Application of a Limited-Area Short-Range Ensemble Forecast System to a Case of Heavy Rainfall in the Mediterranean Region

Dynamical downscaling through coupled regional climate model plays an important role to improve climate information at regional fine-scale, since it modulates information produced by GCM, combining planetary scale processes with regional scale processes. &#160;This study describes the impact of climate change &#160;on rainfall over the Mediterranean region, downscaling, at two different horizontal grid resolutions (0.44 and 0.11 degs), a Global Climate Model (GCM at 0.75 degs) by means of a coupled Regional Climate System Models (RCSM). We analyze the effect of adopting model version with different horizontal resolutions (0.11, 0.44 e 0.75 degs), considering &#160;two climate representative concentration pathways (rcp4.5 and rcp8.5). The spatial pattern on different aspects of precipitation climatology are investigated such as increase/decrease in the intensity of precipitation events, extremes and annual amount of wet days. Moreover, since the grid models cover a wide and complex climate geographic area, the rainfall probability over six sub-regions are calculated: (1) Alps, (2) North-Western coast, (2) South Italy, (3) central part of the Mediterranean sea, (4) Greece Anatolia peninsula and Levantine basin. Although, the evaluation of RCSM downscaling is complex and depends on several factors such as: variables considered, geographic area, topography, model configuration and so on, the results show that it produces an significant improvement, adding information with regards to fine-scale spatial pattern, respect to that provided by GCM.ACKNOWLEDGEMENT: This contribution is based on work conducted by the authors within the SOCLIMPACT project, that has received funding from the European Union&#8217;s Horizon 2020 research and innovation programme under grant agreement No 776661. The fullname of the project is "DownScaling CLImate ImPACTs and decarbonisation pathways in EU islands, and enhancing socioeconomic and non-market evaluation of Climate Change for Europe, for 2050 and Beyond". The opinions expressed are those of the author(s) only and should not be considered as representative of the European Commission&#8217;s official position.Keywords: &#160;widespread heavy rainfall, coupled numerical models, daily rainfall, climate scenarios, climatology of heavy rainfall.&#160;

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Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

Weather and Forecasting ◽

10.1175/waf973.1 ◽

2007 ◽

Vol 22 (1) ◽

pp. 36-55 ◽

Cited By ~ 39

Author(s):

Matthew S. Jones ◽

Brian A. Colle ◽

Jeffrey S. Tongue

Keyword(s):

United States ◽

Wind Speed ◽

Sea Level ◽

Short Range ◽

Warm Season ◽

Ensemble Forecast ◽

Cool Season ◽

Forecast System ◽

Sea Level Pressure ◽

Level Pressure

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

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Short-Range Ensemble Forecasts of Precipitation Type

Weather and Forecasting ◽

10.1175/waf871.1 ◽

2005 ◽

Vol 20 (4) ◽

pp. 609-626 ◽

Cited By ~ 19

Author(s):

Matthew S. Wandishin ◽

Michael E. Baldwin ◽

Steven L. Mullen ◽

John V. Cortinas

Keyword(s):

Short Range ◽

Ensemble Forecast ◽

Ensemble Forecasting ◽

Precipitation Forecast ◽

Initial Condition ◽

Forecast System ◽

Ensemble Forecasts ◽

Probabilistic Forecasts ◽

Precipitation Type ◽

Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

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review of the paper : Sensitivity of forecast skill to the parameterisation of moist convection in a limited-area ensemble forecast system

10.5194/npg-2018-21-rc3 ◽

2018 ◽

Author(s):

Anonymous

Keyword(s):

Ensemble Forecast ◽

Forecast Skill ◽

Limited Area ◽

Moist Convection ◽

Forecast System

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Verification of Extratropical Cyclones within the NCEP Operational Models. Part II: The Short-Range Ensemble Forecast System

Weather and Forecasting ◽

10.1175/2009waf2222170.1 ◽

2009 ◽

Vol 24 (5) ◽

pp. 1191-1214 ◽

Cited By ~ 20

Author(s):

Michael E. Charles ◽

Brian A. Colle

Keyword(s):

United States ◽

Short Range ◽

Wrf Model ◽

Ensemble Forecast ◽

Central Pressure ◽

Extratropical Cyclones ◽

Eastern United States ◽

Forecast System ◽

Eastern Canada ◽

Western Atlantic

Abstract This paper verifies the strengths and positions of extratropical cyclones around North America and the adjacent oceans within the Short Range Ensemble Forecast (SREF) system at the National Centers for Environmental Prediction (NCEP) during the 2004–07 cool seasons (October–March). The SREF mean for cyclone position and central pressure has a smaller error than the various subgroups within SREF and the operational North American Mesoscale (NAM) model in many regions on average, but not the operational Global Forecast System (GFS) for many forecast times. Inclusion of six additional Weather Research and Forecasting (WRF) model members into SREF during the 2006–07 cool season did not improve the SREF mean predictions. The SREF has slightly more probabilistic skill over the eastern United States and western Atlantic than the western portions of the domain for cyclone central pressure. The SREF also has slightly greater probabilistic skill than the combined GFS and NAM for central pressure, which is significant at the 90% level for many regions and thresholds. The SREF probabilities are fairly reliable, although the SREF is overconfident at higher probabilities in all regions. The inclusion of WRF did not improve the SREF probabilistic skill. Over the eastern Pacific, eastern Canada, and western Atlantic, the SREF is overdispersed on average, especially early in the forecast, while across the central and eastern United States the SREF is underdispersed later in the forecast. There are relatively large biases in cyclone central pressure within each SREF subgroup. As a result, the best-member diagrams reveal that the SREF members are not equally accurate for the cyclone central pressure and displacement. Two cases are presented to illustrate examples of SREF developing large errors early in the forecast for cyclones over the eastern United States.

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Evaluation of Short-Range Quantitative Precipitation Forecasts from a Time-Lagged Multimodel Ensemble

Weather and Forecasting ◽

10.1175/2008waf2007053.1 ◽

2009 ◽

Vol 24 (1) ◽

pp. 18-38 ◽

Cited By ~ 18

Author(s):

Huiling Yuan ◽

Chungu Lu ◽

John A. McGinley ◽

Paul J. Schultz ◽

Brian D. Jamison ◽

...

Keyword(s):

Short Range ◽

Mesoscale Model ◽

Horizontal Resolution ◽

Ensemble Forecast ◽

Local Analysis ◽

Lead Times ◽

Multimodel Ensemble ◽

Forecast System ◽

Quantitative Precipitation Forecasts ◽

Time Lagged

Abstract Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.

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