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”. A subtle reframing of the manner and format in which messages are communicated can have a significant impact on whether the information is well-received and acted upon. One important point in relation to information framed in terms of losses is that it is important to also highlight action(s) that can

represents how well the weather forecast at a particular location represents the true weather conditions observed at that location. Perceived accuracy represents how accurate Met Office users/customers believe the forecast(s) to be, based on feedback from market research. Comparative accuracy refers

+/- 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 similar patterns

of 1978. Upavon, Clee Hill and Hameldon Hill were in place by 1980. 31. Launched on 23 November 1977 32. Different types of buoys were tested throughout the 1970’s An example of early global NWP forecast output The Unified Model The next step for NWP was the development of the Unified Model in 1990

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

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

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