Assimilating Earth System Observations at NASA: MERRA and Beyond

Transcription

1 Assimilating Earth System Observations at NASA: MERRA and Beyond Siegfried Schubert, Michael Bosilovich, Michele Rienecker, Max Suarez, Ron Gelaro, Randy Koster, Julio Bacmeister, Ricardo Todling, Larry Takacs, Emily Liu, Gi-Kong Kim, Man-Li Wu, Phil Pegion, Myong-In Lee, Junye Chen, Steve Bloom, Rolf Reichle, Steven Pawson, Ivanka Stajner, Arlindo da Silva, Christian Keppenne, Watson Gregg and many others in NASA s Global Modeling and Assimilation Office Presentation at the 3rd WCRP Conference on Reanalysis Tokyo, Japan 28 January - 01 Feb, 2008

2 Overview MERRA The GEOS-5 DAS and observations Some early results Beyond MERRA Building the components for an Integrated Earth System Analysis Capability 1

3 AGCM Finite-volume dynamical core (S.J. Lin) Moist physics (J. Bacmeister, S. Moorthi and M. Suarez) Physics integrated under the Earth System Modeling Framework (ESMF) Generalized vertical coord to 0.01 hpa Catchment land surface model (R. Koster) Prescribed aerosols (P. Colarco) Interactive ozone Prescribed SST, sea-ice Assimilation Apply Incremental Analysis Increments (IAU) to reduce shock of data insertion (Bloom et al.) IAU gradually forces the model integration throughout the 6 hour analysis period GEOS-5 Atmospheric DAS for MERRA (Supported by NASA MAP Program) q n t Analysis Grid Point Statistical Interpolation (GSI from NCEP) Direct assimilation of satellite radiance data using JCSDA Community Radiative Transfer Model (CRTM) Variational bias correction for radiances total = dynamics (adiabatic ) + physics (diabatic ) +Δq Total observed change Model predicted change Correction from DAS 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z 03Z Raw analysis (from GSI) Background (model forecast) Assimilated analysis (Application of IAU) Initial States for Corrector Analysis Tendencies for Corrector Corrector Segment (1- and 3-hrly products) Analysis 2

4 MERRA Production 2-year spin up at 2-degree resolution 1-year spin up at ½ degree Product Streams begin: Jan , 1989 and 1998 Years in Stream MERRA Stream 1 10 Stream 2 10 Stream ROSB G5-AMIP EOS Spinup years Reduced Observing System Baseline (ROSB) Preview/Validation runs: Jan, Apr, Jul, Oct 2004 July-August 1987 Jan, Jul 2001 Jul degree (scout) runs preliminary look at data and spin-up of satellite bias estimates. 3

5 TOVS ATOVS EOS Aqua GOES Sounders SSM/I TIROS-N Dec Feb NOAA-6 Jul Apr Nov Oct NOAA-7 Sep Feb NOAA-8 May Jun Jul Oct NOAA-9 Jan Nov NOAA-10 Dec Sep Nov Jan Sep Sep NOAA-11 F08 Jul Sep Dec F10 Dec F11 Dec NOAA-14 Jan GOES-08 Apr GOES-10 Apr EOS Aqua Oct Jul NOAA-18 Years in Stream MERRA Stream 1 10 Stream 2 10 Stream 3 9 ROSB 21 G5-AMIP 26 EOS F13 Satellite Radiance Data Streams Jun NOAA-15 Sep NOAA-16 Nov Nov GOES-12 May F14 May F15 Dec NOAA-12 NOAA-17 Dec Jul Jul Nov Jun Dec Aug 4

6 DATA SOURCE/TYPE PERIOD DATA SUPPLIER Conventional Data Radiosondes present NOAA/NCEP PIBAL winds present NOAA/NCEP Wind profiles 1992/5/14 - present UCAR CDAS Conventional, ASDAR, and MDCRS aircraft reports present NOAA/NCEP Dropsondes present NOAA/NCEP PAOB present NCEP CDAS GMS, METEOSAT, cloud 1977 Š present NOAA/NCEP drift IR and visible winds GOES cloud drift winds 1997 Š present NOAA/NCEP EOS/Terra/MODIS winds 2002/7/01 - present NOAA/NCEP EOS/Aqua/MODIS winds 2003/9/01 - present NOAA/NCEP Surface land observations present NOAA/NCEP Surface ship and buoy present NOAA/NCEP observations SSM/I rain rate 1987/7 - present NASA/GSFC SSM/I V6 wind speed 1987/7 - present RSS TMI rain rate 1997/12 - present NASA/GSFC QuikSCAT surface winds 1999/7 - present JPL ERS-1 surface winds 1991/8/5 Š 1996/5/21 CERSAT ERS-2 surface winds 1996/3/19 Š 2001/1/17 CERSAT Satellite Data TOVS (TIROS N, N-6, N-7, 1978/10/30 Š 1985/01/01 NCAR N-8 ) (A)TOVS (N-9; N-10 ; 1985/01/ /07/14 NOAA/NESDIS & NCAR N-11; N-12 ) ATOVS (N-14; N-15; N-16; 1995/01/19 - present NOAA/NESDIS N-18; N-18) EOS/Aqua 2002/10 - present NOAA/NESDIS SSM/I V6 (F08, F10, F11, 1987/7 - present RSS F13, F14, F15) GOES sounder T B 2001/01 - present NOAA/NCEP SBUV2 ozone (Version 8 retrievals) 1978/10 - present NASA/GSFC/Code A special thanks to Jack Woollen for help with the conventional data streams and Leo Haimberger for the radiosonde corrections!!!! 5

7 State Variables 6

8 Zonal Mean u-wind Jan 2004 NCEP OPS EC OPS JRA-25 GEOS-5 Other Δ 7

9 GEOS5 TPW Jan 2004 GEOS5 TPW Jul 2004 SSMI TPW Jan 2004 SSMI TPW Jul 2004 Wentz Retrievals, Version 6 8

10 GEOS-5 TPW - SSM/I Jul

11 Water Vapor Budget for for Jan Jan dynamics condens+re-evap sum = dynamics + (condensation + re-evaporation) + surface evaporation + analysis sfc evaporation analysis residual = (q end -q begin ) - sum sum residual mm/day 10

12 Precipitation 11

13 Area Averaged Precipitation (mm/day) ERA40 Global NCEP R2 JRA25 NCEP R1 MERRA validation and spin-up runs Obs: GPCP CMAP 12

14 Precipitation (mm/day) January 2004 July 2004 GEOS-5 GEOS-5 GPCP GPCP 13

15 Precipitation c.f. GPCP (mm/day) July

16 2004 Tropical Precipitation 15

17 Mississippi River Basin Daily Prec 16

18 MERRA Precipitation 03Z 02 January 2004 Thanks to Matt Sapiano CMORPH mm/day 17

19 3 hourly Precipitation - January 2004 MERRA CMORPH mm/day 18

20 Clouds and Radiation 19

21 20

22 MERRA cloud liquid water path*, compares well with SSMI estimates January 2004 *Comparison with observations is complicated. SSMI LWP contains contributions from convective and precipitating liquid. True, i.e., radiatively active, model LWP (tql) does not contain convective (or precipitating) condensate. The convective contribution (qccu) to LWP can be estimated from MERRA output 21

23 Marine Marine Stratus Deck Deck Off Off Peru Peru Jul 06 T q L CLOUDSAT Cloud Tops Jul 04 Jul 04 EC OPS 22

24 Regional Climate 23

25 Monthly Mean Precipitation over India July 2004 (mm/day) Difference from GPCP GPCP CMAP GEOS5 GEOS5 EC-OPS EC-OPS NCEP R1 NCEP R1 JRA JRA 24

26 Latitude-time Cross Section of Precipitation over India 2004 (72.5E-80E) GPCP (Pentad) latitude TRMM (daily mean) latitude GEOS5 (daily mean) latitude JRA (daily mean) latitude 25

27 Monthly Mean Precipitation over Americas July 2004 (mm/day) Difference from GPCP GPCP CMAP GEOS5 GEOS5 EC-OPS EC-OPS NCEP R1 NCEP R1 JRA JRA 26

28 Seasonal evolution of North American monsoon (2004) NARR Precipitation (mm/d) and 925mb wind Shading: precipitation rate (mm/d), Arrows: 925 mb winds Contours: surface elevation GEOS-5 reproduces the typical structure of the monsoon rainband. Seasonal march of the rainband is reasonable, with a peak in July. Maximum rainfall region is located reasonably well in the windward slope of the mountains (the Sierra Madre Occidental). GEOS-5 Southwesterly flows in the Gulf of California and in the upslope of the mountains seem to be benefit from the high-resolution (1/2- degree) data assimilation. 27

29 Amplitude of Precipitation Diurnal Cycle (24-h harmonic) mm/d Larger diurnal variability over continents than oceans GEOS-5 tends to overestimate the amplitude over continents and underestimate over oceans 28

30 0-6Z 6-12Z 12-18Z 18-0Z GEOS-5 JRA NCEP R1 NCEP R2 TRMM EC-OPS Diurnal variation in precipitation over the United States for July 2004 (mm/day). The July mean is removed. 29

31 Vertical Structure of LLJ: Jul/Aug 2004 v-wind at 35 N NARR GEOS-5 30

32 MERRA FILE COLLECTIONS MERRA products are organized into 24 collections in HDF Data are produced on three horizontal grids: Native (1/2 by 2/3 w/ FV conventions) Reduced (1 1/4 by 1 1/4 Dateline-edge, Pole-edge) Reduced FV -- (1 by 1¼ w/ FV conventions) In the vertical, 3-D data are at: 72 model layers 42 pressure levels Temporal resolution: 3D products are 3-hourly 2D products are hourly and at native resolution Total online collections ~150TB Distributed through a modeling data portal at the Goddard DAAC (including GDS, ftp ) 31

33 Summary MERRA improves upon many features of existing reanalyses Biases generally smaller than climate signals Precipitation issues remain: trends; diurnal cycle, summer land Comprehensive output suite Spin-ups completed and MERRA processing has started Expect to complete processing by mid

34 Next Steps Developing Components of Future Integrated Earth System Analysis, with consistent analyses across all components

35 GMAO Data Assimilation Systems Jan 04 Precipitation in MERRA Assimilation of AURA/MLS and OMI Ozone Aerosol Transport Atmosphere Constituents Aerosols Assimilation of AMSR-E E soil moisture retrievals Assimilation of TOPEX/Jason Altimeter Data Land Surface Ocean Biology Physical Ocean

36 Land data assimilation at the NASA GMAO Surface soil moisture (SMMR, TRMM, AMSR-E, SMOS, SMAP) Soil moisture [m 3 /m 3 ] Snow cover fraction (MODIS) Snow water equivalent (SWE) and snow cover (AMSR-E, SSM/I; MODIS) Ensemble-based land data assimilation system Land surface data products (incl. root zone soil moisture, evaporation) Land surface temperature (MODIS, AVHRR,GOES, ISCCP ) 35

37 Ocean data assimilation in the GMAO Temperature and salinity profiles from Argo floats Sea Level anomalies (TOPEX/JASON) SST (AMSR-E; MODIS) Ensemble-based ocean data assimilation system Surface chlorophyll (CZCS, SeaWiFS, MODIS) Ocean state estimates for climate analysis and for short-term climate forecasts In situ temperature profiles (TAO/PIRATA moorings, XBTs) 36

38 GMAO Assimilation System(s) Atmosphere Meteorological analyses Chemistry constituents: ozone, coupled with meteorology Chemistry constituents: CO, CO 2 NO x under development Chemistry constituents: Aerosol Transport, with source distributions from satellite GEOS-5 AGCM, currently 3Dvar, 4Dvar in test phase Land Surface Soil moisture, surface temperature and snow Catchment LSM with EnKF Ocean Retrospective Ocean analyses for seasonal forecasts MOM4: MvOI, EnKF Assimilation in the CGCM coupled to atmospheric analysis Ocean color analyses: ocean time series, removing cross-satellite biases 37