NAME Climate Process and Modeling Team/ Issues for Warm Season Prediction J. Schemm and D. Gutzler CPC/NCEP/NWS/NOAA University of New Mexico The 30th Climate Diagnostics and Prediction workshop The Pennsylvania State University October 24-28, 2005 Acknowledgements: Myong-In Lee, Soo-Hyun Yoo and Lindsey Williams
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NAME Climate Process and Modeling Team/ Issues for Warm Season Prediction
NAME Climate Process and Modeling Team/ Issues for Warm Season Prediction. J. Schemm and D. Gutzler CPC/NCEP/NWS/NOAA University of New Mexico The 30th Climate Diagnostics and Prediction workshop The Pennsylvania State University October 24-28, 2005 - PowerPoint PPT Presentation
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NAME Climate Process and Modeling Team/Issues for Warm Season Prediction
J. Schemm and D. GutzlerCPC/NCEP/NWS/NOAA
University of New Mexico
The 30th Climate Diagnostics and Prediction workshop
The Pennsylvania State University
October 24-28, 2005
Acknowledgements: Myong-In Lee, Soo-Hyun Yoo and Lindsey Williams
NAME Climate Process and Modeling Team- supported by NOAA/OGP CPPA program
Team members:
David Gutzler, University of New MexicoWayne Higgins, CPC/NCEP/NWS/NOAABrian Mapes, University of MiamiKingtse Mo, CPC/NCEP/NWS/NOAAShrinivas Moorthi, EMC/NCEP/NWS/NOAAJae-Kyung Schemm, CPC/NCEP/NWS/NOAASiegfried Schubert, GMAO/GSFC/NASAGlenn White, EMC/NCEP/NWS/NOAA
Project Objectives:
1. Implementation of the second phase of NAME Model Assessment Project (NAMAP2) focused on the 2004
season.
2. Coordinated efforts with the current NAME Diurnal Cycle Experiment Project in GCM diagnostics.
3. Implementation of their findings to the NCEP GFS/CFS operational forecast system - NOAA Climate Test Bed.
4. Serve as a primary mechanism for collaboration andtechnology transfer between research communities andoperational centers.
NAMAP Analysis: Metrics for model development
• Improved simulation of monsoon onset, especially in global models
• Goals for improvement of precipitation (total amount and diurnal variability) and surface flux simulations, tied to improvements in ground truth to be achieved from NAME 2004 field observations
• Questions regarding the structure of low-level jet circulations and their importance for proper precipitation simulation
NAMAP2 - A coordinated exercise in global and regional atmospheric modeling
of NAMS.
- Summer 2004 is the simulation target.
- Simulation protocols have been developed and announced among potential participants.
- Focus on uncertainties identified in NAMAP, with additional emphasis on verification using enhanced observations from the NAME 2004 field campaign.
- Results based on the first NAMAP published in BAMS, Oct. 2005.
NAMAP2
- Will re-examine the metrics proposed by the first NAMAP.
- For proper specification of SSTs in the Gulf of California, a new SST analysis has been developed by W. Wang and P. Xie of CPC.
Simulation Period 15 May- 30 September 2004
Domain of I nterest 15°N- 45°N 125°W- 75°W
Lateral Boundary Conditions (f or regional models)
NOAA CDAS2 (to be supplied)
Surf ace Boundary Conditions/ oceanic
Multiple- Platf orm- Merged Analysis (to be supplied; see description below)
Surf ace Boundary Conditions/ continental
Chosen by each modeling group
NAMAP2 Protocols
abbrv field abbrv field
Ms sfc soil moisture Ts temperature (surface)
Msub subsfc soil moisture T2m temperature (2m)
Msca sfc soil moisture cap T850 temperature (850 hPa)
Msub subsfc soil mois. cap T500 temperature (500 hPa)
Zacatecas/Guadalupe 22.75N 102.51W Belize City, Belize 17.53N 88.3W
La Paz 24.17N 110.30W
Mexico City 19.4N 99.2W
b) For high-resolution temporal analysis:
Archive "MOLTS"-style time series (at least hourly in time and full vertical resolution). We will consider surface fluxes and profiles of humidity, T, u, v, w, p, resolved and convective precipitation, cloud fraction, radiation, and turbulence at model grid points corresponding to the following NAME sounding sites:
Output Archiving Protocols
A Multi-Platform-Merged (MPM) SST Analysis over the NAME Domain
Wanqiu Wang and Pingping Xie
Climate Prediction Center
NCEP/NWS/NOAA
To create a fine-resolution SST analysis with desirable resolutions and accuracy for NAME Projects
Resolution: 0.25o in space, 3-hour in time
Domain: 180o – 30oW, 30oS – 60oN
Target Period:2001 – present
Input data: All available in-situ and advanced satellite observations
Quality control: Cross verification to ensure data quality
Bias correction: Removal of large-scale/low-frequency bias in satellite observations
OI analysis: Combining SST data from all observations through the Optimal Interpolation (OI)
Input Data In-situ observations
Buoys and ships
Satellite ObservationsGOES: 3-hourly / clear sky
TMI: twice daily / all skyAMSR: twice daily / all sky
NOAA16: twice daily / clear sky
NOAA17: twice daily / clear sky
MODIS: Not included yet.
Input SST for AMJ 2004
Similar Spatial distribution Pattern;
Differences in small-scale features and in magnitude
Current Status
Developed prototype algorithm to define the analysis
Produced analysis for 2004
Conducted preliminary comparison with existing analyses (OI and RTG)
Some quick analysis statistics
OI RTG MPM0.70 K 0.60 K 0.48 K
OI: Weekly Optimum Interpolation
RTG: Real-Time Global analysis (2DVAR)
MPM: Multi-Platform-Merged Analysis
Magnitude of accuracy: RMS difference in daily mean between analyses and moored buoy (May 15 to Sep 30, 2004)
Magnitude of mean bias: RMS difference in seasonal mean between analyses and all in situ (Jun 1 to Aug 31, 2004)
OI RTG MPM0.44 K 0.32 K 0.19 K
Mean difference (K) between analyses
and in situ observations(Jun 1 to Aug 31, 2004)
MPM shows smaller bias
Note: In situ observations were used in all analyses. However, MPM is probably less dependent on the in situ because the use of much larger amount of satellite observations.
Issues involved in warm season predictability over NAME area