Dependence of skill and spread of the ensemble forecasts on the type of perturbation and its relationship with long term norms of precipitation and temperature

• Autorzy: Duniec Grzegorz , Mazur Andrzej

Identyfikatory

DOI

10.1007/s11600-020-00467-4

• Języki publikacji: EN

Abstrakty

EN

A new computing cluster has been operating since 2016 at the Institute of Meteorology and Water Management – National Research Institute. Increasing computing power enabled the implementation of ensemble prediction system forecasts in the operational mode and the use of a new computer for research purposes. As part of the priority project on “Study of Disturbances in the Representation of Modeling Uncertainty in Ensemble Development” and the earlier project entitled “COSMO Towards Ensemble in Km in Our Countries), implemented in the Working Group 7 (Predictability and Ensemble Methods) as part of the COSMO modeling consortium, specifc studies were carried out to test ensemble forecasts. This research concerned the impact of variability of physical felds characterizing the soil surface (a selected parameter determining evaporation from the soil surface and soil surface temperature) using various methods of perturbation. Numerical experiments were completed for the warm period (from June to September) 2013.

Słowa kluczowe

EN

meteorological model; COSMO; perturbation schemes; long-term norms; ensemble prediction system

PL

model meteorologiczny; COSMO; schematy zaburzeń; normy długoterminowe

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 5
• Strony: 1505--1528
• Opis fizyczny: Bibliogr. 9 poz.

Twórcy

autor

Duniec Grzegorz

• Institute of Meteorology and Water Management – National Research Institute, 61 Podleśna street, 01673 Warsaw, Poland

autor

Mazur Andrzej

• Institute of Meteorology and Water Management – National Research Institute, 61 Podleśna street, 01673 Warsaw, Poland

Bibliografia

• 1. Bulletin of the National Hydrological and Meteorological Service (referred to as Bulletin) (2013), Institute of Meteorology and Water Management-National Research Institute, Poland
• 2. Cano R, Sordo C, Gutiérrez JM (2004) Applications of bayesian networks in meteorology. In: Gámez JA, Moral S, Salmerón A (eds) Advances in bayesian networks. Studies in fuzziness and soft computing, vol 146. Springer, Berlin
• 3. Doms G, Foerstner J, Heise E, et al (2011) A description of Nonhydrostatic Regional COSMO Model, Part II: physical parameterization, DWD. (as of June 20th, 2019). http://www.cosmo-model.org/content/model/documentation/core/cosmoPhysParamtr.pdf
• 4. Duniec G, Interewicz W, Mazur A, Wyszogrodzki A (2017) Operational setup of the soil-perturbed, time-lagged ensemble prediction system at the Institute of Meteorology and Water Management – National Research Institute. Meteorol Hydrol Water Manag 5(2):43–51. https://doi.org/10.26491/mhwm/71048
• 5. Hong X, Bishop C (2013) Ocean ensemble forecasting and adaptive sampling. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic and hydrologic applications, vol 2. Springer, Berlin
• 6. Jolliffe IT, Stephenson DB (2012) Forecast verification—a practitioner’s guide in atmospheric science, vol 2. Wiley, Chichester. https://doi.org/10.1002/9781119960003.ch7
• 7. Stensrud DJ (2007) Parameterization schemes: key to understanding numerical weather prediction models. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511812590
• 8. Zängl G, Reinert D, Rpodas P, Baldauf M (2015) The ICON (ICOsahedral Non-hydrostatic) modeling framework of DWD and MPI-M: description of the non-hydrostatic dynamical core. Q J R Meteorol Soc 141(687):563–579. https://doi.org/10.1002/qj.2378
• 9. Zhao Q, Haack T, McLay J, Reynolds C (2016) Ensemble prediction of atmospheric refractivity conditions for EM propagation. J Appl Meteorol Climatol 55(10):2113–2130. https://doi.org/10.1175/JAMC‐D‐16‐0033.1