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GRAS SAF Climate Products

Hans Gleisner & Kent B. Lauritsen

Danish Meteorological Institute

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Contents- GRAS SAF offline profiles and climate gridded data- Status of products- Monitoring and a priori assessment- 1D-Var diagnostics- Prototype data from Metop NRT data

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Overview of GRAS SAF climate gridded data

Climate data product 2D zonal grid: 1

climate + errors Time

resolution Spatial 2

resolutionFormats,graphical

Formats,numerical

CBA: bending angle yes Monthly 5 deg latitude PNG, JPGASCII, netCDF

CRG: refractivity yes Monthly 5 deg latitude PNG, JPGASCII, netCDF

CTE: temperature yes Monthly 5 deg latitude PNG, JPGASCII, netCDF

CHG: spec. humidity yes Monthly 5 deg latitude PNG, JPGASCII, netCDF

CZG: geopot. height yes Monthly 5 deg latitude PNG, JPGASCII, netCDF

1 A latitude-height grid where the height can be expressed in MSL height, geopotential height, or in terms of pressure.

2 The maximum resolution in height is determined by the height resolution of the profiles.

Offline Climate gridded data products – an enhancement of GRAS offline data.

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Some recent achievements

• Delivered a complete RO climate data set based on CHAMP data Sept 2001 - Sept 2008 for the international ROtrends comparison study, November 2010;

• Participated in the Mid-term meeting of the ESA DUE GlobVapour project, ESRIN, Frascati, Italy, 7 March 2011;

• Participated in the GEWEX/ESA DUE GlobVapour workshop on long term water vapour data sets and their quality assessment, ESRIN, Frascati, Italy, 8-10 March 2011;

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NRT, Offline, and Climate processing overviewPhase, amplitude,

ground station observations,near-real time orbits

Phase, amplitude,ground station observations,

NRT/offline data

Bending angle profiles(L1, L2, LC)

Refractivity profiles

1D-Var algorithm

Ancillarytemperature, pressure,

and humidity, fromECMWF forecasts

Temperature,pressure, and

humidity profiles

GRAS SAFNRT Products

Bending angle profiles(ionosphere corrected and

statistically optimized)

Refractivity profiles

1D-Var algorithm

Temperature,pressure, and

humidity profiles

GRAS SAFOffline Products

Level 1a

Level 1b

Level 2

Produced byEUMETSAT

Geometric optics inversion algorithm

Bending angle profiles(statistically optimized)

Abel transformalgorithm

CT2algorithm

Abel transformalgorithm

Level 2

Level 2

Re-processed data(zero/single/double diff.):

Phase, amplitude

Other RO data(COSMIC, CHAMP, ...):

Phase, amplitude

Bending angle profiles(ionosphere corrected and

statistically optimized)

Refractivity profiles

1D-Var algorithm

CT2algorithm

Abel transformalgorithm

Temperature,pressure, and

humidity profiles

Climate algorithms

Bending angle, refractivity,temperature, humidity, andgeopotential height grids

GRAS SAFClimate/Gridded Data

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Status of offline profile and gridded products

Offline products from Metop: offline profiles and gridded climate data- first version based on prototype offline GRAS/Metop-A data via ftp from

EUMETSAT CAF planned from: June 2011- consolidation of new format, netCDF-3 or 4: June - August 2011- demonstration product may be made available for download: Q1, 2012

Offline products from COSMIC: offline gridded climate data- offline processing based on ROPP_PP has started- internal validation planned for Sept 2011- review for offline gridded data: end 2011- operational product made available for download and monitoring: Q1, 2012

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- During 2010 (following the PCR-2 review) we have implemented algorithms to:

- monitor noise on the bending angles- monitor stability of observed errors in refractivity- monitor stability of the errors the 1D-Var a priori- quantify the relative importance of a priori in the refractivity- quantify the relative importance of a priori in 1DVar temperature & humidity

- The climate algorithms are now described in the Algorithm Theoretical Baseline Document (ATBD): Climate Algorithms, ver. 2.1.

Status of offline climate data products

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Monitoring noise on bending angle: Algorithm

The neutral-atmosphere bending angle is contaminated with noise that increases exponentiallywith altitude. The noise is of both instrumental and ionospheric origin and varies considerablyfrom occultation to occultation.

We estimate the upper-level bending angle noise by the smallest standard deviation ofthe bending angle difference obs-clim found over a scale height (here, taken to be 7.5 kilo-

meters) in the interval 60 to 80 kilometer:

n

n 1k

2kclim,kobs,1

1min

Here, n is the number of data points within a sliding window of 7.5 kilometer width. obs is the observed bending angle.

clim is the corresponding bending angle from the MSIS climatology.

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Monitoring noise on bending angle: Example

Biases (left panel) and standard deviations (right panel) of the bendingangle differences obs-clim in CHAMP data during the year 2004.

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For each grid-box and month we compute the quantity:

j2

jobs,2jbg,

2jobs,

iSO

1

MW

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

bending angles, and index j loops over the Mi data points in grid box i.

This quantity provides a measure of the observational information in the optimizedBending angles. As a consequence of the error characteristics, it goes from 0(no background) at low altitudes to 1 (no observational information) at high altitudes.

Relative importance of a priori in refractivity: Algorithm

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Relative importance of a priori in refractivity: Example

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Relative importance of a priori in 1DVar T, q: Algorithm

We quantify the relative importance of the a priori information in the retrievedtemperature and humidity by the error standard deviations used in the 1DVar retrieval:

bgT,

solT,T 100

W

bgq,

solq,q 100

W

where index sol denotes solution and index bg denotes background. The factor 100normalizes the ratio to percent.

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Relative importance of a priori in 1DVar T and q: Example

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1D-Var minimization: Diagnostics

The state xs that minimizes J(x) is a valid estimate of the true atmosphere only if

the error covariance matrixes O and B provide good enough descriptions ofthe actual errors. These errors are not known perfectly known. Desroziers et al. [2005] described how information on the errors can be gainedfrom the statistics of the differences between observation, background, andsolution. In observation space:

))(())((2

1)()(

2

1)( o

1Tob

1Tb xyOxyxxBxxx HHJ

bobo xyd H

soso xyd H

bsbs xxd HH

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If the errors are unbiased, Gaussian, and accurately describe the true errors,then the following set of relations should hold:

The diagnosed error covariances are on the left hand side. The error covariances assumed in the 1D-Var retrieval are on the right hand side. H is the Jacobian of H(x).

Consistency criteria for 1D-Var errors

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Diagnosing 1D-Var errors

The consistency criteria provide a means to monitor the stability of errors, e.g. by aregular diagnosis of the mean error covariance diagonal elements.

i

1jjb,jo,jb,js,

i

2ib,

1 M

yyyyM

i

1jjb,jo,js,jo,

i

2io,

1 M

yyyyM

Here, index i denotes a latitude band and index j loops over the Mj observations in

latitude band i. If the observed quantity is refractivity, which falls off exponentiallywith height, the diagnosed errors are more conveniently expressed in relative terms:

ib,2ib,

diagib, 100 y

io,2io,

diagio, 100 y

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Diagnosed background and observational errors for COSMIC-FM4 in March 2008.

Diagnosing 1D-Var errors

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Metop EUM-BA O-B/B

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Metop EUM-BA O-B/B

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Metop REF O-B/B

2020

Metop REF & T: Polar bias

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Metop REF & T: Polar bias May 2011

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Reprocessing and construction of climate data sets

Major activity in the next phase of the GRAS SAF: Reprocessing of all RO data planned in CDOP-2 in 2014 and 2016 with new algorithms and improved input data:-GRAS data from EUMETSAT CAF-ERA-Clim RO data from EUMETSAT CAF (CHAMP, COSMIC, …)-RO data from CDAAC

Applications and data:- Include QC info, error estimate and usable range; comparisons of reprocessed datasets within a reanalysis system;- Produce RO datasets for testing forecast and climate models (this is more difficult for radiances because they are bias corrected to the models; RO data is assimilated without bias correction);- Produce RO datasets for climate monitoring (RO information content is highest in the upper troposphere/lower-mid stratosphere);

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Overview of reprocessing and validation

Overview of GRAS SAF reprocessing, interfaces, and validation:

WebsiteUsers

Phases, amplitudes, orbits(GRAS, ERA-Clim)

ReprocessingGRAS SAF

EUMETSAT CAF

RO Processing Centers

ECMWF NWP/Reanalysis

ROtrends intercomparison

SAF’s (CM SAF)

ESA DUE GlobVapour

GEWEX Radiation Panel

Intercomparison:- BA, REF, T, P, q (profiles)- Climate data (grids)

Intercomparison:- BA (level 1b)

Offline and climate data

RO Data providers

Phases, amplitudes, orbits

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fin