**5. The Latin American Observatory: An operational research and forecast system**

To illustrate an operational research and forecast system which provides useful tools for decision-makers and stake-holders, in this section the *Latin America Observatory for Climate Events* structure will be discussed briefly. Its goals are similar to those of the Andean Observatory (Muñoz et al., 2010), but in this case the participation of all interested institutions in the Latin American countries is fully brought forth and supported. The idea is to facilitate scientific tools for the decision-makers, thus enabling the continuous interaction between research (universities and centers in the region) and operational activities (basically the National Weather Services and related institutions). The present coordinator of this project is the Centre for Scientific Modelling (Centro de Modelado Científico - CMC, in Spanish) at the University of Zulia, Venezuela.

The Observatory, known as OLE2, currently has got a number of methodologies:

• Dynamical Weather Forecast

At present, OLE2 offers 72-hour weather forecasts on a daily basis using the high resolution downscaling models MM5 (Michalakes, 2000) and WRF (Skamarock et al., 2005). The GFS (Kalnay et. al, 1990) 3-hourly outputs and assimilation of SYNOP, METAR and TEMP reports are used as initial conditions. Each country determines the best set of model parametrisations, typically running at resolutions of 30 km and higher. The model outputs are valuable for the forecasting processes in countries where the Andes Mountain Chain provides complex disturbances that frequently GFS and other global models cannot resolve.

• Dynamical Seasonal Forecast

The NCAR Community Atmospheric Model version 3.1 (CAM3) (Collins et al., 2006) has been configured at T42L26 resolution at CMC by the Atmospheric Model Intercomparison Project (AMIP); it runs through the Green House Gases (GHGs) with monthly variability from 1966 to present. The first 5 years have been discarded for spin-up reasons. The selected climatology corresponds to the 1971-2000 period.

The current seasonal forecast methodology is sketched in Figure 8. On a monthly basis, the CAM runs 6 ensemble members, where as tier-1: (a) two of them follow the persisted SST e-folding methodology (psst, see for example, (Li et al., 2008)), (b) two members use the SST forecast of the CFS model (cfssst, (Saha et al., 2006)), and (c) two realizations are

Fig. 8. Multi-parametric, multi-model ensemble employed for the seasonal forecast in the

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for each country, and then uploaded to the OLE<sup>2</sup> web portal for its publication after internal filters and discussion. Figure 2 depicts an OLE<sup>2</sup> seasonal precipitation anomaly map for South America, with the corresponding observed rainfall anomaly for comparison. These products are also used in each NWS for the generation of agricultural risk maps as

The Regional Oceanic Modeling System (ROMS-AGRIF, (Penven et al, 2007)) has been configured for a computational domain in the Eastern Pacific. At present, the boundary and initial conditions are provided by the ECCO Consortium (Estimating the Circulation and Climate of the Ocean, (Stammer et al., 1999)) and the GFS (Kalnay et. al, 1990). The OLE<sup>2</sup> runs daily ROMS for a 5-day high resolution (30 km) forecast, sharing in the web portal products like SST, surface salinity, vertical velocities (upwelling and downwelling) and marine currents. This kind of product is very useful as a basis for fishery maps,

This OLE2 component has been developed at CMC in collaboration with the *Comisión Permanente del Pacífico Sur* (CPPS) in order to set up the same methodology developed by the NWS for the Marine and Coastal Services of Colombia, Ecuador, Peru, and Chile.

The Dynamical Hydrological Forecast (Level III) process is carried out at OLE<sup>2</sup> by coupling the NOAH Land Surface Model (Schaake et al., 1996) with the Level II models, or directly

indicating locations of nutrient-rich areas due to upwelling processes.

Latin American Observatory.

well as products and tools for decision makers.

Multi-Scale Approach for Research and Decision-Making

• Oceanographic High Resolution Forecast

• Dynamical Hydrological Forecast

Fig. 7. SST behaviour for Jul 2009 (above) and Oct 2009 (below) using two CAM members for a cell located in the equator and the dateline (180W). The green lines corresponds to a 10−<sup>3</sup> K perturbation of the climatological value (in white). After three months the differences are notable.

obtained following the constructed analog (casst, (Van den Dool, 1994)) methodology. For all members the lead's monthly ice fraction coverage is described by the climatological values. For each member's output, the necessary initial and boundary conditions are extracted and written in the special (intermediate) format requested by the climatic versions of MM5 and WRF (CMM5 and CWRF, from now on), and are then available for the Andean NWSs through the OLE2 web portal (http://ole2.org), which has been totally built with Open Source resources by CMC developers.

Each NWS downloads the required files to execute the models in their own computational infrastructures and, since January 2010, using two different sets of physical parametrisations per model. Thus, a multi-parametric multi-model ensemble is produced 16 Numerical Simulations

Fig. 7. SST behaviour for Jul 2009 (above) and Oct 2009 (below) using two CAM members for a cell located in the equator and the dateline (180W). The green lines corresponds to a 10−<sup>3</sup> K perturbation of the climatological value (in white). After three months the differences are

obtained following the constructed analog (casst, (Van den Dool, 1994)) methodology. For all members the lead's monthly ice fraction coverage is described by the climatological values. For each member's output, the necessary initial and boundary conditions are extracted and written in the special (intermediate) format requested by the climatic versions of MM5 and WRF (CMM5 and CWRF, from now on), and are then available for the Andean NWSs through the OLE2 web portal (http://ole2.org), which has been

Each NWS downloads the required files to execute the models in their own computational infrastructures and, since January 2010, using two different sets of physical parametrisations per model. Thus, a multi-parametric multi-model ensemble is produced

totally built with Open Source resources by CMC developers.

notable.

Fig. 8. Multi-parametric, multi-model ensemble employed for the seasonal forecast in the Latin American Observatory.

for each country, and then uploaded to the OLE<sup>2</sup> web portal for its publication after internal filters and discussion. Figure 2 depicts an OLE<sup>2</sup> seasonal precipitation anomaly map for South America, with the corresponding observed rainfall anomaly for comparison. These products are also used in each NWS for the generation of agricultural risk maps as well as products and tools for decision makers.

• Oceanographic High Resolution Forecast

The Regional Oceanic Modeling System (ROMS-AGRIF, (Penven et al, 2007)) has been configured for a computational domain in the Eastern Pacific. At present, the boundary and initial conditions are provided by the ECCO Consortium (Estimating the Circulation and Climate of the Ocean, (Stammer et al., 1999)) and the GFS (Kalnay et. al, 1990). The OLE<sup>2</sup> runs daily ROMS for a 5-day high resolution (30 km) forecast, sharing in the web portal products like SST, surface salinity, vertical velocities (upwelling and downwelling) and marine currents. This kind of product is very useful as a basis for fishery maps, indicating locations of nutrient-rich areas due to upwelling processes.

This OLE2 component has been developed at CMC in collaboration with the *Comisión Permanente del Pacífico Sur* (CPPS) in order to set up the same methodology developed by the NWS for the Marine and Coastal Services of Colombia, Ecuador, Peru, and Chile.

• Dynamical Hydrological Forecast

The Dynamical Hydrological Forecast (Level III) process is carried out at OLE<sup>2</sup> by coupling the NOAH Land Surface Model (Schaake et al., 1996) with the Level II models, or directly

numerous time scales and integrate the physical equations resorting to new meshes and more powerful numerica1l schemes. With the help of such systems, climate science should advance

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Anderson, D. (2008). Overview of Seasonal Forecasting, in *Seasonal Climate: Forecasting and*

Adcroft, A.; Campin, J.M.; Dutkiewicz, S.; et al. (2011). MITGCM USer Manual. Available

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Blackmon, M. B., Boville, B., Bryan, F. et al. (2001) The Community Climate System Model.

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Liang, X., & Xie, Z. (2001) A new surface runoff parameterization with subgrid-scale soil

Marshall, J.; Adcroft, A.; Campin, J. M.; Hill, C. & White, A. (2004). Atmosphere Ocean Modeling Exploiting Fluid Isomorphisms. *Monthly Weather Review*. 132, 2882-2894. Mason, S. (2008). From Dynamical Model Predictions to Seasonal Climate Forecasts, in

Mason, S. and O. Baddour (2008). Statistical Modelling, in *Seasonal Climate: Forecasting and*

MacDonald, G. (1957) The Epidemiology and Control of Malaria. London, U.K. Oxford

Muoz, . G., Lpez, P., Velsquez, R., et al., 2010: An Environmental Watch System for the Andes Countries: El Observatorio Andino. *BAMS*, 91, 1645?1652.

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Michalakes, J. (2000) The same-source parallel MM5. in *Sci. Comput.*, 8, 5?12

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significantly over the next few years.

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**7. References**

using the latter's forecast precipitation, temperatures and wind outputs into the Variable Infiltration Capacity (VIC) Model of (Liang & Xie, 2001). VIC is a macroscale (typical cell resolution > 1 km), semi-distributed hydrologic model that solves full water and energy balances. At OLE<sup>2</sup> the VIC is specifically configured for each basin of interest (the resolution depends on the selected basin) with the corresponding soil and vegetation type data.

For both procedures (coupled LSMs or uncoupled VIC Model), a bias correcting calibration procedure is applied to the raw output using historical, local streamflow data as reference. After the calibration stage, the final outputs can be considered as a main tool for the corresponding Early Warning System in the countries involved.

• Other Applications

Other applications include products related with droughts, floods, fires and ecosystem dynamics. In the case of droughts (Palmer, 1968), indices are employed, while a composite map between runoff and hydrologic capacity of model cells are used to forecast possible floods. Likewise, the (Chandler et al., 1983) index is utilised as a measure of joint probability of fire occurrence and propagation.

Climate and Health applications are focused mainly on malaria seasonal predictability for northwestern South America using the model studied in (MacDonald, 1957). Given the necessary entomological and epidemiological parameters, the high resolution output at OLE<sup>2</sup> supplies the climate information for running this epidemiological tool.

Finally, a new framework is related to Ecosystem Dynamics, especially Lemna (duckweed) population dynamics. In 2004 an important duckweed bloom took place in Maracaibo Lake (Tapias, 2010), the South American largest lake, bringing economic (e.g. fisheries) and health related (e.g. necrotic Lemna at lake shores produce an increase of diseases) problems to human populations in those coastal zones. Recently, the CMC provided an application known as CAVEL ((Tapias, 2010)) that makes use of MODIS VIS and IR data (Barnes et al., 2002 ) for providing normalized vegetation index (NDVI) maps, and time series of total surface coverage.
