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)

•