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

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

Approach related to forthcoming results  analysis carried out on common grid and time period: common period defined as maximum/minimum of start/stop time ( ) 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

Measurement SystemAdvantagesProblems Infrared sensors(e.g., TOVS) Sensors provide total column water vapor and some vertical profile information over large areas. Data are limited to cloud- free regions and can exhibit regional biases. Vertical resolution is poor. Microwave sensors(e.g., SSM/I) Sensors provide total column water vapor data over large regions and are not highly influenced by clouds. Data are limited to ice- free ocean regions, and vertical resolution is poor. Global Positioning System Global water vapor soundings would use existing and planned navigational satellites. Methods are in research and development stage. Rocken, C., R. Ware, T. Van Hove, F. Solheim, C. Alber, J. Johnson, M. Bevis, and S. Businger, Sensing atmospheric water vapor with the Global Positioning System, Geophys. Res. Lett., 20, 2631, Characteristics of Water Vapor Observing Systems

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

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 near global (50°N-50°S) - landocean

Regional time series tropics (20°N-20°S) - landocean

Regional time series northern midlatitudes (20°N-50°N) - landocean

Regional time series southern midlatitudes (20°S-50°S) - landocean

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…

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

H a : Wang, 2008a, J. Appl. Meteor. Climatol., 47, Wang, 2008b, J. Atmos. Oceanic Tech., 25 (No. 3), ; Wang, 2003, J. Climate, 16, Homogeneity tests

F-Test –Testing of differences between two data series –F-statistic: Difference in Variance –Requirements: gaussian, constant linear trend Maximal F-Test –Change point is at maximum of F-statistic Penalized: –empirically constructed penaltiy factor to diminish undesirable effect of dependency of FAR on location in the timeseries PMT test

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

PMF results - Sahara -

PMF results - Africa -

PMF results - SA-

Eye inspec- tion PMF-Test ocean land Timeline of identified difference, breaks… HIRS SSM/I AIRS SSM/I data cov.

Changing observing system… breaks / artefacts due to changes in observing system sensor change change in observation frequency (same sensor, number of sensors) area coverage ….

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-term RS 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).

JANUARY JULY

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)

Comparison vs. HomoRS92

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

Thank you!

Bias relative to ensemble mean JANUARY - For HOAPS, REMSS, ERAint, MERRA, CFSR, NVAP-M -

Bias relative to ensemble mean JULY - 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

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 Setmethodissues NVAP-MMerged: ocean(SSM/I) Land (IR [HIRS + AIRS] + RAOB [IGRA]) dry anomalies in the tropics during the early period of the dataset , large drop in 1991 => drop in SSM/I spatial sampling 1994, global moistening starting in HIRS-NOAA15 starts (2 sats instead of 1 previously) 2002: AIRS starting 2006: HOAPSSSMI- PMW REMSSSSMI- PMW ERAinterimreanalysis1992, 2000 NCEP-CFSRreanalysis1999 MERRAreanalysis1999 Tentative break points - time series -

Individual time / space features Data Setmethodissues NVAP-MMerged: ocean(SSM/I) Land (IR [HIRS + AIRS] + RAOB [IGRA]) dry anomalies in the tropics during the early period of the dataset , large drop in 1991 => drop in SSM/I spatial sampling global moistening starting in HIRS-NOAA15 starts (2 sats instead of 1 previously) 2002: AIRS starting : HOAPSSSMI- PMW , 1992, 2008 REMSSSSMI- PMW , 1992, 2008 ERAinterimreanalysis1992 (SSM/I problem, bug), 2006 NCEP-CFSRreanalysis1992? (ingest of AMSU), 2000 MERRAreanalysis1992, 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.

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. HomoRS92 Lindenberg, Germany (52°N,14°E)

PMF results - ocean -

PMF results - land-