High Resolution Deterministic Prediction System (HRDPS) Simulations of Manitoba Lake Breezes

2016 ◽  
Vol 54 (2) ◽  
pp. 93-107 ◽  
Author(s):  
Scott Kehler ◽  
John Hanesiak ◽  
Michelle Curry ◽  
David Sills ◽  
Neil Taylor
Keyword(s):  
High Resolution ◽  
Prediction System ◽  
Lake Breezes

scholarly journals Assessment of Doppler Radar Radial Wind Observation Quality from Different Echo Sources for Assimilation during the Sydney 2014 Forecast Demonstration Project

2018 ◽  
Vol 35 (8) ◽  
pp. 1605-1620 ◽  
Author(s):  
Susan Rennie ◽  
Peter Steinle ◽  
Alan Seed ◽  
Mark Curtis ◽  
Yi Xiao
Keyword(s):  
Quality Control ◽  
Control System ◽  
Statistical Analysis ◽  
High Resolution ◽  
Radial Velocity ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Demonstration Project ◽  
Prediction System

AbstractA new quality control system, primarily using a naïve Bayesian classifier, has been developed to enable the assimilation of radial velocity observations from Doppler radar. The ultimate assessment of this system is the assimilation of observations in a pseudo-operational numerical weather prediction system during the Sydney 2014 Forecast Demonstration Project. A statistical analysis of the observations assimilated during this period provides an assessment of the data quality. This will influence how observations will be assimilated in the future, and what quality control and errors are applicable. This study compares observation-minus-background statistics for radial velocities from precipitation and insect echoes. The results show that with the applied level of quality control, these echo types have comparable biases. With the latest quality control, the clear air observations of wind are apparently of similar quality to those from precipitation and are therefore suitable for use in high-resolution NWP assimilation systems.

scholarly journals The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts

2003 ◽  
Vol 10 (3) ◽  
pp. 261-274 ◽  
Author(s):  
A. Montani ◽  
C. Marsigli ◽  
F. Nerozzi ◽  
T. Paccagnella ◽  
S. Tibaldi ◽  
...  
Keyword(s):  
High Resolution ◽  
Southern Italy ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Limited Area Model ◽  
Area Model

Abstract. The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so as to generate a highly-populated "super-ensemble". Then, five members are selected from the super-ensemble and used to provide initial and boundary conditions for the integrations with a limited-area model, whose runs generate a Limited-area Ensemble Prediction System (LEPS). The relative impact of targeting the initial perturbations against increasing the horizontal resolution is assessed for the global ensembles as well as for the properties transferred to LEPS integrations, the attention being focussed on the probabilistic prediction of rainfall over a localised area. At the 108, 84 and 60- hour forecast ranges, the overall performance of the global ensembles is not particularly accurate and the best results are obtained by the high-resolution version of EPS. The LEPS performance is very satisfactory in all configurations and the rainfall maps show probability peaks in the correct regions. LEPS products would have been of great assistance to issue flood risk alerts on the basis of limited-area ensemble forecasts. For the 60-hour forecast range, the sensitivity of the results to the LEPS ensemble size is discussed by comparing a 5-member against a 51-member LEPS, where the limited-area model is nested on all EPS members. Little sensitivity is found as concerns the detection of the regions most likely affected by heavy precipitation, the probability peaks being approximately the same in both configurations.

scholarly journals An Evaluation of MÉRA, a High-Resolution Mesoscale Regional Reanalysis

2018 ◽  
Vol 57 (9) ◽  
pp. 2179-2196 ◽  
Author(s):  
Eoin Whelan ◽  
Emily Gleeson ◽  
John Hanley
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Climate ◽  
Prediction System ◽  
High Quality ◽  
Regional Reanalysis ◽  
Horizontal Grid ◽  
Very High

AbstractMet Éireann, the Irish Meteorological Service, has generated a very high resolution (2.5-km horizontal grid) regional climate reanalysis for Ireland called the Met Éireann Reanalysis (MÉRA). MÉRA spans the period from 1981 to 2015 and was produced using the shared ALADIN–HIRLAM numerical weather prediction system. This article includes comparisons with the ERA-Interim and Uncertainties in Ensembles of Regional Reanalyses (UERRA) datasets, analysis of data assimilation outputs, precipitation comparisons, and a focus on extremes of wind and rainfall. The comparisons with the reanalysis datasets show that MÉRA provides a high-quality reconstruction of recent Irish climate and benefits from the use of a very high resolution grid, in particular in relation to wind and precipitation extremes.

Application and Verification of the ECMWF Precipitation Type Forecast Product (PTYPE) in China

2020 ◽  
Author(s):  
Quan Dong ◽  
Feng Zhang ◽  
Ning Hu ◽  
Zhiping Zong
Keyword(s):  
High Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Probability Forecast ◽  
Freezing Rain ◽  
Ensemble Prediction System ◽  
Optimal Probability ◽  
Probability Threshold ◽  
Precipitation Type

<p>The ECMWF (European Centre for Medium-Range Weather Forecasts) precipitation type forecast products—PTYPE are verified using the weather observations of more than 2000 stations in China of the past three winter half years (October to next March). The products include the deterministic forecast from High-resolution model (HRE) and the probability forecast from ensemble prediction system (EPS). Based on the verification results, optimal probability thresholds approaches under criteria of TS maximization (TSmax), frequency match (Bias1) and HSS maximization (HSSmax) are used to improve the deterministic precipitation type forecast skill. The researched precipitation types include rain, sleet, snow and freezing rain.</p><p>The verification results show that the proportion correct of deterministic forecast of ECMWF high-resolution model is mostly larger than 90% and the TSs of rain and snow are high, next is freezing rain, and the TS of sleet is small indicating that the forecast skill of sleet is limited. The rain and snow separating line of deterministic forecasts show errors of a little south in short-range and more and more significant north following elongating lead times in medium-range. The area of sleet forecasts is smaller than observations and the freezing rain is bigger for the high-resolution deterministic forecast. The ensemble prediction system offsets these errors partly by probability forecast. The probability forecast of rain from the ensemble prediction system is smaller than the observation frequency and the probability forecast of snow is larger in short-range and smaller in medium-range than the observation frequency. However, there are some forecast skills for all of these probability forecasts. There are advantages of ensemble prediction system compared to the high-resolution deterministic model. For rain and snow, for some special cost/loss ratio events the EPS is better than the HRD. For sleet and freezing rain, the EPS is better than the HRD significantly, especially for the freezing rain.</p><p>The optimal thresholds of snow and freezing rain are largest which are about 50%~90%, decreasing with elongating lead times. The thresholds of rain are small which are about 10%~20%, increasing with elongating lead times. The thresholds of sleet are the smallest which are under 10%. The verifications show that the approach of optimal probability threshold based on EPS can improve the forecast skill of precipitation type. The proportion correct of HRD is about 92%. Bias1 and TSmax improve it and the improvement of HSSmax is the most significant which is about 94%. The HSS of HRD is about 0.77~0.65. Bias1 increases 0.02 and TSmax increases more. The improvement of HSSmax is the biggest which is about 0.81~0.68 and the increasing rate is around 4%. From the verifications of every kinds of precipitation types, it is demonstrated that the approach of optimal probability threshold improves the performance of rain and snow forecasts significantly compared to the HRD and decreases the forecast area and missing of freezing rain and sleet which are forecasted more areas and false alarms by the HRD.</p><p><strong>Key words: </strong>ECMWF; ensemble prediction system；precipitation type forecast; approach of optimal probability threshold; verification</p>

scholarly journals The Pan-Canadian High Resolution (2.5 km) Deterministic Prediction System

2016 ◽  
Vol 31 (6) ◽  
pp. 1791-1816 ◽  
Author(s):  
Jason A. Milbrandt ◽  
Stéphane Bélair ◽  
Manon Faucher ◽  
Marcel Vallée ◽  
Marco L. Carrera ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Forecast Model ◽  
Added Value ◽  
Precipitation Forecast ◽  
Initial Surface ◽  
Prediction System ◽  
Global Environmental ◽  
Fractional Cloud ◽  
Land Data Assimilation System

Abstract Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

scholarly journals The ALADIN System and its Canonical Model Configurations AROME CY41T1 and ALARO CY40T1

2017 ◽  
Author(s):  
Piet Termonia ◽  
Claude Fischer ◽  
Eric Bazile ◽  
François Bouyssel ◽  
Radmila Brožková ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Forecast Model ◽  
Canonical Model ◽  
Limited Area ◽  
Prediction System ◽  
Climate Simulations ◽  
Process Studies

Abstract. The ALADIN System is a numerical weather prediction system (NWP) developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 Partner Institutes of this consortium. These configurations are called the ALADIN Canonical Model Configurations (CMCs). There are currently three CMCs: the ALADIN baseline-CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs and to document their most recent versions, and (iii) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the Partner Institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

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