Using remotely sensed data to quantify the characteristics of Southeast Asian forest 7ires. Xi Xi Yuk Lunch Seminar at Caltech July 9, 2013

Transcription

1 Using remotely sensed data to quantify the characteristics of Southeast Asian forest 7ires Xi Xi Yuk Lunch Seminar at Caltech July 9, 2013

2 Southeast Asia and Singapore 2 Source: Google Map

3 The Haze Problem in Singapore Worst day: June 20, 2013 PSI (Pollutant Standards Index): 400+ Hazardous Bad days Good days Imagine swimming at the top of the building and enjoying the view 3

4 Forest Fires in SE Asia Clean out land for vegetaton, urbanizaton Illegal biomass burning across SE Asia A major source of trace gases and aerosols Strong winds blowing aerosols away from sources 4

5 Using remotely sensed data to quantify the characteristics of Southeast Asian forest 7ires Jason Blake Cohen*, Xi Xi, Chien Wang Singapore- MIT Alliance for Science and Technology *Dr. Cohen is now an Assistant Professor at Department of Civil and Environmental Engineering, NaTonal University of Singapore 5

6 Methodology Use remotely sensed data to study the characteristcs of aerosols Key indicators: Aerosol OpTcal Depth (AOD), aerosol layer height, and layer thickness AERONET (AErosol ROboTc NETwork) MISR (MulT- angle Imaging SpectroRadiometer) CALIOP (Cloud- Aerosol Lidar with Orthogonal PolarizaTon) EOF (Empirical Orthogonal FuncTons) 6

7 Regional Study: Continental SE Asia (8N to 30N) 7

8 1. AERONET Stations A network of ground- based sun photometers which measure Sun and sky radiances at a number of fixed wavelengths within the visible and near- infra red spectrum 8

9 AERONET AOD 500nm in 2008, 2009, 2010 and 2011 at Chiang Mai Met Station Annual burning from Feb. to May AOD in 2011 AOD in 2010 AOD in 2009 AOD in 2008 AOD 500nm Minor burning from Oct. to Dec Julian Days

10 2. 10 years of MISR data 10 MISR data from Dec to Dec Source: NASA Earth Observatory

11 EOF Analysis Also known as Principal Component Analysis (PCA) We have a set of spatal maps (or vectors) We would like to describe each map as a sum of pa;ern vectors (EOFs) These EOFs are chosen to be orthogonal, and are ordered according to the amount of variance in the dataset that they describe The weights in this sum are known as amplitudes, or Principal Components (PCs) Typically, the EOFs are found by computng the eigenvalues and eigenvectors of a spa%ally weighted anomaly covariance matrix of a field. The derived eigenvalues provides a measure of the percent variance explained by each EOF. E.g. EOF1 explains 90% of the variances Reference book: StaTsTcal Methods in the Atmospheric Sciences by Daniel S. Wilks 11

12 An example: EOF analysis of NCEP/NCAR temperature data Source: EOF Analysis in IDL by David W. Fanning 12

13 Quantifying time- varying emissions Continental Southeast Asia The Cme pa;ern of this spacal pa;ern is (mostly) annual SpaCal pa;ern seems to represent Cme- varying emissions from ConCnental Southeast Asia 13 Cohen, et al. (in prep)

14 3. CALIOP on CALIPSO CALIPSO (Cloud- Aerosol Lidar and Infrared Pathfinder Satellite ObservaTon) CALIPSO probes the verccal structure and propertes of thin clouds & aerosols over the globe since >3- D perspeccves of how clouds and aerosols form, evolve, and affect weather and climate 14

15 Calipso tracks in Feb o N 24 o N Latitude 20 o N Chiang Mai Met Station 16 o N 12 o N 15 8 o N 90 o E 96 o E 102 o E Longitude 108 o E 114 o E 120 o E

16 Aerosol layer products: top height, bo;om height, layer thickness On average, one Calipso track falls within the region of study everyday Extract all Calipso data over the contnental SE Asia and the EOF region Compare the averaged layer characteristcs over the two regions 16

17 10 8 EWeighted Aerosol Layer Base Height over continental SEA (8N to 30N) Feb. to May Layer Base Height (km) EWeighted Aerosol Layer Base Height over EOF2.2 SEA (8N to 30N) Julian Days 20 Comparing Layer Base Height: Over SE Asia mean = 1.94km, standev = 1.27km Over EOF region mean = 1.55km, standev = 2.25km Layer Base Height (km) *the red line in the middle is the mean, and the other two are 3 standev away from mean Julian Days

18 12 EWeighted Aerosol Layer Top Height over continental SEA (8N to 30N) Layer Top Height (km) EWeighted Aerosol Layer Top Height over EOF2.2 SEA (8N to 30N) Julian Days 25 Comparing Layer Top Height: Over SE Asia mean = 3.36km, standev = 1.36km Over EOF region mean = 2.96km, standev = 2.42km Layer Top Height (km) Julian Days

19 3.5 EWeighted Aerosol Layer Thickness over continental SEA (8N to 30N) Layer Thickness (km) Julian Days 3.5 EWeighted Aerosol Layer Thickness over EOF2.2 SEA (8N to 30N) Comparing Layer Thickness: Over SE Asia mean = 1.42km, standev = 0.40km Over EOF region mean = 1.41km, standev = 0.58km Layer Thickness (km) Julian Days

20 Discussion and Conclusion AERONET, MISR, and CALIOP offer three unique perspectves on aerosol propertes. Used together, they could help to understand the rampant forest fires in SE Asia which carry strong annual and seasonal pa;erns. EOF analysis seems to suggest that some smoke plumes over contnental SE Asia have been transported to nearby sea areas. LimitaTons of EOF analysis: difficult to interpret the EOF paqerns, results are dependent on choice of the data domain CALIOP data show that, on average, the aerosol layers over the sea (EOF region) are lower in height, but similar in thickness, compared to those over the entre contnental SE Asia. 20

21 Thank you! 21