Ocean Ensemble Forecasting and Adaptive Sampling - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 397

122:5 ı Wfor( a ) targeting
time 00 UTC 13 Aug 2003 and ( b ) verification time 00 UTC 14 Aug 2003. Signal variance for
nine observations centered at

36:7 ı N,

Fig. 16.6 Signal variance for nine observations centered at

122:1 ı Wfor( c ) targeting time same as ( a ) and verification
time same as ( b ). The black ellipse contour indicates verification region

36:2 ı N,

the targeting time. It shows larger signal variances at the location #1 (Fig. 16.6 a)
compared to the location #2 (Fig. 16.6 c) due to larger ensemble spread at the
targeting time. The signal variance at the verification time has larger values within
the verification region from the location #1 (Fig. 16.6 b) compared to the location
#2 (Fig. 16.6 d). This suggests the first location for the deployment is more likely to
improve the forecast than the second location.
Figure 16.7 a depicts the predicted reduction in forecast error variance at the
verification time due to a surface temperature observation at the targeting time at the
location indicated by the white cross. By integrating this field across the verification
region we obtain a prediction of the reduction in forecast error variance due to an
observation at the white cross. Figure 16.7 b plots the mean reduction in forecast
error variance as a function of the location of the test observation. We refer to maps
like Fig. 16.7 b as a “summary map”.
If gliders are available for adaptive sampling, summary bar charts can be used to
choose among several feasible glider paths. At a particular location, a glider needs
to be directed which direction it will be towards to. To demonstrate how signal
variance summary bar chart can be used, assuming that for a particular location,
a glider can have eight possible tracks (red lines in Fig. 16.7 b). The predicted