NLDAS Wind Problem

NLDAS (National Land Data Assimilation System) from nasa is an awesome data product. That being said, with NLDAS, I have struggled with one big thing: Wind speed.

NLDAS

Time and time again, I have found NLDAS to be an outstanding representation of meterological drivers across the US. At the very least, in the regions I am working. But for some reason, in the upper midwest, myself and colleagues have found a step change in the wind speed data. This seems to come from one of the source datasets, the North American Regional Reanalysis NARR.

In earlier work, I’ve just used a simple multipler correction for data after 2001. But now that this new Macrosystems grant from the NSF is operating at a continental scale, I need to figure out what’s going on more broadly. I noticed the other day that the step change in 2001 is of course different in the Northeastern United States, thus making my simple step change fix no longer applicable.

Joy.

So let’s see how this step change looks across the whole dataset. To do that, I’ll average wind speed for a few years before 2001 and compare it to average wind speed after 2001. This is kinda ugly because my desktop only has 32GB of RAM so I can’t load the whole dataset all at once.

library(ncdf4)

#this happens to be where I store my re-processed NLDAS data
uwind = nc_open('z:/big_datasets/NLDAS/driver_ncdf4_NLDAS/UGRD10m_110_HTGL.nc4')
vwind = nc_open('z:/big_datasets/NLDAS/driver_ncdf4_NLDAS/VGRD10m_110_HTGL.nc4')


time = ncvar_get(uwind, 'time')
time = as.POSIXct(time, origin='1970-01-01', tz='UTC')


#diff(range(which(time > as.POSIXct('1999-01-01') & time < as.POSIXct('2000-01-01'))))
#range(which(time > as.POSIXct('2001-01-01') & time < as.POSIXct('2003-01-01')))

earlyall = list()
for(i in 1995:2000){
  starti = min(which(time > as.POSIXct(paste0(i, '-01-01'))))
  
  early = ncvar_get(uwind, 'UGRD10m_110_HTGL', start=c(1,1,starti), count=c(464,224,8757))
  earlyall[[i]] = apply(early, 1:2, mean)
  gc()
  cat(i, '\n')
}
## 1995 
## 1996 
## 1997 
## 1998 
## 1999 
## 2000
early = apply(abind::abind(earlyall[1995:2000], along = 0), 2:3, mean)


lateall = list()
for(i in 2001:2006){
  starti = min(which(time > as.POSIXct(paste0(i, '-01-01'))))
  
  late = ncvar_get(uwind, 'UGRD10m_110_HTGL', start=c(1,1,starti), count=c(464,224,8757))
  lateall[[i]] = apply(late, 1:2, mean)
  gc()
}

late = apply(abind::abind(lateall[2001:2006], along = 0), 2:3, mean)

So the change from before to after 2001 definitely varies regionally. Much of the northeast is apparently pretty close to 0 change across that time period (which is desirable).

plot of chunk plot

Written on March 7, 2017