Study on Dust Storms Climatological Trends, transportation paths and Sources Identification.

Slides:



Advertisements
Similar presentations
NOAA National Geophysical Data Center
Advertisements

Deep Blue Algorithm: Retrieval of Aerosol Optical Depth using MODIS data obtained over bright surfaces 1.Example from the Saharan Desert. 2.Deep Blue Algorithm.
Introduction Air stagnation is a meteorological condition when the same air mass remains over an area for several days to a week. Light winds during air.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Liang APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region September 2002, Beijing,, China Atmospheric.
A Dictionary of Aerosol Remote Sensing Terms Richard Kleidman SSAI/NASA Goddard Lorraine Remer UMBC / JCET Short.
JERAL ESTUPINAN National Weather Service, Miami, Florida DAN GREGORIA National Weather Service, Miami, Florida ROBERTO ARIAS University of Puerto Rico.
Quantitative Interpretation of Satellite and Surface Measurements of Aerosols over North America Aaron van Donkelaar M.Sc. Defense December, 2005.
Fusion of SeaWIFS and TOMS Satellite Data with Surface Observations and Topographic Data During Extreme Aerosol Events Stefan Falke and Rudolf Husar Center.
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Detector Configurations Used for Panchromatic, Multispectral and Hyperspectral Remote Sensing Jensen, 2000.
Some Significant Current Projects. Landsat Multispectral Scanner (MSS) and Landsat Thematic Mapper (TM) Sensor System Characteristics.
Remote Sensing of Pollution in China Dan Yu December 10 th 2009.
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
Outline Further Reading: Chapter 04 of the text book - satellite orbits - satellite sensor measurements - remote sensing of land, atmosphere and oceans.
Temporal and Spatial Variations of Sea Surface Temperature and Chlorophyll a in Coastal Waters of North Carolina Team Members: Brittany Maybin Yao Messan.
Satellite Remote Sensing of Surface Air Quality
Introduction to Digital Data and Imagery
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
Outline Further Reading: Chapter 04 of the text book - satellite orbits - satellite sensor measurements - remote sensing of land, atmosphere and oceans.
An Overview of Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Originally presented as part.
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
Trajectory validation using tracers of opportunity such as fire plumes and dust episodes Narendra Adhikari March 26, 2007 ATMS790 Seminar (Dr. Pat Arnott)
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
IAAR Seminar 21 May 2013 AOD trends over megacities based on space monitoring using MODIS and MISR Pinhas Alpert 1,2, Olga Shvainshtein 1 and Pavel Kishcha.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
 Introduction  Surface Albedo  Albedo on different surfaces  Seasonal change in albedo  Aerosol radiative forcing  Spectrometer (measure the surface.
MODIS Retrievals for the Amazon Rainforest Dan Sauceda.
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Aerosol Optical Depth during the Northern CA Fires of 2008 In situ aerosol light scattering and absorption measurements in Reno Nevada, 2008, indicated.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Pawan Gupta Satellite.
14 ARM Science Team Meeting, Albuquerque, NM, March 21-26, 2004 Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada Natural.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Long-term drought assessment of Northern Central African continent using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)
Image Interpretation Color Composites Terra, July 6, 2002 Engel-Cox, J. et al Atmospheric Environment.
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Environmental Remote Sensing GEOG 2021 Lecture 8 Observing platforms & systems and revision.
April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.
A Remote Sensing Sampler. Typical reflectance spectra Remote Sensing Applications Consultants -
By Harish Anandhanarayanan Mentor: Dr. Alfredo Huete.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
GEWEX Aerosol Assessment Panel members Sundar Christopher, Rich Ferrare, Paul Ginoux, Stefan Kinne, Jeff Reid, Paul Stackhouse Program Lead : Hal Maring,
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Image: MODIS Land Group,
CAPITA Center for Air Pollution Impact and Trend Analysis.
AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.
Data acquisition From satellites with the MODIS instrument.
North American Visibility. rdyswth Seasonal Bext.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
SCM x330 Ocean Discovery through Technology Area F GE.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
MPI-Meteorology Hamburg, Germany Evaluation of year 2004 monthly GlobAER aerosol products Stefan Kinne.
Dust detection methods applied to MODIS and VIIRS
Satellite Sensors – Historical Perspectives
Diurnal Variation of Nitrogen Dioxide
Presentation transcript:

Study on Dust Storms Climatological Trends, transportation paths and Sources Identification

Content Introduction and Background Methodology Definitions and Indicators Observed Trends of dust storms Diagnosis of causes and identifying sources and hotspots Proposal for observation, monitoring and early warning system for Iraq

LONG-TERM SPATIAL AND TEMPORAL VARIABILITY OF DUST OVER IRAQ Temporal variability of dust episodes and their long-term trends, as well as spatial variability of dust hot spots and sources, are important topics of study In this study we perform a joint analysis of satellite remote sensing data for computing (AOD, AE and NDVI) and horizontal visibility reduction records across Iraq, using the longest data series available in each case.

Two complementary approaches are used to identify regions of high dust loadings and to study seasonal variability and long- term trends of dust Spatial and temporal variability of dust over Iraq using available satellite based observations is presented (MODIS [], MISR[], SeaWiFS [] and MERIS[]). Horizontal visibility observations (Iraqi and Global data ) are used in order to evaluate long-term trends of visibility reductions due to dust episodes.

AOT computed by the Folowing Satellite Sensors MODIS (Moderate-Resolution Imaging Spectroradiometer ) is a payload scientific instrument launched in 1999 on board the Terra(EOS AM) Satellite, and in 2002 on board the Aqua (EOS PM) satellite. The instruments capture data in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm and at varying spatial resolutions (2 bands at 250 m, 5 bands at 500 m and 29 bands at 1 km).payloadTerraEOSAquaµm MISR ( Multi-angle Imaging Spectro Radiometer) is a scientific instrument on the Terra satellite. This device is designed to measure the intensity of solar radiation reflected by the Earth system (planetary surface and atmosphere) in various directions and spectral bands. The MISR instrument consists of an innovative configuration of nine separate digital cameras that gather data in four different spectral bands of the solar spectrum. One camera points toward the nadir, while the others provide forward and aftward view angles at 26.1°, 45.6°, 60.0°, and 70.5°. As the instrument flies overhead, each region of the Earth's surface is successively imaged by all nine cameras in each of four wavelengths (blue, green, red, and near- infrared).Terra satelliteEarthnadirwavelengthsnear- infrared SeaWiFS ( Sea-Viewing Wide-Field-of-View Sensor )was the only scientific instrument on GeoEye's OrbView-2 (AKA SeaStar) satellite. The sensor resolution is 1.1 km (LAC), 4.5 km (GAC). The sensor recorded information in the following optical bands: BandWavelength nm nm nm nm nm nm nm nm.Sea-Viewing Wide-Field-of-View Sensorscientific instrumentGeoEyesatellitesensor resolutionopticalnm MERIS (MEdium Resolution Imaging Spectrometer) is one of the main instruments on board the (ESA)'s Envisat platform. This instrument is composed of five cameras disposed side by side, each equipped with a pushbroom spectrometer provides useful data in 15 spectral bands, generate data that can later be used to create two-dimensional images.Envisatspectrometer

AOD Aerosol Optical Depth (AOD): it depends on the wavelength and represents the vertically- integrated extinction of light by aerosols. AOD, which is a unitless value, provides information about the aerosol amount in the entire column of the atmosphere. The larger the AOD value, the larger the content of aerosols in the column of air

AE Angström Exponent (AE): it is the exponent in the formula that describes the AOD dependency on wavelength. AE is inversely proportional to the size of aerosols, so it is a qualitative indicator of the aerosol particle size of the aerosol present in a given column of air. Desert dust particles are characterized by AE ≤ 0.15 and AOD at 550 nm ≥ 0.15

Normalized Difference Vegetation Index (NDVI): This index indicates whether the area observed by the remote sensor contains green vegetation cover or not. This index is widely used to monitor large variations in vegetation cover and to identify deforestation and desertification processes. NDVI varies between -1 and +1, and it is based on spectral reflectance measurements acquired in the visible (red) and near-infrared regions. Values of NDVI ≤ 0.1 correspond to barren areas of rock, sand, or snow. Moderate values (0.2 to 0.3) represent shrub and grassland. High values (0.6 to 0.8) indicate temperate and tropical rainforests. Monthly and inter-annual climatology of NDVI have been also analyzed in order to detect possible changes in land vegetation cover associated to desertification or changes in land use.

The following MODIS products have been used Monthly averaged MODIS/Terra AOD at 550 nm over land and ocean based on daily measurements. Level 3 data. 1°x1° resolution. March 2000 – April 2013 period. Monthly averaged MODIS/Aqua AOD at 550 nm over land and ocean based on daily measurements. Level 3 data. 1°x1° resolution. July 2002 – June 2013 period. Monthly averaged MODIS/Terra Deep Blue AOD at 550 nm over land based on daily measurements. 1°x1° resolution. Level 3 data. March 2000 – December 2007 period. Monthly averaged MODIS/Aqua Deep Blue AOD at 550 nm over land based on daily measurements. Level 3 data. 1°x1° resolution. July 2002-June 2013 period. Monthly averaged MODIS/Terra Deep Blue Angström exponent over land based on daily measurements. Level 3 data. 1°x1° resolution. March 2000-December 2007 period. Monthly averaged MODIS/Aqua Deep Blue Angström exponent over land based on daily measurements. Level 3 data. 1°x1° resolution. July 2002-June 2013 period. Monthly averaged MODIS/Terra NDVI based on daily measurements. Level 3 data. 1°x1° resolution. March 2000 – April 2013 period.

Angström exponent (AE) and NDVI measurements to compare across them, the period March 2000 – April 2013 was used whenever possible.

Monthly climatology of optical properties of aerosols MODIS/Terra AOD at 550 nm. March 2000 – April 2013

Monthly means of Deep Blue AOD at 550 nm from Terra and Aqua for the period March 2000 – April 2013.

MODIS Deep Blue AOD at 550 nm MODIS/Terra pass over Iraq ≈ 8 UTC MODIS/Aqua pass over Iraq ≈ 10:30 UTC

AOD by different space tools

Means of AOD at 550 nm from MODIS/Aqua for the period Mean of AOD at 550 nm for the period 2008 indicate high dust frequency in this year

Annual means of NDVI from MODIS/Terra for the period

Year to year differences of annual April-May-June-July means of NDVI from MODIS/Terra for the period

Monthly mean variation of MODIS/Terra NDVI for the region limited by lat=[31°N – 36°N], long=[41°E-43°E] (Central Iraq) (a) and of MODIS/Terra NDVI for the region limited by lat=[31- 37N], long=[42E-45E] (Mesopotamian region) (b, after Cuevas, 2013) from July 2002 to April b) a)

Identification of DUST SOURCES By Back trajectories Using HYSPLIT MODEL Statistical analysis using both air mass back-trajectories and AOD measurements is performed in order to identify the extent to which Iraq represents a dust source. Three-dimensional 5-day back trajectories were calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) version 4 (Draxler and Hess, 1998). The time resolution of these back-trajectories is one hour. Eight sets of back-trajectories were calculated, two for each country capital. The end-points were set at ground level and at 1500 m above see level (a.s.l.) at Kuwait City (Kuwait; °N, °E), Manama (Bahrain; °N, °E), Abu Dahbi (UAE; °N, °E) and Riyadh (Saudi Arabia; °N, °E) for each day within the April-August season in the period , at 12 UTC. For the calculation of back-trajectories, 2.5°x2.5° fields from the National Centers for Environmental Prediction/National Center of Atmospheric Research (NCEP/NCAR) reanalysis meteorological dataset was used.

Three-dimensional 5-day Hysplit 4.0 back-trajectories for each day at 12 UTC within the April-August season in the period , with endpoints at ground level at: a) Kuwait City, b) Manama, c) Abu Dahbi and c) Riyadh. a b c d

Three-dimensional 5-day Hysplit 4.0 back-trajectories for each day at 12 UTC within the April-August season in the period , with endpoints at 1500 m a.s.l. at: a) Kuwait City, b) Manama, c) Abu Dahbi and c) Riyadh. a) ) b) ) c) ) d) )

April to August AOD Weighted Trajectories plot for Kuwait City at surface level, for the period. Major source areas are dark red-shaded. Non-source areas are light blue-shaded. A green cross points the location of Kuwait City.

April to August AOD Weighted Trajectories plot for Kuwait City at 1500 m a.s.l., for the period. Major source areas are dark red-shaded. Non-source areas are light blue-shaded. A green cross points the location of Kuwait City

April to August AOD Weighted Trajectories plot for Manama at surface level, for the period. Major source areas are dark red-shaded. Non-source areas are light blue-shaded. A green cross points the location of Manama.

April to August AOD Weighted Trajectories plot for Manama at 1500 m a.s.l., for the period. Major source areas are dark red-shaded. Non-source areas are light blue-shaded. A green cross points the location of Manama.

Conclusions According to Model Results In general, the AOD Weighted Trajectories analysis performed for Kuwait, Bahrain, UAE and Saudi Arabia for the April to August months within the period shows that: Iraq and desert areas in the northern half of Saudi Arabia followed by Syria and other areas in Saudi Arabia on both its border have dust sources. The identification of spatial and temporal dust and sand sources needs deep study.