Report on the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS)

Transcription

1 Report on the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Taichu Y. Tanaka Meteorological Research Institute, Japan Meteorological Agency 19 Sep. 2017, SCOPE-Nowcasting Executive Panel, First Geneva

2 What is SDS-WAS? Topics Background: Sand and dust storms SDS-WAS Research to operation Linkage with SCOPE-Nowcasting Aerosol data assimilation Development of Himawari-8 AOD data assimilation by JMA/MRI and JAXA

3 WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Established in 2007 as a joint WWRP and GAW activity Mission: To enhance the ability of countries to deliver: timely and quality forecasts of SDS observations of dust aerosols Provide information and knowledge to users through an international partnership of research and transfer of experimental products to operations

4 SDS-WAS Structure: established as a federation of partners organized around regional nodes. Pan-American Node Hosted by the Arizona University and Caribbean Institute for Meteorology and Hydrology Northern Africa-Middle East-Europe Node Hosted by Barcelona Supercomputing Centre/AEMET Asian Node Hosted by china meteorological administration The West Asian Node will also be established in near future.

5 From research to operations In May 2013, AEMET and the BSC-CNS was designated to create the first Regional Specialized Meteorological Center with activity specialization on Atmospheric Sand and Dust Forecast (RSMC-ASDF). Barcelona Dust Forecast Center started its operations in March WMO EC-69: China is designated as the RSMC-ASDF Beijing.

6 Protecting People from Sand and Dust Storms

7 SDS-WAS activities Dust forecast product/evaluation Model inter-comparison: Joint visualization Generation of multi-model products (multi-model ensemble) Common forecast evaluation Calculation of statistical metrics Sharing model output data files Observation data exchange Website portal Capacity building: Training course, seminars

8 Linkage with SCOPE-Nowcasting Pilot project #4 of SCOPE-Nowcasting: Real-time Atmospheric Composition products for sand and dust forecasting Consistent, well-characterized satellite products are crucial for dust monitoring and forecasting products of SDS-WAS.

9 SDS-WAS Asia node observation data Currently, SDS-WAS Asia WWW portal provides FY2E/VISSR IDDI FY3B/VIRR DII Visibility Weather Phenomena

10 Forecast models and data assimilation of (dust) aerosol

11 Observations for aerosol data assimilation Currently, commonly used observations for aerosol DA are: Aerosol Optical Depth (AOD) Currently, AOD is the most commonly used data for monitoring and data assimilation of (dust) aerosol. Dust detection may be difficult (but can be guessed from fine and coarse mode AOD) Available during daytime only (most cases) Infra-Red Difference Dust Index (IDDI) CMA uses IDDI and visibility data for its operational dust forecasting.

12 CURRENT ICAP OPERATIONS-AS OF JUNE 2017 Organization BSC Copernicus/ JMA Meteo France NASA US Navy NOAA UKMO ECMWF Model NMMB/BSC- MACC MASINGAR MOCAGE GEOS-5 NAAPS NGAC MetUM CTM MONARCH CAMS Status QO O-24 hrs QO O QO O O O Meteorology Inline NMMB Inline IFS inline AGCM Offline ARPEGE Inline GEOS-5 Offline NAVGEM Inline GFS Inline UM Resolution 1.4x1 (0.7x0.5) 0.4x x x x2 1x1 0.25x x0.125 (Cube) (0.14x0.09) 0.33x0.33 1x1 0.35x x0.15 levels (48) DA LETKF p 4DVar 2DVar DVar 2DVar NA 4DVar LETKF p +LDE 3DVar, EnKF p Assimilated Obs DAQ MODIS+DB DAQ MODIS+DB NA Neural Net MODIS NA MODIS Dust AOT Species Size Bins Dust Sea Salt BC, OC (POA,SOA) Sulfate 8 (dust, salt) Bulk (BC, OC, Su) BC, OC Dust Sea Salt Sulfate MODIS, Himawari-8 p, CALIOP p BC, OC Dust Sea Salt Sulfate 3 10 (dust, salt), Bulk (BC, OC, Su) BC, OC Dust Sea Salt Sulfate BC, OC Dust Sea Salt Sulfate Nitrate DAQ MODIS, AVHRR p VIIRS p CALIOP p Anthro+bio B. Burn Dust Sea Salt Dust BC, OC Sea Salt Sulfate Dust p: Proto type demon strated Bio. Burn. Emissions GFAS GFAS GFAS GFAS QFED FLAMBE GBBEPx NA The ICAP-MME is run daily w/ 1x1 deg res at 00Z for 6 hrly fcasts out to 120 hrs w/ a 1-day latency. (From the slide of Peng Xian et al., 2017, 9 th ICAP meeting)

13 An Example of global dust optical depth forecast (ICAP multi-model dust forecasts of aerosol optical depth at 550 nm for January 1, (From Benedetti et al., 2014; Operational dust prediction in Mineral dust )

14 THE EAST ASIAN DUST CASE ICAP MME Merged DT/DB AOT from Terra, Concensus mean Contours of dust AOT of 0.8 AOT too high for MODIS DT /DeepBlue to retrieve. AERONET AOT at Beijing >3.0 high dust AOT warning Spread among the models (From the slide of Peng Xian et al., 2017, 9 th ICAP meeting)

15 JMA Aeolian dust Information JMA has been providing Aeolian dust information based on numerical forecasts and observations since January Global aerosol model MASINGAR JMA Aeolian dust prediction Research & Development in MRI/JMA Climate Projection model (e.g., CMIP6, CCMI) Aeolian dust advisory information JMA also provides aeolian dust prediction results (GPV : GRIB2 format) for private 15 weather services via the Japan Meteorological Business Support Center (JMBSC).

16 JMA/MRI aerosol data assimilation JMA is developing aerosol data assimilation system for its dust forecast, and planning to adopt in after upgrade of supercomputing system) 2D-VAR satellite AOT data assimilation Simplified and computationally reasonable Suitable for high resolution NRT forecast For research purpose, JMA also developed LETKF DA system. LETKF Complex observations can be incorporated (e.g., lidars and imagers) Two research papers of the DA using AOT by Himawari-8 were published (Sekiyama et al. 2016,SOLA; Yumimoto et al. 2016, GRL). Computationally expensive

17 Himawari-8 Aerosol product by JAXA EORC JAXA EORC provides aerosol retrieval products of Himawari-8. (available in netcdf format) JAXA provided the retrieval algorithm of aerosol products (AOD and Angstrom exponent) to JMA/MSC.

18 Data assimilation experiment of AOT with Himawari-8 by JAXA EORC using LETKF Himawari-8 AOT Without DA With DA Yumimoto et al., (2016, GRL)

19 An example of Aerosol data assimilation experiment Aerosol data assimilation experiment with NRT MODIS L2 AOD and JAXA Himawari-8 aerosol L3 product 2D-VAR data assimilation system Aerosol model: 00UTC 03UTC 06UTC 09UTC 12UTC 18UTC 00UTC (analysis) MRI/JMA MASINGAR mk-2 TL319 (0.56 x 0.56deg) (first guess) Assimilation (analysis) (first guess) Assimilation (analysis) (first guess) Assimilation (analysis) (first guess) Assimilation (analysis) (first guess) Assimilation (analysis) (first guess) Assimilation (analysis) (forecast run) Himawari-8 AOD Assimilation MODIS AOD Assimilation Himawari-8/MODIS AOD hybrid assimilation Himawari-8 AOD Assimilation

20 Forecast comparison : 2017/05/04 3z (FT=3) 3Z 4 May 2017 Without DA Himawari-8 DA MODIS DA JAXA Himawari-8 L3 AOD Total AOD Himawari-8 True Color Reproduction image Surface dust Himawari-8 data assimilation reduced the dust concentration over land.

21 Forecast comparison : 2017/05/07 3z (FT=3) 3Z 7 May 2017 Without DA Himawari-8 DA MODIS DA JAXA Himawari-8 L3 AOD Total AOD Himawari-8 True Color Reproduction image Surface dust Himawari-8 data assimilation produced (much) enhanced the dust concentration over ocean.

22 Challenges of Himawari-8 AOD data assimilation From what we learned from DA experiments Better cloud screening required Uncertainty of Surface emissivity Ocean color dependence Snow/Ice Over desert Angle dependence Quality control of AOD data is essentially important.

23 Thank you very much for your attention. SDS-WAS URLs NA-ME-E node Asian node Pan-American node