Project

Latin American Observatory for Climate Events

Goal: Improve climate services in Latin America and the Caribbean

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Project log

Ángel G Muñoz
added a research item
Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth and more frequent travels. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection once they are back in the country if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, environmental suitability for presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. Here, we develop a subseasonal to seasonal monitoring and forecasting system for environmental suitability of transmission of Aedes-borne diseases for the US, Central America, the Caribbean and northern South America, using multiple calibrated ento-epidemiological models, climate models, and quality-controlled temperature observations. We show that the predictive skill of this new system is higher than that of any of the individual models, and illustrate how a combination of deterministic and probabilistic forecasts can inform key prevention and control strategies.
Ángel G Muñoz
added a research item
Canonical Correlation Analysis (CCA) is used to improve the skill of seasonal forecast in the Orinoquía Region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet-days are predicted, as rice yields simulated by a calibrated crop-model is better correlated with wet-days frequency than with precipitation amounts in June-August (JJA). Prediction of frequency of wet-days, using as predictors variables from the NCEP Climate Forecast System version 2 (CFSv2), results in forecast with higher skill than models predicting seasonal precipitation amounts. Using wet-days frequency as an alternative climate variable reveals that the distribution of daily rainfall is both more relevant for rice yield variability and more skillfully predicted than seasonal precipitation amounts. Forecast skill can also be improved by using the Climate Hazards Infrared Precipitation with Stations (CHIRPS) merged satellite-station JJA precipitation as predictand in a CCA model, especially if the predictor is CFSv2 vertically integrated meridional moisture flux (VQ). The probabilistic hindcast derived from the CCA model using CHIRPS as predictand, can successfully discriminate above-normal, normal and below-normal terciles of over 80% of the stations in the region. This is particularly relevant for stations that, due to discontinuity in their time series, are not included in station-only CCA models but are still in need of probabilistic seasonal forecast. Finally, CFSv2 VQ performs better than precipitation as predictor in CCA, which we attribute to CFSv2 being more internally consistent in regards to sea surface temperature (SST)-forced VQ variability than to SST-forced precipitation variability in the Orinoquía.
Ángel G Muñoz
added an update
New study led by Katia Fernandez shows increased predictive rainfall skill for the Colombian Orinoquia, with implications for agriculture in the region.
 
Ángel G Muñoz
added a research item
There exists a vast amount of interest by the academic, operational and applied communities in sub-seasonal pre- dictions in South America, as these predictions have the potential to help inform decisions in various sectors (e.g. water management, food production and tourism), partic- ularly because they bridge the gap left for several years between traditional weather forecasts and seasonal pre- dictions. This interest was substantially boosted after the initiation of the WWRP/WCRP sub-seasonal to seasonal (S2S) project in 2013, which made available in a coor- dinated way retrospective forecasts (hindcasts) and near real time (3-week delayed) forecasts from 11 contributing models through the S2S Database hosted at ECMWF (mir- rored at CMA), and with a subset available through the IRI Data Library.
Ángel G Muñoz
added a research item
During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistentMadden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but amodel output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions.
Ángel G Muñoz
added a research item
The Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to develop an operational air quality forecast system for the Metropolitan Area of Lima-Callao (MALC), Peru, that is affected by high particulate matter concentrations episodes. In this work, we describe the implementation of an operational air quality-forecasting platform to be used in the elaboration of public policies by decision makers, and as a research tool to evaluate the formation and transport of air pollutants in the MALC. To examine the skills of this new system, an air pollution event in April 2016 exhibiting unusually elevated PM2.5 concentrations was simulated and compared against in situ air quality measurements. In addition, a Model Output Statistic (MOS) algorithm has been developed to improve outputs of inhalable particulate matter (PM10) and fine particulate matter (PM2.5) from the WRF-Chem model. The obtained results showed that MOS increased the accuracy in terms of mean normalized bias for PM10 and PM2.5 from-43.1% and 71.3% to 3.1%, 7.3%, respectively. In addition, the mean normalized gross error for PM10 and PM2.5 were reduced from 48% and 92.3% to 13.4% and 10.1%, respectively. The WRF-Chem Model results showed an appropriate relationship between of temperature and relative humidity with observations during April 2016. Mean normalized bias for temperature and relative humidity were approximately-0.6% and 1.1% respectively. In addition, the mean normalized gross error for temperature and relative humidity were approximately 4.0% and 0.1% respectively. The results showed that this modelling system can be a useful tool for the analysis of air quality in MALC.
Ángel G Muñoz
added a research item
The Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to develop an operational air quality forecast system for the Metropolitan Area of Lima-Callao (MALC), Peru, that is affected by high particulate matter concentrations episodes. In this work, we describe the implementation of an operational air quality-forecasting platform to be used in the elaboration of public policies by decision makers, and as a research tool to evaluate the formation and transport of air pollutants in the MALC. To examine the skills of this new system, an air pollution event in April 2016 exhibiting unusually elevated PM2.5 concentrations was simulated and compared against in situ air quality measurements. In addition, a Model Output Statistic (MOS) algorithm has been developed to improve outputs of inhalable particulate matter (PM10) and fine particulate matter (PM2.5) from the WRF-Chem model. The obtained results showed that MOS increased the accuracy in terms of mean normalized bias for PM10 and PM2.5 from-43.1% and 71.3% to 3.1%, 7.3%, respectively. In addition, the mean normalized gross error for PM10 and PM2.5 were reduced from 48% and 92.3% to 13.4% and 10.1%, respectively. The WRF-Chem Model results showed an appropriate relationship between of temperature and relative humidity with observations during April 2016. Mean normalized bias for temperature and relative humidity were approximately-0.6% and 1.1% respectively. In addition, the mean normalized gross error for temperature and relative humidity were approximately 4.0% and 0.1% respectively. The results showed that this modelling system can be a useful tool for the analysis of air quality in MALC.
Ángel G Muñoz
added an update
In this new paper, we discuss some biases in the representation of the Caribbean Low-Level Jet (CLLJ) by low- and high-resolution models of the Geophysical Fluid Dynamics Laboratory (GFDL). Since the CLLJ is associated with key rainfall characteristics in Central America, northern South America and the Caribbean --the so-called Intra-Americas region, we also discuss biases in the modeled rainfall field.
We found that un-coupled models tend to outperform coupled ones in the Intra-Americas region, and discuss problems with the sea-surface temperature representation of those couple models and related physical mechanisms. In addition, we show that Model Output Statistics (MOS) dramatically improves forecast skill, the improvement being region-dependent. Furthermore, overall, no significant difference in skill is obtained when using low or high-resolution models.
 
Ángel G Muñoz
added 2 research items
Vector-borne diseases are highly climate sensitive and favourable climate conditions can trigger and amplify disease transmission. Warm temperatures increase virus replication rates and drive the development of juvenile mosquitoes, adult feeding and egg laying behaviour. Rainfall excess and deficit have similar outcomes in terms of mosquito proliferation, as containers such as domestic pots, tires, drums and tanks tend to create suitable breeding sites in both cases.
Dengue fever, a mosquito-borne arbovirus, is a major public health concern in Ecuador.In this study, we aimed to describe the spatial distribution of dengue risk and identify localsocial-ecological factors associated with an outbreak of dengue fever in the city of Guayaquil,Ecuador. We examined georeferenced dengue cases (n= 4248) and block-level census data variablesto identify social-ecological risk factors associated with the presence/absence and burden of denguein Guayaquil in 2012. Local Indicators of Spatial Association (LISA), specifically Anselin’s LocalMoran’s I, and Moran’s I tests were used to locate hotspots of dengue transmission, and multimodelselection was used to identify covariates associated with dengue presence and burden at the censusblock level. We identified significant dengue transmission hotspots near the North Central andSouthern portions of Guayaquil. Significant risk factors for presence of dengue included poorhousing conditions, access to paved roads, and receipt of remittances. Counterintuitive positivecorrelations with dengue presence were observed with several municipal services such as garbagecollection and access to piped water. Risk factors for increased burden of dengue included poorhousing conditions, garbage collection, receipt of remittances, and sharing a property with morethan one household. Social factors such as education and household demographics were negativelycorrelated with increased dengue burden. These findings elucidate underlying differences withdengue presence versus burden, and suggest that vulnerability and risk maps could be developed toinform dengue prevention and control; this is information that is also relevant for emerging epidemicsof chikungunya and Zika viruses.
Ángel G Muñoz
added an update
Ángel G Muñoz
added a research item
Venezuela possesses a very useful geographical location for doing Radioastronomy. Recently, the Venezuelan Government (via FIDETEL-Ministerio de Ciencia y Tecnología) has aproved to the Laboratorio de Astronomía y Física Teórica (LAFT) of La Universidad del Zulia (Venezuela) the adquisition of four 3 meter diameter parabolic dishes that will be set as a radio-interferometer receiver and that can be used for certain Radioastronomy purposes. The specifications of the instrument will be treated elsewhere (Muñoz and Hernández 2007). To this aim, as ussually, the first step is to characterize the losses due to the atmosphere, and their evolution over time. In previous works (Muñoz et al. 2004, Memoires of V RIAO/VIII OPTILAS, M10-5 Modelling Tropospheric Radio-Attenuation Parameters for Venezuela, 359; Muñoz et al. 2006, CIENCIA, Vol. 14, 4, 428) we have studied some relevant electromagnetic (e-m) attenuation parameters dueto hydrometeors and absortion gases in the lower atmosphere, focused in local telecommunication applications (surface e-m trajectories). In this work we extend our results to include the cenital and quasi-cenital e-m trajectories, characterizing thus the medium losses in the 0.4-4.0 GHz spectral window for several Venezuelan locations. We report refractivity values and their gradients, tropospheric indexes, extinction coefficients and the total rain attenuation for the whole territory under study.
Ángel G Muñoz
added a research item
The Latin American Observatory, or OLE2 (Muñoz et al., 2010), is an informal regional partnership started in 2008 with the aim of enhancing the collaboration between national weather services (NWS) and research and development institutes in Latin America. OLE2 provides scientific support, training, and additional (weather and) climate services to partner organizations in order to increase the local and regional efficiencies of environmental decision-making, especially in terms of risk management strategies and the establishment of early warning systems (García-Solera, 2012; Muñoz et al., 2012). Although regarded as a successful partnership, until now no formal evaluation has been performed on the Latin American Observatory as a network to enhance the provision of climate services (García-Solera, 2012). In this work the evaluation elements suggested by Vaughan & Dessai (2014) are used to diagnose the use of OLE2 products by its partners, the network structure and governance, communication methods, and the efficacy of its technology and knowledge transference. This study uses 28 online semi-structured surveys (a total of 14 countries participate in the partnership) and a case-by-case analysis of OLE2’s climate services reported in the literature. In the following pages the conclusions of this study are summarized.
Ángel G Muñoz
added a research item
La Cuenca del Lago de Maracaibo, ubicada en el occidente de Venezuela, es la zona con mayor descargas electro-atmosféricas a nivel mundial, superando los 200 rayos/km2 por año. Estos eventos son tan comunes que tienen nombre propio --los Relámpagos del Catatumbo--, estando asociados a un conjunto de tormentas que ocurre en promedio unas 260 noches al año, y que tienden a aparecer más frecuentemente cerca de la desembocadura del río Catatumbo, al sur del Lago de Maracaibo. Los rayos nube-tierra que ocurren aquí son una amenaza a la vida de los habitantes de la región, así como a la seguridad industrial de múltiples sectores socio-económicamente importantes en una de las regiones más activas de Venezuela en términos de consumo eléctrico, producción agropecuaria, y exploración y explotación petrolera y gasífera. Una serie de estudios, los Catatumbo Experiments (CatEx), que involucran campañas observacionales con globos cautivos, análisis de datos satelitales y de superficie, y el uso de modelos teóricos y computacionales, ha permitido identificar los agentes climáticos que controlan la variabilidad de rayos en el Norte de Sudamérica. El Niño-Oscilación del Sur es uno de esos agentes, cambiando los patrones de circulación atmosférica, la disponibilidad de humedad y ocurrencia de precipitaciones y rayos en la zona de interés; sin embargo, también contribuyen otros modos de variabilidad. En particular, interacciones entre las corrientes de viento en chorro a baja altura del Caribe y de la Cuenca del Lago de Maracaibo permiten explicar la variabilidad de rayos a múltiples escalas de tiempo. Con los resultados de CatEx nuestro equipo de trabajo ha generado los primeros modelos dinámicos-estadísticos de pronóstico climático (escala de 3 meses) para rayos, con una capacidad predictiva que supera la de la precipitación en la región. Un conjunto de nuevos servicios climáticos asociados a descargas eléctricas está disponible en la forma de un Sistema Integrado de Vigilancia y Pronóstico para la Cuenca del Lago de Maracaibo (SIVIGILA), que puede implementarse en algunos otros sitios del planeta.
Ángel G Muñoz
added a research item
During the austral summer 2015-16, severe flooding displaced over 170000 people on the Paraguay River system in Paraguay, Argentina, and Southern Brazil. These floods were driven by repeated heavy rainfall events in the Lower Paraguay River Basin. Alternating sequences of enhanced moisture inflow from the South American Low-Level Jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-timescale interactions of a very strong El Niño event, an unusually persistent Madden-Julian Oscillation in phases four and five, and the presence of a dipolar SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and sub-seasonal heavy rainfall predictions could have provided decision makers useful information about the start of these flooding events from at least two-to-four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December-February. Raw sub-seasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a Model Output Statistics approach involving principal component regression substantially improved the spatial distribution of skill for Week 3 relative to other methods tested including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of bias-corrected heavy precipitation seasonal and sub-seasonal forecasts, may help improve flood preparedness for the austral summer season in this part of the world.
Ángel G Muñoz
added 2 research items
Throughout at least the past several centuries, El Niño-Southern Oscillation (ENSO) has played a significant role in human response to climate. Over time, increased attention on ENSO has led to a better understanding of both the physical mechanisms, and the environmental and societal consequences of the phenomenon. The prospects for seasonal climate forecasting emerged from ENSO studies, and were first pursued in ENSO studies. In this paper, we review ENSO's impact on society, specifically with regard to agriculture, water, and health; we also explore the extent to which ENSO-related forecasts are used to inform decision making in these sectors. We find that there are significant differences in the uptake of forecasts across sectors, with the highest use in agriculture, intermediate use in water resources management, and the lowest in health. Forecast use is low in areas where ENSO linkages to climate are weak, but the strength of this linkage alone does not guarantee use. Moreover, the differential use of ENSO forecasts by sector shows the critical role of institutions that work at the boundary between science and society. In a long-term iterative process requiring continual maintenance, these organizations serve to enhance the salience, credibility, and legitimacy of forecasts and related climate services.
Se implementó el modelo de mesoescala Weather Research and Forecast (WRF) en la Universidad Nacional de Colombia de forma operativa para realizar pronósticos a 48 horas de precipitación, temperatura en superficie, viento en dirección e intensidad y radiación solar, con salidas cada 6 horas en un dominio de resolución horizontal de 24 Km que cubre el territorio colombiano. Se realizó su validación a través de los parámetros estadísticos RSME, MAE, BE y C a partir de los registros horarios de 5 estaciones para los días 28 de Junio y 12 de Julio de 2011, obteniendo buenos resultados para los ciclos diarios de todas las variables, pero con errores de sobreestimación y subestimación en la temperatura y la radiación solar. http://www.congremet.prmarg.org/upload/aragonginna.pdf
Ángel G Muñoz
added an update
In this paper we analyze predictive skill of rainfall characteristics for the May-June season ("Primera" crop season) in Central America, using a hybrid dynamical/statistical forecast approach.
The analysis suggests that transport of Convective Available Potential Energy tends to be a better predictor for rainfall characteristics (accumulation, frequency, and the 80th and 20th percentiles) than the actual rainfall model output. We suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.
 
Ángel G Muñoz
added a research item
This study explores the predictive skill of seasonal rainfall characteristics for the irst rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.
Ángel G Muñoz
added an update
We are about to submit a new paper on the American Monsoon Systems' onset, demise and duration.
 
Ángel G Muñoz
added a research item
The objective of this work is to evaluate the impact of vehicular emissions on the formation of fine particles (PM2.5; ≤2.5 μm in diameter) in the Sao Paulo Metropolitan Area (SPMA) in Brazil, where ethanol is used intensively as a fuel in road vehicles. . Weather Research and Forecasting with Chemistry (WRF–Chem) model is used as photochemical modeling tool to describe the physico–chemical processes leading to evolution of number and mass size distribution of particles through gas-to-particle conversion. A vehicular emission model based on statistical information of vehicular activity is applied to simulate vehicular emissions over the studied area. The study period during a month, between 7 August and 6 September 2012, is considered to perform the numerical simulations due to the availability of experimental data from the NUANCE–SPS (Narrowing the Uncertainties on Aerosol and Climate Changes in Sao Paulo State) project that aims to characterize emissions of atmospheric aerosols in the SPMA. Results show that the emission of primary gases from vehicles led to an increase between 20 and 30% due to new particles formation in relation to the total mass concentration of PM2.5 in the downtown SPMA. Dust and sea-salt aerosols contributed with 40–50% of the total PM10 (PM10; ≤10 μm in diameter) concentration. Furthermore, ground level O3 concentration decreased by about 2% when the aerosol–radiation feedback is taken into account. Over 40% of the formation of fine particles, by mass, was due to the emission of hydrocarbons, mainly aromatics. An increase in the number of small particles impaired the solar radiation and induced a decrease in ozone formation. Availability of experimental measurements of atmospheric aerosols and the application of the WRF–Chem model, which simulates feedbacks between meteorological variables and chemical species, made possible to represent some of the most important properties of fine particles in the SPMA such as the mass size distribution and chemical composition in addition to evaluate its formation potential through the gas–to–particle conversion processes.
Ángel G Muñoz
added an update
Our paper on a new experimental forecast system for Aedes-borne disease potential risk of transmission (via the basic reproduction number) has been published today in Frontiers: Infectious Diseases.
 
Ángel G Muñoz
added an update
New SPI forecasts are ready to be published in the Observatory's Datoteca. We're finishing up the texts explaining the new product.
Los nuevos pronósticos de SPI están listos para ser publicados en la Datoteca del Observatorio. Estamos terminando de escribir los textos explicativos de este nuevo producto.
 
Ángel G Muñoz
added an update
 
Ángel G Muñoz
added 18 research items
In Ecuador, forecasts of seasonal total rainfall could mitigate both flooding and drought disasters through warning systems if issued at useful lead time. In Ecuador, rainfall from December to April contributes most of the annual total, and it is crucial to agricultural and water management. This study examines the predictive skill for February April and December February seasonal rainfall totals using statistical and dynamical approaches. Fields of preceding observed sea surface temperature (SST) are used as predictors for a purely statistical prediction, and predictions of an atmospheric general circulation model (AGCM) are used as predictors with a model output statistics correction design using canonical correlation analysis. For both periods, results indicate considerable predictive skill in some, but not all, portions of the Andean and especially coastal regions. The skill of SST and AGCM predictors comes mainly through skillful rainfall anomaly forecasts during significant ENSO events. Atlantic Ocean SST plays a weaker predictive role. For the simultaneous diagnostic highest skill is obtained using the eastern Pacific Ocean domain, and for time-lagged forecasts highest scores are found using the global tropical ocean domain This finding suggests that, while eastern Pacific SST is what matters most to Ecuadorian rainfall, at sufficient lead time these local SSTs become most effectively predicted using basinwide ENSO predictors. In Ecuador's coastal region, and in some parts of the Andean highlands, skill levels are sufficient for warning systems to reduce economic losses associated with flood and drought. Accordingly, the Instituto Nacional Meteorologia e Hidrologia of Ecuador issues forecasts each month using methods described here-also implemented by countries of the Latin American Observatory partnership, among other South American organizations.
Given knowledge at the time, the recent 2015-2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction was for potential risk of an Aedes-borne disease epidemic. Here we use a recently published two-vector capacity model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower –but still of potential use to decision-makers– for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted in several zika hotspots. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.
Ángel G Muñoz
added an update
Project goal
Improve climate services in Latin America and the Caribbean
Background and motivation
http://ole2.org (Spanish)
 
Ángel G Muñoz
added 2 research items
The Observatorio Latinoamericano de Eventos Extraordinarios (OLE²), or Latin American Observatory, is a regional collaborative network that, in addition to the existing infrastructure in each country, aims to ultimately increase the efficiency of the decision-making processes, especially in terms of getting more accurate environmental information and exchanging experiences on data, methodologies and scientific products, all of which are done with a standardized methodology and a Web-sharing service.