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winter_olympic_forecast_3D_webgl.R
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winter_olympic_forecast_3D_webgl.R
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#==========================================================
#
# 本程序用于批量制作冬奥1km/3km分辨率模式预报的三维图形产品.
#
# * 从Cassandra服务器上读取CMA_MESO_1km数据气温和10米U/V风速.
# * 采用rayshader将温度场和风流线场叠加到三维场上.
# * 在命令窗口中运行:
# * R --vanilla --verbose CMD BATCH /media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D/winter_olympic_forecast_3D.R
# 实验用webgl出图, 但非常慢, 且字体不能正常显示.
#==========================================================
#==========================================================
# 设置渲染方式
options(rgl.useNULL=TRUE, rgl.printRglwidget=TRUE)
# 载入所有用到的软件库
library(ggplot2)
library(stringr)
library(raster)
library(png)
library(rayshader)
library(leaflet)
library(metR)
library(geosphere)
library(magrittr)
# 从Cassandra服务器上读取模式数据
# 安装命令, devtools::install_github("nmcdev/nmcMetIO")
library(nmcMetIO)
#==========================================================
# 设置本程序用到的文件及相关参数
#
# 以下参数需要手工配置
# 设置工作目录(请使用绝对路径, 其后的产品都在该目录下运行)
work_dir <- "/media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D"
setwd(work_dir)
# 设置图片产品的输出目录
# sudo mount -t cifs -o username=share,password=nmcwft,dir_mode=0755,file_mode=0755,uid=1000,gid=1000 //10.28.16.160/public /media/winter_olympic_160
# output_dir <- file.path(work_dir, 'data', 'winter_olympic_images')
# output_dir <- "/media/winter_olympic_160"
output_dir <- "/media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D/products"
# 设置GMOSRR数据目录
# \\10.20.90.104\winter_olympic\GRD\NMCGRID-GMOSRR
GMOSRR <- "/media/winter_olympic_gmosrr"
# 地形数据文件,数据提前从下面网站下载, 输出为geotiff格式, 分辨率选择最大
# https://www.gmrt.org/services/gridserverinfo.php#!/services/getGMRTGrid
# 在工作目录下建立 /data/winter_olympic的目录, 把地形文件拷贝到该目录即可
elev_file <- file.path(work_dir, 'data',"stadium_topo.tif")
#
# 以下参数不用手工配置
# 地图图像文件,如果该文件不存在,则会采用nmcMetIO::get_arcgis_map_image从
# Arcgis网站上自动下载.
map_file <- file.path(work_dir, 'data', "stadium_map.png")
# 地形阴影数据文件, 如果文件不存在, 程序会自动生成.
shademat_file <- file.path(work_dir, 'data', "shademat.rds")
ambmat_file <- file.path(work_dir, 'data', "ambmat.rds")
raymat_file <- file.path(work_dir, 'data', "raymat.rds")
#==========================================================
# 读入和处理地形数据
# load topography data
elev_img <- raster(elev_file)
# set coordinates
crs(elev_img) <- CRS('+init=EPSG:4326')
# convert it to a matrix which rayshader can handle.
elev_matrix <- raster_to_matrix(elev_img)
# fill NA values
elev_matrix[!is.finite(elev_matrix)] <- min(elev_matrix, na.rm=TRUE)
#============================
# 准备地图图像文件
# get map extent
bbox = extent(elev_img)
bbox = list(p1 = list(long = bbox[1], lat = bbox[3]),
p2 = list(long = bbox[2], lat = bbox[4]))
# get map image size
image_size <- define_image_size(bbox, major_dim = 2000)
# 下载和读入地图图形文件
if(!file.exists(map_file)){
get_arcgis_map_image(bbox, map_type = "World_Topo_Map", file = map_file,
width = image_size$width, height = image_size$height,
sr_bbox=4326)}
#============================
# 准备地形阴影数据
if(!file.exists(shademat_file)){
shademat <- sphere_shade(elev_matrix, texture = "imhof4")
saveRDS(shademat, file=shademat_file)}
shademat <- readRDS(shademat_file)
if(!file.exists(ambmat_file)){
ambmat <- ambient_shade(elev_matrix, zscale=30)
saveRDS(ambmat, file=ambmat_file)}
ambmat <- readRDS(ambmat_file)
if(!file.exists(raymat_file)){
raymat <- ray_shade(elev_matrix, zscale=30, lambert = TRUE)
saveRDS(raymat, file=raymat_file)}
raymat <- readRDS(raymat_file)
#==========================================================
# 循环每个预报场, 生成相应的图片产品
while (TRUE) {
start_time <- Sys.time()
# 设置模式名称
model_names = c('CMA_MESO(1km)', 'CMA_MESO(3km)')
for (model in model_names){
# 获得模式的最新起报时间
if (model == 'CMA_MESO(1km)'){
filename <- gds_get_latest_filename("GRAPES_1KM/TMP/2M_ABOVE_GROUND/",
filter="*.001")
fhours <- seq(1:24)
} else {
filename <- gds_get_latest_filename("GRAPES_3KM/TMP/2M_ABOVE_GROUND/",
filter="*.001")
fhours <- seq(0:72)
}
basefile <- sub('\\..[^\\.]*$', '', filename)
# 构建输出文件夹
output_subdir <- file.path(output_dir, model, basefile)
if (!dir.exists(output_subdir)){
dir.create(output_subdir, recursive=TRUE)}
# 循环每个预报时效
for (fhour in fhours){
# 构建数据文件名
filename <- paste0(basefile, '.', str_pad(fhour,3,pad="0"))
# 构建输出图形文件名, 并检查是否已经生成
outfilename <- file.path(output_subdir, paste0(filename,'.png'))
if (file.exists(outfilename)){
next}
#============================
# 制作温度场填充等值线图
if (model == 'CMA_MESO(1km)'){
dataT <- retrieve_micaps_model_grid("GRAPES_1KM/TMP/2M_ABOVE_GROUND/", filename=filename)
} else {
dataT <- retrieve_micaps_model_grid("GRAPES_3KM/TMP/2M_ABOVE_GROUND/", filename=filename)
}
if (is.null(dataT)) next
# extract time information
init_time_str <- format(dataT$initTime[1], '%Y/%m/%dT%H')
valid_time_str <- format(dataT$time[1], '%Y/%m/%dT%H')
fhour_str <- str_pad(fhour,3,pad="0")
# extract region
dataT <- dataT[lon >= bbox$p1$long & lon <= bbox$p2$long & lat >= bbox$p1$lat & lat <= bbox$p2$lat]
# smooth the data
out <- smooth2d(dataT$lon, dataT$lat, dataT$var1, theta=0.02, nx=128, ny=128)
# plot contour image
colors <- c("#3D0239","#FA00FC","#090079","#5E9DF8","#2E5E7F",
"#06F9FB","#0BF40B","#006103","#FAFB07","#D50404","#5A0303")
p_temp <- ggplot(out, aes(x, y, z=z)) +
geom_contour_fill(breaks=seq(-16, 12, by=0.25)) +
geom_contour(breaks=seq(-16, 8, by=0.25), color="black", size=0.5) +
scale_fill_gradientn(name="Temp", colours=colors, limits=c(-12, 8)) +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
guides(fill="none")+
theme(panel.spacing=grid::unit(0, "mm"),
plot.margin=grid::unit(rep(-1.25,4),"lines"),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank())
# export png file
overlay_file_temp <- file.path(output_dir,paste0(model,"_temperature_image.png"))
png(overlay_file_temp, width=image_size$width, height=image_size$height,units="px", bg="transparent")
print(p_temp)
dev.off()
#============================
# 制作风场流线图
# load fine gridded forecast
if (model == 'CMA_MESO(1km)'){
dataU <- retrieve_micaps_model_grid("GRAPES_1KM/UGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataU)) next
dataV <- retrieve_micaps_model_grid("GRAPES_1KM/VGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataV)) next
} else {
dataU <- retrieve_micaps_model_grid("GRAPES_3KM/UGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataU)) next
dataV <- retrieve_micaps_model_grid("GRAPES_3KM/VGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataV)) next
}
# compute wind speed at points
point = list(lon=116.39200, lat=39.99150)
pointU = dataU[, Interpolate(var1 ~ lon + lat, point$lon, point$lat, grid=FALSE)]
pointV = dataV[, Interpolate(var1 ~ lon + lat, point$lon, point$lat, grid=FALSE)]
pointWS = sqrt(pointU$var1 * pointU$var1 + pointV$var1 * pointV$var1)
# extract region
dataU <- dataU[lon >= bbox$p1$long & lon <= bbox$p2$long & lat >= bbox$p1$lat & lat <= bbox$p2$lat]
dataV <- dataV[lon >= bbox$p1$long & lon <= bbox$p2$long & lat >= bbox$p1$lat & lat <= bbox$p2$lat]
dataUV <- merge(dataU, dataV, by=c("lon", "lat", "lev", "time", "initTime", "fhour"))
# plot wind streamlines
p_wind <- ggplot(dataUV, aes(lon, lat)) +
geom_streamline(aes(dx=dlon(dataUV$var1.x,lat), dy=dlat(dataUV$var1.y), size=..step..,
alpha=..step.., color=sqrt(..dx..^2 + ..dy..^2)),
L=0.05, res=3, arrow=NULL, n=25, S=NULL, lineend="round", size=5) +
viridis::scale_color_viridis(guide="none") +
scale_size(range=c(0,3), guide="none") +
scale_alpha(guide="none") +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
theme(panel.spacing=grid::unit(0, "mm"),
plot.margin=grid::unit(rep(-1.25,4),"lines"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank())
# export png file
overlay_file_wind <- file.path(output_dir,paste0(model,"_wind_streamline_image.png"))
png(overlay_file_wind, width=image_size$width, height=image_size$height, units="px", bg="transparent")
print(p_wind)
dev.off()
#============================
# 制作三维地图
# 读入图形数据
overlay_img <- readPNG(map_file)
overlay_img_temp <- png::readPNG(overlay_file_temp)
overlay_img_wind <- png::readPNG(overlay_file_wind)
# 设置垂直尺度
zscale <- 10
# 设置标题
title <- paste0("2022北京冬奥",model," 风场及温度预报(",valid_time_str,")\n",
"起报: ",init_time_str, "; 时效: ",fhour_str)
rgl::clear3d()
shademat %>%
add_overlay(overlay_img, alphalayer = 0.95) %>%
add_overlay(overlay_img_temp, alphalayer = 0.6) %>%
add_overlay(overlay_img_wind, alphalayer = 0.8) %>%
add_shadow(raymat, max_darken = 0.4) %>%
add_shadow(ambmat, max_darken = 0.4) %>%
plot_3d(elev_matrix, zscale = zscale, windowsize = c(1000, 1000), background = "white",
water = FALSE, soliddepth = -150, wateralpha = 0)
label <- list(text=paste0("Olympic Stadium: ", as.character(round(pointWS,digits=2)), "m/s"))
label$pos <- find_image_coordinates(long=point$lon, lat=point$lat, bbox=bbox,
image_width=dim(elev_matrix)[1], image_height=dim(elev_matrix)[2])
render_label(elev_matrix, x = label$pos$x, y = label$pos$y, z = 6000,
zscale = zscale, text = label$text, textsize=1.2, linewidth=5, freetype=TRUE)
dist = distm(c(extent(elev_img)[1], extent(elev_img)[3]),
c(extent(elev_img)[1], extent(elev_img)[4]), fun = distHaversine)[,1]
render_scalebar(limits=round(dist/1000.,1),label_unit = 'km')
render_compass(position = "N",compass_radius=120, scale_distance = 1.2)
rgl::par3d(windowRect=c(-450, -100, 1000, 1100))
rgl::view3d(theta=20, phi=30, fov=45, zoom=0.6)
render_snapshot(outfilename, title_text = title, instant_capture=FALSE, bring_to_front=TRUE,
title_size = 45, title_color = "black", title_font = "SimHei", webshot=TRUE)
}
}
# 运行完毕后暂停一段时间
end_time <- Sys.time()
dt <- difftime(start_time, end_time, units="secs")
if (dt < 120) {
Sys.sleep(900)
} else {
Sys.sleep(300)
}
}
# close rgl window
rgl::rgl.close()