deletions | additions       diff --git a/untitled.tex b/untitled.tex  index a2e0039..0ea2a7b 100644  --- a/untitled.tex  +++ b/untitled.tex 
...
\textit{Oh, an empty article!} \textbf{Abstract}  You can get started by \textbf{double clicking} this text block and begin editing. You can \section{Introduction}  \subsection{Bushfire history and impacts}  \subsection{Environmental conditions}  \subsection{ENSO impacts in Ecuador}  \section{Data and methods}  \subsection{Data}  \subsubsection{Weather station}  This study required weather station data from the Andean region in Ecuador. The Andean region or 'la Sierra' is where most of the introduced eucalyptus forest exists \cite{Anchaluisa2013}. Thus, the availability of fuels makes la Sierra the most wildfire-prone region. A limitation with this dataset was that only six stations had enough records for this study. The selected period of analysis was from 1997-2012. This period had almost complete daily information. Additionally, this data was not homogenized. The variables for this research are air temperature, relative humidity, wind speed and precipitation. Each of the stations provided data on an eight-hourly basis. The hour selected for the computations was 1 p.m. local time (GMT 18:00). Table 1 describes each of the stations.  \subsubsection{Reanalysis}  This research also used reanalysis data from the Twentieth Century Reanalysis Project (20CR). The 20CR relies on a data assimilation algorithm. Surface pressure and sea ice extent are inputs to represent the state of the atmosphere. This is the best possible representation based on an ensemble mean. This dataset produces six-hourly data on 24 vertical levels. The 1000 hPa-level data is the first vertical level. Thus, it is the most likely to represent surface values. The hour selected for calculations was 4 p.m. local time (GMT 06:00). This is the time closest to the hour adopted for most wildfire potential computations 3 p.m. The 20CR has data with a resolution of 2o x 2o. Thus, the extracted data is a grid in the Ecuadorian Andean region (include coordinates).   This dataset  also click the \textbf{Insert} button below to add new block elements. Or you can \textbf{drag and drop presents limitations common to all reanalysis products. There is greater uncertainty in regions were there are less observations. This is a particular problem in Africa, some regions in Asia and South America. There is less reliability in the first half of the century due to lack of observations. Although there have been studies that reflect this data represents well climate variability. Yet, studies for South America are not available. Additionally, some variables are better represented than others. Temperature and relative humidity, are better represented than wind speed and precipitation.   \subsubsection{ENSO Indices}  A common ENSO metric is the Southern Oscillation Index (SOI). The SOI represents the difference in normalized surface pressure between Taihiti and Darwin, Australia. The index reflects the changes in air-mass fluxes in the tropical Pacific Ocean. This fluxes, are a manifestation of changes in sea surface temperature (SST). The SOI has an open ended scale with positive and negative values. A negative SOI value represents above-average warm SST conditions the eastern tropical Pacific Ocean. This represents an 'El Niño' phase of the phenomenon. El Niño brings wet conditions to the coasts of Southern Ecuador and Northen Peru. The opposite pattern occurs with a positive SOI value, representing a 'La Niña' phase. This index does not establish thresholds to classify the intensity of ENSO events.   Another ENSO index is the Oceanic Niño Index (ONI). The ONI measures monthly SST values in the tropical Pacific Ocean. The measurements take place in the region from 5 oN to 5 oS latitude and 120 oW to 170 oW longitude. This is the 'Niño 3.4' region. The index requires the calculation of 3-month running means and its climatology. The ONI is the difference between these two values. An 'El Niño' condition exits if there is at least an anomaly of +0.5 oC in this computation. An anomaly of -0.5 oC indicates a 'La Niña' phase.   \subsection{Methods}  \subsubsection{Data Pre-processing}  Weather station data Pre-processing involved several steps. The first step was 'cleaning' the raw data. The original data presentation was not designed for scientific analysis. Thus,formating the original data was a necessary step for the later analysis.   The computation of wildfire potential usually requires continuous daily data. The required variables are temperature, relative humidity, wind speed and precipitation. Thus, only six stations in the Ecuadroian Andean region were suitable for this study. The selected stations are representative of the ecosystems that are prone to wildfire. The available records in these stations spanned the period 1997-2012. Yet, the period of analysis is short. Reanalysis data was bias-corrected to extend the availability of records.   The bias-correction was necessary for several reasons. First, 1000 h-hPa reanalysis data does not represent surface level values. There was a 3-hour difference between the time of the two dataset's values . Reanalysis data in the highlands may differ in quality to those at sea-level. Thus, we plotted probability density functions (PDFs) of the reanalysis and weather station datasets. This allowed to determine the differences between their data distributions. Applying linear scaling techniques was a suitable approach to bias-correct the required variables.   The aim of the study was to estimate wildfire potential for the entire country. Thus, a calculation of daily averages for each variable yielded regional values. With the reanalysis dataset the procedure is straightforward. These data is spatially and temporally continuous. However, the weather stations data had some missing days. Therefore, calculating averages over daily anomalies made the result less sensitive to missing data.   \subsubsection{Forest Fire Danger Index (FFDI) calculation}  Ecuador currently does not have any scientifically designed wildfire potential index. Therefore, an alternative approach was to use  an image} right onto international index suitable for this region. The McArthir Forest Fire Danger Index is commonly used in Australia. This index uses daily values of maximum temperature, relative humidity and wind speed. The index also incorporates a drought factor. This factor represents moisture in soil. This is indirect measure of the wildfire fuel availability. This empirical index is valid for the Southeastern forest ecosystem of Australia. In this wildfire-prone ecosystem, the eucalyptus is the dominant vegetation. Latin-America experienced a massive introduction of  this text. Happy writing! specie. This occurred for commercial purposes in the early years of the 19th century. Thus, is reasonable to expect that this index may represent the Ecuadorian wildfire potential   \subsubsection{Investigation of ENSO influence over wildfire seasons}    \section{Results}  \section{Discussion}  \section{Concluding remarks}