Recent upgrades to the Met Office convective-scale ensemble: An hourly time-lagged 5-day ensemble

被引:32
作者
Porson, Aurore N. [1 ]
Carr, Joanne M. [2 ]
Hagelin, Susanna [2 ,3 ]
Darvell, Rob [2 ]
North, Rachel [2 ]
Walters, David [2 ]
Mylne, Kenneth R. [2 ]
Mittermaier, Marion P. [2 ]
Willington, Steve [2 ]
Macpherson, Bruce [2 ]
机构
[1] Univ Reading, Dept Meteorol, MetOff Reading, Reading RG6 7BE, Berks, England
[2] Met Off, Exeter, Devon, England
[3] Swedish Meteorol & Hydrol Inst, Norrkoping, Sweden
关键词
Ensemble spread; time lagging; ensemble verification; PRECIPITATION FORECASTS; PROBABILISTIC FORECASTS; PREDICTION; VERIFICATION; ATMOSPHERE; STRATEGY; WEATHER; SPREAD;
D O I
10.1002/qj.3844
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
In this article, we introduce a new configuration of the Met Office convective-scale ensemble for numerical weather prediction, for the Met Office Global and Regional Ensemble Prediction System over the United Kingdom (MOGREPS-UK). The new version, which became operational in March 2019, uses an hourly time-lagged configuration to take advantage of the hourly 4D-Var data assimilation run in the deterministic UK model with variable horizontal resolution, the UKV. An 18-member ensemble is created by running three members every hour and time-lagging these over a 6 hr window. This configuration is compared against the previous operational configuration, a 6-hourly convective-scale ensemble running 12 members. The main benefits of the time-lagged ensemble are to increase the ensemble size, to add small-scale uncertainties in the initial conditions and to generate more timely forecasts. The time-lagged configuration is shown to objectively improve the forecast at all lead times, with larger improvements in the first few hours. The improvement is seen in the ranked probability scores and is mainly associated with the improvements in the spread of the ensemble with an increase of about 5 to 10% in both summer and winter seasons. A larger ensemble size is necessary in the time-lagged configuration for it to outperform or maintain as good a performance against the previous 6-hourly configuration for all lead times. Alongside the update to an hourly configuration, the forecast length is more than doubled to 120 hr. Objective verification shows that the time-lagged configuration performs better than the high-resolution deterministic, UKV, and the global ensemble, MOGREPS-G, up to T + 120 hr. Increasing the size of the time-lagged ensemble through lagging over additional cycles leads to small but significant improvements, larger in most cases than those that can be obtained through neighbourhood processing.
引用
收藏
页码:3245 / 3265
页数:21
相关论文
共 64 条
  • [1] Development and verification of two convection-allowing multi-model ensembles over Western Europe
    Beck, Jeffrey
    Bouttier, Francois
    Wiegand, Lars
    Gebhardt, Christoph
    Eagle, Chloe
    Roberts, Nigel
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2016, 142 (700) : 2808 - 2826
  • [2] The MOGREPS short-range ensemble prediction system
    Bowler, Neill E.
    Arribas, Alberto
    Mylne, Kenneth R.
    Robertson, Kelvyn B.
    Beare, Sarah E.
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2008, 134 (632) : 703 - 722
  • [3] BRANKOVIC C, 1990, Q J ROY METEOR SOC, V116, P867, DOI 10.1002/qj.49711649405
  • [4] HINTON DIAGRAMS - VIEWING CONNECTION STRENGTHS IN NEURAL NETWORKS
    BREMNER, FJ
    GOTTS, SJ
    DENHAM, DL
    [J]. BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS, 1994, 26 (02): : 215 - 218
  • [5] Brier G.W., 1950, Mon. Weather Rev, V78, P1, DOI DOI 10.1175/1520-0493(1950)0782.0.CO
  • [6] 2
  • [7] UNIFIED MODELING AND PREDICTION OF WEATHER AND CLIMATE A 25-Year Journey
    Brown, Andrew
    Milton, Sean
    Cullen, Mike
    Golding, Brian
    Mitchell, John
    Shelly, Ann
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2012, 93 (12) : 1865 - 1877
  • [8] The first Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL1
    Bush, Mike
    Allen, Tom
    Bain, Caroline
    Boutle, Ian
    Edwards, John
    Finnenkoetter, Anke
    Franklin, Charmaine
    Hanley, Kirsty
    Lean, Humphrey
    Lock, Adrian
    Manners, James
    Mittermaier, Marion
    Morcrette, Cyril
    North, Rachel
    Petch, Jon
    Short, Chris
    Vosper, Simon
    Walters, David
    Webster, Stuart
    Weeks, Mark
    Wilkinson, Jonathan
    Wood, Nigel
    Zerroukat, Mohamed
    [J]. GEOSCIENTIFIC MODEL DEVELOPMENT, 2020, 13 (04) : 1999 - 2029
  • [9] THE COMMUNITY LEVERAGED UNIFIED ENSEMBLE (CLUE) IN THE 2016 NOAA/HAZARDOUS WEATHER TESTBED SPRING FORECASTING EXPERIMENT
    Clark, Adam J.
    Jirak, Israel L.
    Dembek, Scott R.
    Creager, Gerry J.
    Kong, Fanyou
    Thomas, Kevin W.
    Knopfmeier, Kent H.
    Gallo, Burkely T.
    Melick, Christopher J.
    Xue, Ming
    Brewster, Keith A.
    Jung, Youngsun
    Kennedy, Aaron
    Dong, Xiquan
    Markel, Joshua
    Gilmore, Matthew
    Romine, Glen S.
    Fossell, Kathryn R.
    Sobash, Ryan A.
    Carley, Jacob R.
    Ferrier, Brad S.
    Pyle, Matthew
    Alexander, Curtis R.
    Weiss, Steven J.
    Kain, John S.
    Wicker, Louis J.
    Thompson, Gregory
    Adams-Selin, Rebecca D.
    Imy, David A.
    [J]. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2018, 99 (07) : 1433 - 1448
  • [10] Assessing spatial precipitation uncertainties in a convective-scale ensemble
    Dey, Seonaid R. A.
    Plant, Robert S.
    Roberts, Nigel M.
    Migliorini, Stefano
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2016, 142 (701) : 2935 - 2948