assimilating earth system observations at nasa: …jra.kishou.go.jp/jra-25/3rac/program/v1-104.pdf1...
TRANSCRIPT
Assimilating Earth System Observations at NASA: MERRA
and BeyondSiegfried 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
1
Overview
• MERRA– The GEOS-5 DAS and observations– Some early results
• Beyond MERRA– Building the components for an
Integrated Earth System Analysis Capability
2
AGCMFinite-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 hPaCatchment land surface model (R. Koster)Prescribed aerosols (P. Colarco)Interactive ozonePrescribed SST, sea-ice
AnalysisGrid Point Statistical Interpolation (GSI from NCEP)Direct assimilation of satellite radiance data using
JCSDA Community Radiative Transfer Model (CRTM)
Variational bias correction for radiances
AssimilationApply 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)
∂qn
∂t⎛
⎝ ⎜
⎞
⎠ ⎟
total
= dynamics (adiabatic ) + physics (diabatic ) + Δq
Model predicted change Correction from DASTotal “observed change”
Analysis
Background (model forecast)Raw analysis (from GSI)
Assimilated analysis(Application of IAU)
03Z 06Z 09Z 12Z 18Z15Z 21Z 00Z 03Z
Initial States for CorrectorAnalysis Tendencies for CorrectorCorrector Segment (1- and 3-hrly products)
3
MERRA Production
2-year spin up at 2-degree resolution1-year spin up at ½ degreeProduct Streams begin: Jan 1 – 1979, 1989 and 1998
Years inStream
MERRA 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08Stream 1 10Stream 2 10Stream 3 9ROSB 21G5-AMIP 26EOS
Spinup years Reduced Observing System Baseline (ROSB)
• Preview/Validation runs:• Jan, Apr, Jul, Oct 2004• July-August 1987• Jan, Jul 2001• Jul 2006
• 2 degree (scout) runs ⇒ preliminary look at data and spin-up of satellite bias estimates.
1010112230
4
Years inStream
MERRA 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08Stream 1 10Stream 2 10Stream 3 9ROSB 21G5-AMIP 26EOS
NovNOAA-18
EOS AquaOct
F08
F15
SSM/IJul DecF10
NovF11
Dec
F13
MayF14
May
Dec Aug
Dec Dec
Apr Jul
Apr Jun
Jul
GOES-08
GOES-10
GOES-12
TIROS-NDec Feb
Jul Apr OctNovNOAA-6
FebSep NOAA-7
May Jun Jul OctNOAA-8
Jan NovNOAA-9
NOAA-10Dec Sep
Nov Jan Sep Sep NOAA-11
JunSep NOAA-12
Satellite Radiance Data Streams
JanNOAA-14
SepNOAA-15NovNOAA-16
JulNOAA-17
Dec
TOVS
ATOVS
EOS Aqua
GOESSounders
5
Satellite Data TOVS (TIROS N, N-6, N-7, N-8 )
1978/10/30 Š 1985/01/01 NCAR
(A)TOVS (N-9; N-10 ; N-11; N-12 )
1985/01/01 - 1997/07/14 NOAA/NESDIS & NCAR
ATOVS (N-14; N-15; N-16; N-18; N-18)
1995/01/19 - present NOAA/NESDIS
EOS/Aqua 2002/10 - present NOAA/NESDIS SSM/I V6 (F08, F10, F11, F13, F14, F15)
1987/7 - present RSS
GOES sounder TB 2001/01 - present NOAA/NCEP SBUV2 ozone (Version 8 retrievals)
1978/10 - present NASA/GSFC/Code 613.3
DATA SOURCE/TYPE PERIOD DATA SUPPLIER Conventional Data Radiosondes 1970 - present NOAA/NCEP PIBAL winds 1970 - present NOAA/NCEP Wind profiles 1992/5/14 - present UCAR CDAS Conventional, ASDAR, and MDCRS aircraft reports 1970 - present NOAA/NCEP
Dropsondes 1970 - present NOAA/NCEP PAOB 1978 - present NCEP CDAS GMS, METEOSAT, cloud drift IR and visible winds
1977 Š present NOAA/NCEP
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 1970 - present NOAA/NCEP Surface ship and buoy observations
1977 - present NOAA/NCEP
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
A special thanks to Jack Woollen for help with the conventional data streams and Leo Haimberger for the radiosonde corrections!!!!
6
State Variables
7
Zonal Mean u-windJan 2004
NCEP OPS EC OPS JRA-25
GEOS-5
Other
Δ
8
GEOS5 TPW Jan 2004
SSMI TPW Jul 2004
GEOS5 TPW Jul 2004
SSMI TPW Jan 2004
Wentz Retrievals, Version 6
9
TPW - SSM/IJul 2004GEOS-5
10
Water Vapor Budgetfor Jan 2004
Water Vapor Budgetfor Jan 2004 dynamicsdynamics condens+re-evapcondens+re-evap
sfc evaporationsfc evaporation analysisanalysis
sumsum residualresidual
residual =
(qend - qbegin) - sum
sum =
dynamics + (condensation + re-evaporation) + surface evaporation + analysis
mm/day
11
Precipitation
12
Global NCEP R2
JRA25
� MERRA validation and spin-up runs
ERA40
NCEP R1
Area Averaged Precipitation (mm/day)
GPCP
CMAPObs:
13
Precipitation (mm/day)January 2004 July 2004
GEOS-5 GEOS-5
GPCPGPCP
14
Precipitation c.f. GPCP (mm/day)July 2004
15
2004 Tropical Precipitation
16
Mississippi River Basin Daily Prec
17
03Z 02 January 2004
mm/day
Precipitation
MERRA CMORPH
Thanks to Matt Sapiano
18
mm/day
3 hourly Precipitation - January 2004
MERRA CMORPH
19
Clouds and Radiation
20
21
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
22
Jul 06
Jul 04
Jul 04EC OPS
qLT
CLOUDSATCloud Tops
Marine Stratus DeckOff Peru
Marine Stratus DeckOff Peru
23
Regional Climate
24
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
GPCPGPCP
EC-OPSEC-OPS
CMAPCMAP
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
EC-OPSEC-OPS
Monthly Mean Precipitation over India July 2004 (mm/day)
Difference from GPCP
25
Latitude-time Cross Section of Precipitation over India 2004 (72.5E-80E)
GPCP(Pentad)
TRMM (daily mean)
JRA(daily mean)
GEOS5(daily mean)
GEOS5(daily mean)
latit
ude
latit
ude
latit
ude
latit
ude
26
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
GPCPGPCP
EC-OPSEC-OPS
CMAPCMAP
GEOS5GEOS5
JRAJRA
NCEP R1NCEP R1
EC-OPSEC-OPS
Monthly Mean Precipitation over Americas July 2004 (mm/day)
Difference from GPCP
27
Precipitation (mm/d) and 925mb wind
NARR
GEOS-5
Seasonal evolution of North American monsoon (2004)
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).
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.
Shading: precipitation rate (mm/d), Arrows: 925 mb windsContours: surface elevation
28
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
29
0-6Z 6-12Z 12-18Z 18-0Z
GEOS-5GEOS-5
JRAJRA
NCEP R1NCEP R1
NCEP R2NCEP R2
EC-OPSEC-OPS
TRMM
Diurnal variation in precipitation over the United States for July 2004 (mm/day). The July mean is removed.
TRMM
30
Vertical Structure of LLJ: Jul/Aug 2004 v-wind at 35°N
NARR
GEOS-5
31
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 )
32
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-2009
Next Steps
Developing Components of Future Integrated Earth System
Analysis, with consistent analyses across all
components
GMAO Data Assimilation Systems
Atmosphere Constituents Aerosols
Land Surface Ocean Biology Physical Ocean
Assimilation of AMSRAssimilation of AMSR--E soil moisture retrievalsE soil moisture retrievals
Assimilation of TOPEX/Jason Altimeter DataAssimilation of TOPEX/Jason Altimeter Data
Assimilation of AURA/MLS and OMI OzoneAssimilation of AURA/MLS and OMI OzoneJan 04 Precipitation inJan 04 Precipitation in MERRA MERRA Aerosol Transport Aerosol Transport
35
Soil moisture [m3/m3]
Land data assimilation at the NASA GMAOLand data assimilation at the NASA GMAO
Surface soil moisture (SMMR, TRMM, AMSR-E, SMOS, SMAP)
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”)
260 280 300
Snow cover fraction (MODIS)
36
Ocean data assimilationOcean data assimilation in the GMAOin the GMAO
Temperature and salinity profiles from Argo floats
Surface chlorophyll(CZCS, SeaWiFS, MODIS)
Ensemble-based ocean data assimilation system
Ocean state estimates for climate analysis and for short-term climate forecasts
In situ temperature profiles (TAO/PIRATA moorings, XBTs)
Sea Level anomalies (TOPEX/JASON)
SST (AMSR-E; MODIS)
37
AtmosphereMeteorological analysesChemistry constituents: ozone, coupled with meteorologyChemistry constituents: CO, CO2 NOx under developmentChemistry constituents: Aerosol Transport, with source distributions from
satelliteGEOS-5 AGCM, currently 3Dvar, 4Dvar in test phase
Land SurfaceSoil moisture, surface temperature and snowCatchment LSM with EnKF
OceanRetrospective Ocean analyses for seasonal forecastsMOM4: MvOI, EnKF Assimilation in the CGCM coupled to atmospheric
analysisOcean color analyses: ocean time series, removing cross-satellite biases
AtmosphereMeteorological analysesChemistry constituents: ozone, coupled with meteorologyChemistry constituents: CO, CO2 NOx under developmentChemistry constituents: Aerosol Transport, with source distributions from
satelliteGEOS-5 AGCM, currently 3Dvar, 4Dvar in test phase
Land SurfaceSoil moisture, surface temperature and snowCatchment LSM with EnKF
OceanRetrospective Ocean analyses for seasonal forecastsMOM4: MvOI, EnKF Assimilation in the CGCM coupled to atmospheric
analysisOcean color analyses: ocean time series, removing cross-satellite biases
GMAO Assimilation System(s)