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Areas of expertise Ocean temperature and salinity observations. Quality control. Observational biases and uncertainties. Objective analysis. My Publications - Good, S Current activities Simon is the manager of the Marine Observations Processing and Analysis team within the Ocean Forecasting

: ……………………………………climatic zone(s) Morning Afternoon Night Rainfall distribution Maximum temperature Minimum temperature Hazards Tuesday Wednesday Thursday Friday Saturday Sunday Monday Key to rainfall distribution symbols: rain likely to fall in few places (less than 33%), rain likely to fall in many places (33% -67

: ……………………………………climatic zone(s) Morning Afternoon Night Rainfall distribution Maximum temperature Minimum temperature Hazards Tuesday Wednesday Thursday Friday Saturday Sunday Monday Key to rainfall distribution symbols: rain likely to fall in few places (less than 33%), rain likely to fall in many places (33% -67

of 2018. Weather, 74: 390- 396. https://doi.org/10.1002/wea.3628 Murphy JM, Harris GR, Sexton DMH, Kendon EJ, Bett PE, Clark RT, Eagle KE, Fosser G, Fung F, Lowe JA, McDonald RE, McInnes RN, McSweeney CF, Mitchell JFB, Rostron JW, Thornton HE, Tucker S, Yamazaki K. (2019) UKCP18 Land Projections

value forecast to airline operators. Performance measure: ≤ 2.85m/s for Wind and ≤ 0.56K for Temperature (based on 12-month mean values) Root Mean Square Vector Error 4.00 3.80 3.60 3.40 3.20 3.00 2.80 Northern Hemisphere T+24 Wind (m/s) at 250hPa Monthly Rolling 12-month mean Target Latest value

information for any warnings by omission of missing timesteps. The metadata header will only expose the timesteps that have been generated successfully, therefore if a timestep/s is missing then it should be concluded that they have failed to be generated for that time/s

, to regional drought and fire risk in S. America and to UK energy and hydrological applications.   Career background Since joining the Met Office in 2002 Hazel has worked on improving the assimilation of humidity observations in the stratosphere, on climate change predictions, impacts and adaptation before

and healthcare practitioners as well as being a useful tool to assist self-management or treatment of allergy sufferers. It ‘s hoped that in the future these more detailed source maps can be used, alongside wind direction and precipitation patterns to provide more detailed and local warnings to sufferers, which pollen is impacting their area and in what concentrations.

for the whole Tamil Nadu Model Mann-Kendall statistic (S) Kendall’s tau p value (two-tailed test) Alpha Test interpretation 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s HadCM3Q0 227 173 −42 0.488 0.398 −0.111 <0.0001 0.002 0.422 0.05 R-H0 R-H0 A-H0 HadCM3Q1 77 251 108 0.166 0.577

-Kendall trend test for mean temperature data from all the six models for the whole Tamil Nadu Model Mann-Kendall statistic (S) Kendall’s tau p value (two-tailed test) Alpha Test interpretation 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s HadCM3Q0 227 173 −42 0.488 0.398 −0.111