MPI-Meteorology Hamburg, Germany Evaluation of year 2004 monthly GlobAER aerosol products Stefan Kinne

the task an evaluation of  GlobAER 2004 global maps for aerosol optical depth(info on amount) for Angstrom parameter(info on size)  by ASTR (dual view, global, at best 10 per month)  by MERIS (nadir view, global, at best 1 per day)  by SEVIRI (nadir view, regional, at best 30 per day)  by a merged (ATSR / MERIS / SEVIRI) composite

the questions  how well do the data compare to trusted data references (e.g. AERONET) ?  how well do the data compare to existing data sets – even for same sensor data ?  can the performance be quantified ? more specifically …  what are the scores of a new (outlier- resistant) method … examining bias, spatial and temporal variability ?

the investigated properties  aerosol optical depth (AOD) extinction along a (vertical) direction due to scattering and absorption by aerosol  here for the entire atmosphere  here for the mid-visible (0.55  m wavelength)  Angstrom parameter (Ang) spectral dependence of AOD in the visible spectrum  small dependence (Ang ~ 0)  aerosol > 1  m size  strong decrease (Ang > 1.2)  aerosol < 0.5  m size

monthly data-sets GlobAER 2004 maps  GAa - ATSR  GAs - SEVIRI  GAm - MERIS  GAx – merged other multi-ann. maps  med – model median  clim – med & aer(sun)  sky – med & aer(sky)  TO – TOMS other 2004 maps  ATs - ATSR Swansea  SEb- SEVIRI Bruxelles  Mdb - MODIS deep blu  MO - MODIS std coll.5  MI – MISR version 22  Ag – AVHRR, GACP  Ap – AVHRR, Patmos  Aer – AERONET 2004

AOD map comparisons  AOD annual maps all available data  AOD seasonal maps ATSR GlobAER vs Swansey SEVIRI GloabAER vs RUIB-Bruexelles  difference maps … to a remote sensing ‘best’ composite all available data-sets focus on the four GloabAER products

AOD – 2004 annual maps

ATSR / SEVIRI – seasonal AOD

AOD diff to ‘composite’  underestimates overestimates 

quick (annual) AOD check  ATSR underestimates in dust regions overestimate in biomass regions  SEVIRI severe biomass overestimates useful over-land estimates ?  MERIS apparent land snow cover issue  merged not the envisioned improvement  ‘-’ ’+’ 

Angstrom map comparisons  Angstrom annual maps all available data  Angstrom seasonal maps ATSR GlobAER vs Swansey SEVIRI GloabAER vs RUIB-Bruexelles  difference maps … to a climatology (model & AERONET) all available data-sets focus on the four GloabAER products

Angstrom – 2004 annual maps

ATSR / SEVIRI – seasonal Angstr.

Angstrom diff to ‘climatology’  underestimates overestimates 

quick annual Angstrom check  ATSR underestimates in tropics overestimates in south. oceans  SEVIRI underestimates over oceans strong overestimates over land  MERIS overestimates over land  merged not the envisioned improvement  ‘-’ ’+’ 

the SCORING challenge  quantify data performance by one number  develop a score such that contributing errors to be traceable back to bias spatial correlation temporal correlation spatial sub-scale (e.g. region) temporal sub-scale (e.g. month, day)  make this score outlier resistant

one number !

info on overall bias sign of the bias

| 1 | is perfect …. 0 is poor sign of the bias the closer to absolute 1.0 … the better

product of sub-scores = 0.9 *- 0.7 * 0.8 the closer to absolute 1.0 … the better temporal correlation sub-score bias sub- score spatial correlation sub-score sign of the bias

spatial stratification = 0.9 * -0.7 * 0.8 time score bias score spatial score spatial sub-scale scores overall score regional surface area weights TRANSCOM regions

temporal stratification = 0.9 * -0.7 * 0.8 time score bias score spatial score spatial sub-scale scores overall score temporal sub-scale scores (e.g. month or days) averaging in time instantaneous median data

sub-score definition  each sub-score S is defined by an error e and by an error weight w 0.9 * -0.7 * 0.8 S = 1 – w * e time score S bias score S spatial score S spatial sub-scale scores temporal sub-scale scores (e.g. month or days) instantaneous median data

definition of errors e  S = 1 – w * e   all values for the errors e are rank - based for “time score” and “spatial score” rank correlation coefficients for data pairs are determined  e, correlation = (1- rank_correlation coeff.) /2 (correlated: e = 0, anti-correlated: e = 1) time score bias score spatial score

definition of errors e  S = 1 – w * e for “bias score” all all data-pairs are placed in a single array and ranked by value then ranks are separated according to data origin, summed and (rank-sums are) compared  e, bias = (sum1 – sum2) / (sum1 + sum2) (strong neg.bias e = -1, strong pos.bias e= +1) an example (“how does the rank bias error work ?”)  set 1: value: rank-sum 1: 11  set 2: rank: rank-sum 2: 10 e = (1-2)/(1+2) = (11-10)/21 ~zero  no clear bias time score bias score spatial score

definition of error weight w  S = 1 – w * e  w is a weight factor based on the inter-quartile range / median ratio w = (75%pdf - 25%pdf) / 50%pdf … but not larger than 1.0 (w<1.0) simply put … if there is no variability an error does not matter bias score spatial score time score

scoring summary  one single score …  … without sacrificing spatial and temporal detail !  stratification into error contribution from bias spatial correlation temporal correlation  robustness against outliers still … just one of many possible approaches  now to some applications …

questions  how did GlobAER products score? overall ? seasonality ? spatial correlation ? bias ? in what regions ? in what months ?  how did scores place to other retrievals … with the same sensor (for the same year 2004) with other sensors (for the same year 2004)

monthly data-sets GlobAER 2004 maps  GAa - ATSR  GAs - SEVIRI  GAm - MERIS  GAx – merged other multi-ann. maps  med – model median  clim – med & aer(sun)  sky – med & aer(sky)  TO – TOMS other 2004 maps  ATs - ATSR Swansea  SEb- SEVIRI Bruxelles  Mdb - MODIS deep blu  MO - MODIS std coll.5  MI – MISR version 22  Ag – AVHRR, GACP  Ap – AVHRR, Patmos  Aer – AERONET 2004

ann global scores – AOD / Angstrom  year AOD  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  MOD  best  year Angstrom  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  med  MISR vs sun-photometry

annual AOD scores – diff refs  year 2004 – AOD (aeronet)  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  MOD  best  year 2004 – AOD (climat.)  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  aer  MISR  MOD  best

ATSR AOD - regional errors / data vs sun-photometry

merged AOD - regional errors / data vs sun-photometry

ATSR-s AOD - regional errors / data vs sun-photometry

ann. AOD scores – land/ocean  year 2004 – land AOD  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  MOD  best  year 2004 – ocean AOD  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  MOD  best

ATSR AOD – temporal total errors vs sun-photometry

monthly / regional ‘error’- change  improvement deteriation  ATSR (GlobAer) minus ATSR (Swan) :  total AOD error (=1-|S|) vs sun-photometry

monthly / regional ‘error’- change  improvement deteriation  SEVIRI (GlobAer) vs SEVIRI (Brux) :  total AOD error (=1-|S|) vs sun-photometry

AOD summary  ATSR by GlobAer poorer than MODIS, MISR and even ATSR-s stronger deductions over land than oceans  ocean scores are usually better than land scores low bias over land, high bias over oceans errors are larger for the northern hemisphere  MERIS by GlobAer poorer than ATSR … and also the ‘merged’

ATSR Angstrom - regional errors vs sun-photometry

ann. Ang scores – land/ocean  year 2004 – land Angstr  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  med  year 2004 – ocean Angstr  TOTAL seas bias corr  GAa  GAm  GAx  GAs  ATs  SEb  clim  MISR  med

ATSR Angstr. – temporal total errors vs sun-photometry

monthly / regional ‘error’- change  improvement deteriation  ATSR (GlobAer) vs ATSR (Swansey) :  total Ang. error (=1-|S|) vs sun-photometry

Angstrom summary  ATSR by GlobAer (model-based !) poorer than MODIS, MISR, better than ATSR-s ocean scores are slightly above land scores  (ocean scores are usually better than land scores) high bias over land, low bias over oceans higher errors during (continental) summers  no benefits from merged products Lack of MERIS Angstrom data over oceans

outlook  focus should be on (long-term) ATSR still improvement needed Angstrom constrain should help collaborate with Swansey  merged data is conceptually interesting … but limited by the poorest link (MERIS) if using diff sensors …use the same model !

extras

AODATSR – GlobAER 2004

AODSEVIRI – GlobAER 2004

AODMERIS – GlobAER 2004

AODmerged – GlobAER 2004

AODSEVIRI – RUIB 2004

AODATSR – Swansea 2004

annual maps – AOD 2004 med median cli ‘climatology’ sunsun-photo Globaer products MOMODIS Efcforecast MIMISREasassimilation SEVIRIAATSRMERIS

annual maps – Angstrom 2004 med median cli ‘climatology’ sunsun-photo Globaer products MOMODIS Efcforecast MIMISREasassimilation AATSRMERISSEVIRI

ATSR AOD - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

SEVIRI AOD - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

MERIS AOD - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

‘merged’ AOD - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

ATSR AOD – temporal total errors vs sun-photometry

ATSR-s AOD – temporal total errors vs sun-photometry

SEVIRI AOD – temporal total errors vs sun-photometry

ATSR/SEVIRI – temporal AOD errors GlobAERother sources ATSR SEVIRI vs sun-photometry

ATSR Angstrom - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

SEVIRI Angstrom - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

MERIS Angstrom - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

‘merged’ Angstr. - regional errors e_T  total error e_C  corr error e_B  bias error s_B  bias sign e_S  seas error r_B  rel. bias r_E  rel. error m_d  median dev  med. diff vs sun-photometry

ATSR Angstr. – temporal total errors vs sun-photometry

SEVIRI Ang. – temporal total errors vs sun-photometry

ATSR/SEVIRI – temporal Ang errors other sources ATSR GlobAER SEVIRI vs sun-photometry

monthly / regional ‘error’- change  improvement deteriation  ATSR (GlobAer vs Swansey) : total Ang. error (=1-|S|) change vs sun-photometry

monthly / regional ‘error’- change  improvement deteriation  SEVIRI (GlobAer vs Bruxelles) : total Ang. error (=1-|S|) change vs sun-photometry