The New Nature Run
Contributors to NOAA-NASA and International OSSEs
NCEP: Michiko Masutani, John S. Woollen, Yucheng Song,
Stephen J. Lord, Zoltan Toth, Russ Treadon
JCSDA: John LeMarshal, Jim Yoe, Waymen Baker,
NESDIS: Thomas J. Kleespies, Haibing Sun,
SWA: G. David Emmitt, Sidney A. Wood, Steven Greco,
NASA/GFSC: Lars Peter Riishojgaard, Oreste Reale, Joe Terry,
Ron Errico, Runhua Yang, Juan Juseum, Gail McConaughy
NOAA/ESRL:Tom Schlatter, Yuanfu Xie, Steve Weygandt, Gil Compo
KNMI: Gert-Jan Marseille, Ad Stoffellen
Japan: JMA, MRI and Earth Simulator Center
OSSEs: Observing Systems Simulation Experiments
JCSDA: Joint Center for Satellite Data Assimilation
SWA: Simpson Weather Associates
ESRL: Earth System Research Laboratory (formerly FSL, CDC, ETL)
Existing data +
Current observing system Nature Run Proposed data
DWL, CrIS, ATMS,
Real OSSE Simulated
TOVS Quality Control TOVS
AIRS data) AIRS
NWP forecast NWP forecast
Need for OSSEs
Quantitatively–based decisions on the design &
implementation of future observing systems
Evaluate possible future instruments without cost of
developing, maintaining & using observing systems.
There are significant time lags between instrument
deployment and eventual operational NWP use.
OSSEs are a very labor intensive project.
DA (Data Assimilation) system will be
prepared for the new data
OSSE helps understanding and
formulation of observational errors
Enable data formatting and handling in
advance of “live” instrument
DA system will be different when the actual data
If we cannot simulate observation, how could
we assimilate observation?
We need to present levels of confidence of the results from OSSEs.
Comparison of OSSE by various DA system will be very important.
Nature Run: Serves as a true atmosphere for
Preparation of the Nature Run and simulation of basic
observations consume a significant amount of resources.
If different NRs are used by various DAs, it is hard to
compare the results.
Need one good new Nature Run which will be used
by many OSSEs.
Share the simulated data to compare the OSSE results by
various DA systems to gain confidence in results.
Forecast run is used for the Nature Run
Because the real atmosphere is a chaotic system governed mainly by conditions at
its lower boundary, it does not matter that the Nature Run diverges from the real
The Nature Run should be a separate universe, ultimately independent from but
parallel to the real atmosphere.
The Nature Run must have the same statistical behavior as the real atmosphere in
every aspect relevant to the observing system under scrutiny.
A succession of analyses is a collection of snapshots of the real atmosphere. Each
analysis marks a discontinuity in model trajectory. Considering a succession of
analyses as truth seems to be a serious compromise in the attempt to conduct a
I favor a long, free-running forecast as the basis for defining “truth” in an OSSE.
-- from Tom Schlatter
Posted at http://www.emc.ncep.noaa.gov/research/osse/NR
New Nature Run by ECMWF
Based on Recommendations by
JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL
Low resolution Nature Run High resolution Nature Run
Spectral resolution : T511 for a selected period
Vertical level: L91
T799 resolution, 91 levels,
3 hourly dump
Initial condition: 12Z May 1st, 2005 one hourly dump
End at: 0Z Jun 1,2006 Get initial conditions from L-NR
Daily SST and ICE (Provided by NCEP)
1x1 degree 31 level pressure data
To be archived in the MARS system Potential temperature level data
on the THORPEX server at ECMWF
Accessed by external users
Selected time series of 1x1 degree are also
Copies for US users known as available
Convective precipitation, Large scale precipitation, MSLP,
designated users and users
Z1000, Z500, U500, V500, T2m,TD2m, U10,V10,
known to ECMWF HCC,LCC,MCC,TCC,Sfc Skin Temp
Nature Run home page
Contacts for the New Nature Run
ECMWF Erik Andersson collaboration within Meteorological
NCEP Michiko Masutani community is essential for timely
NASA/GSFC and reliable OSSEs
Oreste Reale(GLA) JCSDA , NCEP, NESDIS,NASA,
Joe Terry (SIVO) ESRL
JCSDA John LeMarshall ECMWF, ESA, EUMETSAT
NESDIS Thomas J. Kleespies THORPEX, IPO
SWA Steven Greco Operational Test Center OTC – Joint
ESRL Tom Schlatter THORPEX/JCSDA
Zoltan Toth (GIFS) Simulation of the data must be
Met Office Richard Swinbank done from model levels and at
Meteo FranceJean Pailleux full resolution.
KNMI Gert-Jan Marseille
EUMETSAT Jo Schmetz Pressure level data will be available
ESA Eva Oriol for diagnostics and evaluation; only
JMA Munehiko Yamaguchi, limited isentropic level data will
Kozo Okamoto become available.
MRI Tetsuo Nakazawa, Masahiro Hosaka BUFR format will be used
ES Takeshi Enomoto
Nature run home page
Strategies for simulation of
Summary of some results
The results depend on the representativeness error
We have to assign representativeness error carefully.
The discussions of representativeness error are posted at
Lorenc,A.C et al 1992, Lorenc.1992.TIDCCR4129.pdf
Some initial diagnostics
The SST, ice and Ts fields look OK, with the expected seasonal
variations. The Z 500 also looks OK. Looking quickly at daily 1000
hPa Z maps for the Caribbean, I've been able to spot nine
hurricanes between June and November. One made landfall in
Florida (see attached ps-file). There might be some more
hurricanes visible in the wind field?
-- Erik Andersson
Tropical Cyclones in The Nature Run August 22-27
Cyclone tracks in the Nature Run
Thomas Jung, ECMWF
By Juan Carlos Jusem. NASA/GSFC
JJA Precipitation anomaly
Comparison between the ECMWF T511
Nature Run against climatology.
20050601-20060531, exp=eskb, cycle=31r1
Adrian Tompkins, ECMWF
TechMemo 452 Tompkins et al. (2004)
Jung et al. (2005) TechMemo 471
Plot files are also posted at
The description of the data
SSMI 10m wind Quickscat SFC wind
- Quikscat does not provide winds in rainy areas
- Shows known bias in the W Pacific. Model winds are too low in
deep convective areas.
GPCP, SSMI, and
TRMM, NASDA and RSS
- These comparisons confirm the lack of rainfall over the
tropical land masses.
- We have an overestimation of precip over the high-SST
regions in the tropics.
- There is a tendency for deep convection to become
locked in with the highest SSTs, which in the east Pacific
results in a narrow ITCZ.
- The TRMM NASDA-3b43 algorithm is presumed to be the
most accurate of the two TRMM retrieval products.
Area averaged precipitation
Large Scale precipitation
It takes about one month to settle tropical precipitation.
More diagnostics are being conducted at
NASA/GLA, ESRL/PSD, JMA, NCEP, etc.
Some recent results from
at NCEP andJCSDA
(using T213 Nature Run)
Targeted DWL experiments
Combination of two lidar
DWL-Upper: An instrument that provides mid and
upper tropospheric winds down only to the levels of
significant cloud coverage.
Operates only 10% (possibly up to 20%) of the time.
DWL-Lower: An instrument that provides wind
observations only from clouds and the PBL.
Operates 100% of the time and keeps the instruments warm.
DWL-NonScan: DWL covers all levels without scanning
(Feb13 - Mar 6 average )
10% Upper Level Adaptive
Doubled contour (based on the difference between first
100% Upper Level guess and NR, three minutes of segments
are chosen – the other 81 min discarded)
10% Uniform DWL Upper NonScan DWL
Target and 200U
Target in Jet region
Target and 200V
Target in North America and
Eurasia associated with
(Feb13 - Mar 6 average )
10% Upper Level Adaptive
100% Upper Level Doubled
10% Uniform DWL Upper NonScan DWL
Anomaly correlation difference from control
Synoptic scale Meridional wind (V)
200hpa NH Feb13-Feb28
DWL-Lower is better than DWL-NonScan only
With 100% DWL-Lower DWL-NonScan is
better than uniform 10% DWL-Upper
Targeted 10% DWL-Upper performs
somewhat better than DWL-NonScan in the
DWL-NonScan performs somewhat better
than Targeted 10% DWL-Upper in 36-48 hour
100% Lower + 100% Upper
100% Lower + 10% Targeted Upper
100% Lower + Non Scan
100% Lower + 10% Uniform Upper
Non Scan only
No Lidar (Conventional + NOAA11 and NOAA12 TOVS)
Reduction of RMSE from NR
for V by adding NonScan V850
lidar to low level scan lidar.
NonScan lidar by itself showed a reasonable
impact but exhibited some negative impact
with data from scanning lidar at lower levels.
V500 Note: the experiments are performed using
old NCEP SSI DA system. This problem is
expected to be resolved in the new GSI DA
We have to work on a DA system for lidar
and new instruments before the data
Do not show the difference
AC to Nature Run
noDWL with TOVS
Z500 presents a very limited story
Data and model resolution
OSSEs with Uniform Data
More data or a better model?
Fibonacci Grid used in the uniform data coverage
40 levels equally-spaced data
100km, 500km, 200km are tested
Skill is presented as Anomaly Correlation %
The differences from selected CTL are presented - Yucheng Song
Time averaged from Feb13-Feb28
200mb U and 200mb T are presented
U 200 hPa Benefit from increasing the number of levels
500km Raob T62L64 anal T62L64 fcst
500km Raob T62L64 anal T62L28 fcst
500km RaobT62L28 anal & fcst
L64 anl L28 fcst
1000km RaobT170L42 analT62L28 fcst
1000km RaobT62L28 anal & fcst
T 200 hPa
T170 L42 model 500km
High density observation give a
High density observationsgive better
analysis but it could cause poor
better analysis but could cause a poor
Increasing the vertical resolution was
important for high density observations L64 anl L28 fcst
High density observations cannot help forecasts
if the model does not have good resolution.
Increasing vertical resolution in the analysis is
important for high density observations.
We have to work on a DA system for new
instruments before the data become available.
OSSE will be a very useful tool to prepare the DA
system for new instruments.
The current NCEP/JCSDA system has shown that
OSSEs can provide critical information for
assessing observational data impacts.
The results also showed that theoretical explanations
will not be satisfactory when designing future
The new Nature Run has been prepared with
international teamwork: ECMWF, NOAA, NASA,
THORPEX EUMETSAT, ESA
Extended international collaboration
within the Meteorological community
is essential for timely and reliable
OSSEs to influence decisions.
OSSE and its evaluation will become
affordable to the University and academic
Some figures need to be improved.
Revised version will be available from the web site.