Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Polarimetric X-band weather radar measurements in the tropics: radome and rain attenuation correction

A polarimetric X-band radar has been deployed during one month (April 2011) for a field campaign in Fortaleza, Brazil, together with three additional laser disdrometers. The disdrometers are capable of measuring the raindrop size distributions (DSDs), hence making it possible to forward-model theoretical polarimetric X-band radar observables at the point where the instruments are located. This setup allows to thoroughly test the accuracy of the X-band radar measurements as well as the algorithms that are used to correct the radar data for radome and rain attenuation. For the campaign in Fortaleza it was found that radome attenuation dominantly affects the measurements. With an algorithm that is based on the self-consistency of the polarimetric observables, the radome induced reflectivity offset was estimated. Offset corrected measurements were then further corrected for rain attenuation with two different schemes. The performance of the post-processing steps was analyzed by comparing the data with disdrometer-inferred polarimetric variables that were measured at a distance of 20 km from the radar. Radome attenuation reached values up to 14 dB which was found to be consistent with an empirical radome attenuation vs. rain intensity relation that was previously developed for the same radar type. In contrast to previous work, our results suggest that radome attenuation should be estimated individually for every view direction of the radar in order to obtain homogenous reflectivity fields.

Air quality simulations for Londrina region, Brazil

The objective of this work were apply and provide a preliminary evaluation of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) performance, for Londrina region. We performed comparison with measurements obtained in meteorological stations. The model was configured to run with three domains with 27,9 and 3 km of grid resolution, using the ndown program and also was realized a simulation with the model configured to run with a single domain using a land use file based in a classified image for region of MODIS sensor. The emission files to supply the chemistry run were generated based in the work of Martins et al., 2012. RADM2 chemical mechanism and MADE/SORGAM modal aerosol models were used in the simulations. The results demonstrated that model was able to represent coherently the formation and dispersion of the pollution in Metropolitan Region of Londrina and also the importance of using the appropriate land use file for the region.

The impact of different surface parameterizations and vertical resolutions on the simulation of seabreeze episodes at the island of Fuerteventura

[EN] On 8-10 April 2007, several episodes of intense sea-breeze fronts were registered at the island of Fuerteventura (Canary Islands). The sea-breeze circulation was primary driven by daytime heating contrasts between land and the Atlantic Ocean during a period of weak trade winds. Numerical simulations of these events were carried out using the 3.1.1 version of the Weather Research and Forecasting (WRF) Model. Two different domains with 6.6-km and 2.2-km horizontal grid spacing and two sets with 27 and 51 vertical sigma levels were defined. The simulation was performed using two-way interactive nesting between the first and the second domain, using different land surface model parameterizations (Thermal diffusion, Noah LSM and RUC) for comparison. Initial conditions were provided by the NCAR Dataset analysis from April 2007, which were improved using surface and upper-air observations. The poster is focused on the 9 April episode.

Local flow under weak trade wind regime

[EN] On 8-10 April 2007, several episodes of intense sea-breeze fronts were registered at the islands of Fuerteventura and Lanzarote (Canary Islands). The sea-breeze circulation was primary driven by daytime heating contrasts between land and the Atlantic Ocean during a period of weak trade winds. Numerical simulations of these events were carried out using the 3.1.1 version of the Weather Research and Forecasting (WRF-ARW) Model. Three different domains with 6.6-km, 2.2-km and 0.7-km horizontal grid spacing and two sets with 51 and 70 vertical sigma levels were defined. The simulation was performed using two-way interactive nesting between the first and the second domain, using different land surface model parameterizations (Thermal diffusion, Noah LSM and RUC) for comparison. Initial conditions were provided by the NCAR Dataset analysis from April 2007, which were improved using surface and upper-air observations. The poster is focused on the 10 April episode.

Firsts results on the modelling of the Gran Canaria atmospheric wake

[EN] This poster shows the first attempt to modelize the Gran Canaria Island wake, an obstacle with almost a conical shape (60 km diameter and about 2000 m height). The leeside circulation was modelized for two well-defined street vortex cases during June 2010 and March 2011. Numerical simulations of these events were carried out using the 3.1.1 version of the Weather Research and Forecasting (WRF-ARW) Model. Three different domains with 4.5-km, 1.5-km and 0.5-km horizontal grid spacing and 70 vertical sigma levels were defined. The simulations were performed using two-way interactive nesting between the first and the second and third domains, using different land surface model parameterizations (Thermal diffusion, Noah LSM and RUC) for comparison. Initial conditions were provided by the NCAR Dataset analysis from April 2007. The poster is focused on both episodes using NoahLSM parameterizations.

Forecasting the need for medical specialists in Spain: application of a system dynamics model

[EN] Background: Spain has gone from a surplus to a shortage of medical doctors in very few years. Medium and long-term planning for health professionals has become a high priority for health authorities. Methods: We created a supply and demand/need simulation model for 43 medical specialties using system dynamics. The model includes demographic, education and labour market variables. Several scenarios were defined. Variables controllable by health planners can be set as parameters to simulate different scenarios. The model calculates the supply and the deficit or surplus. Experts set the ratio of specialists needed per 1000 inhabitants with a Delphi method. Results: In the scenario of the baseline model with moderate population growth, the deficit of medical specialists will grow from 2% at present (2800 specialists) to 14.3% in 2025 (almost 21 000). The specialties with the greatest medium-term shortages are Anesthesiology, Orthopedic and Traumatic Surgery, Pediatric Surgery, Plastic Aesthetic and Reparatory Surgery, Family and Community Medicine, Pediatrics, Radiology, and Urology. Conclusions: The model suggests the need to increase the number of students admitted to medical school. Training itineraries should be redesigned to facilitate mobility among specialties. In the meantime, the need to make more flexible the supply in the short term is being filled by the immigration of physicians from new members of the European Union and from Latin America.

Wildfire prevention and management in a 3D virtual environment

[EN] This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A new open source framework is presented for the development of 3D graphics applications over large geographic areas offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfire event. The user is enabled to simulate and visualize a wildfire spreading on the terrain under conditions of spatial information on topography and fuels along with weather and wind files.

Assimilation of Meteosat Second Generation (MSG) satellite data in a regional numerical weather prediction model using a one-dimensional variational approach

The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.

Forecasting and visualization of wildfires in a 3D geographical information system

[EN]This paper describes a wildfi re forecasting application based on a 3D virtual environment and a fi re simulation engine. A novel open source framework is presented for the development of 3D graphics applications over large geographic areas, off ering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfi re event.

Ensemble forecasting in the Mediterranean sea

A new methodology is being devised for ensemble ocean forecasting using distributions of the surface wind field derived from a Bayesian Hierarchical Model (BHM). The ocean members are forced with samples from the posterior distribution of the wind during the assimilation of satellite and in-situ ocean data. The initial condition perturbations are then consistent with the best available knowledge of the ocean state at the beginning of the forecast and amplify the ocean response to uncertainty only in the forcing. The ECMWF Ensemble Prediction System (EPS) surface winds are also used to generate a reference ocean ensemble to evaluate the performance of the BHM method that proves to be eective in concentrating the forecast uncertainty at the ocean meso-scale. An height month experiment of weekly BHM ensemble forecasts was performed in the framework of the operational Mediterranean Forecasting System. The statistical properties of the ensemble are compared with model errors throughout the seasonal cycle proving the existence of a strong relationship between forecast uncertainties due to atmospheric forcing and the seasonal cycle.

Wind forecasting over complex terrain

[EN]In this paper we introduce a new methodology for wind field forecasting over complex terrain. The idea is to use the predictions of the HARMONIE mesoscale model as the input data for an adaptive finite element mass consistent wind model [1, 2]. A description of the HARMONIE Non-Hydrostatic Dynamics can be found in [3]. The HARMONIE results (obtained with a maximum resolution about 1 Km) are refined in a local scale (about a few meters)...