This document summarizes research on improving volcanic ash forecasts using an ensemble Kalman filter data assimilation method with the LOTOS-EUROS model and OpenDA software. Aircraft measurements of volcanic ash are assimilated to improve forecast accuracy compared to models alone. A two-way tracking localized ensemble Kalman filter is developed to reduce sampling errors from small ensembles. A mask-state ensemble Kalman filter is also created to accelerate volcanic ash data assimilation by focusing computations only on relevant grid points containing ash. Validation shows both methods successfully improve volcanic ash concentration forecasts.

1. Data assimilation to improve volcanic ash
forecasts using LOTOS-EUROS and
OpenDA Presenter: Guangliang Fu (TU Delft)
In cooperation with
Sha Lu, Arjo Segers, Hai Xiang Lin, Arnold Heemink, Martin Verlaan,
Nils van Velzen (the Netherlands),
Thorgeir Palsson (Iceland), Konradin Weber (Germany),
Tongchao Lu (China), Fred Prata (Norway)
OpenDA
user talk
1 Nov 2016

2. Hazard of volcanic ash:
An accurate forecast is important!

3. Observations of volcanic ash activity

4. LOTOS-EUROS simulation
Compare
Model simulations
Far from
measurements
Aircraft
Data
assimilation
Only relying
on Model is
not
accurate
Model & measurements

5. Fu, G., Lin, H.X., Heemink, A.W., Segers, A.J., Lu, S., and Palsson, T.: Assimilating aircraft-
based measurements to improve Forecast Accuracy of Volcanic Ash Transport, Atmospheric
Environment, 115, 170-184, 2015.
Aircraft
Data
assimilation

6. Ensemble Kalman Filter
forecast Analysis at 09:40 Analysis at 11:10
No assimilation
Big difference between
With and no Assimilation
Which
Is
Better
?
Aircraft
Data
assimilation

7. Validation
Ensemble Kalman Filter
Improve
volcanic ash concentration
Fu, G.* , Heemink, A., Lu, S., Segers, A., Weber, K. and Lin, H.-X.: Model-based aviation advice on
distal volcanic ash clouds by assimilating aircraft in situ measurements, Atmospheric Chemistry
and Physics , 16 (14), 9189-9200, 2016.
Aircraft
Data
assimilation

8. Two-way-tracking localized EnKF (TL-EnKF)
to reduce sampling errors (using small ensemble size)
Spurious noises
Estimated covariances
between measurements
and volcanic ash state

9. Characteristics of forecast error covariances
Aircraft
Data
assimilation
Characteristic 1: Two-way-anisotropic

10. Characteristics of forecast error covariances
Aircraft
Data
assimilation
Characteristic 2: Standard-deviation-dependent

11. Methodology of TL-EnKF
Aircraft
Data
assimilation
At each analysis step
(place one point
concentration at only
measurement location):
(1) Forward tracking
downwind Correlations,
using forward model.
(2) Backward tracking
upwind Correlations
Using a backward
Model.
(3) Combine two-way
Correlations
(4) Include standard
Deviations
(5) Create a covariance
mask for localization

12. Results
Two-way-tracking only adds 10 % computational cost than normal assimilation
Much Cheaper than the computational cost with large ensemble size
Fu, G.*., Lin, H.X., Heemink, A.W., Segers, A.J., Verlaan, M., Lu, T., and Lu, S.: A two-way-tracking
localized ensemble Kalman filter for assimilating aircraft in situ volcanic ash measurements, under
review in Monthly Weather Review.
Aircraft
Data
assimilation

13. Mask-state EnKF (MS-EnKF) to accelerate volcanic ash
data assimilation
Parallel Framework (on Cartesius)
Cartesius: the Dutch
supercomputer

14. Implementation Results
Computational time
(4.36 h) is more than
twice
the model run (3 h).
Goal:
Speed up to Acceptable level.
(in this case study, i.e. within 3 hours equal to the
simulation window
(09:00-12:00 UTC, 18 May, 2010))
Analysis step costs
(3.14 h) which is
expensive

15. Computational evaluation of the analysis step.
a, Illustration of the analysis step.
b, Computational cost of all sub-part of the analysis step.
Most time-consuming
Mask-state for the Analysis step

16. First analyze Characteristic of volcanic ash state, which is as one column of .
a, volcanic ash forecast at 09:40 UTC, 18 May, 2010.
b, volcanic ash forecast at 11:00 UTC, 18 May, 2010.
How to reduce the computational cost of ?
➔ Large no-ash grids point to the rows of with all zero elements.
● These zero rows have nothing to do with the computations of .
● Therefore the computations related to these rows are completely redundant
and it can easily be up to 2/3 of the total computations.

17. The Mask-state algorithm (MS) includes 5 steps:

18. Results of second-step acceleration
➔ The results showed
that by employment
of Mask-state
algorithm, the
volcanic ash
assimilation
system can be
speeded up to an
acceptable level.
➔ The acceleration is
generic, can be used
in all ensemble-
based data
assimilation system.
Fu, G.*, Lin, H.-X., Heemink, A., Segers, A., van Velzen, N., Lu, T., Xu, S., and Lu, S.: A
mask-state algorithm to accelerate volcanic ash data assimilation, Geoscientific Model
Development Discussions 1-19, 2016.

19. Others
Jianbing Jin at TU Delft is working with
LOTOS-EUROS and OpenDA for Dust Storm
forecasts in China .
Thank you!