An Environmental Watch System for the Andean Countries

Muñoz, Ángel G.; López, Patricio; Velásquez, Ramón; Monterrey, Luis; Leon, Gloria R.; Ruiz, Franklyn; Recalde, Cristina; Cadena, Jaime; Méjía, Raúl; Paredes, Marco; Bazo, Juan; Reyes, Carmen Elena Salinas; Carrasco, Gualberto; Castellón, Yaruska; Villarroel, Cynthia; Quintana, Juan Carlos López; Urdaneta, Avel

doi:10.1175/2010bams2958.1

Cited by 29 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SL schemes discussed in Skamarock et al () are NCAR Mesoscale Model Version 5 = MM5, which has to be combined with Yonsei University (Korea) = YSU (Hong et al , ) as a PBL scheme; Eta, which has to be combined with Mellor‐Yamada‐Janjic = MYJ as a PBL scheme; and Similarity theory = PX (Pleim, ). Two SF schemes were chosen for parameterization: NOAH LSM and Urban canopy = Noah (Chen and Dudhia, ; Kusaka et al , ), which is the most complex and complete scheme, and also the simple thermal scheme that was already used in Ecuadorian regions (Muñoz et al, ). PBL schemes recommended are: YSU, MYJ and Asymmetrical Convective Model version 2 = ACM2 (Pleim, ) taking into account that YSU has to be combined with MM5 and MYJ with Eta scheme; thus, ACM2 is combined with PX.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study

Ochoa

Campozano

Sánchez

et al. 2015

Intl Journal of Climatology

View full text Add to dashboard Cite

The downscaling of global climate models (GCMs) aims at incorporating finer scale information to their horizontal resolution in order to represent regional and local processes better. There are two main approaches to downscaling: statistical (based on data relationships between synoptic atmospheric variables and observations of local variables) and dynamical (based on the modelling of regional atmospheric processes and land–surface interactions). In this study, some predictive capabilities regarding the generation of station‐scale mean monthly temperature and rainfall of both a statistical artificial neural network (ANN‐based) and a dynamical weather research and forecasting (WRF‐based) downscaling approach are assessed. We have devised two versions of the statistical downscaling approach. One of them includes regional orographic variables as predictors to allow for spatial extrapolation; the other is purely local. Historical observational data, from the period 1990 to 1999, of two major watersheds in the Ecuadorian Southern Andes, the Jubones and Paute river basins, were used. Since, to a certain extent, the value added by downscaling techniques can be attributed to terrain information, it is worth noting that some characteristics of the selected catchments (as notorious altitude differences and the presence of qualitatively different precipitation regimes) provide a scientifically interesting location for exploring how finer scale effects are captured. For this reason, we concentrate on the ability of downscaling techniques to reproduce seasonality. A decade of evaluation proved that both approaches were able to qualitatively describe precipitation and temperature seasonal variations for different regimes at representative weather stations. Furthermore, the seasonality of precipitation represented by both downscaling approaches surpassed the seasonality representation of reanalysis data. However, shortcomings on the estimates were found. Specifically, dynamical downscaled precipitation estimates were prone to overestimation. Despite the fact that the considered downscaling approaches are different in nature, their ability to represent the high spatio‐temporal variability in this region highlights the importance of evaluating their strengths and limitations.

show abstract

Section: Methodsmentioning

confidence: 99%

Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study

Ochoa

Campozano

Sánchez

et al. 2015

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…The Another Assimilation System for WRF-Chem (AAS4WRF) chemical emissions pre-processor developed by the Latin American Observatory (OLE2; Muñoz et al, 2010Muñoz et al, , 2012) was used to scale emission rates on WRF curvilinear coordinates. AAS4WRF is appropriate to write chemical emission rates from both surface and elevated sources in the proper WRF data file format, providing an alternative tailored way to assimilate emissions to WRF-Chem.…”

Section: Anthropogenic Emissionsmentioning

confidence: 99%

Impact of vehicular emissions on the formation of fine particles in the Sao Paulo Metropolitan Area: a numerical study with the WRF-Chem model

et al. 2016

View full text Add to dashboard Cite

Abstract. 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. The Weather Research and Forecasting with Chemistry (WRF-Chem) model, which simulates feedbacks between meteorological variables and chemical species, is used as a photochemical modelling tool to describe the physico-chemical processes leading to the 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 simulation has been performed for a 1-month period (7 August–6 September 2012) to cover 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. The availability of experimental measurements of atmospheric aerosols and the application of the WRF-Chem model made it possible to represent some of the most important properties of fine particles in the SPMA such as the mass size distribution and chemical composition, besides allowing us to evaluate its formation potential through the gas-to-particle conversion processes. Results show that the emission of primary gases, mostly from vehicles, led to a production of secondary particles between 20 and 30 % in relation to the total mass concentration of PM2.5 in the downtown SPMA. Each of PM2.5 and primary natural aerosol (dust and sea salt) contributed with 40–50 % of the total PM10 (i.e. those ≤  10 µm in diameter) concentration. Over 40 % of the formation of fine particles, by mass, was due to the emission of hydrocarbons, mainly aromatics. Furthermore, an increase in the number of small particles impaired the ultraviolet radiation and induced a decrease in ozone formation. The ground-level O3 concentration decreased by about 2 % when the aerosol-radiation feedback is taken into account.

show abstract

“…In Ecuador, satellite products have been used as an important source for climate prediction (Muñoz et al, 2010). However, in * Corresponding author order to use these products to its fullest potential, it is imperative to assess it.…”

Section: Introduction*mentioning

confidence: 99%

Validation of Satellite Precipitation (Trmm 3b43) in Ecuadorian Coastal Plains, Andean Highlands and Amazonian Rainforest

Ballari

Castro

Campozano

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission VI, WG VIII/4KEY WORDS: TRMM, validation, statistical error metrics, detection indexes, Ecuador, ungagged watershed basins ABSTRACT:Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 -2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

show abstract

An Environmental Watch System for the Andean Countries

Cited by 29 publications

References 14 publications

Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study

Evaluation of downscaled estimates of monthly temperature and precipitation for a Southern Ecuador case study

Impact of vehicular emissions on the formation of fine particles in the Sao Paulo Metropolitan Area: a numerical study with the WRF-Chem model

Validation of Satellite Precipitation (Trmm 3b43) in Ecuadorian Coastal Plains, Andean Highlands and Amazonian Rainforest

Contact Info

Product

Resources

About