CHIRTS FAQ

Just before we operationalized the IRTmax process, Seth was working in the Southern US. There he noticed some years where the geo-registration could be off by quite a bit. He decided to avoid years,

   1980, 1981, 1983, 1989,1990, 1991, 1992, 1993, 1994 and 1995

There are 25 good years left between 1982 and 2014.

The attached figure shows the various Geostationary sensors in use over the equator. The problem area for the US SW coincides with the GOES-7 period when there was only a single GOES for the US, so it had to cover both coasts.

This should have no bearing on Africa which has consistent coverage when it has any, just wanted you to know why we selected these 25 years to make a climatology.

Start by making climatologies of expected brightness temperature for each pixel for each month 8 times a day.

Eight time a day, at each pixel, look at a 5x5 array centered on it over 25 good years over the calendar month. Exclude any points colder than 180 K or warmer than 340 K.

Look at the histogram of all points (binned every 3 degrees to avoid spurious maxima), find the first maxima starting from the right hand side (hot). These histograms can be pretty noisy on the left hand side.

A histogram of all of the temperatures for a given pixel for a given month for a given hour will have a really long tail on the low end, and a near gaussian distribution of good temperatures. the low end is all of the solidly cloudy to partly cloudy data values. My first cut at this was to fit a gaussian to the distribution and only keep values +/- 3 standard deviations from the mean. this works fine for africa as long as the amount of good values is >> the amount of cloudy values. for the western US, especially in winter, distributions become distinctly non-gaussian, so i came up with the method that pete mentioned, which essentially seeks to find the gaussian part of the distribution. the first maxima on the right hand side bit is because there were often bimodal histograms. then to mimic the +3 S.D. bit i found the 99th percentile, which becomes the upper threshold. to get the lower threshold we find the difference between the maxima and the 99th, and then subtract that from the maxima, so we have a symmetric, gaussian like distribution of good values. getting the clouds out is incredibly important, obviously, and this works pretty well.

Use the maxima found and mirror the right hand side to the left hand side of the maxima. This acts to some degree as a cloud mask. We are left with a 4 dimensional array,

     31 days, 25 years, 5, 5

take the maximum over the 5x5 neighborhoods,

  then the median over 25 years,
     then the median over all days in the month.

This results in 8 maps (00, 03, 06, 09, 12 ,15, 18 and 21) for each of the B1 observations in a day

Now we go back to each day (8 maps). at each point take the maximum of the 8 clim maps, maxclim.

for each of the 8 obs/day take an anomaly, Tb - maxclim

take the maximum of the anomaly over the day, maxanom

add these two together to get IRTmax for the day,

     IRTmax = maxclim + maxanom.

The idea here is that if it is cloudy at noon, but clear so other part of the day, we use the clear values to estimate IRTmax.

This is run for all years, 1980 to 2015

That's a very rough draft of what we do. All of this is up for discussion/improvements.

Daily SADC .bils are ready for Jan-April and July and August here,

  ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/experimental/Tmax/daily/SADC.bils/

Will fill in the rest of the months as they become available.

These are hot off the press. No one has taken a look at these yet.... Have fun.