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governmental organisations) ▶▶ Level of resources and expertise in the organisation(s) ▶▶ Perceived advantage of using forecasts and warnings particularly in the private sector ▶▶ Ability to develop specific products in regions where forecasts and warnings reliability is higher (e.g. tropics) ▶▶
and working change almost overnight, with uncertainty and disruption affecting us in ways we could never have predicted. Two examples of COVID-19’s impact on Met Office operations spring to mind. Firstly, due to reduced aircraft flying, there were fewer observations to feed into the Numerical Weather
of the author(s) and do not necessarily reflect the views of WMO, the UK Met Office, the NMHS, EAC or NCAR. Authors: Paul Watkiss, Robert Powell, Alistair Hunt, Federica Cimato Supported by: Fund Manager: Delivery Partners: Introduction Background and objectives The HIGHWAY project (HIGH impact
believe the forecast(s) to be, based on feedback from market research. Comparative accuracy refers to how accurate the Met Office is compared to other weather providers, and this can be judged via actual accuracy measures or and perceived accuracy measures. However, the definitive comparison is based
fishers of looming danger but sometim es it crows and the wind does not change Sam (small flies) Bad – proceed s a strong wind Low wave Good Good height High wave Dangerous Bad height No rain Good Good How do they obtain this information? water cold, which reduces the catch) Bad Bad Irrelevant - Good
, with changes of the order +/- 1.5 m/s in places (not shown). The direction of change in future wave climate is consistent, with the mean SWH seen to reduce in both configurations. Stronger changes are seen in the regional model than the global model. Additional simulations under RCP4.5 (not shown) show
with the ICAO regions, and the content of each is described in table 15. -29- Chapter 6 SADIS User Guide locationId EUR-NAT NAM-CAR SAM AFI MID ASIA-PAC GLOBAL ICAO “CCCC” E*** L*** B*** U*** C*** K*** M*** T*** P*** S*** G*** D*** F*** H*** O*** A*** N*** R*** V*** W*** Y*** Z*** all ICAO codes Table 15
2,481 0.0 2.6 516 0.6 9 46 46 72 64 50 52 47 35 95 S Africa 889 775 12.8 63.6 344 13 58 21 21 94 83 78 81 85 75 6 Madagascar 12,83 2 12,832 0.0 11.3 516 23 96 1 3 53 36 12 8 34 11 18 Seychelles ND ND 0.0 ND 141 17 7 28 66 97 97 100 100 100 100 0 Mauritius 2171 2171 0.0 21.5 470 20 51 2 47 99 99 96 95
. -29- Chapter 6 SADIS User Guide locationId EUR-NAT NAM-CAR SAM AFI MID ASIA-PAC GLOBAL ICAO “CCCC” E*** L*** B*** U*** C*** K*** M*** T*** P*** S*** G*** D*** F*** H*** O*** A*** N*** R*** V*** W*** Y*** Z*** all ICAO codes Table 15 – OPMET API regional collection names 6.1.5. It is possible
with medium confidence that a collapse of AMOC was not anticipated before 2100, and Armstrong McKay et al. (2022)’s review of tipping points assigned medium confidence to AMOC collapse based on estimated critical temperature thresholds. Note that ‘Gulf Stream Collapse’ (a phrase often used in media
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