SCT dual with FG

Work in progress

Returned values are: the p-vector of the quality flags

flag	description
-999	missing flag (observation not checked)
0	good observation
1	bad observation
11	isolated observation, it is the only observation inside the inner circle
12	isolated observation, less than num_min_outer observations inside outer circle

The constants to keep in mind are:

• M, the number of observation providers
• B, the number of background fields (see --fg.files)
• N, the total number of tests. Each test is applied to just one background field. The N-vector sct_fglab.fg matches the tests with the background fields. Example: sct_fglab.fg equals to (1,1,2), then: three tests are defined; the first two tests will make use of the first background file; the third test will make use of the second background field. The order of the background fields is given by the input order in fg.file

The list of parameters is (type vector = it is possible to specify a sequence of buddy checks):

parameter	description	type
sct_fg	do it, true or false	logical
sct_fg.code	code identifying bad observation flagged by the test	scalar
i.sct_fg	number of repetitions of the entire sequence of checks	scalar
break.sct_fg	break the loop over the tests if less than this number of observations has been flagged	scalar
transf.sct_fg	transform values before doing the check	scalar
doit.sct_fg	specify on a provider basis if the observations have to be tested (0=no; 1=yes)	M-vector
prio.sct_fg	specify on a provider basis the priorities the observations have (the smaller the number, the higher the piority)	M-vector
inner_radius.sct_fg	radius (m) of the inner circle	N-vector
outer_radius.sct_fg	radius (m) of the outer circle	N-vector
sct_fglab.sct_fg	labels identifying which of the background fields have to be used in the test	N-vector
tpos.sct_fg	threshold when the observed value is greater than the background	(N*M)-vector
tneg.sct_fg	threshold when the observed value is smaller than the background	(N*M)-vector
eps2.sct	relative precision	(N*M)-vector
num_min_outer.sct_fg	minimum number of observations required inside the outer circle	N-vector
num_max_outer.sct_fg	maximum number of observations used	N-vector
min_horizontal_scale.sct_fg	lower limit for the horizontal decorrelation length scale in OI correlation function	N-vector
max_horizontal_scale.sct_fg	lower limit for the horizontal decorrelation length scale in OI correlation function	N-vector
kth_closest_obs_horizontal_scale.sct_fg	horizontal decorrelation length scale in OI correlation function is the average distance between an observation location and the k-th closest location	N-vector
vertical_scale.sct_fg	vertical decorrelation length scale in OI correlation function	N-vector
aggn_radius.sct_fg	radius defining the background aggregation area	N-vector
num_max_aggn.sct_fg	maximum number of points considered inside the background aggregation area	N-vector

Notes:

• (N*M)-vectors. for each check in the sequence and for each provider, specify one value.
• doit.sct_fg. one value for each provider. If only one value is specified, it is assumed it is the same for all providers.
• prio.sct_fg. one value for each provider. If only one value is specified, it is assumed it is the same for all providers.
• tpos(tneg).sct_fg. one value for each pair provider/test. If only one value is specified, it is assumed it is the same for all providers.
• other vectors, if just one scalar value is passed, then it is usually recycled for all elements.
• eps2.sct_fg. ratio between observation error variance and background error variance. One value for each pair provider/test. If only one value is specified, it is assumed it is the same for all providers.
• The observation operator used to extract the background at an observation location is the average of all (up to num_max_aggn.fg) background values within a circle of aggn_radius.fg units (m) from the observation location. If a digital elevation model is associated to the background, then the extracted background value is adjusted considering the elevation difference between the observation location and the average of the digital elevation model adjusted_backg_value = original_backg_value + gamma * ( observation_elevation - aggregated_backg_elevation_from_dem) where gamma is usually set to -0.0065 °C/m. The background uncertainty is set to the standard deviation of the same background values used to compute the average.

R-examples