Blended TPW Products AlgorithmThere are two different blending algorithms used for TPW overocean and overland. This page contains simple descriptions of the blending algorithms used for the blended TPW products. More details please refer to Kidder and Jones. 1. Over Ocean Algorithm1.1 Histogram Correction
The first step in the ocean blending algorithm is the construction of histograms of TPW values for a fiveday period.
A histogram is constructed for each satellite instrument at each scan position. The assumption is that in a fiveday period,
each scan position of each instrument will sample the global distribution of TPW. The second step in the process is constructing a reference PDF. While the "true" TPW distribution is not known, one set of observations can be chosen to be the reference and an adjustment can be calculated. When the adjustment is applied to other observations it makes the distribution approximate the reference distribution. Currently the average TPW PDF (scan positions 6–25 only) of the AMSU instrument is used as the reference PDF. Figure1 shows an example of the reference for the five day period as the red line.
Figure 1. Illustration of the blending algorithm. [From K&J Fig. 1.]The third step in the process is the construction of an adjustment for each scan position of each instrument. The blue line in Fig. 1 shows the cumulative PDF for scan position 32 on the DMSP F14 SSM/I for the time period above. The TPW histograms are tabulated with 1mmwidth bins centered at 0.5 mm, 1.5 mm, etc. For each bin from 5.5 mm to 68.5 mm (the 64 x_{i} values) a y_{i} is interpolated such that the observed cumulative PDF has the same value as the cumulative reference PDF. This step is illustrated in Fig. 1 for a subset of the x_{i} and y_{i} values. Then, applying the adjustment is just a simple process of selecting the coefficients (as a function of satellite and scan position), using the observed TPW as x, and calculating y: adjusted TPW = a_{0} + a_{1}TPW + a_{2}TPW^{2} + a_{3}TPW^{3} (2)Figure 2 shows the adjusted cumulative PDF (dashed line) for the data shown in Fig. 1. Figure 2. The dashed line shows the results of adjusting the observed TPW data with the blending algorithm.

Table 1. Current Barnes analysis parameters for GPS data.  

Barnes Analysis Parameter  Value 
Minimum number of GPS data points within range for Barnes analysis to proceed  3 
1/e decay length  250 km 
Maximum number of stations in an analysis  100 stations 
Maximum distance of closest station  300 km 
Maximum distance of a station to consider (stations further than this not used)  600 km 
The GOES Sounder data are mapped directly into the Mercator projection, and then a 3 x 3 grid box expansion is performed, or up to roughly a 48 km^{2} areal coverage. This is strictly to eliminate holes in the analysis from insufficient coverage of the gridded data. In the future, the GOES Sounder results could be remapped or averaged together to eliminate missing values.
In order to augment the oceanonly TPW product, each point in the starting map, which contains missing data after the addition of the microwavederived TPW is examined. At each point, the TPW value is selected in the following order:
The search for a TPW value ends as soon as one is found, so that the blended TPW values over ocean remind unchanged, and GPS data are selected preferentially over GOES PW values. No averaging or errorweighting of the data is currently performed, although such approaches are worthy of further study.
To give forecasters an idea about how abnormally moist or dry the Blended TPW Product is, the TPW values from the blended product are divided by the weekly mean TPW values from the NVAP Dataset for 19881999 (Randel et al. 1996). NVAP is a daily, 1degree resolution analysis which was created with TPW derived from SSM/I (ocean), NOAA TOVS (ocean and land), and radiosonde (land). This gives "percent of normal." There are 52 weekly mean fields used as normal from NVAP. No moving mean or 313 (three days before, the current day, and the next three days) calculation of the weekly mean is currently performed. The percent of normal product was developed at CIRA for research purposes but has proven useful for tracking atmospheric rivers, return flow of moisture from the Gulf of Mexico, and abnormally dry conditions associated with fire danger.
For more details about the blending algorithm, see amsu.cira.colostate.edu/kidder/Blended_TPW.pdf.