1. How to visualize .nc data in R using RNCEP library Exempli Gratia: Mean Precipitation and Temperature Regime Map for European Countries in 2019 (w/o rasterizing) Technical part: this part will briefly explain the importance of proper data visualization of meteorological/climatological data, especially in NWP (numerical weather prediction). The brief, comprehensive hierarchy of .nc data will be enlisted within the presentation and clarified for the audience. As an alternative, GRIB2 type will be also mentioned. Practical part: once the technical part is clear, the programmable code will be briefly shown to audience on how to visualize the precipitation and temperature map. This will be achieved using variety of libraries and corresponding methods under CRAN repository, such as sf, lubridate, tidyverse and the most pivotal - RNCEP. 2. Our own developed NWP (numerical weather prediction) model: NOTHAS NWP Logic: the algorithmic approach behind NOTHAS will be briefly explained as part of visualizing and parametrizing the .nc and GRIB2 data within the integrated WRF domain inside the Southeastern Europe Domain using ICON-EU, GFS, ECMWF and/or ICON-EU model data as initial parameters. The algorithm itself will be shown and onwards briefly explained for parameterized data to the audience. Final result will include the results of visualized parameters for specific scenarios. 3. Why Stripes? Logic: a simple, yet effective way of showing the importance of global/local temperature rise caused by effects of climate change. Three colors and bunch of stripes inside one simple piece of R code will be demonstrated on the example of our country. 4. OpenGrADS Technical part: as a software tool that has been widely used in the meteorological circles, we will briefly explain the logic behind OpenGrADS. Exempla Gratia: show both results: the existing visual, and code our visual for 500 hPa altitude pressure anomaly using CFS data.

1. October, 19 – 23, 2020.
Meteorological Data Visualization using
Numerical Weather Prediction Models:
ICON-EU, GFS and CFS GRIB/NETCDF4
Data Formats
Nedim Sladić, BSc.

2. October, 19 – 23, 2020.
Agenda
• Data Visualization Importance in Meteorology and Climatology;
• Data Formats – GRIB2, NETCDF4;
• Effective Data Visualization Example – Climate Stripes;
• Development of our Numerical Weather Prediction (NWP) System;
• OpenGrADS

3. October, 19 – 23, 2020.
Data Visualization Importance in Meteorology
and Climatology
• Reducing text redundancy and improving general
comprehensiveness, mostly in the reports;
e.g. ”June 2019 was warmer than the 30-year
annual average in southern Europe”.
General Question: on what 30-year annual
average we refer?
(what year span is taken into comparison?)
• Alternative Approach: Graphs and Maps
• Importance: Colours, Lines

4. October, 19 – 23, 2020.
Data Visualization Importance in Meteorology
and Climatology (cont.)
Example: June 2019 was at least 3-4 ºC
warmer w.r.t. the 30-year annual mean
1981-2010 in the northern Balkans.
• For mass audience this sentence is:
– lacking comprehensiveness!
– very hard to visualize!
– causing redundancy!
• Maps
– Colours depicting the anomaly
– the darker the colour, the stronger anomaly
– easier to comprehend and interpret! Figure 1: Textual context transformed into map.

5. October, 19 – 23, 2020.
Data Visualization Importance in Meteorology
and Climatology (cont.)
• Climatology and Meteorology highly depends on data visualization!
Figure 2: Precipitation Anomaly for June 2006. Figure 3: Temperature Anomaly for June 2019.

6. October, 19 – 23, 2020.
Data Visualization Importance in Meteorology
and Climatology (cont.)
Figure 4: European Z500 Geopotential Heights for July 29 Figure 5: Southern Europe Z500 Geopotential Heights for July 29
Beside colours, lines are also useful, especially in detecting pressure anomalies!

7. October, 19 – 23, 2020.
Data Visualization Importance in Meteorology
and Climatology (cont.)
• Numerical models – ECMWF, ICON, GFS, UKMO, GEM-CMCC, etc.
• Data collection: soundings, commercial flights, satellites, ships, buoys, etc.
• Supercomputers
– immense computation strength for vast datasets
– computing complex sets of partial differential equations
• Forecast Skill
– the measure of accuracy of the model prediction w.r.t. the observed (predictand)
– numerical errors due to chaotic nature of the atmosphere!
– some atmospheric systems due to the Earth limitations have not been developed!
• Differential Equations based on the Laws of Physics
– Fluid motion, Thermodynamics, Chemistry, etc.
• Different domains: Global and Regional (horizontal coordinate grid)

8. October, 19 – 23, 2020.
Data Formats
• Array-oriented scientific data
• Multidimensional data
– Latitude
– Longitude
– Level/Height
– Time
– Step/Band
– Ensemble
• Format Types: GRIB2, NETCDF4
– GRIB2: rNOMADS (GFS, ICON)
Description of NETCDF4 file from CFS in Panoply

9. October, 19 – 23, 2020.
NETCDF4 visualization – rasterization example
library(raster)
library(tidyverse) #data manipulation and visualization
library(RColorBrewer) #color schemes
library(sf) #to import a spatial object and to work with geom_sf in ggplot2
setwd("~/Downloads") #Set the directory path
dset <- raster("air.mon.anom.nc", band = 1674) #Band: step; #Level for pressure.
dset
print(dset)
plot(dset) #First plain visualization
dset_r <- rotate(dset) #Due to lat-lon projection!
dset_r
plot(dset_r)
library(ggplot2)
df <- as.data.frame(dset_r, xy = TRUE) #return spatial coordinates
View(df)
#df$Near.Surface.Air.Temperature <- df$Near.Surface.Air.Temperature - 273.15
str(df)

10. October, 19 – 23, 2020.
NETCDF4 visualization - rasterization (cont.)
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "white",
"yellow", "#F8D568", "#FF7F00", "red", "#7F0000"))
library(mapproj)
library(metR)
ggplot()+
geom_tile(data = df, aes(x=x, y=y, fill=Surface.Air.Temperature.and.SST.Monthly.Anomaly)) +
geom_raster(data = df, aes(x=x, y=y, fill=Surface.Air.Temperature.and.SST.Monthly.Anomaly),
interpolate = TRUE)+ borders('world', size = 1, xlim=range(df$x), ylim=range(df$y),
colour='black')+
scale_fill_gradientn(colors = jet.colors(9), limits=c(-7,7), breaks=seq(-7,7,1))+
guides(fill = guide_colorbar(barwidth = 60, barheight = 1,
title.vjust = 0.85, nbin = 9, ticks = FALSE, draw.ulim = FALSE,
draw.llim = FALSE, direction = "horizontal"))+
coord_sf(ylim=c(39,49),xlim=c(12,30))+
scale_x_continuous(breaks=seq(12,30,1),expand=c(0,0))+
scale_y_continuous(breaks=seq(39,49,0.5),expand=c(0,0))+
labs(x="Longitude",y="Latitude",fill="Anomaly (ºC)")+
theme_tq(base_size = 32, base_family = "Arial")+
ggtitle("Temperature Anomaly for June 2019 w.r.t the 30-year annual mean 1981-2010")+
labs(caption = "Data: NCEP-NOAA ©n Visualization: Nedim Sladićn17.10.2020.")

11. October, 19 – 23, 2020.
Data accessible via NCEP-NOAA
server.
Rasterization: image described in
a vector graphics format.
Converting it (triangles,
polygons) into series of pixels,
dots and lines, final image is
obtained.

12. October, 19 – 23, 2020.
GRIB2 Visualization from ICON-EU Data Input
Video 1: ICON-EU precipitation
simulation on ADRIA domain.

13. October, 19 – 23, 2020.
Effective Data Visualization Example – Climate Stripes
• Autor: Ed Hawkins, Assoc. Assist. Prof. Dr. at
University of Reading
• Matching colour scheme to visualize the
temperature anomaly with the
corresponding year.
– Synonym for the Climate Change

14. October, 19 – 23, 2020.
Development of our Numerical Weather Prediction System
- NOTHAS
The Idea of prof. dr. Vlado Spiridonov,
prof. dr. Mlađen Ćurić and Nedim Sladić
• Advanced Forecast ad Diagnostic System for
an early assessment of storm intensity and
alert category.
• Consists out of WRF model forecast outputs
and a diagnostic algorithm based on Weibull
distribution.
• Initial and Boundary condition: GFS

15. October, 19 – 23, 2020.
OpenGrADS
• Software tool widely used for visualization of meteorological and climatological data.
• Other software tools:
– ArcGIS
– NCAR Command Langauge (NCL)
– Panoply
– etc.

16. October, 19 – 23, 2020.