 
previous and next slides)
Day-1 Ensemble Summary Fields
(18z 29 March to 18z 30 March)
• Highest lightning probabilities over central Bangladesh and SE Nepal into northeastern India
• Higher threshold probability (> 1 flash/min) focused over central and northern Bangladesh
Postage Stamp: Max lightning flash rates in first 24h Probability of lightning > 1 fl/min within 20 km 
of a point during first 24 hours
Bangl.
India
Nepal
Bhutan
Hourly Paintball of any lightning (LFA; left) Hourly probability of lightning (right)
30 Mar 2018 Wind and Hail Event
Lightning Validation Workplan
• Verify ensemble model lightning against ENTLN network
– Hourly LFA snapshots from ensemble model output
– ENTLN flashes collected at +/- 10 minutes at top of each hour and gridded
• Run through Model Evaluation Tools (MET) verification package
– Group lightning into various time bins (1, 3, 6, 12, and 24 hour “accumulations”)
– Compute verification skill scores much like precipitation (e.g., FBIAS, CSI, HSS)
• Summarize results for individual cases and collectively for Spring Campaign
• Also verify lightning during summer monsoon months (Jun – Aug)
– Ensemble LFA and ENTLN already collected for these months
– Compare behavior and accuracy of LFA during pre-monsoon and summer monsoon
20
Future Efforts
• Finish validating severe events against available observational assets
– Human casualties / storm reports from news sites and regional collaborators
– Lightning [ENTLN] / GPM hail algorithm / IMERG precipitation estimates
– In situ and other regional datasets (e.g., India radars)
• Optimize diagnostic output for HKH: (e.g., graupel/hail relationship in Nepal)
• Satellite / SAR signals for damage and flood inundation for the HKH region
• Automate product generation into SERVIR visualization App called “Tethys”
• Transition ownership of modeling tools to NASA SERVIR in Nepal
21
Thank you!! (Questions and Comments?)
NASA/SERVIR web: https://www.servirglobal.net/
Twitter: @SERVIRGlobal / Facebook: servirglobal
NASA/SPoRT web: https://weather.msfc.nasa.gov/sport/
Twitter: @NASA_SPoRT / Facebook: NASA.SPoRT
**ICIMOD/SERVIR-Himalaya Collaborator: Bhupesh Adhikary
Funding acknowledgement: Project funded by NASA’s Applied Science 
Capacity Building Program Manager (Nancy Searby)
Backup Slides
Backup Slides
Project Overview
Goal:  Use [NASA] modeling and remote-sensing assets to build early 
warning capabilities and facilitate timely disaster response for high-impact 
weather events in the Hindu Kush Himalayan (HKH) region
Objectives:  
1. High-Impact Weather Assessment 
Toolkit (HIWAT) for the HKH region 
2. Jointly develop HIWAT 
capabilities/training with the 
International Centre for Integrated 
Mountain Development (ICIMOD)
3. Demonstrate capability in end-user 
environment
4. Transition HIWAT to ICIMOD
ICIMOD is the NASA/SERVIR hub in Nepal
HQ of Bangl. Met. Dept.
HIWAT overview at 
Tribhuvan Univ. (Nepal)
DECISION-
MAKER
ICIMOD
TRAINING
HIWAT
What is HIWAT?
REGIONAL NWP MODEL HIGH-IMPACT WEATHER ENSEMBLE PROGNOSTICS
Short-term forecasting / situational awareness
GPM SATELLITE OBSERVATIONS
Threat Assessment
SATELLITE LAND IMAGERY
Impact Assessment
Hailstorm badly damaged hybrid bitter 
gourd seedbeds in Bangladesh
Source: thedailystar.net
2018 Spring Demonstration:
Computing Platform and Software
Computing environment: “SOCRATES” system
– SERVIR Operational Cluster Resource for Applications - Terabytes for Earth 
Science [SOCRATES]
– Series of network-connected Virtual Linux nodes running Ubuntu OS
– Each node has 32 processors (16 dual cores), 128 GB RAM
– Two network-mounted disks
• “shared” disk: model software installation and run-time environment; 1.2 TB disk space
• “storage” disk: archive location for model run output; post-processed GRIB2 files; 96 TB
Numerical Weather Prediction (NWP) and supporting software
– Unified Environmental Modeling System (UEMS), version 15
• NOAA/NWS SOO Science and Training Resource Center
• Based on Weather Research and Forecasting (WRF) v3.7.1 community NWP model
• Manages data acquisition, model initialization/execution, and post-processing
• Includes numerous utilities for system execution, creating graphics, and manipulating data
• Freely available to international community
– Software for generating products (python, ImageMagick, etc.)
2018 Spring Demonstration: 
Ensemble Model Simulation Strategy
Deliver daily, real-time 2-day ensemble guidance for severe thunderstorm hazard 
forecasting in HKH region
– Generate sufficient spread in ensemble members to capture uncertainty
– Cover two full diurnal heating cycles for day-1 and day-2 outlooks
– Minimize latency for product relevancy and usefulness
Based on these criteria, we chose:
– Ensemble system of 12 individual model runs with combination of initial condition and physics 
variability to generate spread
– 18z initialization; initial conditions from 12z UTC GFS/GEFS 6-hour forecasts
• Local midnight initialization provides several hours model spin-up prior to day-1 sunrise
• Enables earlier start to ensemble; no need to wait for 18z cycle to become available
– 48-hour model forecasts to cover 2 diurnal cycles; hourly output frequency
– Concurrent post-processing; products become available as ensemble system runs, rather than 
waiting until after all model simulations complete
Boundary Conditions:
Ens#1: 12z GFS every 3 hours; Ens#2-12: GEFS every 6 hours
Initial Conditions: 
Ensemble#1: 12z GFS 6-h fcst
Ensemble#2-12: GEFS 6-h fcst
t=0 (18z) t+48 ht+24 hours
NASA/SERVIR Web Mapping Service App “Tethys” to
convey information for end-user/decision-makers
Chance of 
Intense Storms
HIWAT Thunderstorm Hazards Forecasts
Chance of 
Damaging WindsChance of 
Frequent 
Lightning
Chance of Hail
Point-based forecasts Severe weather reports 
(30 March 2018)


Ensemble Lightning Forecasts and Validation over the Hindu Kush Himalayan Region

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