1. THE NORTH AMERICAN MONSOON EXPERIMENT (NAME)
National Science Foundation
March 6, 2003
NAME Homepage:

2. OUTLINE PROGRAM RATIONALE AND HYPOTHESES
NAME MODELING AND DATA ASSIMILATION R&D
3. LINKAGES (PROGRAMS, AGENCIES, COUNTRIES)
4. NAME 2004 FIELD CAMPAIGN

3. WHAT IS NAME?
NAME is an internationally coordinated, joint CLIVAR – GEWEX process study aimed at determining the sources and limits of predictability of warm season precipitation over North America.

4. RATIONALE FOR NAME
Coupled models do not accurately simulate or predict warm season precipitation, especially in tropical / subtropical land-ocean interaction regions.
NAME seeks improved understanding of the key physical processes that must be parameterized for improved simulations and predictions with coupled models.
A fundamental first step towards improved prediction is the clear documentation of the major elements of the NAMS and their variability within the context of the evolving O-A-L annual cycle.

5. NORTH AMERICAN MONSOON EXPERIMENT (NAME)
HYPOTHESIS
The NAMS provides a physical
basis for determining the degree of
predictability of warm season
precipitation over the region.
Topographic and Sea-Land Influence
OBJECTIVES:
Better understanding and
simulation of:
warm season convective
processes in complex terrain
(TIER I);
intraseasonal variability of
the monsoon (TIER II);
response of warm season
circulation and precipitation
to slowly varying boundary
conditions (SST, soil
moisture) (TIER III);
monsoon evolution and
variability (TIER I, II, III).
Intraseasonal Variability
Boundary Forcing?
YEAR (2000+)
Planning |
Preparations |
Data Collection |
Principal Research |
Data Management |

6. NAME IMPLEMENTATION
Empirical and modeling studies that carry forward the joint PACS/GAPP Warm Season Precipitation Initiative (2000 onward), and initiate new elements.
NAME Field Campaign (JJAS 2004) including build-up, field, analysis and modeling phases.

7. ROLE OF THE LAND SURFACE
The land surface has its strongest influence during the warm season (continents are warm; evaporation is large).
The land surface has many memory mechanisms (e.g. soil moisture, snow, vegetation) that may influence monsoon variability.

8. ROLE OF LOW-LEVEL JETS
There are 2 important low-level jets that transport significant
moisture to the continent and that play an important role in the
diurnal cycle of precipitation.

9. ROLE OF OCEANIC FORCING OF CONTINENTAL CLIMATE ANOMALIES
Ocean memory components evolve slowly and are to some degree predictable in their own right.
Warm season correlations between SST and continental precipitation are at least marginal.
Motivates studies of remote-vs-local SST’s; antecedent influences; influences of MJO, ENSO,PDO.

10. NAME MODELING –OBSERVATIONS TEAM
Charge:
Provide guidance on needs and priorities for NAME field observations.
“NAMAP” – 10 modeling groups; 1990 monsoon, global and regional models
Identify the path to improved warm season precipitation prediction:
“White Paper” - NAME Modeling and Data Assimilation R&D
Identify additional process studies necessary to reduce uncertainties in coupled models.

11. NAME MODELING AND DATA ASSIMILATION R&D
Objective: To improve predictions of warm season precipitation in the NAME region. Approaches:
I. 3-Tiered Approach to link spatial scales
- Model Development
- Data Assimilation and Analysis
- Predictability and Forecast Skill
II. Multi-scale Modeling Approach
Links Tiers I-III by employing cloud-resolving models to understand
local processes and to address parameterization (and other) issues in
regional and global models.

12. MODEL DEVELOPMENT Premise:
The leading factors that limit precipitation forecast skill in both global and regional models during the warm season are deficiencies in how we model “local” processes that modulate deep convection.
In order to achieve the desired improvements, NAME will focus on the diurnal cycle of convection in the core monsoon region of NW Mexico, a region of complex terrain and land/sea contrasts.
NAME 2004 will provide improved estimates of the 3-d structure of the monsoon and its variability on diurnal to monthly time scales.

13. DIURNAL VARIABILITY
The amplitude of the diurnal cycle in the core monsoon region is
larger than the amplitude of the annual cycle.
There are large-scale shifts in the regions of deep convection
during the day from over land to over water.
There is large intraseasonal and interannual variability of the
diurnal cycle, but it is not well understood.
Improved monitoring and modeling of the diurnal cycle will go a
long way towards improved warm season precipitation forecasts
not just for Tier I, but for Tiers II and III.

14. DATA ASSIMILATION AND ANALYSIS
Objectives:
1) To improve both global and regional data assimilation;
2) To identify uncertainties in analyses caused by model deficiencies;
3) To examine NAME data impacts on analyses;
4) To use the analyses to study the regional / global connections of the important processes within the NAME region:
* relationships between moisture surges and precipitation
* tropical easterly wave – moisture surge relationships
* out-of-phase relationship between precipitation in the core
monsoon region and the U.S. Great Plains

15. PREDICTABILITY AND FORECAST SKILL
Objectives:
1) To examine the predictability of warm season precipitation over
the NAM region;
2) To quantify error growth due to model errors versus that due to
uncertainties in analyses and boundary conditions;
3) To assess the value of NAME observations for prediction.
Key Questions (ultimately critical for climate prediction):
How is the life cycle of the monsoon related to the evolution of oceanic and continental boundary conditions?
2) Can models reproduce the observed summertime precipitation in average years and years with ENSO influence?

16. NAME MODELING AND DIAGNOSTIC STUDIES
Activity
Focus
Sponsor and Status
PI's
Cloud Resolving Models
Explicit convection; effect of terrain; mesoscale aspects of surges
Proposed
M. Moncrieff (NCAR)
North American Monsoon Assessment Project (NAMAP)
1990 Monsoon
NOAA/OGP - Unfunded
D. Gutzler (UNM)
(10 modeling groups)
Moisture Budget of Intra-Americas Sea
Moisture Transport
NOAA/OGP – Funded
C. Zhang (RSMAS)
Moisture Budget of NAME Tiers 1-3
Moisture Transport / Precipitation
NOAA/OGP - Funded
W. Higgins (NOAA/CPC)
E. Yarosh (NOAA/CPC)
Daily Precipitation Analysis (US_Mexico)
Monitoring and Assessment of floods / droughts
W. Shi (NOAA/CPC)
Diurnal Cycle and Precipitation
Linkage to TRMM/GPM
P. Arkin (U. Md.)
Subseasonal Variability
Role of MJO/ Model Uncertainties
NASA/IDS - Proposed
S. Schubert (NASA/GSFC)
Global / Regional Analyses and NAME Data Impact
NCEP Analyses and Forecasts With Eta Model and Eta Model Data Assimilation System (EDAS)
NOAA/OGP - Proposed
K. Mo (NOAA/CPC)
F. Meisenger (UCAR)
H. Berbery (U. Md)
Hydrological interpretation of monthly to seasonal precipitation forecasts
Relationship of Precipitation characteristics to streamflow
NOAA/OGP
D. Gochis (NCAR)
B. Njissen (UAZ)
WJ Shuttleworth (UAZ)
Assessment of land Surface Hydrologic Predictability
Extend the long term land surface dataset retrospectively from LDAS to Tiers I and II.
D. Lettenmaier (UW)
T. Cavazos-Perez (CISESE)

18. NAME AND CLIVAR
NAME is the North American Implementation of the WCRP/ CLIVAR VAMOS Program
NAME is endorsed by the US CLIVAR SSC as a warm season process study of the North American monsoon under the US CLIVAR Pan American Panel. US CLIVAR’s interests in NAME were listed in the June 2001 meeting report.
These interests would lead to the following deliverables:

19. CLIVAR - NAME DELIVERABLES
Coupled climate models capable of predicting North American monsoon variability months to seasons in advance;
More comprehensive understanding of North American summer climate variability and predictability;
Infrastructure to observe and monitor the North American monsoon system;
Contributions to the assessment of climate variability and long-term climate change in the North American monsoon region;
Strengthened multinational scientific collaboration across the Americas. 17

20. NAME AND GEWEX NAME is directly responsive to GEWEX Science Goals
NAME is included in the GEWEX/GAPP Science and Implementation plan, with emphasis on topographic influences on precipitation, hydrology and water resources, and land-surface memory processes.
NAME will contribute to GEWEX’s Cloud System Study (GCSS) by bringing a focus on the effects of complex coastal terrain on precipitating convection into the agenda of GCSS Working Group 4 (Precipitating Convective Cloud Systems).

22. INTERNATIONAL PARTNERSHIPS
SMN Meteorological Infrastructure
79 synoptic stations
16 radiosonde sites
60 automated weather stations (15 more in 2003)
12 radars (4 in northwestern Mexico)
Historical and real-time data
NAME Forecast Operations Center (meteorologists,technicians)
(2) Universities and Institutions in Mexico and Central America
(UNAM, IMTA, CICESE, University of Costa Rica, Univ. of Vera Cruz, Univ. of Guadalajara)
Equipment, personnel, transportation, data collection, research

23. NAME PROJECT STRUCTURE
3-Pronged
NAME Science Working Group (Science Focus)
VAMOS / NAME Project Office (Field Implementation, Data Management, Logistics)
NAME Forecast Operations Center
NAME International Project Support Team
NAME Program Management

24. NAME FIELD OBSERVATIONS (JJAS 2004)
R.V. Ron Brown
Enhanced Precipitation Gauge Network
Radiosondes/PIBALS
Radar/Profiling/Radiosondes

25. NAME FIELD NETWORKS (JJAS 2004)
Regional Networks (Tiers II and III; Integrated with Tier I Obs)
Radiosonde
PIBAL
Tier I Instrumentation (Focus of NSF facility request)
UHF wind profiler (1)
VISS (5) (SMN sounding+UHF-RASS profiler)
NCAR ISS (4) (UHF-RASS profiler+sounding +sfc)
SMN 5cm Doppler radars (4) (3 Enterprise; 1 Eriksson)
10cm Doppler-polarimetric radar (NCAR S-POL)
Ron Brown shipboard platform (VISS, 5cm Doppler radar, sfc fluxes)
NOAA/ETL shipboard flux system (on UNAM/PUMA)
Advanced Lightning Direction Finder (5)
Raingauges (100 event logging; 1600 simple)
Research Aircraft (NOAA P-3, NASA P-3B)
Soil Moisture Sensors 14

26. NAME FIELD OBSERVATIONS and RELATED DIAGNOSTICS
Bolded red – NSF Deployment Pool; bolded blue –other NSF support; black – NOAA OGP or NASA THP
Platform / Data
Instrumentation
Sponsor - Status
PI's
Integrated Sounding Systems /S-POL Radar
SMN Radars
Profilers, Soundings
Ship (see detail below)
4 Integrated Sounding Systems; S-POL Doppler Polarimetric radar;
Upgrade for 4 SMN 5-cm Doppler radars; 6 UHF profilers; RV Ron Brown
NCAR/ATD -
NSF Field Deployment –   
NOAA/ETL and/or AL -NOAA/OGP
- Proposed
R. Carbone (NCAR), R. Cifelli(CSU), R. Johnson (CSU), M. Moncrieff (NCAR), W. Petersen (UAH)
S. Rutledge (CSU)
C. Fairall (NOAA/ETL), K. Gage (NOAA/AL), G. Kiladis (NOAA/AL)
Lightning Detectors
5 sites (Mexico)
8 sites (US)
NSF/Hydrology and Physical Meteorology
Proposed
W. Petersen (UAH), P. Krider (UAZ), S. Rutledge (CSU) , R. Blakeslee (NASA), S. Goodman (NASA),
B. Maddox (UAZ)
Radiosondes (Mexico)
17 sites (up to 2x-4x daily)
NOAA/OGP - Proposed
M. Cortez (SMN),
A. Douglas(Creighton) , M. Cortez SMN
Pilot Balloons
6 US sites / 25 Mexican sites
M. Douglas (NSSL)
NOAA P-3Aircraft
Doppler radar, dropsondes,
Flight level inst.
NOAA - Proposed
M. Douglas (NOAA), W. Cotton (CSU)
D. Jorgensen (NOAA),
Surface Observations Network (Mexico)
Coop Raingauge Network
79 synoptic stations; 60 automated met stations; 12 radars; 17 radiosonde sites;
1600 simple gauges
SMN - N/A
N/A
Lobato/ Higgins
ASIS Buoys (Mexico)
1 site (central GOC)
CICESE - Funded
F. Torres (CICESE)
GPS (Suominet)
3 sites (NW Mexico)
A. Hahmann (UAZ) ,R. Kursinsky (UAZ)
C. Sosa (IMADES) , C. WattsIMADES
Hydrometeorology Network (Mexico, AZ) ;
Soil Moisture Field Campaign;
NASA P-3B Aircraft
Unified Geographic Network (Terrain, Hydrography, Landcover); Agromet stations; soil moisture sensors (Walnut Gulch,AZ Hermosillo, MX)
event logging raingauges- funded (OGP)
NASA THP 
D. Gochis (NCAR), J. Shuttleworth UAZ, C. Watts (IMADES), J. Garatuza ITSON,
T. Cavazos (CICESE)
D. Lettenmaier (UW), D. Lettenmaier (UW), T. Jackson

27. NAME TIER-I Tier I Issues and Opportunities (Carbone)
Scientific Objectives
Experiment Design and Observational Requirements
Observing clouds / storms (Rutledge)
Forcing and budgets (Johnson)
Simulation, Parameterization (Moncrieff)