Aerosol Data Assimilation Aerosol Data Assimilation with Lidar Observations with Lidar Observations

and Ensemble Kalman Filterand Ensemble Kalman FilterT. Thomas Sekiyama (MRI/JMA, Japan)T. Y. Tanaka (MRI/JMA, Japan)A. Shimizu (NIES, Japan)T. Miyoshi (Univ. of Maryland, US)

The Second GALION Workshop22 September 2010, Geneva, Switzerland

2

AgendaAgenda

• Introduction

• Data Assimilation System– Global Aerosol Model (MASINGAR)

– 4-Dimensional Ensemble Kalman Filter (4D-EnKF)

– Observational Data #1 (CALIPSO/CALIOP)

– Observational Data #2 (Asian Dust Network, AD-Net)

• Results on Asian Dust– Comparison with Independent Observations

– Comparison between CALIOP exp. and AD-Net exp.

• Summary and Future Work

3

IntroductionIntroduction

• Aerosol observation:Available data are limited or very sparse spatio-temporally! ( weather obs.)

• Model simulation:It’s useful, but not real! ( virtual reality)

• Data assimilation:It’s a fusion of observation and simulation with highly informative techniques to extract hidden information from data on hand.

4

Global Aerosol Model (MASINGAR)Global Aerosol Model (MASINGAR)

• The Model of Aerosol Species in the Global Atmosphere (MASINGAR) was developed by MRI/JMA.

• MASINGAR simulates dust (partitioned into 10-size bins), seasalt, and sulfate aerosols with a resolution of 2.8º by 2.8º.

• The meteorological field is assimilated with the JMA reanalysis (6-hourly).

• JMA is using MASINGAR to forecast Asian dust storms operationally.

A snapshot of MASINGAR’s dust simulation

5

4-Dimensional Ensemble Kalman Filter4-Dimensional Ensemble Kalman Filter

4D-Var 4D-EnKF

Background error statistics

Flow-dependent Flow-dependent

Program code Complicated Simple

Adjoint matrix Necessary Unnecessary

Observation operatorRequires tangent linear & adjoint

operators

Requires only a forward transform

operator

Asynchronous observations

Handles at each observational time

Handles at each observational time

Analysis error covariance

Not provided Explicitly provided

6

Experiment #1: Satellite Lidar AssimilationExperiment #1: Satellite Lidar Assimilation

• Observational Data CALIPSO/CALIOP– global– but, longitudinally

sparse– attenuated

backscattering coefficients from the the Level 1B dataset are used

The CALIPSO orbit has an about 1000 km longitudinal interval per day at mid-latitudes; but its vertical and latitudinal resolution is extremely high.

CALIOP observation

7

CALIOP Data Screening by CAD ScoreCALIOP Data Screening by CAD Score

(a) CALIOP Level 1B attenuated backscattering coefficients at 532nm;

(b) before data assimilation in model;(c) after data assimilation in model.

White squares are areas where the Cloud-Aerosol-Discrimination scores are less than -33.

8

Results (comparison with an independent lidar)Results (comparison with an independent lidar)

532nm extinction coefficients for non-spherical particles ( dust aerosol).

The X-axis shows date in May 2007.

(a) Independent ground-based lidar observation; (b) free model-run result without assimilation; (c) data assimilation result with CALIPSO data.

9

Instrument Location of Matsue StationInstrument Location of Matsue Station

10

Results (comparison with weather reports)Results (comparison with weather reports)

Contours and gray shades are surface dust concentrations.

(a) Free model-run result without assimilation.(b) Data assimilation result.

Red and blue circles are weather stations. The Red ones observed aeolian dust on this day. Blue ones did not observe any dust events.

(c) MODIS optical Thickness on 28May07.

11

Experiment #2: AD-Net Lidar Experiment #2: AD-Net Lidar AssimilationAssimilation• Observational Data

8 stations of the NIES Asian Dust Network – only in East Asia– but, temporally

dense– aerosol extinction

coefficients (provided by the NIES team) are used.

Lidar data of 8 stations (indicated green circles) were used for this data-assimilation experiment.

12

AD-Net Results (comparison with CALIOP results)AD-Net Results (comparison with CALIOP results)

532nm extinction coefficients for non-spherical particles ( dust aerosol).

The X-axis shows date in April 2007.

(a) AD-Net ground-based lidar observation;

(b) free model-run result without assimilation;

(c) data assimilation result with CALIPSO data,

(d) with AD-Net lidar data.

13

Instrument Location of Toyama stationInstrument Location of Toyama station

14

532nm extinction coefficients for spherical particles ( dust excluded).

The X-axis shows date in April 2007.

(a) NIES ground-based lidar observation;

(b) free model-run result without assimilation;

(c) data assimilation result with CALIPSO data,

(d) with NIES lidar data.

AD-Net Results (comparison with CALIOP results)AD-Net Results (comparison with CALIOP results)

15

AOT on 24Apr2007: data assimilation resultsAOT on 24Apr2007: data assimilation results

16

SummarySummary

• CALIOP assimilation results were validated by independent dust observations in East Asia: ground-based lidars and weather reports of aeolian dust events.

• The assimilation system was successfully performed with CALIOP aerosol observations in springtime 2007.

• This assimilation system can potentially provide global aerosol reanalyses for various particle types and sizes.

17

SummarySummary

18

Future WorkFuture Work

• Dust forecasting– To apply this data assimilation system to the

operational dust prediction service of JMA. – The 4D-EnKF with lidar data makes it possible to

supply the initial conditions for aerosol forecasting.

• Predictability in the Chaotic system– Data assimilation with lidar data can

provide plenty of information to explore the scientific frontier.