Data Visualization Techniques in Meteorological and Climatological World using variety of techniques and software tools by Nedim Sladić

Information about Data Visualization Techniques in Meteorological and Climatological World...

Published on October 25, 2020

Author: BosniaAgile

Source: slideshare.net

Content

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.

#data presentations

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