
Monitoring: Description
Monthly zonal grids  Time series  Diagnostics 1  Diagnostics 2  Validation
Monthly Zonal Grids
Monthly mean
This plot shows weighted averages of the RO data within 5degree latitude bins at a 200meter height grid. The
averaging includes all data from the selected RO mission that passed the QC screening.
Monthly standard deviation
This plot shows the standard deviations of the RO data within 5degree latitude bins at a 200meter
height grid. The standard deviation includes all data from the selected RO mission that passed the QC screening.
The standard deviation is dominated by the variability of the atmosphere, but also contains a small component
due to the observational errors of the underlying profile data. We can estimate the uncertainty of the
monthly mean by the standard error:
$$\sigma_m \approx \sigma_m^{se} = \frac{s}{\sqrt{N}}$$
where $\sigma_m^{se}$ is the standard error of the mean, $s$ is the observed standard deviation, and $N$ is the
monthly data number.
Observational error
This plot shows the observational errors of the monthly means. They are computed from the assumed
observational errors of the individual profiles, and scales inversely with the square root of the monthly data
number.
The total error of the mean (i.e the difference between the observed mean and the true mean) is assumed to
be caused by two effects. First, each measurement has a random observational error, or measurement
error, associated with it. This error can only be described in terms of a statistical uncertainty. Secondly, the
finite number of measurements is not able to fully account for all variability within the monthly bin, resulting in a
sampling error. Unlike the observational errors, the actually realized sampling errors can be estimated,
and eventually subtracted.
Sampling error
This plot shows an estimate of the error due to undersampling of the atmospheric variability. We sample an
ECMWF operational analysis field (at a 2.5x2.5 degree horizontal resolution  roughly corresponding to the
resolution of the RO measurements) at the same time and locations as the actual observations, and compute
the error made by averaging the sampled values rather than the whole field. This error gives us an
estimate of the sampling error.
The total error of the mean (i.e the difference between the observed mean and the true mean) is assumed to
be caused by two effects. First, each measurement has a random observational error, or measurement
error, associated with it. This error can only be described in terms of a statistical uncertainty. Secondly, the
finite number of measurements is not able to fully account for all variability within the monthly bin, resulting in a
sampling error. Unlike the observational errors, the actually realized sampling errors can be estimated,
and eventually subtracted.
Data number
The data number plots show the number of data available for processing before the QC screening.
There is a variation with latitude due to the satellite orbits and the particular scan mode of the RO technique.
The number of data also decreases at low altitudes as a consequence of the gradual loss
of signal in the moist troposphere.
Rel. data number
The relative data number plots show the number of data available for processing before the QC screening,
relative to the number of data at high altitudes. The plots demonstrate the gradual loss of
signal as the radio waves prober deeper into the atmosphere.
A priori fraction
The a priori fraction plots show estimates of the fraction of a priori information that enter the
monthly mean climate data. This a priori information is taken from a background model, e.g. ECMWF, or from
some sort of climatology, e.g. MSIS90.
Mean ecmwf@obs
This plot shows weighted averages within 5degree latitude bins at a 200meter height grid generated from ECMWF
data colocated (in latitude, longitude, and time) with the observed data.
Stdev ecmwf@obs
This plot shows the standard deviation of the data within 5degree latitude bins at a
200meter height grid generated from ECMWF data colocated (in latitude, longitude, and time) with the observed
data. Like the observed standard deviations, this plot demonstrates the degree of variability of the atmosphere.
(OB)
This plot shows the differences between zonal monthly means computed from RO data and
from ECMWF shortterm forecast data. The ECMWF monthly means were computed from model data colocated (in latitude,
longitude, and time) with the observed data.
(OB)/B
This plot shows the relative differences between zonal monthly means computed from RO data and
from ECMWF shortterm forecast data. The ECMWF monthly means were computed from model data colocated (in latitude,
longitude, and time) with the observed data.
Time Series
The climate data sets are fundamentally 3dimensional: time series of zonal monthly means on a 2D
latitudeheight grid
$$f_{ijm} = f({\phi}_i,h_j,m)$$
where $f$ is a climate variable (refractivity, temperature, etc.), indices $i$ and $j$ denote the latitude and height bins,
and $m$ denotes the time (number of months).
We define the longterm climate mean as the mean over the time dimension for the
full length of the time series
$$f_{ij}^{\rm C} = f^C({\phi}_i,h_j) = \frac{1}{M}\sum\limits_{m=1}^{M} f_{ijm}$$
where $M$ is the number of months in the climate data record. The longterm climate mean
is used as a reference for constructing anomalies of the climate variables.
We define a mean annual cycle as
$$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n1)+s ~, ~~ s=1,{\ldots},12$$
where s denote the season and N is the number of years in the climate data record.
The mean annual cycle is used to construct deseasonalized anomalies, i.e. anomalies with the dominating
seasonal cycle removed.
Based on these quantities, we define the anomalies as
$$\Delta f_{ijm} = f_{ijm}  f_{ij}^{\rm C}$$
and the fractional anomalies
$$\Delta f_{ijm} = (f_{ijm}  f_{ij}^{\rm C}) / f_{ij}^C$$
where the latter are used for quantities that has a predominantly
exponential altitude dependence.
Similarly, we define the deseasonalised anomalies as
$$\Delta f_{ijm}^{\rm des} = f_{ijm}  f_{ijs}^{\rm A}$$
and the deseasonalised fractional anomalies
$$\Delta f_{ijm}^{\rm des} = (f_{ijm}  f_{ijs}^{\rm A}) / f_{ijs}^A$$
where s is the season (1,...,12) of month m.
The anomaly fields are still 3D as they depend on latitude, altitude, and time. For plotting, the number
of dimensions needs to be reduced. This is done by averaging over a set of latitude bands and height layers.
Averaging over a latitude band gives a 2D timeheight plot, averaging over a
height layer gives a 2D timelatitude plot, and averaging over both a latitude band and a height layer
gives a 1D time series plot.
Lat mean
These 2D timeheight plots show monthly anomalies at a 200meter height grid as a
function of time. The anomalies are based on longterm means computed from the full length
of the time series (see the details below). The latitudinal averaging is done using a simple cosine
weighting of the fundamental 5degree latitude bins.
The plots are dominated by the seasonal cycle. In the tropics, were the seasonal cycle is weaker, influence
from the QuasiBiennial Oscillation (QBO) is evident.
Time series data
The climate data sets are fundamentally 3dimensional: time series of zonal monthly means on a 2D
latitudeheight grid
$$f_{ijm} = f({\phi}_i,h_j,m)$$
where $f$ is a climate variable (refractivity, temperature, etc.), indices $i$ and $j$ denote the latitude and height bins,
and $m$ denotes the time (number of months).
We define the longterm climate mean as the mean over the time dimension for the
full length of the time series
$$f_{ij}^{\rm C} = f^C({\phi}_i,h_j) = \frac{1}{M}\sum\limits_{m=1}^{M} f_{ijm}$$
where $M$ is the number of months in the climate data record. The longterm climate mean
is used as a reference for constructing anomalies of the climate variables.
We define a mean annual cycle as
$$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n1)+s ~, ~~ s=1,{\ldots},12$$
where s denote the season and N is the number of years in the climate data record.
The mean annual cycle is used to construct deseasonalized anomalies, i.e. anomalies with the dominating
seasonal cycle removed.
Based on these quantities, we define the anomalies as
$$\Delta f_{ijm} = f_{ijm}  f_{ij}^{\rm C}$$
and the fractional anomalies as
$$\Delta f_{ijm} = (f_{ijm}  f_{ij}^{\rm C}) / f_{ij}^C$$
where the latter are used for quantities that has a predominantly
exponential altitude dependence.
Similarly, we define the deseasonalised anomalies as
$$\Delta f_{ijm}^{\rm des} = f_{ijm}  f_{ijs}^{\rm A}$$
and the deseasonalised fractional anomalies as
$$\Delta f_{ijm}^{\rm des} = (f_{ijm}  f_{ijs}^{\rm A}) / f_{ijs}^A$$
where s is the season (1,...,12) of month m.
The anomaly fields are still 3D as they depend on latitude, altitude, and time. For plotting, the number
of dimensions needs to be reduced. This is done by averaging over a set of latitude bands and height layers.
Averaging over a latitude band gives a 2D timeheight plot, averaging over a
height layer gives a 2D timelatitude plot, and averaging over both a latitude band and a height layer
gives a 1D time series plot.
Lat mean (deseason)
These 2D timeheight plots show monthly deseasonalized anomalies in various latitude
zones at a 200meter height grid as a function of time. The anomalies are based on a mean annual cycle
computed from the full length of the time series (see the details below). The latitudinal averaging is done
using a simple cosine weighting of the fundamental 5degree latitude bins. Since the dominating seasonal
cycle has been removed, we can see other structures in the climate data more clearly: trends and periodic
phenomena, like the QBO.
Time series data
The climate data sets are fundamentally 3dimensional: time series of zonal monthly means on a 2D
latitudeheight grid
$$f_{ijm} = f({\phi}_i,h_j,m)$$
where $f$ is a climate variable (refractivity, temperature, etc.), indices $i$ and $j$ denote the latitude and height bins,
and $m$ denotes the time (number of months).
We define the longterm climate mean as the mean over the time dimension for the
full length of the time series
$$f_{ij}^{\rm C} = f^C({\phi}_i,h_j) = \frac{1}{M}\sum\limits_{m=1}^{M} f_{ijm}$$
where $M$ is the number of months in the climate data record. The longterm climate mean
is used as a reference for constructing anomalies of the climate variables.
We define a mean annual cycle as
$$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n1)+s ~, ~~ s=1,{\ldots},12$$
where s denote the season and N is the number of years in the climate data record.
The mean annual cycle is used to construct deseasonalized anomalies, i.e. anomalies with the dominating
seasonal cycle removed.
Based on these quantities, we define the anomalies as
$$\Delta f_{ijm} = f_{ijm}  f_{ij}^{\rm C}$$
and the fractional anomalies as
$$\Delta f_{ijm} = (f_{ijm}  f_{ij}^{\rm C}) / f_{ij}^C$$
where the latter are used for quantities that has a predominantly
exponential altitude dependence.
Similarly, we define the deseasonalised anomalies as
$$\Delta f_{ijm}^{\rm des} = f_{ijm}  f_{ijs}^{\rm A}$$
and the deseasonalised fractional anomalies as
$$\Delta f_{ijm}^{\rm des} = (f_{ijm}  f_{ijs}^{\rm A}) / f_{ijs}^A$$
where s is the season (1,...,12) of month m.
The anomaly fields are still 3D as they depend on latitude, altitude, and time. For plotting, the number
of dimensions needs to be reduced. This is done by averaging over a set of latitude bands and height layers.
Averaging over a latitude band gives a 2D timeheight plot, averaging over a
height layer gives a 2D timelatitude plot, and averaging over both a latitude band and a height layer
gives a 1D time series plot.
Lat/layer mean
These time series plots show monthly anomalies in various
latitude zones and height layers as a function of time. The latitudinal averaging is done using a simple cosine
weighting of the fundamental 5degree latitude bins. The plots are dominated by the seasonal cycle. In the tropics,
were the seasonal cycle is weaker, influence from the QuasiBiennial Oscillation (QBO) is evident.
Time series data
The climate data sets are fundamentally 3dimensional: time series of zonal monthly means on a 2D
latitudeheight grid
$$f_{ijm} = f({\phi}_i,h_j,m)$$
where $f$ is a climate variable (refractivity, temperature, etc.), indices $i$ and $j$ denote the latitude and height bins,
and $m$ denotes the time (number of months).
We define the longterm climate mean as the mean over the time dimension for the
full length of the time series
$$f_{ij}^{\rm C} = f^C({\phi}_i,h_j) = \frac{1}{M}\sum\limits_{m=1}^{M} f_{ijm}$$
where $M$ is the number of months in the climate data record. The longterm climate mean
is used as a reference for constructing anomalies of the climate variables,
We also define a mean annual cycle as
$$f_{ijs}^{\rm A} = f^C({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n1)+s ~, ~~ s=1,{\ldots},12$$
where s denote the season and N is the number of years in the climate data record.
The mean annual cycle is used to construct deseasonalized anomalies, i.e. anomalies with the dominating
seasonal cycle removed.
Based on these quantities, we define the anomalies
$$\Delta f_{ijm} = f_{ijm}  f_{ij}^{\rm C}$$
and the fractional anomalies
$$\Delta f_{ijm} = (f_{ijm}  f_{ij}^{\rm C}) / f_{ij}^C$$
where the latter are used for quantities that has a predominantly
exponential altitude dependence.
Similarly, we define the deseasonalised anomalies
$$\Delta f_{ijm}^{\rm des} = f_{ijm}  f_{ijs}^{\rm A}$$
and the deseasonalised fractional anomalies
$$\Delta f_{ijm}^{\rm des} = (f_{ijm}  f_{ijs}^{\rm A}) / f_{ijs}^A$$
The anomaly fields are still 3D as they depend on latitude, altitude, and time. For plotting, the number
of dimensions needs to be reduced. This is done by averaging over a set of latitude bands and height layers.
Averaging over a latitude band gives a 2D timeheight plot, averaging over a
height layer gives a 2D timelatitude plot, and averaging over both a latitude band and a height layer
gives a 1D time series plot.
Lat/layer mean (deseason)
These time series plots show monthly deseasonalized anomalies in various latitude
zones and height layers as a function of time. The latitudinal averaging is done using a simple cosine weighting
of the fundamental 5degree latitude bins. The dominating seasonal cycle has been removed, which allow us to
see other structures in the climate data: trends and periodic phenomena, like the QBO.
Time series data
The climate data sets are fundamentally 3dimensional: time series of zonal monthly means on a 2D
latitudeheight grid
$$f_{ijm} = f({\phi}_i,h_j,m)$$
where $f$ is a climate variable (refractivity, temperature, etc.), indices $i$ and $j$ denote the latitude and height bins,
and $m$ denotes the time (number of months).
We define the longterm climate mean as the mean over the time dimension for the
full length of the time series
$$f_{ij}^{\rm C} = f^C({\phi}_i,h_j) = \frac{1}{M}\sum\limits_{m=1}^{M} f_{ijm}$$
where $M$ is the number of months in the climate data record. The longterm climate mean
is used as a reference for constructing anomalies of the climate variables.
We define a mean annual cycle as
$$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n1)+s ~, ~~ s=1,{\ldots},12$$
where s denote the season and N is the number of years in the climate data record.
The mean annual cycle is used to construct deseasonalized anomalies, i.e. anomalies with the dominating
seasonal cycle removed.
Based on these quantities, we define the anomalies
$$\Delta f_{ijm} = f_{ijm}  f_{ij}^{\rm C}$$
and the fractional anomalies
$$\Delta f_{ijm} = (f_{ijm}  f_{ij}^{\rm C}) / f_{ij}^C$$
where the latter are used for quantities that has a predominantly
exponential altitude dependence.
Similarly, we define the deseasonalised anomalies
$$\Delta f_{ijm}^{\rm des} = f_{ijm}  f_{ijl}^{\rm A}$$
and the deseasonalised fractional anomalies
$$\Delta f_{ijm}^{\rm des} = (f_{ijm}  f_{ijl}^{\rm A}) / f_{ij}^A$$
The anomaly fields are still 3D as they depend on latitude, altitude, and time. For plotting, the number
of dimensions needs to be reduced. This is done by averaging over a set of latitude bands and height layers.
Averaging over a latitude band gives a 2D timeheight plot, averaging over a
height layer gives a 2D timelatitude plot, and averaging over both a latitude band and a height layer
gives a 1D time series plot.
Diagnostics 1
This page presents a range of diagnostic information related to sampling distributions, quality of the
data, quality control (QC), and data numbers. The information is presented on a monthly basis, and
some of the plots also show data broken down to daily numbers.
File numbers
The file numbers plots show the daily number of files available at different steps in the ROM SAF
processing:
• occ files: the original level 1a input files converted to ROPP format,
• atm files: level 1b and 2a data (bending angle, refractivity, dry temperature),
• wet files: level 2b data (1DVar T, p, and q),
The file numbers, relative plots show the daily number of files relative to the
number of occ files used as input to the processing.
The atm failed plots show the number of cases where an occ file
did not result in a nominal atm file (i.e. a file with status=0).
The wet failed plots show the number of cases where a nominal atm file
did not result in a wet file.
Data numbers
The data numbers plot shows the daily number of input files to the ROM SAF processing chain.
Data numbers & sampling
The distrib plots show the distribution of occultations over latitude, longitude,
and local time. In all four plots, the data have been divided into 36 bins. There are two latitude
plots  one for equalangle bins and one for equalarea bins. All plots are based on data numbers
before the QC screening, and before the gradual loss of occultations in the troposphere.
The scatter plots visualize the distribution of occultations over latitude, longitude,
azimuth, and local time. While the distributions over longitude and local time are relatively uniform,
the distributions over latitude and, in particular, azimuth are not uniform. The nonuniform distributions
are mainly due to the orbits of the LEO and GNSS satellites, while the RO antenna characteristics act
to modify the distributions.
QC screening
The number of occs passing QC shows how the data numbers are reduced by the
quality screening procedure:
• input: number of occ files,
• ropp_pp: number of atm files with status=0,
• QC0: observational data below 20 km and above 60 km, monotonous height series
• QC2: quality of L2 signal (L2score), statistical optimization (LCindex, SOreg1, SOreg2),
• QC3: detection of outliers using model data (e.g., ECMWF) or fixed limits,
• QC4: quality of 1DVar solution (Jpen, Niter).
The fraction of occs passing QC shows how the relative data numbers are reduced
by the quality screening procedure. A systematic variation of these fractions with time may indicate a
variation of data quality, or the impact of factors affecting the processing chain.
The rejection rate plots show the fraction of data rejected at each QC step: (inout)/in.
QC screening
The plot QC impact on OB stat shows the impacts of the QC screening procedure on
the refractivity OB statistics.
Diagnostics, bending angle
The 6080 km noise floor is the smallest standard deviation of the raw LC bending
angle (with respect to a fitted background bending angle)
over a 7.5 km height layer (roughly one scale height) in the 6080 km interval. This quantity can
be regarded as an estimate of the underlying instrumental noise level for an occultation. A
variation of the noise floor distribution with time may indicate time varying instrumental effects.
The L2 quality score quantifies the degradation of the L2 signal through the RMS
difference of the L1 and L2 impact parameter series obtained from a radioholographic analysis.
This quantity plays a central role in the ROM SAF quality screening procedure.
Diagnostics, BA optimization
The 6080 km bias is the mean difference between the raw LC bending angle and
a fitted background bending angle in the 6080 km interval.
The 6080 km stdev is the standard deviation of the raw LC bending angle (with
respect to a fitted background bending angle) in the 6080 km interval.
Diagnostics, 1DVar
TBD
Diagnostics 2
This page presents a range of diagnostic information related to quality of the data,
quality control (QC), and data numbers. The information is mainly presented as time
series of monthly data for the full length of the data record.
File numbers
The file numbers plots show the mean daily file numbers available at different steps in the
ROM SAF processing.
• occ files: the original level 1a input files converted to ROPP format,
• atm files: level 1b and 2a data (bending angle, refractivity, dry temperature),
• wet files: level 2b data (1DVar T, p, and q),
The file numbers, relative plot shows the mean daily file numbers relative to the number
of occ files used as input to the processing.
The file numbers, accumulated plot shows the total number of files accumulated since
a certain starting date.
Data numbers
The data numbers plot shows the (monthly mean) daily number of excessphase data files.
The data numbers, accumulated plot shows the total number of excessphase data files
accumulated since a certain starting date.
QC screening
The number of occs passing QC shows how the data numbers are reduced by the
quality screening procedure:
• input: number of occ files,
• ropp_pp: number of atm files with status=0,
• QC0: observational data below 20 km and above 60 km, monotonous height series
• QC2: quality of L2 signal (L2score), statistical optimization (LCindex, SOreg1, SOreg2),
• QC3: detection of outliers using model data (e.g., ECMWF) or fixed limits,
• QC4: quality of 1DVar solution (Jpen, Niter).
The fraction of occs passing QC shows how the relative data numbers are reduced
by the quality screening procedure. A systematic variation of these fractions with time may indicate a
variation of data quality, or the impact of factors affecting the processing chain.
The rejection rate plots show the fraction of data rejected at each QC step: (inout)/in.
Diagnostics, bending angle
The 6080 km noise floor is the smallest standard deviation of the raw LC bending
angle (with respect to a fitted MSIS bending angle)
over a 7.5 km height layer (roughly one scale height) in the 6080 km interval. This quantity can
be regarded as an estimate of the underlying instrumental noise level for an occultation. A
variation of the noise floor distribution with time may indicate time varying instrumental effects.
The L2 quality score quantifies the degradation of the L2 signal through the RMS
difference of the L1 and L2 impact parameter series obtained by radioholographic analysis.
This quantity plays a central role in the ROM SAF quality screening procedure.
Diagnostics, BA optimization
The 6080 km stdev is the standard deviation of the neutral bending angle (with
respect to a fitted MSIS bending angle) in the 6080 km interval.
The SO quality score quantifies the relative error (expressed as a percentage)
of the neutral bending angle, estimated by the degree of best fit to a MSIS background bending angle
profile. This quantity only plays a marginal role in the ROM SAF quality screening procedure.
Service Specs
This page is mainly intended for internal data quality monitoring relative to
the requirements defined by project documents. It may be of a broader interest but
requires an understanding of the quantities being monitored, and the requirements used.
Compliance with PRD
For each climate variable and month, and within specified latitudinal zones and height
layers, we can plot the distribution of absolute deviations from ERAInterim.
The median and the 60%, 70%, and 80% percentiles of this distribution are compared
with the three levels of PRD accuracy requirements. Using color codes we show, for each percentile,
whether threshold, target, or optimal accuracies were reached.
PRD requirements
The Product Requirements Document
(PRD) states the required accuracies for the data
products, and suggests how to demonstrate compliance to these accuracies. For each
climate variable, three accuracy levels are specified (threshold, target, and optimal),
and the PRD states that the actual data should be evaluated against these accuracies by comparison
with reanalysis data.
The zonal fields are divided into three latitudinal zones: tropics (30°S30°N), midlatitudes
(30°S60°S and 30°N60°N), and polar regions (60°S90°S and 60°N90°N).
Each zone is further separated into three height layers (08 km, 815 km,
and 1540 km). For each of these 9 latitudeheight regions, the median and three percentiles (60%, 70%, and 80%)
of the absolute deviation from ERAInterim are computed. The 67% percentile corresponds approximately to one standard
deviation for a normal distribution. Formally, we validate the Level 3 data by the compliance
of the 60% percentile of the absolute deviations from ERAInterim, with the PRD requirements.
Compliance with PRD
For each climate variable and month, and within specified latitudinal zones and height
layers, we can plot the distribution of estimated errors of the monthly mean.
The median and the 60%, 70%, and 80% percentiles of this distribution are compared
with the three levels of PRD accuracy requirements. Using color codes we show, for each percentile,
whether threshold, target, or optimal accuracies were reached.
PRD requirements
The Product Requirements Document
(PRD) states the required accuracies for the data
products, and suggests how to demonstrate compliance to these accuracies. For each
climate variable, three accuracy levels are specified (threshold, target, and optimal),
and the PRD states that the actual data should be evaluated against these accuracies by comparison
with reanalysis data.
The zonal fields are divided into three latitudinal zones: tropics (30°S30°N), midlatitudes
(30°S60°S and 30°N60°N), and polar regions (60°S90°S and 60°N90°N).
Each zone is further separated into three height layers (08 km, 815 km,
and 1540 km). For each of these 9 latitudeheight regions, the median and three percentiles (60%, 70%, and 80%)
of the absolute deviation from ERAInterim are computed. The 67% percentile corresponds approximately to one standard
deviation for a normal distribution. Formally, we validate the Level 3 data by the compliance
of the 60% percentile of the absolute deviations from ERAInterim, with the PRD requirements.
Compliance with Service Specifications
For each climate variable and month, and within specified latitudinal zones and height
layers, we can plot the distribution of estimated errors of the monthly mean.
The 60% percentiles of the distributions are compared
with the three levels of PRD accuracy requirements. Using color codes we show
whether threshold, target, or optimal accuracies were reached.
To comply with the
Service Specifications, the 60% percentile must at least
reach the target accuracy.
PRD requirements
The Product Requirements Document
(PRD) states the required accuracies for the data
products, and suggests how to demonstrate compliance to these accuracies. For each
climate variable, three accuracy levels are specified (threshold, target, and optimal),
and the PRD states that the actual data should be evaluated against these accuracies by comparison
with reanalysis data.
The zonal fields are divided into three latitudinal zones: tropics (30°S30°N), midlatitudes
(30°S60°S and 30°N60°N), and polar regions (60°S90°S and 60°N90°N).
Each zone is further separated into three height layers (08 km, 815 km,
and 1540 km). For each of these 9 latitudeheight regions, the median and three percentiles (60%, 70%, and 80%)
of the absolute deviation from ERAInterim are computed. The 67% percentile corresponds approximately to one standard
deviation for a normal distribution. Formally, we validate the Level 3 data by the compliance
of the 60% percentile of the absolute deviations from ERAInterim, with the PRD requirements.
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