GPSRO Monitoring - description
This page contains improved NRT monitoring of RO data, with effect from 1st May 2017. The monitoring of data up to that point can be found at old NRT monitoring.
O-B profiles |
Zonal plots |
Time series |
1D-Var stats |
Map plots |
Time trend |
Spatial plots |
VAR stats |
Delay histograms |
Availability charts |
Refractivity, N=(n - 1)*1E6 where n=refractive index of the medium
The monitored radio occultation data are provided to the Met Office in
BUFR format via the Global Telecommunication System (GTS) by EUMETSAT, DMI (the
ROM SAF), UCAR and GFZ. Other data providers will be added if new data becomes
available. This near-real-time (NRT) data includes bending angle and
refractivity. Most of the plotted statistics are calculated as comparisons of
the observed values and the equivalent forward-modelled values using Met Office
"background" (i.e. 6-hour forecast) fields. Both monthly and daily statistics
are plotted four times a day, in parallel with the operational schedule of the
Met Office global model. Several hours after each of these runs, the plots are
updated to allow "late" data to be included in the statistics. Some statistics
from the Met Office 4D-Var system (VAR) are shown, which can help to show how
the analysis fit to RO compares to the background fit.
Data Quality Monitoring
(O-B)/B profile plots
These plots allow us to compare statistics of the observed GPSRO bending angle or refractivity with equivalent values calculated using fields from the Met Office's global model. The fractional difference in bending angle or refractivity, referred to as (Observation-Background)/Background or (O-B)/B, is calculated for each occultation profile at a fixed set of altitude levels (200 m intervals, the (O-B)/B values were interpolated on to these levels linearly. The altitude data are geopotential height with respect to the geoid for refractivity and impact height with respect to WGS-84 ellipsoid for bending angle.
The mean of (O-B)/B over many occultations for each altitude level is calculated. Over many thousands of occultations this mean indicates the typical differences (biases) between the observed and forward-modelled values. We also show the standard deviation of (O-B)/B, indicating the width of the distribution. The plots show the number of occultations used in each mean and standard deviation calculation i.e. number that were accepted by the Met Office quality control.
Unless otherwise specified, solid lines show mean values, dashed lines show standard deviations and dash-dot lines show the number of observations.
Monthly bending angle (O-B)/B statistics provided by ECMWF have been added to a selection of (O-B)/B plots, and some ECMWF refractivity statistics are displayed for the GRAS instruments. These plots help to distinguish between observation problems and model issues.
These plots should be useful for comparing satellites/models/processing centres and thus for making decisions on which data to assimilate into NWP models.
Some notes for individual plots: Rising/Setting - this is determined by the flags in the BUFR data.
These plots show the mean and standard deviation of (O-B)/B and the observation count averaged over latitudinal bands around the globe. Each band is 1 degree wide in latitude to ensure sufficient data for the average. The zonal averages are calculated on each altitude level, and the result is shown as a contour plot. Zonal plots for day and night are also produced which may assist in determining the cause of data problems.
Time series plots
These plots show how global (O-B)/B statistics vary with time on altitude levels. These are calculated for each 6-hour period, and are shown on a colour scale. The plots always show the data from the previous month. These plots are useful as they allow changes in the data quality of the observations or background to be seen. Also available are ECMWF and Met Office bending angle statistics against time on the same plot for a selection of impact heights; with Met Office statistics plotted every 6 hours, ECMWF every 12 hours. They help to determine whether changes with time are related to observations or the models.
To get a qualitative feel for observation error covariance, the vertical correlation matrices of (O-B)/B for refractivity bending angle and refractivity are plotted against height. Although some of the correlations of (O-B)/B come from the background we can still get a feel for the observation part of error correlation by performing comparisons between satellites/processing centres, when B should be the same (although different satellites sample different local times). Also the difference between refractivity and bending angle provide an interesting comparison, and changes in correlation lengths can indicate adjustments to smoothing parameters made by the processing centres.
At the Met Office we pass radio occultation data through a 1D-Var procedure for QC purposes prior to assimilating the data in 4D-Var; the aim of this is to black-list poor quality occultations. We run this 1D-Var in a non-operational capacity for monitoring. In operations, some instruments have their height range restricted, but for monitoring we pass observations from all heights to the 1D-Var.
The 1D-Var hist plot shows histograms of (1), 2J/m when J is the initial cost function (m is the number of observations), (2), 2J/m at convergence and (3) , the number of iterations required to obtain convergence.
Four types of map plot are produced for each satellite/combination:
This plot shows a histogram of the azimuth angle, i.e. the bearing from North of the vector from the GNSS satellite to the receiving LEO satellite.
There are two histogram figures, each of which contain histograms of (O-B)/B for a given height. These are useful to determine if biases are caused by outliers or if the whole distribution is shifted.
There is one figure for six different height ranges, and these show:
Where available, ECMWF statistics are also plotted, but note that these are provided in 1km height bins, so the statistics may show variation. In particular, the number of observations from the ECMWF bins have been scaled to provide a better match to the Met Office values. Therefore, the plotted values should only be interpreted as a proxy for the number of observation; however, the trends themselves can be useful.
These plots show statistics of co-located observations over the
period of a month. Co-located pairs of observations are identified if they occur
within 300km and 3 hours of each other. Checks are in place to ensure that
observations are not compared with themselves and pairs are ignored if they have
already been included in the opposite order (i.e. if O2-O1 has been included in
the stats, O1-O2 will subsequently be screened out).
Spatially mapped statistics
These plots show various statistics for a range of heights that have been binned onto a lat-lon grid and overlaid on a map. These plots include:
The Met Office data assimilation system, called VAR, produces a
series of statistics for each assimilation window. This includes the
contribution to the observation term of the cost function from each observation
type. Therefore we can plot time series of initial and final cost functions, as
well as the difference between these.
Data Flow Monitoring
The key at the bottom of the plot shows the colours used. Blank
areas mean that no profiles were available in MetDB for that hour
(at the time of the analysis). The total number of profiles for
each day is shown on the right margin of the plot. It should be
noted that the flow of data provided to the Met Office can stop
occasionally for reasons beyond our control, hence why some of
the plots may be missing.
For data to be available for operational assimilation, its observation time must be within the model analysis window and be delivered to MetDB before the cut-off time for that window. Hence data points must lie in the yellow triangles for inclusion in the Main Runs. Late-arriving data lying in the dark pink area would meet the cut-off for the U12 analysis, which isused as the starting point for the main 6-day forecast. Data not lying in a yellow or pink zone would never be assimilated in our operational system (but could be included in an off-line (re-)analysis or case study to maximize data coverage)The date and time when the plots were generated are shown in the bottom right corner.