Real Time Forecast

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File NameDate & Time
Verification_MISO.ppt September 29 2014 09:27:23. hrs
Verification_spatial.ppt September 29 2014 09:19:49. hrs
Verification_anim_Aug.ppt September 29 2014 09:11:39. hrs
Verification_anim_Jul.ppt September 09 2014 10:40:58. hrs
Verification_anim_May.ppt August 13 2014 09:26:08. hrs
Verification_anim_Jun.ppt August 13 2014 09:26:05. hrs


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Archieval of Real Time Forecast

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Archieval of Forecast Verification

File NameDate & Time
Verification_20140725.ppt July 26 2014 06:54:09. hrs
Verification_20140720.ppt July 21 2014 07:37:17. hrs
Verification_20140715.ppt July 16 2014 10:58:52. hrs
Verification_20140710.ppt July 11 2014 11:28:24. hrs
Verification_20140705.ppt July 06 2014 08:37:28. hrs
Verification_20140630.ppt July 01 2014 11:22:20. hrs
Verification_20140625.ppt June 26 2014 10:27:58. hrs
Verification_20140620.ppt June 21 2014 08:12:02. hrs
Verification_20140615.ppt June 16 2014 14:37:46. hrs
FV_2013_Spatial_June_July.ppt September 10 2013 11:00:28. hrs
FV_2013_Spatial_August_September.ppt September 10 2013 11:00:28. hrs


Forecast Skills

The correlations coefficients (CC) between the observed and the forecasted rainfall anomaly at different pentad leads and for the five selected regions are shown in Figure. It is observed from the figure that except NEI, there is no significant improvement in the prediction skill of CFST382 run and both T382 and T126 skills are comparable in all the four pentad leads. On the other hand if we compare the same with that of GFSbc, it is observed that GFSbc shows better prediction skill in the pentad lead 2, 3 and 4 over CEI, SPI and MZI and in pentad lead 2 over NEI. Over NWI none of the models shows any improvement in the prediction skill.

The verification skill scores for the ensemble mean deterministic forecast are presented in

Figure (a),
Figure (b),
Figure (c),
Figure (d),
Figure (e).

The different skill score measures used in this study are: The Kupper skill score (KSS) and bias score proposed, the Heidke skill score (HSS) and Gerity score, the correlation coefficient and the root mean square error (RMSE). All these scores are shown over MZI and four homogeneous regions. It is observed that over all the regions the large correlation at pentad lead 1 significantly drops at pentad lead 4. CC values of 0.5 are chosen as the threshold value for useful prediction on pentad scale. After pentad 2 lead, the deterministic ensemble mean prediction skill drops below 0.5 over all the selected regions.

This confirms the uselessness of deterministic forecast alone after pentad 2 lead. However, considering large sample size of 288, all CC values above 0.14 are significant at 99% confidence level. The HSS and GSS are found to be significant at 99% confidence level even at pentad lead 4 over CEI and NWI. It is also evident from the figure that skill in predicting break is higher, followed by active and then normal over MZI and CEI at all lead pentads and at pentad lead 1 and 2 over SPI. Over NWI, skill in predicting break is higher at all lead pentads but active follows normal at pentad lead 4.

ROCs for the discussed three observed categories are also evaluated for the four homogenous regions and also for MZI using CFST382 and compared with CFST126 and GFSbc in

Figure (a),
Figure (b),
Figure (c),
Figure (d),
Figure (e).

The area under the ROC curve is used for the calculation of the probabilistic skill score and is plotted in Figure. It is observed that break is more predicable in all the pentad leads over MZI and CEI and in pentad lead 1 and 2 over NWI and SPI. It is also observed that GFSbc outperforms CFS in predicting the active phases over MZI, CEI (pentad lead 2, 3, 4) and SPI (pentad lead 3, 4).