GOME NO 2 data product HDF data file user manual

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1 GOME NO 2 data product HDF data file user manual Folkert Boersma and Henk Eskes 23 April 2002

2 Contents Content of the no2trackyyyymmdd.hdf files 3 The use of the averaging kernel 6 IDL sample code to read the ozone fields 7 2

3 Content of the no2trackyyyymmdd.hdf files The no2trackyyyymmdd.hdf file contains NO 2 data for one day. These files are organised as follows: SDS Global Attributes (contains information about the file) Vdata (the actual data) SDS Global Attributes An example of the SDS Global Attributes is given below. Data_created_by refers to the version of the retrieval-assimilation software that was used to generate the NO 2 column data. SDS Global Attributes Name Value Author H.J. Eskes, K.F. Boersma Affiliation KNMI (Royal Netherlands Meteorological Institute) eskes@knmi.nl, boersma@knmi.nl Data_created_by TM3-NO2A, version 1 Creation_date 8 Mar 2003 Unit_of_NO2_column 1e15 molecules/cm2 VData The Vdata tables contain the data product, and consists of: pressure_grid NO2_ymmddttt (contains the main NO 2 retrieval data for one GOME track) GEO_ymmddttt (contains all geometric data associated with retrievals in this track) ANC_ymmddttt (contains all ancillary data associated with retrievals in this track) The VData contains only one pressure_grid. This table defines the hybrid pressure levels at which the averaging kernel is provided. The VData table attribute also provides a recipe to convert the level constants a_lev, b_lev and surface pressure p_surf into the hybrid level pressures p (in Pascal). The equation is: p = a_lev + p_surf * b_lev (1) The variable p_surf is stored in the NO2_ymmddttt table: the surface pressure is provided for each GOME pixel separately. The VData may contain between 0 and 15 (maximum number of satellite orbits in one day) NO2\_ymmddttt, GEO\_ymmddttt and ANC\_ymmddttt tables. The table NO2\_ymmddttt is accompanied by a VData Table Attribute that contains the name of the track, and the start- and end-time (year, month, day, hour, minutes, and seconds): 3

4 VData Table Attributes Name Value track_identifier start_time 1997, 2, 1, 3, 57, 43 end_time 1997, 2, 1, 4, 40, 45 The main data product table is NO2\_ymmddttt and it contains 15 columns. They are: date time lon lat vcd sigvcd vcdtrop sigvcdt vcdstrat sigvcds fltrop psurf sigvcdak sigvcdtak kernel Date of GOME retrieval (yyymmdd) Time of measurement ((h)hmmss) Centre longitude of pixel Centre latitude of pixel Retrieved total vertical column density (in 1e15 molec. cm-2) Error in the total vertical column density (in 1e15 molec. cm-2) Retrieved tropospheric vertical column density (in 1e15 molec. cm-2) Error in the tropospheric vertical column density (in 1e15 molec. cm-2) Assimilated stratospheric vertical column density (in 1e15 molec. cm-2) Error in the stratospheric vertical column density (in 1e15 molec. cm-2) Flag that indicates if the tropospheric retrieval is meaningful; 0 = yes, -1 = no Surface pressure of the pixel (in Pa) Error in total vertical column density when averaging kernel information is used, in 1e15 molec. cm-2 (without profile error contribution) Error in tropospheric vertical column density when averaging kernel information is used, in 1e15 molec. cm-2 Averaging kernel vector, corresponding to the kernel values at the pressure levels as defined above The geolocation data is stored in GEO\_ymmddttt, and it contains 6 columns: sza vza raa ssc loncorn latcorn satellite solar zenith angle satellite viewing zenith angle satellite relative azimuth angle GOME subset counter, 0 = West, 1 = Center, 2 = East, 3 = Backscan longitudes of the four corners of the pixel latitudes of the four corners of the pixel 4

5 Additional retrieval information is provided by the ancillary data table ANC\_ymmddttt, and it contains 10 columns: scd slant column density, in 1e15 molec. cm-2 (from IUP-Heidelberg) amf total air-mass factor used to compute vcd (= scd / amf) amftrop tropospheric air-mass factor used to compute vcdtrop (= [scd-scdstr] / amftrop ) amfgeo geometrical air-mass factor scdstr stratospheric slant column density (= amfgeo * vcdstrat ) clfrac cloud fraction from FRESCO cltpres cloud top pressure from FRESCO albclr surface albedo for clear part of the pixel from TOMS/GOME database crfrac cloud radiance fraction, i.e. percentage of light coming from the cloudy part of the scene ltropo index of pressure level where tropopause occurs (2K/km level) 5

6 The use of the averaging kernel Users will generally have a range of interests in employing the data. We can basically distinguish two applications of the NO 2 data sets. (1) For plotting purposes, qualitative comparisons, and when no additional source of information on NO 2 is available: In these cases the users will be mainly interested in the total NO 2 column (vcd) and its error estimate (sigvcd), or in the tropospheric column (vcdtrop) and its error estimate (sigvcdt). The NO 2 columns vcd and vcdtrop are the best estimate of the total and tropospheric column. However, it is important to note that these retrieved quantities are sensitive to assumptions concerning the NO 2 profile shape. (2) For comparisons with measured NO 2 profiles, with chemistry-transport models, or when the data is assimilated into a chemistry model: In these cases the users should combine the retrieved columns with the averaging kernels (AK) provided. The averaging kernel also gives a very useful interpretation of the data product: it is the sensitivity of the GOME measurement to NO 2 at a given altitude. The averaging kernel vector provides the relation between the actual NO 2 profile x and the retrieved quantity y (either vcd or vcdtrop), y = A x (2) with A the averaging kernel vector, specified at pressure levels as described in the previous section. The actual NO 2 profile x should consist of partial columns of NO 2, in molecules cm 2. For data assimilation purposes A is the observation operator that converts the NO 2 model forecast x f into a forecast of the GOME retrieved column y f. For detailed comparisons with chemistry models one should first interpolate the model field to the longitude/latitude and time of the GOME observation. Next, this collocated model profile is multiplied by the averaging kernel to result in a model prediction of the observation. This can be directly compared with for instance vcd as retrieved from the GOME observation. For these calculations, the error in y reduces to sigvcdak, which is smaller than sigvcd, since uncertainties on the vertical NO 2 profile are no longer contributing. Note that sigvcdak combines both measurement errors (in y) and averaging kernel errors. The application of the averaging kernel involves an interpolation to the pressure grid on which the averaging kernel is defined. This should be done with some care: abrupt changes in the AK will occur especially near the cloud top pressure. For tropospheric retrievals (with y now the tropospheric column), equation 2 reduces to: y = A trop x trop (3) where A trop is the averaging kernel for the tropospheric column, defined as: A trop = A amf amftrop (4) Here x trop is the profile defined for the tropospheric levels only (levels up to level number ltropo as specified in ANC\_ymmddttt). The pressure at level ltropo corresponds to the pressure of the layer in which the tropopause occurs according to the WMO 1985 temperature tropopause criterion. 6

7 IDL sample code to read the NO 2 fields The following IDL program reads an HDF data file and stores the data in a structure no2. pro readno2colhdf ; ; Read data from GOME HDF file and stores in structure "no2" ; ;"maxorbits" maximum number of orbits in a file ;"maxpix" maximum number of pixels in an orbit ; ; Folkert Boersma, KNMI, April 2003 ; maxorbits = 15 maxpix = 2500 file = no2track hdf ; Define structure for GOME data no2 = { $ norbits : 0, $ npix : intarr(maxorbits), $ a_lev : make_array(19,/float,value=-999), $ b_lev : make_array(19,/float,value=-999), $ date : make_array(maxorbits,maxpix,/int,value=-999), $ time : make_array(maxorbits,maxpix,/int,value=-999), $ lon : make_array(maxorbits,maxpix,/float,value=-999.9), $ lat : make_array(maxorbits,maxpix,/float,value=-999.9), $ vcd : make_array(maxorbits,maxpix,/float,value=-999.9), $ sigvcd : make_array(maxorbits,maxpix,/float,value=-999.9), $ vcdtrop : make_array(maxorbits,maxpix,/float,value=-999.9), $ sigvcdt : make_array(maxorbits,maxpix,/float,value=-999.9), $ vcdstrat : make_array(maxorbits,maxpix,/float,value=-999.9), $ sigvcds : make_array(maxorbits,maxpix,/float,value=-999.9), $ fltrop : make_array(maxorbits,maxpix,/int,value=-999.9), $ psurf : make_array(maxorbits,maxpix,/int,value=-999.9), $ sigvcdak : make_array(maxorbits,maxpix,/float,value=-999.9), $ sigvcdtak : make_array(maxorbits,maxpix,/float,value=-999.9), $ kernel : make_array(maxorbits,maxpix,19,/float,value=-999), $ sza : make_array(maxorbits,maxpix,/float,value=-999.9), $ vza : make_array(maxorbits,maxpix,/float,value=-999.9), $ raa : make_array(maxorbits,maxpix,/float,value=-999.9), $ ssc : make_array(maxorbits,maxpix,/int,value=-999.9), $ loncorn : make_array(maxorbits,maxpix,4,/float,value=-999), $ latcorn : make_array(maxorbits,maxpix,4,/float,value=-999), $ scd : make_array(maxorbits,maxpix,/float,value=-999), $ amf : make_array(maxorbits,maxpix,/float,value=-999), $ amftrop : make_array(maxorbits,maxpix,/float,value=-999), $ 7

8 amfgeo : make_array(maxorbits,maxpix,/float,value=-999), $ scdstr : make_array(maxorbits,maxpix,/float,value=-999), $ clfrac : make_array(maxorbits,maxpix,/float,value=-999), $ cltpres : make_array(maxorbits,maxpix,/float,value=-999), $ albclr : make_array(maxorbits,maxpix,/float,value=-999), $ crfrac : make_array(maxorbits,maxpix,/float,value=-999), $ ltropo : make_array(maxorbits,maxpix,/int,value=-999)} id = hdf_sd_start(file,/read) ; The HDF_SD_FILEINFO procedure retrieves the number of datasets and ; global attributes in an HDF file. hdf_sd_fileinfo,id,datasets,attributes help,datasets,attributes iret = 0 for i=0,attributes-1 do begin hdf_sd_attrinfo,id,i,name = name, data = d command = name+ =d iret = execute(command) print, Retrieved Attribute:,name,,d hdf_sd_end,id ; Open file and initialize vdata reading file_id=hdf_open(file,/read) vd_id = -1 vd_handle = -1 end_of_file=0 vds = hdf_vd_lone(file_id) nvds = n_elements(vds) if (nvds eq 0) then begin print, ERROR: No vdatas found in file ; Loop over vdatas for i=0,attributes+nvds-1 do begin vd_id = hdf_vd_getid(file_id,vd_id) vd_handle=hdf_vd_attach(file_id,vd_id,/read) hdf_vd_get,vd_handle,nfields=nf,name=vd_name,count=count,fields=fields ; Read pressure fields a_lev and b_lev into variables if (strmid(vd_name,0,4) eq pres ) then begin for j=0,nf-1 do begin hdf_vd_getinfo,vd_handle,j,name=fieldname,size=size,type=type name = strcompress(fieldname,/remove_all) iret = execute( nread=hdf_vd_read(vd_handle, +$ 8

9 name+,fields=" +fieldname+ ") ) if (iret ne 1) then begin print, Error dataset,i no2.a_lev(0:18) no2.b_lev(0:18) = transpose(a_lev) = transpose(b_lev) if (strmid(vd_name,0,4) eq NO2_ ) then begin if (vd_name ne start_time and vd_name ne end_time ) then begin track_date = long64(strmid(vd_name,strlen(vd_name)-8,8)) track_date = track_date * ; Check whether orbit fits in data structure if (no2.norbits ge maxorbits) then begin print, ERROR: more orbits in file than, maxorbits if (count gt maxpix) then begin print, ERROR: orbit, no2.norbits+1, has more pixels (, $ count, ) than, maxpix ; Read fields into variables for j=0,nf-1 do begin hdf_vd_getinfo,vd_handle,j,name=fieldname,size=size,type=type name = strcompress(fieldname,/remove_all) iret = execute( nread=hdf_vd_read(vd_handle, + $ name+,fields=" +fieldname+ ") ) if (iret ne 1) then begin print, Error dataset,i ; Add variables to structure no2.date(no2.norbits,0:count-1) no2.time(no2.norbits,0:count-1) no2.lon(no2.norbits,0:count-1) no2.lat(no2.norbits,0:count-1) no2.vcd(no2.norbits,0:count-1) no2.sigvcd(no2.norbits,0:count-1) no2.vcdtrop(no2.norbits,0:count-1) no2.sigvcdt(no2.norbits,0:count-1) no2.vcdstrat(no2.norbits,0:count-1) = date = time = lon = lat = vcd = sigvcd = vcdtrop = sigvcdt = vcdstrat 9

10 no2.sigvcds(no2.norbits,0:count-1) = sigvcds no2.fltrop(no2.norbits,0:count-1) = fltrop no2.psurf(no2.norbits,0:count-1) = psurf no2.sigvcdak(no2.norbits,0:count-1) = sigvcdak no2.sigvcdtak(no2.norbits,0:count-1) = sigvcdtak no2.kernel(no2.norbits,0:count-1,*) = transpose(kernel) no2.npix(no2.norbits) = count if (strmid(vd_name,0,4) eq GEO_ ) then begin ; Check whether orbit fits in data structure if (no2.norbits ge maxorbits) then begin print, ERROR: more orbits in file than, maxorbits if (count gt maxpix) then begin print, ERROR: orbit, no2.norbits+1, has more pixels (, $ count, ) than, maxpix ; Read fields into variables for j=0,nf-1 do begin hdf_vd_getinfo,vd_handle,j,name=fieldname,size=size,type=type name = strcompress(fieldname,/remove_all) iret = execute( nread=hdf_vd_read(vd_handle, + $ name+,fields=" +fieldname+ ") ) if (iret ne 1) then begin print, Error dataset,i ; Add variables to structure no2.sza(no2.norbits,0:count-1) = sza no2.vza(no2.norbits,0:count-1) = vza no2.raa(no2.norbits,0:count-1) = raa no2.ssc(no2.norbits,0:count-1) = ssc no2.loncorn(no2.norbits,0:count-1,*) = transpose(loncorn) no2.latcorn(no2.norbits,0:count-1,*) = transpose(latcorn) if (strmid(vd_name,0,4) eq ANC_ ) then begin ; Check whether orbit fits in data structure if (no2.norbits ge maxorbits) then begin print, ERROR: more orbits in file than, maxorbits 10

11 if (count gt maxpix) then begin print, ERROR: orbit, no2.norbits+1, has more pixels (, $ count, ) than, maxpix ; Read fields into variables for j=0,nf-1 do begin hdf_vd_getinfo,vd_handle,j,name=fieldname,size=size,type=type name = strcompress(fieldname,/remove_all) iret = execute( nread=hdf_vd_read(vd_handle, +$ name+,fields=" +fieldname+ ") ) if (iret ne 1) then begin print, Error dataset,i ; Add variables to structure no2.scd(no2.norbits,0:count-1) no2.amf(no2.norbits,0:count-1) no2.amftrop(no2.norbits,0:count-1) no2.amfgeo(no2.norbits,0:count-1) no2.scdstr(no2.norbits,0:count-1) no2.clfrac(no2.norbits,0:count-1) no2.cltpres(no2.norbits,0:count-1) no2.albclr(no2.norbits,0:count-1) no2.crfrac(no2.norbits,0:count-1) no2.ltropo(no2.norbits,0:count-1) no2.norbits = scd = amf = amftrop = amfgeo = scdstr = clfrac = cltpres = albclr = crfrac = ltropo = no2.norbits+1 hdf_vd_detach, vd_handle hdf_close, file_id end References [1] H.J. Eskes and K.F. Boersma, Averaging kernels for DOAS total-column satellite retrievals, Atmos. Chem. Phys. Discuss., 3, ,

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