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# Monitoring: Description

## Monthly Zonal Grids

Monthly mean
This plot shows weighted averages of the RO data within 5-degree latitude bins at a 200-meter 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 5-degree latitude bins at a 200-meter 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 under-sampling 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. MSIS-90.

Mean ecmwf@obs
This plot shows weighted averages within 5-degree latitude bins at a 200-meter height grid generated from ECMWF data co-located (in latitude, longitude, and time) with the observed data.

Stdev ecmwf@obs
This plot shows the standard deviation of the data within 5-degree latitude bins at a 200-meter height grid generated from ECMWF data co-located (in latitude, longitude, and time) with the observed data. Like the observed standard deviations, this plot demonstrates the degree of variability of the atmosphere.

(O-B)
This plot shows the differences between zonal monthly means computed from RO data and from ECMWF short-term forecast data. The ECMWF monthly means were computed from model data co-located (in latitude, longitude, and time) with the observed data.

(O-B)/B
This plot shows the relative differences between zonal monthly means computed from RO data and from ECMWF short-term forecast data. The ECMWF monthly means were computed from model data co-located (in latitude, longitude, and time) with the observed data.

## Time Series

The climate data sets are fundamentally 3-dimensional: time series of zonal monthly means on a 2D latitude-height 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 long-term 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 long-term climate mean is used as a reference for constructing anomalies of the climate variables.

We define a mean annual cycle as $$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n-1)+s ~, ~~ s=1,{\ldots},12$$ 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 de-seasonalized 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 de-seasonalised anomalies as $$\Delta f_{ijm}^{\rm des} = f_{ijm} - f_{ijs}^{\rm A}$$ and the de-seasonalised 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 time-height plot, averaging over a height layer gives a 2D time-latitude plot, and averaging over both a latitude band and a height layer gives a 1D time series plot.

Lat mean
These 2D time-height plots show monthly anomalies at a 200-meter height grid as a function of time. The anomalies are based on long-term 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 5-degree latitude bins. The plots are dominated by the seasonal cycle. In the tropics, were the seasonal cycle is weaker, influence from the Quasi-Biennial Oscillation (QBO) is evident.

Time series data
The climate data sets are fundamentally 3-dimensional: time series of zonal monthly means on a 2D latitude-height 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 long-term 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 long-term climate mean is used as a reference for constructing anomalies of the climate variables.

We define a mean annual cycle as $$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n-1)+s ~, ~~ s=1,{\ldots},12$$ 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 de-seasonalized 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 de-seasonalised anomalies as $$\Delta f_{ijm}^{\rm des} = f_{ijm} - f_{ijs}^{\rm A}$$ and the de-seasonalised 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 time-height plot, averaging over a height layer gives a 2D time-latitude plot, and averaging over both a latitude band and a height layer gives a 1D time series plot.

Lat mean (de-season)
These 2D time-height plots show monthly de-seasonalized anomalies in various latitude zones at a 200-meter 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 5-degree 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 3-dimensional: time series of zonal monthly means on a 2D latitude-height 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 long-term 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 long-term climate mean is used as a reference for constructing anomalies of the climate variables.

We define a mean annual cycle as $$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n-1)+s ~, ~~ s=1,{\ldots},12$$ 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 de-seasonalized 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 de-seasonalised anomalies as $$\Delta f_{ijm}^{\rm des} = f_{ijm} - f_{ijs}^{\rm A}$$ and the de-seasonalised 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 time-height plot, averaging over a height layer gives a 2D time-latitude 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 5-degree latitude bins. The plots are dominated by the seasonal cycle. In the tropics, were the seasonal cycle is weaker, influence from the Quasi-Biennial Oscillation (QBO) is evident.

Time series data
The climate data sets are fundamentally 3-dimensional: time series of zonal monthly means on a 2D latitude-height 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 long-term 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 long-term climate mean is used as a reference for constructing anomalies of the climate variables,

We also define a mean annual cycle as $$f_{ijs}^{\rm A} = f^C({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n-1)+s ~, ~~ s=1,{\ldots},12$$ 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 de-seasonalized 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 de-seasonalised anomalies $$\Delta f_{ijm}^{\rm des} = f_{ijm} - f_{ijs}^{\rm A}$$ and the de-seasonalised 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 time-height plot, averaging over a height layer gives a 2D time-latitude plot, and averaging over both a latitude band and a height layer gives a 1D time series plot.

Lat/layer mean (de-season)
These time series plots show monthly de-seasonalized 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 5-degree 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 3-dimensional: time series of zonal monthly means on a 2D latitude-height 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 long-term 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 long-term climate mean is used as a reference for constructing anomalies of the climate variables.

We define a mean annual cycle as $$f_{ijs}^{\rm A} = f^A({\phi}_i,h_j,s) = \frac{1}{N}\sum\limits_{n=1}^{N} f_{ijm} ~, ~~ m=12{\cdot}(n-1)+s ~, ~~ s=1,{\ldots},12$$ 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 de-seasonalized 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 de-seasonalised anomalies $$\Delta f_{ijm}^{\rm des} = f_{ijm} - f_{ijl}^{\rm A}$$ and the de-seasonalised 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 time-height plot, averaging over a height layer gives a 2D time-latitude 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 (1D-Var 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 equal-angle bins and one for equal-area 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 non-uniform 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 1D-Var 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: (in-out)/in.

QC screening

The plot QC impact on O-B stat shows the impacts of the QC screening procedure on the refractivity O-B statistics.

Diagnostics, bending angle

The 60-80 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 60-80 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 radio-holographic analysis. This quantity plays a central role in the ROM SAF quality screening procedure.

Diagnostics, BA optimization

The 60-80 km bias is the mean difference between the raw LC bending angle and a fitted background bending angle in the 60-80 km interval.

The 60-80 km stdev is the standard deviation of the raw LC bending angle (with respect to a fitted background bending angle) in the 60-80 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 (1D-Var 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 excess-phase data files.

The data numbers, accumulated plot shows the total number of excess-phase 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 1D-Var 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: (in-out)/in.

Diagnostics, bending angle

The 60-80 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 60-80 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 radio-holographic analysis. This quantity plays a central role in the ROM SAF quality screening procedure.

Diagnostics, BA optimization

The 60-80 km stdev is the standard deviation of the neutral bending angle (with respect to a fitted MSIS bending angle) in the 60-80 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 ERA-Interim. 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°S-30°N), mid-latitudes (30°S-60°S and 30°N-60°N), and polar regions (60°S-90°S and 60°N-90°N). Each zone is further separated into three height layers (0-8 km, 8-15 km, and 15-40 km). For each of these 9 latitude-height regions, the median and three percentiles (60%, 70%, and 80%) of the absolute deviation from ERA-Interim 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 ERA-Interim, 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°S-30°N), mid-latitudes (30°S-60°S and 30°N-60°N), and polar regions (60°S-90°S and 60°N-90°N). Each zone is further separated into three height layers (0-8 km, 8-15 km, and 15-40 km). For each of these 9 latitude-height regions, the median and three percentiles (60%, 70%, and 80%) of the absolute deviation from ERA-Interim 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 ERA-Interim, 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°S-30°N), mid-latitudes (30°S-60°S and 30°N-60°N), and polar regions (60°S-90°S and 60°N-90°N). Each zone is further separated into three height layers (0-8 km, 8-15 km, and 15-40 km). For each of these 9 latitude-height regions, the median and three percentiles (60%, 70%, and 80%) of the absolute deviation from ERA-Interim 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 ERA-Interim, with the PRD requirements.