Aerosol data assimilation with lidar observations and ensemble kalman filter
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Aerosol Data Assimilation with Lidar Observations and Ensemble Kalman Filter. T. Thomas Sekiyama ( MRI/JMA, Japan ) T. Y. Tanaka( MRI/JMA, Japan ) A. Shimizu( NIES, Japan ) T. Miyoshi( Univ. of Maryland, US ). The Second GALION Workshop 22 September 2010, Geneva, Switzerland. Agenda.

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Aerosol Data Assimilation with Lidar Observations and Ensemble Kalman Filter

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Aerosol data assimilation with lidar observations and ensemble kalman filter

Aerosol Data Assimilation with Lidar Observations and Ensemble Kalman Filter

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

The Second GALION Workshop

22 September 2010, Geneva, Switzerland


Agenda

Agenda

  • 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


Introduction

Introduction

  • 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.


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


4 dimensional ensemble kalman filter

4-Dimensional Ensemble Kalman Filter


Experiment 1 satellite lidar assimilation

Experiment #1: Satellite Lidar Assimilation

  • Observational Data CALIPSO/CALIOP

    • global

    • but, longitudinally sparse

    • attenuated backscattering coefficients from the the Level 1B dataset are used

CALIOP observation

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


Aerosol data assimilation with lidar observations and ensemble kalman filter

CALIOP 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.


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.


Instrument location of matsue station

Instrument Location of Matsue Station


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.


Experiment 2 ad net lidar assimilation

Experiment #2: AD-Net Lidar Assimilation

  • 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.


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.


Instrument location of toyama station

Instrument Location of Toyama station


Ad net results comparison with caliop results1

AD-Net Results (comparison with CALIOP results)

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.


Aot on 24apr2007 data assimilation results

AOT on 24Apr2007: data assimilation results


Summary

Summary

  • 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.


Summary1

Summary

  • AD-Net lidar assimilation results were compared with CALIOP assimilation results.

  • If the model works well in dust source regions, the AD-Net lidar assimilation yields very similar results to the CALIOP results.

  • If the model doesn’t work well in dust source regions, the AD-Net lidar assimilation also doesn’t work well.

    • At that time, we need lidar stations near the dust source regions.


Future work

Future 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.


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