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)
•