Satellite observation of atmospheric dust

Similar documents
Preparation for Himawari 8

Monitoring Sand and Dust Storms from Space

FUTURE PLAN AND RECENT ACTIVITIES FOR THE JAPANESE FOLLOW-ON GEOSTATIONARY METEOROLOGICAL SATELLITE HIMAWARI-8/9

Non-meteorological Applications for Next Generation Geostationary Meteorological Satellites

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

A 2016 CEOS Chair Initiative. Non-meteorological Applications for Next Generation Geostationary Satellites

Arctic Weather Every 10 Minutes: Design & Operation of ABI for PCW

Data Assimilation of Satellite Lidar Aerosol Observations

Sand and Dust Monitoring in RA II

Recent Update on MODIS C6 and VIIRS Deep Blue Aerosol Products

McIDAS support of Suomi-NPP /JPSS and GOES-R L2

RGB Experts and Developers Workshop - Introduction Tokyo, Japan 7-9 Nov 2017

4.1 New Generation Satellite Data and Nowcasting Products: Himawari

EUMETSAT products and services for monitoring storms - New missions, more data and more meteorological products

NOAA Report. Hal Bloom Mitch Goldberg NOAA/NESDIS

Lecture 4b: Meteorological Satellites and Instruments. Acknowledgement: Dr. S. Kidder at Colorado State Univ.

PHEOS - Weather, Climate, Air Quality

Instrumentation planned for MetOp-SG

Country Report - Singapore

Near real-time monitoring of the April-May 2010 Eyjafjöll s ash cloud

Atmospheric Measurements from Space

Himawari 8/9 data distribution/dissemination plan

TEMPO Aerosols. Need for TEMPO-ABI Synergy

A new perspective on aerosol direct radiative effects in South Atlantic and Southern Africa

The use of Direct Broadcast Processing System in Poland

Land Surface Temperature Measurements From the Split Window Channels of the NOAA 7 Advanced Very High Resolution Radiometer John C.

NESDIS Polar (Region) Products and Plans. Jeff Key NOAA/NESDIS Madison, Wisconsin USA

Ground-based Validation of spaceborne lidar measurements

Some NOAA Products that Address PSTG Satellite Observing Requirements. Jeff Key NOAA/NESDIS Madison, Wisconsin USA

Monitoring Air Pollution from Space

Aerosol measurements from Space. Gerrit de Leeuw FMI & Uni of Helsinki, Finland & TNO, Utrecht, Netherlands

MSG system over view

History of Aerosol Remote Sensing. Mark Smithgall Maria Zatko 597K Spring 2009

Benefits of the new-generation Himawari-8 geostationary satellite for the Asia-Pacific region. Toshihiko HASHIDA Japan Meteorological Agency (JMA)

EUMETSAT AGENCY REPORT 2014/15 INSTRUMENT CAL/VAL ACTIVITIES

Extending the Deep Blue aerosol record from SeaWiFS and MODIS to NPP-VIIRS

GENERATION OF HIMAWARI-8 AMVs USING THE FUTURE MTG AMV PROCESSOR

REMOTE SENSING KEY!!

Development of a System for Quantitatively Analyzing Volcanic Clouds

Himawari-8/9 and GCOM-C Non-Meteorological Application Opportunities

The Sentinel-4 Mission and its Atmospheric Composition Products

Recommendation proposed: CGMS-39 WGII to take note.

P3.24 EVALUATION OF MODERATE-RESOLUTION IMAGING SPECTRORADIOMETER (MODIS) SHORTWAVE INFRARED BANDS FOR OPTIMUM NIGHTTIME FOG DETECTION

EUMETSAT NEWS. Marianne König.

On the Satellite Determination of Multilayered Multiphase Cloud Properties. Science Systems and Applications, Inc., Hampton, Virginia 2

Introduction of the Hyperspectral Environmental Suite (HES) on GOES-R and beyond

Launched on May 4, K. Hokusai

Satellite Data For Applications: Aviation/Volcanic Ash. Richard Eckman NASA 27 May 2013

Feature-tracked 3D Winds from Satellite Sounders: Derivation and Impact in Global Models

Global Space-based Inter-Calibration System (GSICS) Infrared Reference Sensor Traceability and Uncertainty

Advanced Geostationary Observations for the OzEWEX Community. Leon Majewski Bureau of Meteorology

REMOTE SENSING TEST!!

The CEOS Atmospheric Composition Constellation (ACC) An Example of an Integrated Earth Observing System for GEOSS

1. History and Current Status

A Guide to Satellite Data Appropriate for Solar Energy Applications in Ireland

Updates on Chinese Meteorological Satellite Programs

EUMETSAT atmospheric composition products and support to CAMS

Operational systems for SST products. Prof. Chris Merchant University of Reading UK

Interpretation of Polar-orbiting Satellite Observations. Atmospheric Instrumentation

Operational Uses of Bands on the GOES-R Advanced Baseline Imager (ABI) Presented by: Kaba Bah

Cross-calibration of Geostationary Satellite Visible-channel Imagers Using the Moon as a Common Reference

Overview of Himawari-8/9

Current capabilities and limitations of satellite monitoring and modeling forecasting of volcanic clouds: and example of Eyjafjallaj

Aerosol Impact on Infrared METOC Data Assimilation

THE GLI 380-NM CHANNEL APPLICATION FOR SATELLITE REMOTE SENSING OF TROPOSPHERIC AEROSOL

Global observations from CALIPSO

Status and Plans of Next Generation Japanese Geostationary Meteorological Satellites Himawari 8/9

CEOS Atmospheric Composition Constellation

Advancing Environmental Intelligence via Next-Generation Satellite Observations

Observation of Smoke and Dust Plume Transport and Impact on the Air Quality Remote Sensing in New York City

Aerosol Impact on Infrared METOC Data Assimilation

Polar Multi-Sensor Aerosol Product: User Requirements

Preparation for FY-4A. (Submitted by Xiang Fang, CMA)

Vicarious calibration of GLI by global datasets. Calibration 5th Group Hiroshi Murakami (JAXA EORC)

Study of the Influence of Thin Cirrus Clouds on Satellite Radiances Using Raman Lidar and GOES Data

CURRENT STATUS OF OPERATIONAL WIND PRODUCT IN JMA/MSC

Meteosat Third Generation (MTG): Lightning Imager and its products Jochen Grandell

MSGVIEW: AN OPERATIONAL AND TRAINING TOOL TO PROCESS, ANALYZE AND VISUALIZATION OF MSG SEVIRI DATA

Satellite remote sensing of aerosols & clouds: An introduction

Report on the SCOPE-Nowcasting Pilot Projects January 2017

AMVs in the ECMWF system:

Space Based Global Observing System Requirements for Satellite Sounders. Dr. Jian Liu Space Programme World Meteorological Organization

Atmospheric Lidar The Atmospheric Lidar (ATLID) is a high-spectral resolution lidar and will be the first of its type to be flown in space.

EUMETSAT STATUS AND PLANS

JMA Assimilation Update

Himawari-8 True Color RGB

STATUS OF JAPANESE METEOROLOGICAL SATELLITES AND RECENT ACTIVITIES OF MSC

Future NASA Atmospheric Missions: Adding to the A-Train Calipso OCO NPP CloudSat Glory

WMO SDS-WAS. Observational needs. Enric Terradellas, AEMET, Barcelona chair of the WMO SDS-WAS Steering Committtee

EUMETSAT PLANS. K. Dieter Klaes EUMETSAT Darmstadt, Germany

Remote Sensing I: Basics

EUMETSAT SAF NETWORK. Lothar Schüller, EUMETSAT SAF Network Manager

H-SAF future developments on Convective Precipitation Retrieval

Ash RGB Detection of Volcanic Ash

RGB Products: an easy and practical way to display multispectral satellite data (in combination with derived products)

Aerosol impact and correction on temperature profile retrieval from MODIS

Projects in the Remote Sensing of Aerosols with focus on Air Quality

RETRIEVAL OF AEROSOL PROPERTIES FROM SEVIRI USING VISIBLE AND INFRA-RED CHANNELS.

CLAVR-x is the Clouds from AVHRR Extended Processing System. Responsible for AVHRR cloud products and other products at various times.

The potential impact of ozone sensitive data from MTG-IRS

Transcription:

Satellite observation of atmospheric dust Taichu Y. Tanaka Meteorological Research Institute, Japan Meteorological Agency 11 April 2017, SDS WAS: Dust observation and modeling @WMO, Geneva

Dust observations by satellite Satellite observations are powerful tools for monitoring and data assimilation of atmospheric dust. Pros Wide coverage of horizontal area Enable observations where human observer can go/maintain High temporal resolution (geostationary) Cons Cannot observe dust under clouds Uncertainties in retrievals: quantitative observations are still difficult Especially over grounds with high reflectance (e.g., deserts!) requires validations with ground observations!

Earth observing satellites: categorize by orbits Low Earth Orbit (LEO) Polar/no polar orbit Sun synchronous/non sun synchronous Geostationary Image from https://www.e education.psu.edu/eme810/node/477 Spatial resolution Low Earth Orbit (Polar orbit) High (MODIS: 0.25 1km) Geostationary Low* (*Himawari 8: 0.5 2km) Temporal resolution Low High Area coverage Global Localized disk

Low Earth orbit satellites Terra MODIS (MODerate resolution Imaging Spectroradiometer) MISR (Multi angle Imaging Spectro Radiometer) Aqua MODIS (MODerate resolution Imaging Spectroradiometer) AIRS (Atmospheric Infrared Sounder) CALIPSO CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) Aura OMI (Ozone Monitoring Instrument) PARASOL POLDER (Polarization and Directionality of the Earth's Reflectances) Suomi NPP VIIRS (Visible Infrared Imaging Radiometer Suite) OMPS (Ozone Mapping and Profiler Suite) MetOp ( A/B) GOME 2 AVHRR/3 PMAp A Train Upcoming: GCOM C: SGLI EarthCARE: ATLID, MSI JPSS series MetOp C

Geostationary Satellites Meteosat (EUMETSAT) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) Now: MSG (Meteosat Second Generation): Next gen. MTG Himawari 8/9 (JMA) AHI (Advanced Himawari Imager): 16 spectral bands GOES (NOAA) Next generation: GOES 16 with ABI (Advanced Baseline Imager) will be operational: 16 spectral bands FY 2/4 (CMA) Next generation: FY 4A: AGRI (Advanced Geostationary Radiation Imager) will be operational: 14 spectral bands

Next generation geostationary satellites Specs: Multi spectral : ~ 16 bands Horizontal : 500m ~ 2 km Full disk scan: ~ 10min, Regional rapid scan: ~2.5 min Long operational periods: ~15 years Himawari 8 True Color reproduction Suitable for aerosol observation! Upcoming: GOES 16(/17), FY 4 series, Meteosat Third Generation

Dust observations: Categorization by sensor type products used for dust Wavelengths Imager Horizontal Dust RGB Aerosol Optical Depth (AOD) Angstrom Exponent DEBRA Dust Infrared Difference Dust Index (IDDI) Absorbing Aerosol Index (OMI, OMPS) Dust score (by AIRS) Ultraviolet to infrared (depends on the sensor) Lidar Vertical Aerosol extinction Attenuated backscatter Depolarization ratio Limited to specific laser bands (typically, 355/532/1064nm) Satellite orbit Any Low earth orbit only

Dust RGB image EUMETSAT algorithm to identify airborne dust Exploits the difference in emissivity of dust/desert surfaces, temperature difference of desert surface(hot) and the dust cloud (cooler). Pink to magenta colors indicate the presence of dust Available during day and nighttime Rather qualitative yet effective to monitor the evolution of dust storms From SDS WAS NA ME E website

Real Time Image of Himawari 8 User s Guide to RGB composite image is available. Select Dust RGB http://www.data.jma.go.jp/mscweb/data/himawari/sat_img.php

Dust storm traveling over Mongolia and China, March 2016 by Himawari 8 DustRGB. The 10 minute interval movie of DustRGB captured the evolution of dust storm clearly.

Dust Plume over Desert Southwest, U.S. by GOES 16 (2017/03/24) Dust RGB with GOES 16 NOAA, From YouTube Dust Plume over Desert Southwest

Aerosol Optical Depth Quantitative value of the total aerosol (not only dust) in the atmospheric column Used for aerosol monitoring and data assimilation Dust detection may be difficult (but can be guessed from fine and coarse mode AOD) Available during daytime only (most cases)

Currently available AOD products MODIS (Collection 6) Long period: 2002 Most widely used for monitoring and data assimilation PMAp VIIRS AOD SEVIRI dust AOD (MSGAOD) Himawari 8 aerosol product Coming next generation geostationary satellites (GOES 16, FY 4A, MTG) are expected to provide aerosol products

MODIS AOD (NASA Worldview) https://worldview.earthdata.nasa.gov/

Himawari 8 Aerosol product by JAXA EORC JAXA EORC provides aerosol retrieval products of Himawari 8. (available in netcdf format)

Satellite Lidar observation Vertical profile of aerosol extinction and depolarization Used for aerosol monitoring and data assimilation research Depolarization ratio can be used to distinguish non spherical particles dust, volcanic ash From https://svs.gsfc.nasa.gov/4273 Used for data assimilation on research base Available during daytime and nighttime (daytime may be noisy)

3 D structure of dust transport captured by CALIOP (Yumimoto et al. 2009, ACP)

Space lidar platform Currently available CALIOP on CALIPSO product End of lifetime is approaching CATS on International Space Station (ISS) Future ATLID on EarthCARE (late 2018?)

DEBRA dust Other dust indices A New multi spectral satellite algorithm that utilizes ancillary surface data (SDS WAS NA ME E node website) Absorbing Aerosol Index (OMI, OMPS) Logarithmic difference between observed and pure Rayleigh atmosphere in a UV band. Indicates the presence of light absorbing aerosols (dust or black carbon) Infrared Difference Dust Index From https://ozoneaq.gsfc.nasa.gov/omps/blog/2014/04/dust overunited kingdom Dust detection from difference of infrared channels

Useful resources of satellite observation of atmospheric dust SDS WAS NA ME E Regional Center http://sds was.aemet.es/forecast products/dust observations SDS WAS Asia Regional Center http://eng.nmc.cn/sds_was.asian_rc/ NASA worldview https://worldview.earthdata.nasa.gov/ Goddard Earth Sciences Data and Information Service Center (GES DISC) https://disc.gsfc.nasa.gov/ JAXA Himawari Monitor: http://www.eorc.jaxa.jp/ptree/ Meteorological Satellite Center of JMA http://www.data.jma.go.jp/mscweb/data/himawari/sat_img.php The Regional and Mesoscale Meteorology Branch (RAMMB) of NOAA/NESDIS http://rammb.cira.colostate.edu/ramsdis/online/index.asp EUMETSAT http://www.eumetsat.int/website/home/index.html

Specification of Himawari 8/9 Imager (AHI) MTSAT 1R/2 VIS: 1km, IR: 4km AHI = Advanced Himawari Imager Band Wavelength [μm] Spatial Resolution VIS 1 0.46 1Km 2 0.51 1Km 3 0.64 0.5Km 4 0.86 1Km RGB band Composited True Color Image IR4 IR3 IR1 IR2 5 1.6 2Km 6 2.3 2Km 7 3.9 2Km 8 6.2 2Km 9 7.0 2Km 10 7.3 2Km 11 8.6 2Km 12 9.6 2Km 13 10.4 2Km 14 11.2 2Km 15 12.3 2Km 16 13.3 2Km NIR SO2 O 3 CO2 Similar to ABI for GOES R, but 0.51 μm(band 2) instead of ABI s 1.38 μm Water vapor Atmospheric Windows Products Volcanic Ash Global Instability Index Nowcasting Typhoon Analysis Atmospheric Motion Vector Clear Sky Radiance Sea Surface Temperature Yellow Sands Snow and Ice Coverage

Airflow brings Sahara dust to the UK (2017/03/24 25) MetOffice, From YouTube Airflow brings Sahara dust to the UK