South. America Level. Jet. field EXperiment
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1 South America Low Level Jet field EXperiment SALLJEX legacy and main findings
2 Outline SALLJ Scientific Goals and Specific Objectives SALLJEX Components - Participant Countries SALLJEX observations and data sets Main results
3 VAMOS/SALLJ Program Specific objectives obtain an improved description of the temporal and spatial structure of the SALLJ based on expanded monitoring activities and special field experiments, evaluate the veracity of numerical representation (forecasts and analyses) of SALLJ against special observations and, determine improvements of initial state representation and model parameterizations required to improve prediction.
4 Salljex: Partners and Field Components Participant countries: Argentina, Bolivia, Brazil, Chile, USA, Paraguay, Perú Main funding sources: NOAA/OGP and NSF (USA), FAPESP (Brazil), ANPCyT (Argentina) Upper-air network enhancement Daily precipitation network enhancement NOAA/P-3 Missions Modeling NOAA/OGP Educational and outreach Program: Teachers in the field
5 SALLJEX Timeline October November December January 1 15 February 1 15 March Basic observing period (Upper-air obs) Special Observing Period (Upper-air obs & P-3 Missions) Intensive Observing Periods (Upper-air obs) SALLJEX raingauge observating system
6 SALLJEX observational components: Upper air 4504 Pilot balloon observations in 26 stations 279 extra Radiosondes over Argentina, 200 over Bolivia and Paraguay and 120 over Brazil Approximately between 4 and 6 observers at each site ~140 extra observers
7 Wind Profiles and quantity of observations at some stations over SALLJEX area Mariscal Estigarribia Resistencia UTC times Santiago del Estero Córdoba Tostado Nicolini et al, 2004
8 PACS web page :// pacs/web web/html/salljex.html Average wind from pibals during SALLJEX experiment. These values were calculated during LLJ events
9 SALLJEX observational components: Raingauges 795 installed raingauges More than 1500 raingauges were made available
10 Evaluate the veracity of numerical representation (forecasts and analyses) of SALLJ related paterns against special observations
11 Observational components: NOAA P flight hours to cover 13 missions
12 The SALLJEX web page :// projects/salljex salljex/ Salljex publications CLIVAR/Exchanges SALLJEX Special Issue, March The South American Low Level Jet experiment,, Vera et al., BAMS 2006
13 SALLJEX components: Modeling Groups from Argentina, Brazil, Chile and US issued daily weather forecasts NOGAPS model Analysis University of Utah Model Analysis ETA/CPTEC 40 km res., 00 and 12 UTC FC ETA/CPTEC 20 km res., 12 UTC FC ETA/UMD 80 km res., 00 UTC FC LAHM/CIMA 60 km res., 00 UTC FC NCEP/AVN 100 km res., 00 UTC FC RAMS/UBA 20 km res., 00 UTC FC RAMS/USP 25 km res., 00 and 12 UTC FC U of Chile MM5 30 km res., 12 UTC FC The SALLJEX coordinated numerical model experiment was the starting point to the development of a regiontal super-ensemble
14 Integration of models: Concept of Super Model Ensemble Several models are available: global, (CPTEC, NCEP, ECMWF, UKMO, ) aprox. 12; Regional models in S. America: CPTEC (ETA,BRAMS), INMET (DWD regional model), MASTER (BRAMS), SIMEPAR (ARPS, BRAMS), UFRJ (MM5, WRF,RAMS), FURGS (BRAMS), EPAGRI (BRAMS), LNCC (ETA), CIMA/UBA (WRF), aprox. 26 models! Differences in physical processes parameterization, data assimilation, data source
15 Bias V-comp. - at Santa Cruz/Bolivia SALJJEX Mean Meridional Wind Component Bias up to 7days forecast MESMES is the optimal statistical combination of all available forecasts (near zero bias)
16 The reanalysis using SALLJEX data Herdies et al, J. Clim 2007 Local impact: Mean low level wind at 850 hpa
17 Mean low level wind at Santa Cruz
18 Global impact From Nogués Paegle and Saulo,, AGU meeting 2006
19 Impact on medium range forecasts with the Utah Global model From Paegle et al., 2007
20 MCS and LLJ during SALLJEX Nicolini et al., 2004
21 Summary Enhanced data sets: Upper air: soundings over regions where there was no possibility to describe vertical structure Precipitation: large area coverage. Good for calibration of derived precip data sets Enhanced re-analysis Recommendations for Long Term Monitoring can be done Modeling: Super-ensemble Hypothesis testing: many case studies Collaboration: valuable links between institutions and researchers Training, capacity building, outreach programs Enormous advantage: to have many people looking at the same phenomena by the same time!!!
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