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Publications and Resources

Publications Collection 002 (Issued on Aug 2019)

Jun 24,2020

S. Ma et al, 2019, An analysis of perturbation features of convection-allowing ensemble prediction based on the local breeding mode. Wea & Forecasting,34, 289-303, DOI: 10.1175/WAF-D-18-0111.1

 

E. D. Loken et al, 2019, Spread & Skill in mixed- and single-0physics convection allowing ensembles. Wea & Forecast, 34, 305-330. DOI: 10.1175/WAF-D-18-0078.1

 

Generaux et al, 2019, Psychosocial management before, during and after emergencies and disasters – results from the Kobe expert meeting. Int. J. Environ. Res. Public Health, 16, 1309; doi:10.3390/ijerph16081309

 

E. E. H. Doyle et al, 2019, Communciating model uncertainty for natural hazards: a qualitative systematic thematic review. Int J Disaster Risk Reduction, 33, 449-476.  https://doi.org/10.1016/j.ijdrr.2018.10.023

 

K. M. Lambrecht et al, 2019, Improving visual communication of weather forecasts with rhetoric. Bull Amer Meteorol S, 557-563.  DOI:10.1175/BAMS-D-18-0186.1

 

Y. Wehbe et al, 2019, Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station and satellite data. Nat. Hazards Earth Syst. Sci., 19, 1129-1149. https://doi.org/10.5194/nhess-19-1129-2019

 

M-C. Oliver et al, 2019, Disaster risk resilience in Colima-Villa de Alvarez, Mexico: application of the resilience index to flash flooding events. In. J. Env. Res & Pub Health,16, 2128; doi:10.3390/ijerph16122128

 

A.Nori-Sarma et al, 2019, Advancing our understanding of heat wave criteria and associated health impacts to improve heat wave alerts in developing country settings. Int. J. Environ. Res. Public Health, 16, 2089; doi:10.3390/ijerph16122089

 

S. Heo et al, 2019, The use of a quasi-experimental study on the mortality effect of a heat wave warning system in Korea. Int. J. Environ. Res. Public Health, 16, 2245; doi:10.3390/ijerph16122245

 

P. Masselot et al, 2019, Toward an improved air pollution warning system in Quebec. Int. J. Environ. Res. Public Health, 16, 2095; doi:10.3390/ijerph16122095

 

J. G. Pinto et al, 2019, From Atmospheric Dynamics to Insurance Losses: an interdisciplinary workshop on European Storms. Bull Amer Meteorol S, ES175-ES178, DOI:10.1175/BAMS-D-19-0026.1

 

C. Poku et al, 2019, How important are aerosol-fog interactions for the successful modelling of nocturnal radiation fog. Weather, 74, 237-243. doi:10.1002/wea.3503

 

J. C. Kealy, 2019, Probing the “grey zone” of NWP – is higher resolution always better? Weather 74, 246-249. doi:10.1002/wea.3506

 

E. Zmudzka et al, 2019, Assessment of modern hydrometeorologocal hazards in a big city – identification for Warsaw. Meteorol Appl. 26, 500-510. DOI: 10.1002/met.1779

 

X. Pedruzo-Bagazgoitia et al 2019, Shallow cumulus representation and its interaction with radiation and surface at the convective grey zone. Mon Wea Rev, 147, 2467-2483, DOI: 10.1175/MWR-D-19-0030.1

 

V. Karsisto & L. Loven, 2019, Verification of road surface temperature forecasts assimilating data from mobile sensors. Mon Wea Rev, 147, 539-558, DOI: 10.1175/WAF-D-18-0167.1

 

J. Du et al, 2019, Measure of Forecast Challenge & Predictability Horizon Diagram Index for ensemble models. Mon Wea Rev, 147, 603-615. DOI: 10.1175/WAF-D-18-0114.1

 

C. Morcrette et al, 2019, Development and evaluation of in-flight icing index forecast for aviation. Mon Wea. Rev. 147, 731-750. DOI: 10.1175/WAF-D-18-0177.1

 

N. Snook et al, 2019, Evaluation 0f convection-permitting precipitation forecast products using WRF, NMMR & FV3 for the 2016-7 NOAA Hydrometeorology Testbed flash flood and intense rainfall experiments. Mon. Wea. Rev. 147, 781-804. DOI: 10.1175/WAF-D-18-0155.1

 

G. K. Zewdie et al, 2019, Applying deep neural networks and ensemble machine learning methods to forecast airborne Ambrosia pollen. Int. J. Environ. Res. Public Health 2019, 16, 1992; doi:10.3390/ijerph16111992

 

W. J. Keat et al, 2019, Convection initiation and storm life cycles in convection-permitting simulations of the Met Office Unified Model over South Africa. Quart J Roy Meteorol S, 145, 1323-1336. DOI: 10.1002/qj.3487

 

A.Tsiringakis et al, 2019, On- and off-line evaluation of the single-layer urban canopy model in London summertime conditions. Quart J Roy Meteorol S, 145, 1474-1489. DOI: 10.1002/qj.3505

 

H. W. Lean et al, 2019, The impact of spin-up and resolution on the representation of a clear convective boundary layer over London in order 100m grid-length version of the Met Office Unified Model. Quart J Roy Meteorol S, 145,1674-1689.  DOI: 10.1002/qj.3519

 

R. Pohorsky et al, 2019, The Climatological Impact of recurving North Atlantic tropical cyclones on downstream extreme precipitation events. Mon. Wea. Rev. 147, 1513-1532. DOI: 10.1175/MWR-D-18-0195.1

 

J. D. Duda et al, 2019, Comparing the Assimilation of radar reflectivity using the direct GSI-based ensemble-variational (EnVar) and indirect cloud analysis methods in convection-allowing forecasts over the continental United States. Mon. Wea. Rev. 147, 1655-1678. DOI: 10.1175/MWR-D-18-0171.1

 

M. Baumgart et al, 2019, Quantitative view on the processes governing the upscale error growth up to planetary scale using a stochastic convection scheme. Mon Wea Rev, 147, 1713-1731. DOI: 10.1175/MWR-D-18-0171.1

 

M. Lagasio et al, 2019, Predictive capability of a high-resolution hydrometeorological forecast framework coupling WRF cycling 3DVar and Continuum. J Hydromet, 20,, 1307-1337. DOI: 10.1175/JHM-D-18-0219.1

 

S. Han & P. Coulibaly, 2019, Probabilistic flood forecasting using hydrologic uncxertainty processor with ensemble weather forecasts. J Hydromet, 20, 1379-1398. DOI: 10.1175/JHM-D-18-0251.1

 

L. Zhou et al, 2019, Toward convective-scale prediction within the next generation global prediction system. Bull Amer Meteorol S, 1225-1243. DOI:10.1175/BAMS-D-17-0246.1

 

I.M. Karaye et al, 2019, Factors associated with self-reported mental health of residents exposed to Hurricane Harvey. Progress in Disaster Sci, 2, http://dx.doi.org/10.1016/j.pdisas.2019.100016

 

R. Ciurean et al, 2018, Review of multi-hazards research and risk assessments. British Geological Survey OR/18/057. http://nora.nerc.ac.uk/id/eprint/524399/.

 

Fakhruddin, B., Bostrom, A., Cui, P., Yu, L., Zou, Q., Sillmann, J., Johnston, D., Jimenez, V., Ying, E., Chan,Y., Chan, G.K.W., Hung, H., Huang, Z., Wong, C.K.P., Lim, C.K.P., Anuar T., Komoo J.I.K., Schueller, L.,Thiebes, B., Booth, L., Abad, J., Baills, A., Fleming, K., Zuccaro, G., Lian, F., Lucy Jones, L., Han, Q., Shaw, R., Lwasa, S. (2019). Integrated Research on Disaster Risk (IRDR). Contributing Paper to the 2019 edition of the Global Assessment Report on Disaster Risk Reduction (GAR 2019). https://www.preventionweb.net/files/65873_f301fahkruddinintegratedresearchond.pdf

Golding, B., Mittermaier, M., Ross, C., Ebert, B., Panchuk, S., Scolobig, A., Johnston, D. (2019). A Value Chain Approach to Optimising Early Warning Systems. Contributing Paper to the 2019 edition of the Global Assessment Report on Disaster Risk Reduction (GAR 2019). https://www.preventionweb.net/files/65828_f212goldingetalvaluechain.pdf

Fundel, V. J., Fleischhut, N., Herzog, S. M., Göber, M., & Hagedorn, R. (2019). Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers, and end-users. Quarterly Journal of Royal Meteorological Society. doi.org/10.1002/qj.3482

 

Lewis HW, Castillo Sanchez JM, Arnold A, Fallmann J, Saulter A, Graham J, Bush M, Siddorn J, Palmer T, Lock A, Edwards J, Bricheno L, Martínez-de la Torre A, Clark, J (2019): The UKC3 regional coupled environmental prediction system. Geoscientific Model Development. 12 (6). 2357-2400. https://doi.org/10.5194/gmd-12-2357-2019

Lewis HW, Castillo Sanchez JM, Siddorn J, King RR, Tonani M, Saulter A, Sykes P, Pequignet A-C, Weedon GP, Palmer T, Staneva J, Bricheno L (2019): Can wave coupling improve operational regional ocean forecasts for the north-west European Shelf? Ocean Science. 15. 669-690. https://doi.org/10.5194/os-15-669-2019

Lewis HW, Siddorn J, Castillo Sanchez JM, Petch J, Edwards JM, Smyth T (2019): Evaluating the impact of atmospheric forcing and air–sea coupling on near-coastal regional ocean prediction. Ocean Science. 15. 761-778. https://doi.org/10.5194/os-15-761-2019

Di Muzio, E., M. Riemer, A. H. M. Fink, and M. Maier-Gerber, 2019: Assessing the predictability of Medicanes in ECMWF ensemble forecasts using an object-based approach, Quart. J. Roy. Meteor. Soc., 145, 1202-1217
JL Catto, S Raveh-Rubin, 2019. Climatology and dynamics of the link between dry intrusions and cold fronts during winter. Part I: global climatology, Climate Dynamics, 53, 1873–1892
S Raveh-Rubin, JL Catto, 2019. Climatology and dynamics of the link between dry intrusions and cold fronts during winter, Part II: Front-centred perspective. Climate Dynamics, 53, 1893–1909
Taylor, A. L., Kause, A., Summers, B., & Harrowsmith, M. (2019). Preparing for Doris: Exploring public responses to impact-based weather warnings in the UK. Weather, Climate, and Society, (2019).


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