First results from the inter-comparison Maarit Lockhoff, Marc Schröder

First results from the inter-comparison

Maarit Lockhoff, Marc Schröder

Overview

Key science questions and activities from Assessment Plan

Data sets

First results:

Inter-comparison Homogeneity assessment Comparison vs. long-term radiosonde obs.

Summery / Next steps

Key questions+activities(focus on long-term analysis)

How large are the differences in observed temporal changes in atmospheric water vapour on global and regional scales?

What is the degree of homogeneity and stability of each satellite data record?

Are the observed changes and anomalies in line with theoretical expectations? What are the reasons for the differences, inhomogeneities (breaks) etc. found?

Assessment plan

Activity D Q ECV Lead Partner 1) Analyse temporal averages of long-term satellite data records on original grid basis and Hovmoeller diagrams in absolute and relative space on full temporal coverage using monthly means.

f 1 TCWV WV UTH

M. Lockhoff (DWD)

A. Walther (UW), R. Bennartz (UW), M. Schröder (DWD)

2) Inter-comparison of long-term satellite data records. Analyse bias and standard deviation relative to ensemble monthly mean of long term satellite data records and differences in 10 year averages.

f

1, 3

TCWV WV

M. Lockhoff (DWD)

A. Walther (UW), M. Schröder (DWD)

UTH L. Shi (NOAA)

V. John (UKMO), M. Schröder (DWD)

3) Analyse degree of homogeneity of long-term satellite data records, e.g., following Wang et al. (2007) and Wang (2008) in anomaly space (e.g., PMF method, anomaly with respect to data record average).

f, 2

1

TCWV WV UTH

M. Lockhoff (DWD)

Data records(up to now)

The following six long-term data records (+25 yrs) are considered so far:

CM SAF/HOAPS 3.2 RSS/SSMI Version 7 NASA/NVAP-M NCEP/CFSR ECMWF/ERA Interim NASA/MERRA

SSM/I - PMW

Merged product (HIRS, SSMI, radiosondes)

reanalysis

Approachrelated to forthcoming results

analysis carried out on common grid and time period:• common period defined as maximum/minimum of start/stop

time (1988-2008)• common grid defined by the minimum integer multiple

applicable to all grids. This leads to a resolution of 2°x2°.

area means are latitude-weighted averages

anomalies calculated as departures from climatological mean per month

Overview

Key science questions and activities from Assessment Plan

Data sets

First results:

Inter-comparison Homogeneity assessment Comparison vs. long-term radiosonde obs.

Summery / Next steps

Climatological Maps 1988-2008

Ensemble mean, stdd, rel.stdd

TCWV ensemble ALL MONTHS

TCWV ensemble JANUARY

TCWV ensemble JULY

Rain forest, deserts, Andes, ITCZ,… Arctic, Antarctic, deserts, Andes, coasts during

winter (?)

Regional time series 1988-2008- near global (50°N-50°S) -

land ocean

Hovmoeller Plots Common features: El Nino, Arctic/Antarctic Zonal averages and annual cycle available (not

shown)

ERA

MERRA

CFSR

NVAP-M

HOAPS

REMSS

To sum up…

ERAintMerraNCEPNVAP-MREMSSHOAPS

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

ERAintMerraNCEPNVAP-MREMSSHOAPS

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Overview

Key science questions and activities from Assessment Plan

Data sets

First results:

Inter-comparison Homogeneity assessment Comparison vs. long-term radiosonde obs.

Summery / Next steps

Ha :

Wang, 2008a, J. Appl. Meteor. Climatol., 47, 2423-2444.

Wang, 2008b, J. Atmos. Oceanic Tech., 25 (No. 3), 368-384.; Wang, 2003, J. Climate, 16, 3383-3385.

Homogeneity tests

1. over ocean– anomalies of all individual data sets over ocean

– anomalies difference vs. HOAPS

2. over land– anomalies of all individual data sets over land

– anomalies difference vs. ERAinterim

3. in selected regions– anomalies of all individual data sets

PMF test set up

PMF results- ocean -

PMF results- land-

Sahara

ERAintMerraNCEPNVAP-MREMSSHOAPS

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

ERAintMerraNCEPNVAP-MREMSS

Eye inspec-tion

PMF-Testocean

land

MerraNCEPNVAP-M

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Timeline of identified difference, breaks…

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

HIRSSSM/IAIRSSSM/I data cov.

• set up a data base of changes in

observing systems

• set up a data base of max/min gradient

in major climate indices (e.g. ENSO

index)

• homogeneity test at selected reference

sites (GRUAN / ARM / NDACC stations)

• assess stability (bias vs. reference)

Next steps..

JMA ENSO Index 3.4

Overview

Key science questions and activities from Assessment Plan

Data sets

First results:

Inter-comparison Homogeneity assessment Comparison vs. long-term radiosonde obs.

Summery / Next steps

Comparison to long-termRS data record

Monthly means were calculated for those months having at least 20 days with min. of two measurements per day: basis – Dai et al. (2011). Other currently available options: Analysed Radiosounding Archive (ARSA, ARA/LMD) and GNSS (NCAR).

Comparison vs. HomoRS92 Sodankylä, Finland (67°N, 26°E)

Comparison vs. HomoRS92 Albany (42°N, 73°W)

Comparison vs. HomoRS92 Salem, USA (45°N, 123°W)

Comparison vs. HomoRS92 St. Paul Island, USA (57°N, 170°W)

Summary

differences observed on global and regional scales• Largest differences over land (rain forest, deserts, Andes, coast)

• Several break points and artefacts visible in the timeseries

What is the degree of homogeneity of each satellite data record?• break points found for all data sets

• many artefacts can be attributed to changes in observing system: sensor change change in observation frequency, area coverage

Comparison vs. long-term radiosonde obs.– differences in performance between datasets, seasonal effects

Update/Refine incomparison for +25yrs data sets – add HIRS, JRA55

– set up a data base changes in obs. system an gradients of major climate indices

– homogeneity test at selected reference sites & stability assessment

– extend analysis to +10yrs data sets

Next steps

8) Areas and periods of distinct differences or limitations observed in activities 1)-7) will be analysed in more detail to find reasons for them.

h, 1-6

1, 3

TCWV WV

M. Lockhoff (DWD)

M. Schröder (DWD)

8) Areas and periods of distinct differences or limitations observed in activities 1)-7) will be analysed in more detail to find reasons for them.

h, 1-6

1, 3

UTH L. Shi (NOAA)

V. John (UKMO), M. Schröder (DWD)

9) Assess quality of satellite data records at near surface layers and in the upper troposphere using high quality ground-based and in-situ observations such as GRUAN or ARM and new, high quality satellite data (each as Level 2). The activity will be based on existing processing and analysis tools. Other activities will contribute to answer Q3, in particular 3, 5 and 8.

3

WV A. Reale (NOAA), M. Lockhoff (DWD)

A. Gambacorta (NOAA), J. Remedios (U Leicester), M. Schröder (DWD), T. Trent (U. Leicester)

Thank you!

Bias relative to ensemble mean1988-2008JANUARY

- For HOAPS, REMSS, ERAint, MERRA, CFSR, NVAP-M -

Bias relative to ensemble mean1988-2008JULY

- For HOAPS, REMSS, ERAint, MERRA, CFSR, NVAP-M -

First results:full time series averages

Analyse bias and difference in absolute and relative standard deviation, identify suspicious regions, analyse time series and variability there and carry out Level 2 evaluation using ground-based data records.

Based on first, interim results: Mountainous regions, latitudinal gradients, tropical forest and deserts need to be analysed in more detail (after evaluation of analysis tools and confirmation of first results).

Climatological Maps 1988-2008

Climatological Maps 1988-2008

First results:full time series averages

First analysis of time series/climatological averages leads to: Reload of data record, re-consideration of common period (e.g., after 1992), re-computation of time series for specific data records to ensure

consistent sampling (green).

Data Set method issues

NVAP-M Merged: ocean(SSM/I)Land (IR [HIRS + AIRS] + RAOB [IGRA])

• dry anomalies in the tropics during the early period of the dataset 1988-1992, large drop in 1991 => drop in SSM/I spatial sampling

• 1994, global moistening starting in 1995• 1999 HIRS-NOAA15 starts (2 sats instead of 1 previously)• 2002: AIRS starting• 2006:

HOAPS SSMI- PMW

REMSS SSMI- PMW

ERAinterim reanalysis 1992, 2000

NCEP-CFSR reanalysis 1999

MERRA reanalysis 1999

Tentative break points- time series -

• Individual time / space featuresData Set method issues

NVAP-M Merged: ocean(SSM/I)Land (IR [HIRS + AIRS] + RAOB [IGRA])

• dry anomalies in the tropics during the early period of the dataset 1988-1992, large drop in 1991 => drop in SSM/I spatial sampling

• global moistening starting in 1995• 1999 HIRS-NOAA15 starts (2 sats instead of 1 previously)• 2002: AIRS starting:

HOAPS SSMI- PMW 1988-1990, 1992, 2008

REMSS SSMI- PMW 1988-1990, 1992, 2008

ERAinterim reanalysis 1992 (SSM/I problem, bug), 2006

NCEP-CFSR reanalysis 1992? (ingest of AMSU), 2000

MERRA reanalysis 1992, 2000

Tentative break points- hovmoeller plots -

Same for HOAPS!!, ERA, MERRA, CFSR!!

Vonder Haar et al. (2012)

Temporal coverage of SSM/I instrument aboard DMSP satellite platforms for the HOAPS processing.

http://www.star.nesdis.noaa.gov/

NCEP-satellite Instrument Usage

Radiance instruments included in CFSR and the time period each was assimilated (Suranjana et al., 2010).

Timeline of conventional observations assimilated in ERA-Interim. (Dee et al., 2011)

Timeline of clear-sky radiance observations assimilated in ERA-Interim. (Dee et al., 2011)

Comparison vs. HomoRS92Lindenberg, Germany (52°N,14°E)

PMF results- ocean -

PMF results- land-