From b5b404391c28cd26e51a3a2ec625fa874b1e8aa5 Mon Sep 17 00:00:00 2001 From: mdfrias Date: Mon, 6 Mar 2017 18:35:27 +0100 Subject: [PATCH] Remove dir inst temporally --- inst/doc/visualizeR_vignette.html | 288 ------------------------------ 1 file changed, 288 deletions(-) delete mode 100644 inst/doc/visualizeR_vignette.html diff --git a/inst/doc/visualizeR_vignette.html b/inst/doc/visualizeR_vignette.html deleted file mode 100644 index d726aec..0000000 --- a/inst/doc/visualizeR_vignette.html +++ /dev/null @@ -1,288 +0,0 @@ - - - - - - - - - - - - - -visualizeR. Visualizing and Communicating Uncertainty in Seasonal Climate Prediction - - - - - - - - - - - - - - - - - -

visualizeR. Visualizing and Communicating Uncertainty in Seasonal Climate Prediction

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Santander MetGroup

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2017-02-28

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1 Introduction

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Unlike deterministic weather forecasts (e.g. “tomorrow it will rain 15 mm in Madrid”), - probabilistic seasonal forecasts provide, a few months in advance, -information on how seasonally averaged weather is likely to evolve (e.g. - “there is an 80% chance that the next season will be wetter than usual in central Spain”). - Seasonal forecasting is a problem of probabilistic nature. Ensemble -forecasting is used to sample different realizations (members) produced -from slightly different initial conditions. The resulting ensemble -allows estimating the likelihood of different events/indices related to -the seasonal average weather (e.g. “being wetter than usual”). -Despite their huge potential value for many sectors, there are still a -number of problems which limit the practical application of these type -of predictions, being the probabilistic communication of uncertainty of -central importance.

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visualizeR is an R package implementing a set of -advanced tools for probabilistic forecast visualization and validation. -It allows visualizing the predictions together with the corresponding -validation results in a form suitable to communicate the underlying -uncertainties, both from the prediction itself and from the skill of the - model. Its aim is to translate the probabilistic seasonal predictions -in comprehensible, actionable information for end-users in different -fields of application.

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2 Data load and collocation

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library(visualizeR)
-library(transformeR)
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First of all, the datasets required to produce the example visualizations are loaded. These datasets are included in visualizeR, so they can be automatically loaded. The datasets are:

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  1. The reference historical observations of global mean temperature. The historical records come from the NCEP-NCAR reanalysis (tas.ncep).
    2. -
  2. The re-forecast data (or historical hindcast), -corresponding to the predictions of the model done in the past, over a -period of several decades. In this case, these are winters from 1983 to -2010 (tas.cfs).
    3. -
  3. The operative predictions issued by the model on november 2015 for winter 2016 (tas.cfs.operative.2016).
    4. -
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# Load reanalysis
-data(tas.ncep)
-# Load hindcast
-data(tas.cfs)
-# Load operative predictions
-data(tas.cfs.operative.2016)
-
-

2.1 Adjusting the temporal extent of hindcast and reanalysis

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It is necessary to adjust the length of the hindcast and the -reanalysis data, given that the latter has a more extended period. To -this aim, we first extract the years encompassed by the hindcast:

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(years <- unique(getYearsAsINDEX(tas.cfs)))
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##  [1] 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
-## [15] 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
-

Next, the reanalysis period is “trimmed” to match the hindcast length:

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obs <- subsetGrid(tas.ncep, years = years)
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Finally, for clarity, the hindcast and the operative predictions dataset are renamed as hindcast and forecast respectively:

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hindcast <- tas.cfs
-forecast <- tas.cfs.operative.2016
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2.2 Adjusting the spatial resolution of hindcast, reanalysis and predictions

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The function interpGrid in package transformeR - allows to change from one grid to another (re-gridding). In this case, -we are re-gridding the model data (hindcast and operative predictions) -to match the reanalysis grid (this is easily achieved using the getGrid - command). The chosen method is bilinear interpolation. We are also -using the parallelization option to speed-up the interpolation (Note -that this is not available under Windows or in single-core machines):

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# grid definition as a names list with 'x' and 'y' components
-newgrid <- list(x = c(getGrid(hindcast)$x, 5), y = c(getGrid(hindcast)$y, 5))
-hindcast <- interpGrid(hindcast, new.coordinates = getGrid(obs), method = "bilinear", 
-    bilin.method = "fields", parallel = TRUE)
-forecast <- interpGrid(forecast, new.coordinates = getGrid(obs), method = "bilinear", 
-    bilin.method = "fields", parallel = TRUE)
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3 Spatial Visualization of the verification

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In order to have a spatial overview of the skill of the predictions -globally, bubble plots are particularly effective. Bubble plots combine -in a single map several aspects of the quality of the forecasting -systems by means of three graphical features: bubble color (hue), bubble - size (area) and brightness. These aspects can be combined in different -ways in order to provide different levels of information. In the -following examples different options are of forecast quality -visualization are illustrated.

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# This is a subtitle that will be added to each plot
-subtitle <- sprintf("Reference data: NCEP Reanalysis;  Hindcast: CFSv2 (%d members); %d-%d", 
-    getShape(hindcast, dimension = "member"), getYearsAsINDEX(hindcast)[1], 
-    tail(getYearsAsINDEX(hindcast), 1))
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In this basic example, the most likely tercile (i.e., the one with -the highest probability, or in other words, the one predicted by the -highest number of ensemble members) is indicated by the color of the -bubbles. All bubles have equal size.

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bubblePlot(hindcast = hindcast, obs = obs, forecast = forecast, size.as.probability = FALSE, 
-    bubble.size = 1.5, score = FALSE, subtitle = subtitle)
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In the next example, in addition to the most likely tercile, also the - probability of that tercile is indicated. In this case, the sizes of -the bubbles are relative to the probability, so the largest the bubble -size, the highest the probability for that tercile.

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bubblePlot(hindcast, obs = obs, forecast = forecast, size.as.probability = TRUE, 
-    bubble.size = 1.5, score = FALSE, subtitle = subtitle)
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In the next variant of the bubble plot, we set the argument score = TRUE. - In this case, the ROC Skill Score (ROCSS) is computed considering the -hindcast period. For each tercile, it provides a quantitative measure of - the forecast skill, being a commonly used metric to evaluate the -performance of probabilistic systems. The value of this score ranges -from 1 (perfect forecast system) to -1 (perfectly bad forecast system). A - value zero indicates no skill compared with a random prediction. The -transparency of the bubbles is associated to the ROCSS, so the more -transparent the color is, lower is the ROCSS. By default only positive -ROCSS values are plotted when the score argument is set to TRUE.

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bubblePlot(hindcast, obs, forecast = forecast, size.as.probability = TRUE, bubble.size = 1.5, 
-    score = TRUE, subtitle = subtitle)
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The score.range argument is useful in order to mask from - the map all information deemed non-relevant or visually distracting -depending on each user application. This is a vector of length two used -to rescale the transparency of the bubbles. For instance, a score.range = c(0.5, 0.8) - will turn ROCSS values below 0.5 completely transparent, while values -of 0.8 or more have minimum transparency, i.e., opaque). The default is NULL, that will set a transparency range between 0 and 1, as in the previous plot.

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bubblePlot(hindcast, obs, forecast = forecast, size.as.probability = TRUE, bubble.size = 1.5, 
-    score = TRUE, score.range = c(0.5, 1), subtitle = subtitle)
-

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Another variant of bubble plots consists in replacing the bubbles by -pie charts. Each of the three sectors of the pie-chart provide a -quantitative measurement of the numbers of members falling in each -tercile. The terciles are identified by colors (i.e., red upper, grey -middle, blue lower tercile). The main advantage of this display is that -it allows for the visualization of all terciles simultaneously, instead -of just the most likely one, as with the ordinary bubbles. Furthermore, -as in the previous examples, the ROCSS can be equally represented by -transparency, and masking is also possible using the score.range argument, as before. This is represented in the next plot:

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bubblePlot(hindcast, obs, forecast = forecast, piechart = TRUE, score = TRUE, 
-    score.range = c(0.5, 1), subtitle = subtitle)
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4 Temporal Visualization of the verification

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This type of visualization is envisaged to provide a temporal -overview of forecast skill. For this reason, its use is meaningful only -at single-point locations or relatively small regions with an -homogeneous forecast skill.

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Tercile pots are computed considering seasonally averaged time series - (i.e., interannual series). For rectangular spatial domains (i.e., for -fields), the spatial average is first computed to obtain a unique series - for the whole domain. The corresponding terciles for each ensemble -member are then computed for the hindcast period. Thus, each particular -member and season, are categorized into three categories (above, between - or below), according to their respective climatological terciles. Then, - a probabilistic forecast is computed year by year by considering the -number of members falling within each category. This probability is -represented by the colorbar. For instance, probabilities below 1/3 are -very low, indicating that a minority of the members falls in the -tercile. Conversely, probabilities above 2/3 indicate a high level of -member agreement (more than 66% of members falling in the same tercile). - The observed terciles (the events that actually occurred) are -represented by the white circles. If the forecast object is not NULL, then the probabilities for this season are also ploted next to the hindcast results.

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Finally, the ROC Skill Score (ROCSS) is computed for the hindcast. It is indicated in the secondary (right) Y axis.

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In this example, we illustrate its usage considering the Nino 3.4 - region, a rectangle of coordinates -5 to 5 degrees N and -170 to -120 -degrees East, centered on the equator. To this aim, the subsetting -operations are next performed with the global mean surface temperature -dataset:

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# El Nino 3.4
-crop.nino <- function(x) subsetGrid(x, lonLim = c(-170, -120), latLim = c(-5, 
-    5))
-hindcast.nino <- crop.nino(hindcast)
-obs.nino <- crop.nino(obs)
-forecast.nino <- crop.nino(forecast)
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Additional options include linear detrending of the input data and -selection of various color palettes. The default values are used in the -next example:

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tercilePlot(hindcast = hindcast.nino, obs = obs.nino, forecast = forecast.nino, 
-    subtitle = subtitle)
-

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In order to have a clearer overview of the tercile plot, the above -results, which correspond to a world region where seasonal forecasts are - particularly skillful, can be compared to another region of very -limited forecast skill like the Mediterranean Basin:

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# Europe (35-45N and -10-42)
-crop.eu <- function(x) subsetGrid(x, lonLim = c(-10, 40), latLim = c(35, 47))
-hindcast.eu <- crop.eu(hindcast)
-obs.eu <- crop.eu(obs)
-forecast.eu <- crop.eu(forecast)
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tercilePlot(hindcast.eu, obs.eu, forecast = forecast.eu, subtitle = subtitle)
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