Data assimilation and Forecast activities in support of NAME Data assimilation and Forecast activities in support of NAME The NAME Team at CPC: Kingtse Mo, Wayne Higgins, Jae Schemm, Muthuvel Chelliah, Jae Schemm, Muthuvel Chelliah, Wesley Ebisuzaki, Marco Carrera, Wei Shi, Hyun Kyung Kim, Yucheng Song and Evgeney Yarosh

Best use the NAME data 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 ( ) 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 ( )   CMORPH hourly (2003 on ward)

Precipitation and Surface Temperature Precipitation and Surface Temperature from the RR compare favorably with observations.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 RRThe 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)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) 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 ( ) 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 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 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 observationsThe Gulf of California LLJ (GCLLJ) may be too strong compared to observations

CALLJ May June July August September October 925-hPa Zonal Wind (m s -1 )

Strong diurnal cycle MAX : 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) ( ) 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 soundings at 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 RR obs

RROperational EDAS Vertical cross section of qv at 30N

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 oWe will assimilate all data getting to the GTS within the cut off time o oCarefully monitoring data inputs, perform diagnostics, and comparison with obs. o oPerform data impact studies using both the global and regional data assimilation systems when all data are collected and obtained from JOSS o oSpecial 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)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)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 the monsoon onset to within a week of accuracy (2006) Simulate diurnal cycle of observed precip to within 20% of a monthly means (2007)Simulate diurnal cycle of observed precip to within 20% of a monthly means (2007)