Transcript LightningTalk42008.ppt

Assimilating Lightning Data Into
Numerical Forecast Models: Use
of the Ensemble Kalman Filter
Greg Hakim, Cliff Mass, Phil Regulski, Ryan Torn
Department of Atmospheric Sciences
University of Washington
Vaisala ILMC Meeting
Tucson, April 24-25, 2008
Use of Lightning Data in Numerical Weather
Prediction (NWP): Previous Studies
 Earlier studies have generally used fairly primitive
assimilation approaches or were completed during earlier
periods without the massive amounts of observations that
are now available from satellite and aircraft.
 Several of these studies have noted substantial forecast
improvements using lightning data.
Poorly Forecast 1993 Superstorm
Lots of lightning during its early developmental stages over the Gulf
Alexander Study: 1993
Superstorm
 A relationship between lightning flash rate and convective
precipitation was used to alter the latent heating rate in
the MM5 during a spin-up period.
 Precipitation based on satellite microwave information
was also used.
 The model was then run in forecast mode, improving
predictions when satellite and lightning data were used.
500 mb height errors in meters
Assimilation of Pacific Lightning Data
into a Mesoscale NWP Model
Antti Pessi, Steven Businger, and Tiziana Cherubini
University of Hawaii
K. Cummins, N. Demetriades, and T. Turner
Vaisala Thunderstorm Group Inc. Tucson, AZ
Conversion of Lightning Rate to
Moisture Profile
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Determined the relationship between convective rainfall and
lightning rate.
Determined the relationship of rainfall with the moisture profile using
MM5 data.
Thus, Lightning rate => rainfall rate => moisture profile
Nudged moisture in MM5 model towards the moisture profile
Moisture profiles
Mixing Ratio (kg/kg)
0
0.002
0.004
0.006
0.008
0
5
10
Sigma Level
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15
20
25
30
35
40
No rain
1-3 mm/h
3-6 mm/h
>6 mm/h
Reducing Forecast Error
over the Eastern Pacific
Assimilation of lightning data results in a
significantly improved forecast of storm
central pressure (December 18-19, 2002).
972
L 972
L 983
The Big Questions
What is the potential impact
of lightning, particularly over
the oceans, now that there
are massive amounts of
satellite information from
cloud and moisture track
winds, as well as increasing
number of satellite vertical
soundings and scatterometer
winds. Plus, increasing
aircraft observations.
The Big Questions
 Does lightning data provide
information content and potential
forecast improvements that are not
available from conventional and
satellite assets?
 What is the impact of new data
assimilation approaches that allow
better use of conventional and nonconventional data? Will this allow a
more effective use of lightning data?
Or will it make lightning data
redundant with other data sources?
The University of Washington
Lightning Assimilation Project
 The UW has has been working for several years, both in
research and operational modes, with a new type of data
assimilation that has a number of potential advantages
over more traditional types of data assimilation, such as
nudging and 3D-VAR.
 Known as the Ensemble Kalman Filter (EnKF), this
approach is essentially probabilistic and makes use of the
modeling system as a central component of the data
assimilation process.
EnKF Primer
 Modern data assimilation systems combine the background (or
model first guess) fields and observations to produce an optimal
analysis.
 A key element of such data assimilation systems is the
background error covariance matrix, which spreads errors in the
background fields both spatially and among other parameters.
 Current data assimilation approaches, such a 3D-Var spread the
errors using simplified structures and functions that are not
necessarily realistic.
Covariance structures in 3dvar
Cov(Z500,Z500)
Cov(Z500,U500)
Data Assimilation
•Data assimilation should be probabilistic, providing uncertainty information
regarding the analyses and the forecasts derived from them.
•Data assimilation should also spread information among parameters, say
using a precipitation (or lightning) observation to update other parameters
such as wind or temperature.
•Ensemble-based data assimilation and particularly the Ensemble Kalman
filter offers a way to do this.
•Makes use of an ensemble of forecasts to produce state-dependent error
covariance structures, uncertainty information for analyses and forecasts,
and allows the spread of information among parameters.
State-dependent Covariance
Matrices
Cov(Z500,Z500)
“3DVAR”
EnKF
Cov(Z500,U500)
“3DVAR”
EnKF
Summary of Ensemble Kalman
Filter (EnKF) Algorithm
(1)
Begin with a large ensemble of forecasts.
(2)
Ensemble forecast provides background error
covariance statistics (B) for new analyses. (How to
spread errors)
(3)
Ensemble analyses with new observations using these
covariance structures. Many analyses and uncertainty
information.
(4)
Make short forecasts of all ensemble members until the
next observation time.
Mesoscale Example: cov(|V|, qrain)
A nice example: the Puget Sound Convergence Zone
Experiment Design
 Eastern Pacific Ocean
 Relatively low observation
density; location of important
storm tracks; errors propagate
downstream to mainland United
States
 Other studies with similar domain
 Pessi/Businger previously
studied domain for lightning
assimilation
Experiment Design
 Observations
 Control case
 Radiosondes
 Surface stations (ASOS, ship,
buoy)
 ACARS
 Cloud drift winds (no sat.
radiances)
 Experimental cases
 Control observations
 Lightning
Experiment Design
 The WRF Model.
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WRF 2.1.2 (Jan 27, 2006)
100 by 86 grid
45-km horizontal resolution
33 vertical levels
270 second timestep
Shortwave: Dudhia
Longwave: Rrtm
Surface: Noah land-sfc
PBL: MYJ TKE scheme
Cumulus: Kain-Fritsch (new Eta)
Experiment Design
 EnKF Setup
 90 ensemble members
 6-hr Analyses
 24-hr Forecasts (starting every 12
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hours)
8 assimilations period for “spin-up”
before lightning assimilations
Square root filter (Whitaker and
Hamill, 2002)
Horizontal localization – Gaspari and
Cohn 5th order piecewise
Fixed covariance perturbations to
lateral boundaries
Zhang covariance inflation method
Localization radius – 2000 km
Experiment observations example
ACARS observations spatial distribution
Experiment observations example
Cloud track wind observations spatial distribution
Experiment observations example
Radiosonde, surface station and buoy observations
•Radiosonde Obs
•Surface Stations
•Buoys
Experiment observations example
Lightning Observations
Test Cases
 Test Case #1
 December 16-21, 2002 (already considered by Businger
and Pessi)
 Test Case #2
 October 4-8, 2004
 Test Case #3
 November 8-12, 2006
Lightning Assimilation
Techniques
Converted the density of lighting observations into convective
rainfall using the Pessi/Businger Lightning rate/Convective
rainfall rate relationship
Lightning Assimilation
 Then the convective rainfall was assimilated using the
ensemble-based covariances to influence a wide variety of
parameters.
 We tried thinning and not thinning the lightning
observations.
 We tried assimilating the lightning over various periods.
 We verified both the quality of the analyses and forecasts.
Lightning Assimilation
Techniques
 Non-thinned Lightning Experiment
 Lightning strike observations are converted into 30 minute
lightning density rate from nearby LTNG observations.
 Lightning rate converted into “observation” of convective
rainfall rate using Pessi/Businger convective rain
rate/lightning rate relationship
 Convective rainfall (mm) is assimilated into WRF-EnKF
Lightning Assimilation
Techniques
 Thinned Lightning Experiment
 Same as the previous experiment except that any lightning
strikes used in the density calculation are no longer allowed
to be an assimilation point, resulting in a thinning out of the
lightning “observations” (although strikes will be used to
calculate nearby densities)
 One hour and six hour lightning assimilation
experiments. In all cases we calculate the lightningbased convective rainfall using lighting plus or
minus one hour from the nominal observing time.
 One hour-compared that rate to the one hour convective rainfall
in model.
 Six hour-scaled it to 6 hr and compared to six hour precipitation
in model.
Results from the latest
experiment
 Thinned lightning
 1-hr precipitation assimilation (which should be more realistic)
 Realistic error variance for lightning precipitation retrieval (5
mm)
 Comparisons to GFS analysis
 Although generally the best analysis provided by NCEP, the GFS
analysis is certainly imperfect, especially for fine scale features.
Question 1: Is their a significant
impact from lightning data?
Question 2: Is lightning
improving the analysis compared
to the no-lightning control?
Question 3: Is lightning
improving the 12 and 24h
forecasts compared to the nolightning control?
Future Work
 Evaluation of other approaches to connecting lightning with
meteorological variables:
 One approach would be to connect lightning with graupel, or with
some combination of strong vertical motion and cloud ice. Perhaps
more general.
 Improvements in the WRF EnKF, including experiments with
varying EnKF settings (localization ratios, etc).
 Increasing frequency to 3hr.
 Weight lining with the lightning detection efficiencies.