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Downloading a weather model and using it to predict stuff is all well and good – but what are the sources of error? Specifically, how accurate is the atmospheric parametrization? Luckily, NOMADS provides sets of models with slightly different initialization values. The result is a set of realizations that captures the variability of the model product. Here’s how to get that product, and examples of what you can do with it.

library(rNOMADS) #Get the latest ensemble model run model.urls <- GetDODSDates("gens") latest.model <- tail(model.urls$url, 1) model.runs <- GetDODSModelRuns(latest.model) model.run <- tail(model.runs$model.run[grepl("all", model.runs$model.run)], 1) #Define region of interest: Chapel Hill, NC lon <- -79.052104 lat <- 35.907553 #Set up DODS model grid indices #Use GetDODSModelRunInfo() to figure out how to do this for other products lons <- seq(0, 359, by = 1) lats <- seq(-90, 90, by = 1) lon.diff <- abs(lon + 360 - lons) lat.diff <- abs(lat - lats) model.lon.ind <- which(lon.diff == min(lon.diff)) - 1 model.lat.ind <- which(lat.diff == min(lat.diff)) - 1 #Set up call to NOMADS GrADS-DODS server #Analysis(?) model only time <- c(0, 0) #Longitude grid node.lon <- c(model.lon.ind - 2, model.lon.ind + 2) #Latitude grid node.lat <- c(model.lat.ind - 2, model.lat.ind + 2) #Temperature, wind speeds, and geopotential height variables <- c("tmpprs", "ugrdprs", "vgrdprs", "hgtprs") levels <- c(0, 25) #All available levels ensemble <- c(0, 20) #All available ensembles model.data <- DODSGrab(latest.model, model.run, variables, time, node.lon, node.lat, levels = levels, ensemble = ensemble) #Plot temperature variability plot(c(-65, 25), c(0, 35), xlab = "Temperature (C)", ylab = "Geopotential Height (km)", type = 'n') for(k in ((ensemble[1]:ensemble[2] + 1))) { model.data.sub <- SubsetNOMADS(model.data, ensemble = c(k), variables = c("hgtprs", "tmpprs")) profile <- BuildProfile(model.data.sub, lon, lat) points(profile[[1]]$profile.data[, which(profile[[1]]$variables == "tmpprs"), 1] - 272.15, profile[[1]]$profile.data[, which(profile[[1]]$variables == "hgtprs"), 1] / 1000) } #Plot winds zonal.wind <- NULL merid.wind <- NULL height <- NULL for(k in ((ensemble[1]:ensemble[2] + 1))) { model.data.sub <- SubsetNOMADS(model.data, ensemble = c(k), variables = c("hgtprs", "ugrdprs", "vgrdprs")) profile <- BuildProfile(model.data.sub, lon, lat) hgt <- profile[[1]]$profile.data[, which(profile[[1]]$variables == "hgtprs"),] ugrd <- profile[[1]]$profile.data[, which(profile[[1]]$variables == "ugrdprs"),] vgrd <- profile[[1]]$profile.data[, which(profile[[1]]$variables == "vgrdprs"),] synth.hgt <- seq(min(hgt), max(hgt), length.out = 1000) ugrd.spline <- splinefun(hgt, ugrd, method = "natural") vgrd.spline <- splinefun(hgt, vgrd, method = "natural") zonal.wind[[k]] <- ugrd.spline(synth.hgt) merid.wind[[k]] <- vgrd.spline(synth.hgt) height[[k]] <- synth.hgt } dev.new() PlotWindProfile(zonal.wind, merid.wind, height, lines = TRUE, points = FALSE, elev.circles = c(0, 15000, 30000), elev.labels = c(0, 15, 30), radial.lines = seq(45, 360, by = 45), colorbar = TRUE, invert = FALSE, point.cex = 2, pch = 19, lty = 1, lwd = 1, height.range = c(0, 30000), colorbar.label = "Wind Speed (m/s)")

Here’s atmospheric profiles from GFS ensemble runs on November 24, 2016 for Chapel Hill, NC. Temperature is quite variable at low altitudes (several degrees Celsius) and at 5 km above sea level (a couple degrees). Ensemble runs are more consistent for the rest of the atmosphere.

Wind speeds are pretty consistent, at least at the resolution of the color scale I’m using. Wind direction varies by a few degrees below 15 km, but becomes quite a bit less reliable between 15 and 30 km altitude. This is one reason that our solar balloon trajectories are so hard to predict. The other reason is because we still don’t really understand the physics of solar balloon flight, but that’s a subject for another post.

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