Dated: 24-June-2020
Why “roadblocks are often been overlooked by forecasters” before moving to the “prediction system (PS)”? Can it (PS) be considered a sustainable in the long term?
Now a days most of the forecasting agencies in India are busy in giving seasonal weather forecast (regional) including extremes and making it instantly available on the net. Many are in race of launching new portal to do so without comprehension of the predictability charade. Mostly been done using numerical modelling systems without exploring (disclosing) the some main factor which are essentially are the roadblocks in predictability.
I think, correcting spatial bias via embedded station data network should not only be the focus, though it will be a help but not sustainable solution. Why main problem lies been often overlooked before moving to PS? For example- intraseasonal variability (main roadblock to the predictability) is not well resolved in GFS forecasting model (or alike other models) and these oftenly used by the forecaster as an input data to their chosen prediction model. My question is, if unresolved or inadequate in specific sense (exam.- not having tendency to reproduce intraseasonal signals) inputs goes into the main predictive model then how sustainable will be the forecast in the long run. I feel, to do any less may result in prediction unsustainable. Surely, it may results in few right prediction and leads to self-acclaimed commendations but in longer run chances of failure in prediction will be higher. In terse, these prediction will have no substantial value in the long term.
For example – in a year when these charade processes will be predominant, forecast will be failure and it leads to socio-economic loss and setback to forecasting organizations. In general it will then, as usual, follow with post-mortem which will again highlights the need in the improvement of microphysics, intraseasonal signals variability, lead lag relationship, issues associated to AWS, standards rules or norms, installations, implementations policies, and money etc. aspects and in some cases probably leads to blame game to defend the failure. Remember, these reasoning to defend the prediction sometimes makes other agency competitive and robust. Healthy criticism can substitute constructiveness. I think, scientific failure must be constructively accepted to explore afresh scientific causes behind instead politicization.
If such things continue then it will be followed with actions such as --- Despondent with exiting forecast, Govt. decided to search for new options, leaving or updating the existing.
I think, Obliviousness should not a substitute for decisive forecasting. Forecasters must ensure that all roadblock are properly addressed or informed properly to tackle forecasting related failures and contingency. The truth must not left to postmortem and implications of the words.
Best,
Vaid, B. H.
https://www.researchgate.net/profile/Bakshi_Vaid/questions
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Best regards,
Vaid, B. H.
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