Transcript Document
Data assimilation and Forecast
activities in support of NAME
The NAME Team at CPC:
Kingtse Mo, Wayne Higgins,
Jae Schemm, Muthuvel Chelliah,
Wesley Ebisuzaki, Marco Carrera,
Wei Shi, Hyun Kyung Kim,
Yucheng Song and Evgeney Yarosh
Best use the NAME data
Understand the dynamical processes related to NAME
Improve warm season precipitation forecasts
Approach
Monitoring
Data assimilation & RR
Modeling issues:
resolution,
physical processes : convection in a complex terrain,
Better usage of satellite observations
Prediction: A test bed for hydromet
Monitoring effort in support of
NAME04
Archive GFS (T126, 1 degree) and the
operational EDAS (40-km and 12 km) for
monitoring
Set up web monitoring pages in support
of the NAME 04
Set up rotation of the monitoring director
in support of the CPC/HPC briefing
Regional Reanalysis
Produced by the EMC and post processed and
archived at the CPC
Archive selected daily and 3-hourly variables and
all monthly mean quantities at each synoptic time.
Form Climatology of the above fields (1979-2001
DATA Distribution
Climatology to UCAR/JOSS
Total archive will be distributed by ftp
Regional Reanalysis
(Mesinger et. al. 2004)
Model: Eta 32km, 45 vertical levels
Period: 1 Jan 1979 – 31 Dec 2002
Domain: North America and adjacent oceans
Precipitation assimilation:
U.S.: PRISM corrected gauge analysis;
Mexico: Rain gauge analysis;
other areas: CMAP pentad analysis (1979-2002)
CMORPH hourly (2003 on ward)
Precipitation and
Surface Temperature
• Precipitation and Surface Temperature from the RR
compare favorably with observations.
– Surface Temperature is not assimilated.
• The seasonal cycle of Precipitation is well captured by
the RR
• Relationships between E and P in the RR are consistent
with those reported by Rasmusson (1968,1969),
Rasmusson and Berbery (1996)
Annual Cycle of
Precipitation (mm day-1)
(warm season)
May: Heaviest P in the western Gulf
Coast and lower Mississippi
Valley.
June: P reaches a maximum over the
Central US, while monsoon
rainfall spreads northward along
the western slopes of the SMO.
July: Monsoon P shifts northward into
AZ/NM by early July while P
decreases in Central US.
August: Monsoon P reaches a
maximum over SW and then starts
to retreat. The demise of the
monsoon is more gradual than the
onset.
(Higgins et al. 1998)
Precipitation Difference (mm day-1)
(RR – Obs)
RR assimilates observed P, so
the differences between RR
and obs are expected to be
small.
Largest differences are over
southern Mexico , the
difference is about 8% of the
total rainfall
Annual Cycle of
T2m Temperature (°C)
(warm season)
Surface Temperature Difference (°C)
(RR-OBS)
Seasonal cycle of Moisture
Budget Parameters (32N-36N)
P
E
(E-P)
-DQ
1. E> P over the central
US in summer
2. D(Q) contribution
over the central US is
small
3. Both E and D(Q)
contribute to rainfall
over the Southwest
Rasmusson 1968,1969
Diurnal Cycle P for August (1979-2001)
The RR captures
the eastward
propagation of the
diurnal Max
Low Level Jets
• The LLJ from the Caribbean (CALLJ) is well
captured by the RR.
• The Great Plains LLJ (GPLLJ) in the RR is
similar to that in the operational EDAS and
compares well to wind profiler data.
• The Gulf of California LLJ (GCLLJ) may be
too strong compared to observations
CALLJ
925-hPa Zonal Wind (m s-1)
May
August
June
September
July
October
Meridional Wind (m s-1) at
(36N,97.5W) (GPLLJ)
RR
Wind Profiler
Strong diurnal cycle
MAX :950-975 hPa
Higgins et al. (1997)
Vertically Integrated Meridional
Moisture Flux (kg/ms) (1995-2000)
RR
RR - OpEDAS
RR [qv]
GCLLJ
Over the Guf
of California
are stronger
than EDAS
Differences
can be as
large as
60kg/(ms)
RR and pilot balloon and soundings
at Puerto Penasco
RR
obs
252-m obs wind (Douglas et al. 1998)
Profile of v-wind
1 LT
1LT
16 LT
16LT
RR vwind
captures the
diurnal cycle
but it is 3m/s
higher than
obs,
Vertical cross section of qv at 30N
1998-2000
RR
Operational EDAS
Challenges
The NAME data will give guidance to
the location, strength and variation of the GCLLJ.
Relationship between the GCLLJ, rainfall and the
GPLLJ
We need to understand
The reasons that the RR GCLLJ is stronger than the
operational EDAS
Data impact studies
Both GFS and EDAS
o We will assimilate all data getting to the GTS within the
cut off time
o Carefully monitoring data inputs, perform diagnostics,
and comparison with obs.
o Perform data impact studies using both the global and
regional data assimilation systems when all data are
collected and obtained from JOSS
o Special data impact studies will be made.
Global modeling issues
Model resolution
Physical processes: Convection in
complex terrain,
Predictability
Think globally, act locally
Experiments
• Models: with observed SSTs
• A) T126L28 GFS Model (approx 80 km)
• B) T62 GFS model (approx 200 km)
• C) T62 with RSM80 downscaling
Conclusions
T126 Fcst performs better than T62 over the United States
and Mexico
T62 does not recognize the Gulf of California and can not
capture anomalies associated with monsoon rainfall
The RSM/T62 does not improve Fcsts because the RSM is not
Able to correct errors of the T62 model.
Observed Precip
T62 ensemble mean P
T126 ensemble mean P
RSM/T62 ensemble mean P
P from RSM/T62 is similar to the T62 Fcsts
The RSM can not correct errors in the T62
Fcst to improve P
Physical Processes
Physical Processes : Diurnal cycle Precipitation and
related circulation anomalies in a complex terrain;
(Siegfried Schubert)
NAMAP 1 (Dave Gutzler)
CPT team and NAMAP 2 (Dave Gutzler)
Seasonal Forecast Experiments : Establish of the
Baseline of prediction skill (Jae Schemm)
Improve fcsts in operational centers
III. Prediction
Linkages between climate and weather :A
Hydromet Test bed (Precip QPF fcsts)
improve the precip prediction over the NAME region
associated with the leading patterns of climate variability;
determine the impact of boundary conditions :Coupled
model vs two tier prediction system.
assess the impact of boundary conditions like
vegetation fraction, soil conditions and soil moisture on
precip prediction in the seasonal time scales
Better use of satellite data
Enhance local climate prediction using regional
models
Milestones
• Benchmark and assessment of global and regional model
performance (2004) (NAMAP1,NAMAP2, Fcst Exp)
• Evaluate impact of the data from the NAME campaign on
operational data assimilation and forecasts (2005)
• Simulate the monsoon onset to within a week of accuracy (2006)
• Simulate diurnal cycle of observed precip to within 20% of a
monthly means (2007)
•