ROM SAF CDOP-2 Validation Report: Offline Level 3 Climate ... · Prepared by: Hans Gleisner ROM SAF Scientist, Climate Coordinator 15/5 2013 Reviewed by (internal): Approved by: Kent

Ref: SAF/ROM/DMI/REP/CLM/001

ROM SAF CDOP-2

Validation Report:

Offline Level 3 Climate Data:

GRM-17, 18, 19, 20, 21

Version 1.2

15 May 2013

Danish Meteorological Institute (DMI) European Centre for Medium-Range Weather Forecasts (ECMWF)

Institut d’Estudis Espacials de Catalunya (IEEC) Met Office (METO)

Ref: SAF/ROM/DMI/REP/CLM/001 Issue: 1.2 Date: 15 May 2013

ROM SAF CDOP-2 Validation Report:

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DOCUMENT AUTHOR TABLE

Author(s) Function Date Comment Prepared by: Hans Gleisner ROM SAF Scientist,

Climate Coordinator 15/5 2013

Reviewed by (internal):

Approved by: Kent B. Lauritsen ROM SAF Project Manager 16/5 2013

DOCUMENT CHANGE RECORD

Issue/Revision Date By Description 1.0 12/2 2012 HGL Version for ORR-2.

1.1 15/3 2013 HGL Version for ORR-2 Closeout: addressing RIDs 1-17 and 26-35 from ORR2.

1.2 15/5 2013 HGL Version closing Action 1, points 21 to 26 (raised by A. Steiner) and points 1 to 7 (raised by P. Poli) in Annex 2 of the minutes from the ORR2 Close Out meeting.



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ROM SAF

The Radio Occultation Meteorology Satellite Application Facility (ROM SAF) is a

decentralised processing center under EUMETSAT which is responsible for operational

processing of GRAS radio occultation data from the Metop satellites and radio occultation

(RO) data from other missions. The ROM SAF delivers bending angle, refractivity,

temperature, pressure, and humidity profiles in near-real time and offline for NWP and

climate users. The offline profiles are further processed into climate products consisting of

gridded monthly zonal means of bending angle, refractivity, temperature, humidity, and

geopotential heights together with error descriptions.

The ROM SAF also maintains the Radio Occultation Processing Package (ROPP) which

contains software modules that will aid users wishing to process, quality-control and

assimilate radio occultation data from any radio occultation mission into NWP and other

models.

The ROM SAF Leading Entity is the Danish Meteorological Institute (DMI), with

Cooperating Entities: i) European Centre for Medium-Range Weather Forecasts (ECMWF)

in Reading, United Kingdom, ii) Institut D'Estudis Espacials de Catalunya (IEEC) in

Barcelona, Spain, and iii) Met Office in Exeter, United Kingdom. To get access to our

products or to read more about the ROM SAF please go to: http://www.romsaf.org

Intellectual Property Rights

All intellectual property rights of the ROM SAF products belong to EUMETSAT. The use

of these products is granted to every interested user, free of charge. If you wish to use these

products, EUMETSAT's copyright credit must be shown by displaying the words

“copyright (year) EUMETSAT” on each of the products used.

http://www.romsaf.org/



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List of Contents

1. INTRODUCTION ............................................................................................................................. 5

1.1 PURPOSE OF THE DOCUMENT ......................................................................................................... 5 1.2 APPLICABLE & REFERENCE DOCUMENTS ....................................................................................... 6

1.2.1 Applicable documents ............................................................................................................. 6 1.2.2 Reference documents ............................................................................................................. 6

1.3 ACRONYMS AND ABBREVIATIONS ................................................................................................... 7 1.4 DEFINITIONS .................................................................................................................................... 8

2. BACKGROUND .............................................................................................................................. 9

2.1 LEVEL 1B AND LEVEL 2 PROFILE DATA ........................................................................................... 9 2.2 LEVEL 3 PROCESSING ................................................................................................................... 10 2.3 QUALITY CONTROL ........................................................................................................................ 10 2.4 ECMWF REFERENCE DATA ......................................................................................................... 11

3. VALIDATION ..................................................................................................................................13

3.1 VALIDATION STRATEGY ................................................................................................................. 13 3.2 VALIDATION DATA SET .................................................................................................................. 13 3.3 ZONAL MONTHLY MEANS AND STANDARD DEVIATIONS ................................................................ 17 3.4 ESTIMATES OF A PRIORI INFORMATION ........................................................................................ 28 3.5 ERROR ESTIMATES........................................................................................................................ 34 3.6 COMPARISON TO ECMWF REFERENCE DATA ............................................................................. 45 3.7 VALIDATION AGAINST PRD REQUIREMENTS ................................................................................ 51

3.7.1 PRD requirements ..................................................................................................................... 51 3.7.2 Compliance with PRD requirements ......................................................................................... 52 3.7.3 Service specifications ................................................................................................................ 59

4. OPEN ISSUES ...............................................................................................................................60

4.1 QC PROCEDURES ......................................................................................................................... 60 4.2 SAMPLING ERROR CORRECTION ................................................................................................... 60

5. CONCLUSIONS .............................................................................................................................61



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1. Introduction

1.1 Purpose of the document

This document describes the validation of the ROM SAF Offline Level 3 climate data

products listed in Table 1. The data products are generated by the ROM SAF processing

system using Level 1b & 2 profile data as input together with ancillary information from

ECMWF analyses and forecasts.

The validation is based on data from the FORMOSAT-3/COSMIC mission (henceforth

referred to as COSMIC). The retrievals of bending angle and refractivity profiles, and the

1D-Var processing, are performed using ROPP software version 6.0 and the ROM SAF

1DV code version 2.5. The Level 3 climate processing was done with version 1.1 of the

ROMCLIM software package.

Section 2 describes the Level 1b and Level 2 profile data used as input to the Level 3

processing. It also includes a brief overview of the Offline Level 3 processing at DMI, the

quality screening process, and a summary of the ECMWF data used as a reference in the

validation. The actual validation, including the data used and the compliance with the

formal requirements, is described in Section 3. A few open issues are pointed out in

Section 4. The main conclusions are stated in Section 5.

An extensive range of plots with a direct bearing on the validation of the climate data can

also be found in the climate monitoring section of the ROM SAF web site

(http://www.romsaf.org). Those plots should be studied in conjunction with the present

report.

Product

identifier

Product name Product

acronym

Mission /

satellite

Dissemination

means

Product

version

GRM-17 Bending angle grid CBACO1 COSMIC Web 1.0

GRM-18 Refractivity grid CRGCO1 COSMIC Web 1.0

GRM-19 Temperature grid CTGCO1 COSMIC Web 1.0

GRM-20 Specific humidity grid CHGCO1 COSMIC Web 1.0

GRM-21 Geopotential height grid CZGCO1 COSMIC Web 1.0

Table 1. Offline Level 3 climate data products.




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1.2 Applicable & reference documents

1.2.1 Applicable documents

The following list contains documents with a direct bearing on the contents of this

document:

[AD.1] CDOP-2 Proposal: Proposal for the Second Continuous Development and

Operations Phase (CDOP-2); Ref: SAF/GRAS/DMI/MGT/CDOP2/001

Version 1.1 of 21 March 2011, approved by the EUMETSAT Council in Ref.

EUM/C/72/11/DOC/10 at its 72nd

meeting on 28-29 June 2011;

[AD.2] CDOP-2 Cooperation Agreement: Agreement between EUMETSAT and DMI

on the Second Continuous Development and Operations Phase (CDOP-2) of

the Radio Occultation Meteorology Satellite Applications Facility (ROM

SAF), approved by the EUMETSAT Council; Ref: EUM/C/72/11/DOC/15 at

its 72nd

meeting on 28-29 June 2011 and signed on 29 June 2011 in

Copenhagen;

[AD.3] ROM SAF Product Requirements Document,

SAF/ROM/DMI/MGT/PRD/001.

[AD.4] ROM SAF Service Specifications, SAF/ROM/DMI/RQ/SESP/001.

1.2.2 Reference documents

The following documents provide supplementary or background information, and could be

helpful in conjunction with this document:

[RD.1] ROM SAF ATBD: 1DVAR algorithms, SAF/ROM/DMI/ALG/1DV/002.

[RD.2] ROM SAF ATBD: Climate algorithms, SAF/ROM/DMI/ALG/CLM/001.

[RD.3] ROM SAF Validation Report: GRM-01 NRT Refractivity Profile (NRP),

SAF/ROM/DMI/RQ/REP/001.

[RD.4] GRAS SAF Validation Report: GRM-02 NRT Temperature profile (NTP), GRM-03

NRT Specific humidity profile (NHP), GRM-04 NRT Pressure profile (NPP), GRM-05

NRT Surface pressure (NSP), SAF/GRAS/DMI/RQ/REP/002.

[RD.5] ROM SAF Validation Report: Offline Level 1b & 2a COSMIC bending-angle &

refractivity profiles, SAF/ROM/DMI/REP/COS/001.

[RD.6] ROM SAF Validation Report: Offline Level 2b COSMIC 1D-Var profiles,

SAF/ROM/DMI/REP/COS/002.

[RD.7] The ROPP User Guide – Part III: Pre-processor module,

SAF/ROM/METO/UG/ROPP/006.

[RD.8] GRAS SAF CT2 Processing Code: Operational Processing of CHAMP and COSMIC

data: Mathematical Methods, Data Filtering and Quality Control,

SAF/GRAS/DMI/ALG/CT2/002.

[RD.9] M.E. Gorbunov and K.B. Lauritsen, Analysis of wave fields by Fourier Integral



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Operators and its application for radio occultations, Radio Science, 39(4), 4010,

doi:10.1029/2003RS002971.

[RD.10] M. E. Gorbunov, Ionospheric correction and statistical optimization of radio occultation

data, Radio Science, 37(5), 1084, doi:10.1029/2000RS002370, 2002.

[RD.11] Healy, S. B. and Eyre, J. R., Retrieving temperature, water vapor and surface pressure

information from refractive–index profiles derived by radio occultation: A simulation

study, Quart. J. Roy. Meteorol. Soc., 126, 1661–1683, 2000.

[RD.12] Persson and Grazzini. User guide to ECMWF forecast products. Meteorological

Bulletin, M3.2, (available from ECMWF website).

[RD.13] Rieder, M.J., and G. Kirchengast (2001): Error analysis and characterization of

atmospheric profiles retrieved from GNSS occultation data, J. Geophys. Res., vol. 106,

31755.

[RD.14] Foelsche, U., B. Scherllin-Pirscher, F. Ladstädter, A.K. Steiner, and G. Kirchengast,

Refractivity and temperature climate records from multiple radio occultation satellites

consistent within 0.05%, Atmos. Meas. Tech., 4, 2007-2018, 2011.

[RD.15] Schreiner, W., S. Sokolovskiy, D. Hunt, C. Rocken, and Y.-H. Kuo, Analysis of GPS

radio occultation data from the FORMOSAT-3/COSMIC and the Metop/GRAS

missions at CDAAC, Atmos. Meas. Tech., 4, 2255-2272, 2011.

1.3 Acronyms and abbreviations

ATBD Algorithm Theoretical Baseline Document

CDAAC Cosmic Data Analysis and Archival Center

COSMIC Constellation Observing System for Meteorology, Ionosphere and Climate

CDOP-2 Second Continuous Development and Operations Phase (EUMETSAT)

DMI Danish Meteorological Institute; ROM SAF Leading Entity

ECMWF The European Centre for Medium-range Weather Forecasts

EPS EUMETSAT Polar Satellite System

EUMETSAT EUropean organisation for the exploitation of METeorological SATellites

GNSS Global Navigation Satellite System

GPS Global positioning System (US)

GRAS GNSS Receiver for Atmospheric Sounding (EPS/Metop)

GRIB GRIdded Binary (WMO)

IEEC Institut d’Estudis Espacials de Catalunya

L1 GPS carrier frequency, 1575.42 MHz

L2 GPS carrier frequency, 1227.6 MHz

LC L Corrected (through linear combination of L1 and L2)

LEO Low Earth Orbit

Met Office United Kingdom Meteorological Office

Metop Meteorological Operational Polar satellite (EUMETSAT)

MSL Mean Sea Level

netCDF Network Common Data Format

NRT Near Real Time

NWP Numerical Weather Prediction

PRD Product Requirements Document (ROM SAF)

RO Radio Occultation

ROM SAF Radio Occultation Meteorology SAF (former GRAS SAF)



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ROPP Radio Occultation Processing Package (ROM SAF)

SAF Satellite Application Facility (EUMETSAT)

WMO World Meteorological Organization

WWW World Weather Watch (WMO programme)

1.4 Definitions

RO data products from the GRAS instrument onboard Metop and RO data from other data

providers are grouped in levels and are either NRT or Offline products:

Data levels:

Level 0: Raw sounding, tracking and ancillary data, and other GNSS data before clock

correction and reconstruction;

Level 1a: Reconstructed full resolution excess phases, SNR’s, amplitudes, orbit

information, I, Q, and NCO values, and navigation bits;

Level 1b: Bending angles and impact parameters, Earth location, metadata and quality

information;

Level 2: Refractivity profiles (level 2a), and pressure, temperature, and specific

humidity profiles (level 2b and 2c), Earth location, metadata, and quality information;

Level 3: Gridded level 1 and 2 offline profile products in the form of, e.g., monthly

and seasonal zonal means, metadata, and quality information;

Product types:

NRT product: data product delivered less than 3 hours after measurement;

Offline product: data product delivered less than 30 days after measurement (the

timeliness for some offline level 3 products may be up to 6 months);



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2. Background

2.1 Level 1b and Level 2 profile data

The two GPS radio frequencies (L1 and L2) received by an RO instrument onboard a LEO

satellite are characterised by their amplitude and phase values. The bending angle profiles

are obtained using the positions and velocities of the GPS and LEO satellites [RD.7]. The

raw bending angle profiles are first subject to a correction in order to eliminate the effect of

the ionosphere on the signals (LC). In the case of single ray propagation the phase contains

all the necessary information in order to derive the bending angle whereas in the case of

multiple ray propagation (multipath), caused by strong vertical gradients in the atmosphere,

both the amplitude and the phase are needed to obtain a bending angle profile free of

multipath artifacts. In Offline processing both the phase and the amplitude are used to solve

for the multipath propagation that occurs frequently in the troposphere [RD.8,9].

The observed, raw bending angles undergo ionospheric correction and statistical

optimization, whereby the observed data are smoothed and merged with an MSIS-90

profile mapped to bending angle space [RD.7,10]. A refractivity profile is then obtained

through an inversion using the Abel transform [RD.7]. To retrieve the ROM SAF products

temperature, pressure and humidity (Level 2b), ancillary data are needed. These data are

obtained from ECMWF forecasts, appropriate to the time and location of the occultation. In

combination with the observed refractivity, the ECMWF background is used in a 1D-Var

algorithm to simultaneously estimate the temperature, humidity and pressure profiles,

together with surface pressure [RD.7,11]. The solution is constrained by the assumption

that the atmosphere is in hydrostatic equilibrium. Note that unique humidity profiles cannot

be obtained from radio occultation measurements without using some source of ancillary

information on temperature, something referred to as the temperature-humidity ambiguity.

The starting point for the ROM SAF Level 3 processing is vertical profiles of bending

angle () as a function of impact parameter, microwave refractivity (N) as a function of

mean-sea level altitude (H; geometric height above the geoid), and temperature (T),

pressure (p), and specific humidity (q) as functions of mean-sea level altitude, H. The

geopotential height (Z) as a function of pressure or pressure height (Hp) is an alternative

formulation of the pressure profile [RD.2]

The bending angles and the refractivities are provided on relatively dense vertical grids

reaching up to around 100 km. i.e. above the region where the RO technique provides

useful information on the neutral atmosphere. The temperature, pressure, geopotential

height, and specific-humidity profiles are given on a standard set of ECMWF model levels

ranging from the surface up to around 80 km.



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2.2 Level 3 processing

The climate data are generated from the RO atmospheric profile data through rather

straight-forward binning and averaging [RD.2]. A set of equal-angle latitudinal bands, or

grid boxes, are defined and all valid observations that fall within a latitude band and

calendar month undergo a weighted averaging to form an area-weighted zonal mean for

that latitude and month. The error in the mean is estimated as a combination of the per-

profile observational errors and errors due to under-sampling of the atmosphere.

The RO climate data products are generated by the following steps:

listing of all occultations that were observed within a calendar month

quality control followed by removal of profiles that are deemed “bad”

vertical interpolation of profiles onto the regular climate height grids

weighted averaging into monthly latitude bins

estimation of the errors (observational and sampling) in the zonal monthly means

estimation of the a priori information in the zonal monthly means

formatting of the RO climate data and meta-data into image and data files

The generation of zonal monthly mean climate data may be followed by further averaging

into seasonal and annual means, and into regional, hemispheric, and global means. Further

details of the Level 3 processing are found in [RD.2].

2.3 Quality control

As described in Section 2.2, the Level 3 climate data are generated from Level 2 profile

data. The quality of the climate data depends on the quality of the input profile data

[RD.5,6], and the quality control (QC) procedure is a central part of the Level 3 processing.

It is a stepwise procedure that starts with a basic check to ensure that:

the bending angle profile reaches above 60 km and below 30 km impact altitude

the bending angle profile contains more than 100 valid data points

the impact altitudes for the bending-angle profile form a monotonic series

all bending angles are within the range [-1,100] mrad

the refractivity profile reaches above 60 km and below 30 km altitude

the refractivity profile contains more than 100 valid data points

the altitudes of the refractivity profile form a monotonic series

all refractivities are within the range [0,500] N-units

The number of data points required for the bending angle and refractivity profiles depends

on the sampling density of the profiles.

Next, the noise properties of the L2 signal and the degree of fit of the raw LC bending

angle to the background bending angle is checked. The L2 quality score quantifies the

degradation of the L2 signal through the RMS difference of the L1 and L2 impact



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parameter series obtained from a radio-holographic analysis [RD.7,8]. 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 background bending angle profile [RD.10]. The

settings currently used for these two quality scores are:

the L2 quality score must be less than 30.0

the SO quality score must be less than 1000.0

It is the L2 quality score that dominates the QC screening, whereas the SO quality score

currently is set to a value that only gives it a minor role. The third QC step removes data

that do not seem to belong to the distribution, through a comparison with ECMWF

reference data:

the refractivity profiles must deviate from ECMWF forecasts by less than 10%

between 10 km and 35 km

The above QC screening applies to all data. For the data derived from a 1D-Var procedure

(pressure, temperature, humidity) there is a fourth QC step which provides a check on the

1D-Var solution:

the 1D-Var retrieval algorithm must converge within 25 iterations

the normalized penalty function 2J/Nobs must be smaller than 5.0

2.4 ECMWF reference data

ECMWF short-term forecasts are used both as an a priori in the 1D-Var retrieval of Level

2b profile data [RD.1] and as a reference in the validation of the Level 3 climate data

products [RD.12]. The ECMWF fields are retrieved in the form of GRIB files from the

standard dissemination stream at a 1.0˚x1.0˚ resolution [RD.12]. The use of one and the

same resolution in the 1D-Var processing and in the reference data set allows us to

investigate which information that the observations contributed to the monthly means.

Each occultation has a nominal latitude, longitude, and time associated with it. To retrieve

co-located ECMWF profiles, we first select the ECMWF forecast at the nearest 6-hourly

time step (i.e. at UTC 0, 6, 12, or 18) and then interpolate bi-linearly from the ECMWF

grid to the nominal latitude and longitude of the occultation. Since new ECMWF forecast

streams are started at 0 UTC and 12 UTC the forecasts used in the nominal situation are as

follows:

0:00 UTC - 3:00 UTC: 12-hour forecast for 0 UTC (based on the analysis at 12 UTC)







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For each observed profile we thus obtain a corresponding ECMWF model profile.

Following a forward-modeling step, this model profile can be used as a reference in the

validation.

ECMWF data are also used in the Level 3 processing to estimate sampling errors. These

data are taken from analysis fields on a coarser 2.5˚x2.5˚ degrees horizontal resolution. The

latter grid has been interpolated from an ECMWF spectral model truncated to T63, which

has a horizontal resolution comparable to the observation resolution of an RO instrument.



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3. Validation

3.1 Validation strategy

The purpose of the validation is to demonstrate that the ROM SAF Level 3 data products

meet the standards expected from an operational EUMETSAT data product, and in

particular that they meet the formal requirements as stated in the ROM SAF PRD [AD.2].

The adopted validation procedure aims to demonstrate that the deviations of the COSMIC

monthly mean data from the ECMWF reference data (described in Section 2.4) are

consistent with the required accuracies stated in the PRD (summarized in Table 2).

The formal validation covers two full years: 2010 and 2011. The present report shows

detailed examples of zonal plots for two representative months (January and July 2011). In

addition to this, an extensive range of plots – zonal, monthly, and time series plots for

climate variables as well as diagnostic variables – can be found on the associated ROM

SAF climate monitoring web site (http://www.romsaf.org/climate_monitoring) which

should be studied in conjunction with this report. Many of the plots on the web page have a

direct bearing on the validation of the climate data.

The PRD specifies three sets of requirements for each of the five monthly mean data

products (“threshold”, “target”, and “optimal” accuracies). The requirements are further

separated into two height ranges (0–15 km and 15–40 km), except for the humidity

requirements which only have a single height range (0–12 km). In the lower height range,

the requirements for bending angle and refractivity vary linearly with height. The full set of

PRD requirements applicable to the Level 3 climate data are listed in Table 2, Section 3.7.

Even though the requirements are separated into two height ranges, the actual comparison

between the observed deviations and the formal requirements are performed separately in

three height layers (0-8 km, 8-15 km, and 15-40 km). The formal compliance with the

requirements is summarized in Section 3.7, where the 70% percentile of the absolute

deviation of observed monthly means from the ECMWF reference data are compared with

the PRD requirements. This choice of formal test is roughly in line with what is currently

used in the Level 2 validation, in which one standard deviations are used for comparison

with the PRD requirements (for a normal distribution 68% of the data fall within +/- 1

standard deviation). The 80% and 90% percentiles are also shown for information. The

formal part of the validation is performed for all months in the 2-year validation period,

and in addition to this the climate monitoring web site provides updated validation

information for the full length of the climate data set.

3.2 Validation data set

The validation period consists of the two years 2010-2011. Postprocessed excess-phase

data (version 2010.2640) were obtained from the UCAR’s COSMIC Analysis and

Archiving Center (CDAAC). After conversion from CDAAC’s atmPhs file format to the

ROPP file format, these data were further processed from excess phase (Level 1a) to




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bending angle (Level 1b), refractivity (Level 2a), pressure, temperature and humidity

(Level 2b) profile data using ROM SAF processing software [RD.1,7]. The QC procedures

described in Section 2.3 are applied to the profile data, and the data not rejected by the QC

are then used as input to the Level 3 processing in which the gridded climate data are

generated [RD.2]. The ECMWF short-term forecasts used as a reference in the validation

are taken from GRIB fields at a 1.0˚x1.0˚ horizontal resolution (see Section 2.4).

The number of occultations available for climate data generation varies with time (Fig. 1).

After 2009 this variation is considerable for the COSMIC mission, partially related to

power problems onboard the COSMIC satellites, which have exceeded their nominal

lifetime of 5 years. The COSMIC-1 and 6 satellites in particular caused a loss of data

during the time period, e.g. the apparent seasonal decrease in the number of occultations

passing QC3 can be traced to these two satellites. The QC screening further reduces the

number of data that can be used to generate climate data.

The data numbers are relatively uniform with longitude, and also with local time (Fig. 2;

upper panels). The latter is a consequence of the fact that measurements are made from

several satellites in different orbits, and that the orbits drift in local time. On the other

hand, the data numbers vary substantially with latitude, predominantly a consequence of

the GNSS and LEO satellite orbits in combination with the limb-sounding observing

geometry of the RO instrument (Fig. 3; middle panels). The RO antenna characteristics

also act to modulate these distributions.

Figure 1. Number of occultations available for climate data generation, after the consecutive QC steps described in Section 2.3. QC0 is a fundamental sanity check of bending-angle and refractivity profiles, QC2 is a check based on the L2 and SO quality scores, QC3 consists of systematic removal of outliers through comparison with ECMWF, and QC4 is a check on the 1D-Var solution. The large variation in the number of occultations is partially explained by problems with power onboard the COSMIC satellites, which have exceeded their nominal life time of 5 years.



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Figure 2. Upper panels: distribution of occultations over longitude (left) and over local time (right) for July 2010. On a monthly time scale these distributions are relatively uniform for the COSMIC

mission. Middle panels: distribution of occultations over latitude, for equal-angle latitude bands (left) and for equal-area latitude bands (right). The latitudinal distributions are predominantly governed by the GNSS and LEO satellite orbits, and by the limb-sounding observing geometry of

the RO instrument, and vary relatively little with time. Lower panels: scatter of occultations in azimuth/longitude (left) and in azimuth/latitude (right). These characteristic patterns are a consequence of satellite orbits and observing geometries.



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Figure 3. Number of samples per grid box (upper panel) and relative number of samples (upper panel) for refractivity in July 2010. Data numbers are the same for bending angle, and slightly lower for the 1D-Var quantities (temperature, humidity, geopotential height). The distribution over latitude described by Fig. 2 is clearly visible, as well as a drop-off of data numbers with altitude.



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3.3 Zonal monthly means and standard deviations

For each latitude-height grid box, we compute the monthly means and the corresponding

standard deviations [RD.2]. These are shown in Figures 4a-e and 5a-e, respectively. The

standard deviation gives an indication of the degree of atmospheric variability.

The monthly mean fields in Figures 4a-e show the overall structure expected for winter and

summer months.

The bending angle and refractivity standard deviations show the effects of enhanced

atmospheric variability at the boundary to the stratospheric winter-hemisphere polar

regions. Enhanced variability is also evident in the sub-tropical troposphere. The

temperature and humidity standard deviations indicate that the refractivity variations are

dominated by temperature variability in the high-latitude stratosphere, and by humidity

variability in the low-latitude troposphere.



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Figure 4a: Bending angle zonal monthly means for January and July 2011.



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Figure 4b: Refractivity zonal monthly means for January and July 2011.



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Figure 4c: Temperature zonal monthly means for January and July 2011.



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Figure 4d: Humidity zonal monthly means for January and July 2011.



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Figure 4e: Geopotential height zonal monthly means for January and July 2011.



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Figure 5a: Bending angle zonal monthly standard deviations for January and July 2011.



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Figure 5b: Refractivity zonal monthly standard deviations for January and July 2011.



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Figure 5c: Temperature zonal monthly standard deviations for January and July 2011.



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Figure 5d: Humidity zonal monthly standard deviations for January and July 2011.



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Figure 5e: Geopotential height zonal monthly standard deviations for January and July 2011.



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3.4 Estimates of a priori information

The grid-box means described in Section 3.3 are not a result of observational data alone,

but also depend on a priori data – background atmospheric data taken from a model. There

are two sources of a priori information: (a) background bending angle profiles that are used

to smooth the observed bending angles and extend them to infinity before inverting

bending angles to refractivity, and (b) the background atmospheric states that are used to

resolve the temperature-humidity ambiguity through a 1D-Var procedure.

As described in the ATBD [RD.2], we define the parameter

2

obs

2

bg

2

obs

2

obs

2

bg

2

bg

SO 1

W (1)

where obs and bg are estimates of the errors in the observed and background bending

angles. The parameter WSO provides a measure of the observational information in the

optimized bending angle profile, which is inverted to refractivity through an Abel

transform. As a consequence of the error characteristics, WSO is a monotonic function of

altitude and goes from 0 (no a priori information) at low altitudes to 1 (no observational

information) at high altitudes. The corresponding zonally averaged quantity (with the ratio

normalized to percent) is shown in Fig. 6a.

Through the Abel inversion, the a priori fraction in refractivity is propagated further down

to lower altitudes. We estimate the resulting a priori fraction through

A

WAW SO

ref 100 (2)

where is a bending angle profile and A[ ] denotes the Abel transform. The factor 100

normalizes the ratio to percent. The refractivity a priori fraction is estimated individually

for all profiles, and the corresponding zonally averaged quantity is shown in Fig. 6b.

Following the notation in the ATBD [RD.2], we let O and B denote the observational and

background error covariance matrixes assumed in the 1D-Var retrieval. The solution error

covariance matrix S is then given by

HOHBS-1-1-1 T (3)

in the linear limit, i.e. in the limit where the forward model H(x) can be represented by its

Jacobian H. The second term on the right hand side is the observational error covariances

mapped to the background state space. Following Rieder and Kirchengast [RD.13] we let

the error standard deviations – i.e. the square root of the diagonal elements of the error

covariance matrix – quantify the relative importance of the a priori information in the

retrieved temperature and humidity:



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bgT,

solT,

T 100

W (4)

bgq,

solq,

q 100

W (5)

where index ‘sol’ denotes solution and index ‘bg’ denotes background. The factor 100

normalizes the ratio to percent. The corresponding zonally averaged quantities are shown

in Figs. 6c-d.

The impacts by the background (MSIS) bending angle used in the statistical optimization

procedure are described by Fig. 6a. In general, the impacts are larger at high latitudes. In a

cold, dry atmosphere, the effects of the statistical optimization reach deeper into the

atmosphere. Note that the bending angle monthly means are generated from the raw, non-

optimized bending angle profiles. Hence, the MSIS a priori only enters the refractivity and

the following products.

The impacts by the background used in the 1D-Var retrieval are quantified in Figs. 6c-d.

The temperatures have a broad height range from around 8 km up to 35 km where the

observational information is considerable. For humidity, the observational information is

concentrated to heights below 10 km, and is greatest at low- and mid-latitudes.



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Figure 6a: Estimates of the a priori fraction in the statistically optimized bending angle (only used to generate the refractivity) for January and July 2011. A value of zero means there is only observational data in the corresponding monthly means.



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Figure 6b: Estimates of the a priori fraction in the refractivity for January and July 2011. A value of zero means there is only observational data in the corresponding monthly means.



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Figure 6c: Estimates of the a priori fraction in the 1D-Var temperatures for January and July 2011. A value of zero means there is only observational data in the corresponding monthly means.



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Figure 6d: Estimates of the a priori fraction in the 1D-Var humidity for January and July 2011. A value of zero means there is only observational data in the corresponding monthly means.



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3.5 Error estimates

The observed grid-box mean described in Section 3.4 is an estimate of the true grid-box

mean. The difference between the observed mean and the true mean is referred to as the

error of the climate variable. This error 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 grid box

and time interval, resulting in a sampling error. Unlike the observational errors, it is

possible to estimate the actually realized sampling errors affecting the monthly means.

As a part of the operational climate processing, we specify the assumed observational

uncertainty and provide an estimate of the sampling error associated with the monthly

mean. Detailed descriptions of the assumptions and estimates are found in [RD.2].

The observational errors of the monthly means are based on the observational errors per

profile, scaled with the square root of the weighted number of observations in a grid box.

The observational errors for bending angle profiles are 6% at the surface, decreasing

linearly to 0.6% at the tropopause (here simply taken to be 12 km). Above the tropopause,

the errors are 0.6% or 2 rad, whichever is greatest. These numbers are roughly in line with

the results from several studies of the RMS differences between collocated profiles

observed by different COSMIC satellites soon after launch [e.g., RD.15]. The

corresponding errors in the refractivity profiles are from 2% at the surface to 0.2% at the

tropopause, and above that the greatest of 0.2% and 0.02 N-units. The observational errors

for temperature, pressure, and humidity profiles are the formal errors obtained as a part of

the 1D-Var solution [RD.1]. Hence, the errors vary between profiles, but normally we find

temperature errors in the troposphere around 0.8-1.5 K, and relative errors in the specific

humidity amounting to 20-50%, or even more [RD.6].

The sampling errors are estimated from sampling an ECMWF analysis field at the nominal

time and locations of the observations. A weighted average is generated from the sampled

ECMWF data and the difference to the true mean, obtained from averaging the full grid,

provides an estimate of the sampling error [RD.2].

As described in the ATBD, the observational and sampling errors are independent and can

be combined into a total error according to

2

samp

2

obsclim (6)

Figure 7a-e show the assumed observational uncertainties in the zonal monthly means for

the five climate variables. Figures 8a-e show the corresponding estimates of the sampling

errors. Both show a considerable variation with latitude and height. The distribution of the

observational uncertainties for bending angle and refractivity (Figures 7a and 7b) is a

simple consequence of the assumed per-profile errors and the distribution of the number of

samples per grid box, whereas the sampling errors are related to the variability as indicated

by the grid box standard deviations. The sampling errors can be roughly an order of

magnitude larger than the observational uncertainties, but also show a stronger variability.



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Figure 7a: Estimates of the bending-angle observational uncertainty in the zonal monthly means for January and July 2011. The number of samples per grid box varies from around 200 to more than 1200, and the observational uncertainty of the mean scales accordingly.



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Figure 7b: Estimates of the refractivity observational uncertainty in the zonal monthly means for January and July 2011. The number of samples per grid box varies from around 200 to more than 1200, and the observational uncertainty of the mean scales accordingly.



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Figure 7c: Estimates of the temperature observational uncertainty in the zonal monthly means for January and July 2011. The number of samples per grid box varies from around 200 to more than 1200, and the observational uncertainty of the mean scales accordingly.



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Figure 7d: Estimates of the specific-humidity observational uncertainty in the zonal monthly means for January and July 2011. The number of samples per grid box varies from around 200 to more than 1200, and the observational uncertainty of the mean scales accordingly.



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Figure 7e: Estimates of the geopotential-height observational error in the zonal monthly means for January and July 2011. The number of samples per grid box varies from around 200 to more than 1200, and the observational uncertainty of the mean scales accordingly.



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Figure 8a: Estimates of the bending-angle sampling error in the zonal monthly means for January and July 2011.



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Figure 8b: Estimates of the refractivity sampling error in the zonal monthly means for January and July 2011.



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Figure 8c: Estimates of the temperature sampling error in the zonal monthly means for January and July 2011.



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Figure 8d: Estimates of the specific-humidity sampling error in the zonal monthly means for January and July 2011.



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Figure 8e: Estimates of the geopotential height sampling error in the zonal monthly means for January and July 2011.



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3.6 Comparison to ECMWF reference data

In this section, we demonstrate the difference between the observed zonal monthly means

and monthly means generated from co-located ECMWF forecasts. In Section 3.7, we then

compare statistics of the absolute values of these differences with the requirements

specified in the PRD [AD.2].

There are a few issues to consider when comparing monthly-mean data generated from

observed RO data with ECMWF data. First, ECMWF analyses have assimilated COSMIC

RO data. We should therefore try to avoid comparing COSMIC with ECMWF analyses.

Secondly, ECMWF forecast data have been used as an a priori in the 1D-Var processing of

the Level 2 profile data used as input to the Level 3 processing.

These two factors complicate the choice of reference for the comparison. We have chosen

to use the ECMWF forecast data described in Section 2.4. For bending angle and

refractivity, the comparison should therefore be unaffected by impacts on the ECMWF data

by the RO data itself. Impacts on RO data by ECMWF data will, however, affect the

comparisons of the temperature, humidity, and geopotential height, particularly at altitudes

and/or latitudes where the a priori dominates the 1D-Var solution (see Section 2.4).

Figs. 9a-e show the difference between the observed monthly means and the monthly

means generated from co-located ECMWF forecast, for two months in 2011.



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Figure 9a: Deviations of COSMIC monthly means from the corresponding collocated ECMWF short-term forecast field during January and July 2011.



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Figure 9b: Deviations of COSMIC monthly means from the corresponding collocated ECMWF short-term forecast field during January and July 2011.



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Figure 9c: Deviations of COSMIC monthly means from the corresponding collocated ECMWF short-term forecast field during January and July 2011.



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Figure 9d: Deviations of COSMIC monthly means from the corresponding collocated ECMWF short-term forecast field during January and July 2011.



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Figure 9e: Deviations of COSMIC monthly means from the corresponding collocated ECMWF short-term forecast field during January and July 2011.



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3.7 Validation against PRD requirements

3.7.1 PRD requirements

The formal requirements for the ROM SAF data products are stated in the PRD [AD.2].

The particular requirements applicable to the Level 3 climate data products are here

reiterated in Table 2. There are three sets of requirements for each climate data product

(threshold, target, and optimal). The requirements are further separated into two height

layers (0-15 km and 15-40 km), except for the humidity requirements which only covers 0-

12 km. In the lower height range, the requirements for bending angle and refractivity vary

linearly with height.

As indicated in Table 2 below, the accuracy requirements have been color coded: threshold

is yellow, target is light green, and optimal is dark green. For each climate variable and

month, and within specified latitudinal bands and height layers (see Section 3.7.2), we

define one observed quality figure to compare with the requirements. If this reaches

threshold, but not target, the variable is indicated by yellow color for that month, latitude

band and height layer. If the quality figure reaches target, but not optimal accuracy, it is

indicated by light green color. Dark green is shown if the quality figure reaches optimal

accuracy. Red color means that the quality figure does not reach the threshold value

specified by the PRD.

PRD

REQUIREMENTS

Threshold

0–15 km

(0-12 km)*

Target

0–15 km

(0-12 km)*

Optimal

0–15 km

(0-12 km)*

Threshold

15–40 km

Target

15–40 km

Optimal

15–40 km

CBA: bending angle 1.0 – 4.0 % 0.4 – 2.0 % 0.2 – 1.0 % 2.5 rad 1.0 %

1.0 rad 0.4 %

0.5 rad 0.2 %

CRG: refractivity 0.6 – 2.0 % 0.3 – 1.0 % 0.1 – 0.5 % 0.6 % 0.3 % 0.1 %

CTG: temperature* 1 K 0.2 K 0.1 K 2 K 0.4 K 0.2 K

CHG: spec. humidity 0.25 g/kg

10 % 0.05 g/kg

2 % 0.03 g/kg

1 % N/A N/A N/A

CZG: geopot. height 50 gpm 12 gpm 5 gpm 50 gpm 12 gpm 5 gpm

Table 2. Formal requirements in terms of the threshold, target, and optimal accuracies in two height layers; 0–15 km and 15–40 km (for humidity: 0–12 km), according to the PRD [AD.2]. In the cases where both absolute and relative numbers are given, the requirement is given by the largest of these two. The colour codes are used in Tables 3 to 7 to refer to the accuracy levels met by the actual data quality measures.



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3.7.2 Compliance with PRD requirements

The zonal field is separated into three latitudinal zones: tropics (30˚S–30˚N), mid-latitudes

(30˚N–60˚N and 30˚S–60˚S), and polar regions (60˚N–90˚N and 60˚S–90˚S). Each of these

zones is further separated into three height layers (0-8 km, 8-15 km, and 15-40 km). Since

the climate grid is 200 meters by 5 degrees latitude there are several hundred monthly

values of the absolute deviation from ECMWF, di, in each of the nine latitude-height

regions. Each value, di, is compared with a certain PRD requirement, Ri, valid for that

particular height (and, possibly, latitude). If more than 70% of the values

i

ii

R

dq (7)

within a latitude-height region are smaller than one (i.e., di is smaller than Ri), the observed

data comply with that particular requirement. Differently stated, the 70% percentile of the

distribution of qi’s should be smaller than one,

170 q (8)

for compliance. In the upper panels of Tables 3 to 7, the percentile q70 is compared to the

PRD requirements for every month in the two year validation period 2010-2011. As

described in Section 3.7.1, the color codes refer to the most strict PRD accuracy level

reached.

Hence, the formal compliance with the PRD requirements is based on the 70% percentiles

of the absolute deviation from ECMWF. This quantity is also used in the formal

comparison with the Service Specification [AD.4], and is roughly in line with the current

validation of Level 2 data which use one standard deviation values to compare with PRD

requirements (68% of the samples fall within +/- 1 standard deviation for a normal

distribution).

For bending angle climatologies, with only a few exceptions the 70% percentile values of

the absolute deviations from ECMWF are within target. In the 8-15 km layer they all reach

optimal values. Most of the 80% percentile values also reach target, but a larger fraction

only reach threshold values, particularly in the lower tropical troposphere. This is even

more pronounced for the 90% percentile values which often are outside of threshold in the

lower tropical troposphere, but otherwise reach either target or threshold, and in the 8-15

km layer even optimal values.

The refractivity climatologies show a better compliance to the PRD requirements in the

troposphere than the bending angles. They are never outside of threshold in the 0-8 km

layer, not even for the 90% percentile values. On the other hand, in the polar stratosphere a

fraction of the 80% percentile values are outside of threshold. On these occasions, the 70%

percentile values do not reach target but are still within threshold. The largest deviations of

the refractivity climatologies from the PRD requirements appear to occur high in the

stratosphere during southern hemisphere winter.



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For the 1D-Var quantities (temperature, humidity, and geopotential height) the comparison

of the absolute deviations from ECMWF is only meaningful where the observational

information is significant (see Section 3.4). For the 1D-Var temperatures that would

primarily be the atmospheric regions above 8 km, while for the 1D-Var humidity the

observational information is significant below 8 km and at low- and mid-latitudes. We find

that the 70% percentile values always reach target, except for humidity which show a few

values that only reach threshold. The 80% and 90% percentile values exhibit more cases

that only reach threshold, but they are never outside of threshold.

From these comparisons, we conclude that the observed COSMIC climate data comply

with the Level 3 requirements specified in the PRD [AD.2].



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Table 3a-c. Comparison of the 70%, 80%, and 90% percentile values of the absolute deviation from ECMWF, with the formal PRD requirements (Table 2). Bending angle during 2010-2011.

70%

80%

90%



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Table 4a-c. Comparison of the 70%, 80%, and 90% percentile values of the absolute deviation from ECMWF, with the formal PRD requirements (Table 2). Refractivity during 2010-2011.

70%

80%

90%



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Table 5a-c. Comparison of the 70%, 80%, and 90% percentile values of the absolute deviation from ECMWF, with the formal PRD requirements (Table 2). 1D-Var temperature during 2010-2011.

70%

80%

90%



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Table 6a-c. Comparison of the 70%, 80%, and 90% percentile values of the absolute deviation from ECMWF, with the formal PRD requirements (Table 2). 1D-Var specific humidity during 2010-2011.

70%

80%

90%



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Table 7a-c. Comparison of the 70%, 80%, and 90% percentile values of the absolute deviation from ECMWF, with the formal PRD requirements. 1D-Var geopotential height during 2010-2011.

70%

80%

90%



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3.7.3 Service specifications

The Service Specifications [AD.4] describes the commitments by the ROM SAF related to

the services and products provided to the users. These commitments include a set of

operational accuracy targets that should be met by the Level 3 climate data products, and

which should be regularly monitored and documented as a part of normal operations. Even

though these targets should be consistent with the PRD [AD.3], they are not necessarily

identical to the PRD requirements.

The accuracy targets defined in the service specifications for the Level 3 climate data

products GRM-17…21 are listed in Table 8 below. They are defined similarly to the target

accuracies in the PRD requirements, described in Section 3.7.1.

We monitor the compliance to the service specification by comparing the 70% percentiles

of the absolute deviation from the ECMWF reference data with the accuracies stated in the

Service Specifications Document [AD.4]. Hence, the procedure is similar to the validation

described in Section 3.7.2. The outcome of the regular monitoring against the service

specifications is provided on the ROM SAF web page.

SERVICE

SPECIFICATIONS

Accuracy

0–15 km

(0-12 km)*

Accuracy

15–40 km

CBA: bending angle 0.4 – 2.0 % 1.0 rad

0.4 %

CRG: refractivity 0.3 – 1.0 % 0.3 %

CTG: temperature 0.2 K 0.4 K

CHG: spec. humidity* 0.05 g/kg

2 % N/A

CZG: geopot. height 12 gpm 12 gpm

Table 8. Service specifications according to the Service Specifications Document [AD.4]. The accuracies are stated separately in two height layers; 0–15 km and 15–40 km (for humidity: 0–12 km). In the cases where both absolute and relative accuracies are stated, the accuracy is given by the largest of these two. For bending angle and refractivity, the specified accuracies vary linearly with height in the lower layer.



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4. Open issues

A few open issues remain that will be addressed in future updates of the Level 3 climate

data generation procedures. The issues of highest priority concern the QC procedures and

the correction of the climate data for sampling errors.

4.1 QC procedures

During the validation period about 1000-2000 occultations became available for processing

every day. Of these, about 5% are rejected by the first QC step (the basic sanity check; see

Section 2.3). Another 15% of the original occultations are rejected by the L2 quality-score

criterium in the second QC step. The SO quality-score criterium only affects a few

occultations. In the third QC step, roughly 1% of the original profiles are rejected by the

comparison of observed refractivity with ECMWF. The QC screening of the 1D-Var

solution finally discards a few of the temperature and pressure profiles, typically 0.1-0.3%

of the original data.

The number of profiles rejected is relatively large due to intentionally conservative settings

of the QC criteria – we want to be sure that no erroneous data significantly affect the

climate statistics. These settings will be revised in future updates of the processing

software.

4.2 Sampling error correction

Studies have shown that the errors in the climatological means can be reduced by

subtraction of the estimated sampling errors [RD.14]. This is not done in the present

version of the climate data products. The data provided to the users are the original

climatologies, and not the sampling error subtracted climatologies. However, the estimated

errors are provided as a part of the climate data product, and users may consider to apply a

sampling error correction to the monthly means.



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5. Conclusions

During the 2-year validation period 2010-2011, on average about 1600 COSMIC

occultations per day were available in the form of post-processed excess phase data. After

QC screening, around 80% of these occultations were available for processing into Level 3

climate data at the ROM SAF.

The results in this report, and the related plots on the ROM SAF climate monitoring web

page, show that the ROM SAF Level 3 climate data are of sufficient quality for use in

science studies and in climate monitoring applications.

The deviations of the observed climatologies from the ECMWF reference data (based on

collocated ECMWF short-term forecast profiles) are consistent with the requirements

specified by the PRD. The 70% percentile values of the deviations are well within the

limits, while a small fraction of the 80% and 90% percentile values for bending angles and

refractivity are outside the limits stated in the PRD.

A few open issues are pointed out, but none of them are judged to be critical for the use of

the climate data.

Documents

ROM SAF CDOP-2 Validation Report: Offline Level 3 Climate ... · Prepared by: Hans Gleisner ROM SAF Scientist, Climate Coordinator 15/5 2013 Reviewed by (internal): Approved by: Kent