Data assimilation and Forecast activities in support of NAME
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Data assimilation and Forecast Data assimilation and Forecast activities in support of NAMEactivities in support of NAME
The NAME Team at CPC:The NAME Team at CPC:
Kingtse Mo, Wayne Higgins,Kingtse Mo, Wayne Higgins,
Jae Schemm, Muthuvel Chelliah,Jae Schemm, Muthuvel Chelliah,
Wesley Ebisuzaki, Marco Carrera, Wesley Ebisuzaki, Marco Carrera,
Wei Shi, Hyun Kyung Kim, Wei Shi, Hyun Kyung Kim,
Yucheng Song and Evgeney YaroshYucheng Song and Evgeney Yarosh
Best use the NAME dataBest 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 Monitoring effort in support of NAME04NAME04
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 ReanalysisRegional 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 ReanalysisRegional Reanalysis(Mesinger et. al. 2004)(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 Precipitation and Surface TemperatureSurface Temperature
• Precipitation and Surface Temperature from the RR Precipitation and Surface Temperature from the RR compare favorably with observations. compare favorably with observations.
– Surface Temperature is not assimilated.Surface Temperature is not assimilated.
• The seasonal cycle of Precipitation is well captured by The seasonal cycle of Precipitation is well captured by the RR the RR
• Relationships between E and P in the RR are consistent Relationships between E and P in the RR are consistent with those reported by Rasmusson (1968,1969), with those reported by Rasmusson (1968,1969), Rasmusson and Berbery (1996)Rasmusson and Berbery (1996)
Annual Cycle of Annual Cycle of Precipitation (mm dayPrecipitation (mm day-1-1) )
(warm season) (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 dayPrecipitation Difference (mm day-1-1) )
(RR – Obs)(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 Annual Cycle of T2m Temperature (°C)T2m Temperature (°C)
(warm season)(warm season)
Surface Temperature Difference (°C)Surface Temperature Difference (°C)(RR-OBS)(RR-OBS)
Seasonal cycle of Moisture Budget Parameters (32N-36N)
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
P
E
(E-P)
-DQ
Diurnal Cycle P for August (1979-2001)Diurnal Cycle P for August (1979-2001)
The RR captures the eastward propagation of the diurnal Max
Low Level JetsLow Level Jets
• The LLJ from the Caribbean (CALLJ) is well The LLJ from the Caribbean (CALLJ) is well captured by the RR.captured by the RR.
• The Great Plains LLJ (GPLLJ) in the RR is The Great Plains LLJ (GPLLJ) in the RR is similar to that in the operational EDAS and similar to that in the operational EDAS and compares well to wind profiler data.compares well to wind profiler data.
• The Gulf of California LLJ (GCLLJ) may be The Gulf of California LLJ (GCLLJ) may be too strong compared to observationstoo strong compared to observations
CALLJCALLJ
May
June
July
August
September
October
925-hPa Zonal Wind (m s-1)
Strong diurnal cycle
MAX :950-975 hPa
Meridional Wind (m s-1) at (36N,97.5W) (GPLLJ)
RR Wind Profiler
Higgins et al. (1997)
Vertically Integrated Meridional Moisture Flux (kg/ms) (1995-2000)
GCLLJ
RR RR - OpEDAS
RR [qv]
Over the Guf of
California are stronger than EDAS
Differences can be as large as
60kg/(ms)
RR and pilot balloon and soundingsRR and pilot balloon and soundingsat Puerto Penascoat Puerto Penasco
252-m obs wind (Douglas et al. 1998)
RR vwind captures the diurnal cycle but it is 3m/s higher than obs,
Profile of v-wind
1 LT
16 LT
1LT
16LT
RRRR obsobs
RR Operational EDAS
Vertical cross section of qv at 30N
1998-2000
ChallengesChallenges
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 studiesData impact studiesBoth GFS and EDASBoth 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 issuesGlobal modeling issues
Model resolution
Physical processes: Convection in complex terrain,
Predictability
Think globally, act locally
ExperimentsExperiments
• Models: with observed SSTs
• A) T126L28 GFS Model (approx 80 km)
• B) T62 GFS model (approx 200 km)
• C) T62 with RSM80 downscaling
ConclusionsConclusions
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 PrecipObserved Precip
T62 ensemble mean PT62 ensemble mean P
T126 ensemble mean PT126 ensemble mean P
RSM/T62 ensemble mean PRSM/T62 ensemble mean P
P from RSM/T62 is similar to the T62 FcstsP from RSM/T62 is similar to the T62 Fcsts
The RSM can not correct errors in the T62 The RSM can not correct errors in the T62 Fcst to improve PFcst to improve P
Physical ProcessesPhysical 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 scalesBetter use of satellite data Enhance local climate prediction using regional models
MilestonesMilestones
• Benchmark and assessment of global and regional model Benchmark and assessment of global and regional model performance (2004) (NAMAP1,NAMAP2, Fcst Exp)performance (2004) (NAMAP1,NAMAP2, Fcst Exp)
• Evaluate impact of the data from the NAME campaign on Evaluate impact of the data from the NAME campaign on operational data assimilation and forecasts (2005)operational data assimilation and forecasts (2005)
• Simulate the monsoon onset to within a week of accuracy (2006)Simulate the monsoon onset to within a week of accuracy (2006)
• Simulate diurnal cycle of observed precip to within 20% of a Simulate diurnal cycle of observed precip to within 20% of a monthly means (2007)monthly means (2007)
•