We conclude that the original HPB mechanism using all pairs of receivers produces a smaller localization error than the other two approaches, when a large number of receiving devices are available.
By applying the approach to the test beams, it is observed that crack can be confidently located with a relatively small localization error.
However, as far as the authors know, there has been no study that assimilated PWV data with a horizontal resolution less than 10 km, using a small horizontal localization radius for PWV data over a precipitating region.
Even when using accurate atlases, small localization errors may remain as a result of individual differences or pathologies.
This is because we eliminated the spurious error correlations of PWV between distant locations by applying the small localization radii over the precipitating region.
The result was improved because we eliminated the spurious error correlations of PWV between distant locations by applying small localization radii over the precipitating region.
The use of small localization radii was also effective in reducing the influence of observation error correlation of PWV around the hyper-dense GNSS receiver network.
Second, the classifier is trained to be insensitive to small localization error [3, 8, 9] so the classifier can handle different face variations.
Although a single localization radius of 34.2 km was used in the earlier conventional LETKF experiment, we applied small localization radii, depending on the rain rates (R [mm/h]) observed by weather radar, i.e., 30.8 km (R < 0.1), 7.5 km (0.1 ≤ R < 10), and 4.8 km (R ≥ 10).
Although increasing the ensemble size or model resolution is effective alternative methods to estimate the appropriate background error covariance, we fixed the ensemble size and model resolution in order to evaluate the effects of using small localization radii.
