Compared with observations and experiment CNTL, experiment ALLDATA generally improves the forecast by moving the precipitation region in Guangxi southward although still have the problem of rainfall overestimation.
The precipitation patterns from CNTL, RADSND, and ALLDATA (Figures 7(b)-7(d))are quite close to each other; all are successful in generating the basic rainband.
Figure 10 presents the time series of RMSEs for 2 m temperature and 10 m wind speed from experiments CNTL and RADAWS for both cases.
For the verifications with the threshold of 1mm, the biases (Figure 11(a))in RADSND and ALLDATA are generally smaller than those in CNTL and RADAWS, and the experiment RADAWS has the highest bias errors during the latter 5h.
RADAWS produces larger biases than CNTL both in the first and latter 6 h forecasts, indicating that addition of AWS data increases the degree of rainfall overprediction for this case.
3) shows that each of the CNTL, NOPM, and OPS runs have the highest or lowest scores at multiple times throughout the experiment period.
3) is 0.005 and 0.013 larger for CNTL than for NOPM in the NH and SH, respectively--about equal to approximately one year of increase in the GFS annual mean Z500AC (appendix B).
The comparison of overall CNTL and NOPM 1-10-day forecast performances is summarized by a scorecard (Fig.
larger, the CNTL is significantly better for both SON and DJF.
The OPS mean 0000 UTC Z500AC score for the entire experimental period is 0.002 and 0.001 higher than CNTL in the NH and SH, respectively (Fig.