Limited-area Ensemble Prediction System Research Articles

LENS-GRM Applicability Analysis and Evaluation

Recently, there have been many abnormal natural phenomena caused by climate change. Anthropogenic factors associated with insufficient water resource management can be another cause. Among natural causes, rainfall intensity and volume often induce flooding. Therefore, accurate rainfall estimation and prediction can prevent and mitigate damage caused by these hazards. Sadly, uncertainties often hinder accurate rainfall forecasting. This study investigates the uncertainty of the Korean rainfall ensemble prediction data and runoff analysis model in order to enhance reliability and improve prediction. The objectives of this study include: (i) evaluating the spatial characteristics and applicability of limited area ensemble prediction system (LENS) data; (ii) understanding uncertainty using parameter correction and generalized likelihood uncertainty estimation (GLUE) and grid-based rainfall-runoff model (GRM); (iii) evaluating models before and after LENS-GRM correction. In this study, data from the Wicheon Basin was used. The informal likelihood (R2, NSE, PBIAS) and formal likelihood (log-normal) were used to evaluate model applicability. The results confirmed that uncertainty of the behavioral model exists using the likelihood threshold when applying the runoff model to rainfall forecasting data. Accordingly, this method is expected to enable more reliable flood prediction by reducing the uncertainties of the rainfall ensemble data and the runoff model when selecting the behavioral model for the user’s uncertainty analysis. It also provides a basis for flood prediction studies that apply rainfall and geographical characteristics for rainfall-runoff uncertainty analysis.

Probabilistic Forecast of Visibility at Gimpo, Incheon, and Jeju International Airports Using Weighted Model Averaging

In this study, weighted model averaging (WMA) was applied to calibrating ensemble forecasts generated using Limited-area ENsemble prediction System (LENS). WMA is an easy-to-implement post-processing technique that assigns a greater weight to the ensemble member forecast that exhibits better performance; it is used to provide probabilistic visibility forecasting in the form of a predictive probability density function for ensembles. The predictive probability density function is a mixture of discrete point mass and two-sided truncated normal distribution components. Observations were obtained at Gimpo, Incheon, and Jeju International Airports, and 13 ensemble member forecasts were obtained using LENS, for the period of December 2018 to June 2019. Prior to applying WMA, a reliability analysis was conducted using rank histograms and reliability diagrams to identify the statistical consistency between the ensembles and the corresponding observations. The WMA method was then applied to each raw ensemble model, and a weighted predictive probability density function was proposed. Performances were evaluated using the mean absolute error, the continuous ranked probability score, the Brier score, and the probability integral transform. The results showed that the proposed method provided improved performance compared with the raw ensembles, indicating that the raw ensembles were well calibrated using the predicted probability density function.

Model Uncertainty Representation in a Convection-Permitting Ensemble—SPP and SPPT in HarmonEPS

Abstract The stochastically perturbed parameterizations scheme (SPP) is here implemented and tested in HarmonEPS—the convection-permitting limited area ensemble prediction system by the international research program High Resolution Limited Area Model (HIRLAM) group. SPP introduces stochastic perturbations to values of chosen closure parameters representing efficiencies or rates of change in parameterized atmospheric (sub)processes. The impact of SPP is compared to that of the stochastically perturbed parameterization tendencies scheme (SPPT). SPP in this first version in HarmonEPS perturbs 11 parameters, active in different atmospheric processes and under various weather conditions. The main motivation for this study is the lack of variability seen in cloud products in HarmonEPS, as reported by duty forecasters. SPP in this first version is able to increase variability in a range of weather variables, including the cloud products. However, for some weather variables the root-mean-squared error of the ensemble mean is increased and the mean bias is impacted, especially in winter. This indicates that (some) parameter perturbation distributions are not optimal in the current configuration, and a further sensitivity analysis is required. SPPT resulted in a smaller increase in variability in the ensemble, but the impact was almost completely masked out when combined with perturbations of the initial state, lateral boundaries, and surface properties. An in-depth investigation into this lack of impact from SPPT is here presented through examining, among other things, accumulated tendencies from the model physics. Significance Statement Small inaccuracies, simplifications, or errors in any part of a complex and nonlinear system like a weather model can amplify and in a short time become significant. We wanted to introduce a physically consistent way of representing these uncertainties in a model that is used in several European countries. To do this we introduce variations in a few parameters that are used in the model description, and that we know are uncertain. By doing this we were able to increase the variability of the cloud products as desired. We see this as a promising approach for capturing the possibilities of fog occurring or not in this model. Further refinements are needed before it can be used in operational weather forecasts.

Probabilistic Forecast of Low Level Wind Shear of Gimpo, Gimhae, Incheon and Jeju International Airports Using Ensemble Model Output Statistics

Wind shear can occur at all flight levels; however, it is particularly dangerous at low levels, from the ground up to approximately 2000 feet. If this phenomenon can occur during the take-off and landing of an aircraft, it may interfere with the normal altitude change of the aircraft, causing delay and cancellation of the aircraft, as well as economic damage. In this paper, to estimate the probabilistic forecasts of low-level wind shear at Gimpo, Gimhae, Incheon and Jeju International Airports, an Ensemble Model Output Statistics (EMOS) model based on a left-truncated normal distribution with a cutoff zero was applied. Observations were obtained from Gimpo, Gimhae, Incheon and Jeju International Airports and 13 ensemble member forecasts generated from the Limited-Area Ensemble Prediction System (LENS), for the period December 2018 to February 2020. Prior to applying to EMOS models, statistical consistency was analyzed by using a rank histogram and kernel density estimation to identify the uniformity of ensembles with corresponding observations. Performances were evaluated by mean absolute error, continuous ranked probability score and probability integral transform. The results showed that probabilistic forecasts obtained from the EMOS model exhibited better prediction skills when compared to the raw ensembles.

Inter-Comparison of Ensemble Forecasts for Low Level Wind Shear against Local Analyses Data over Jeju Area

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operations and management. In this study, we conducted an accuracy and reliability analysis using a rank histogram, Brier score, and reliability diagram to verify LLWS ensemble member forecasts based on grid points over the Jeju area of the Republic of Korea. Thirteen LLWS ensemble member forecasts derived from a limited area ensemble prediction system (LENS) were obtained between 1 July 2016 and 30 May 2018, and 3-h LLWS forecasts for lead times up to 72 h (three days) were issued twice a day at 0000 UTC (9 am local time) and 1200 UTC (9 pm local time). We found that LLWS ensemble forecasts have a weak negative bias in summer and autumn and a positive bias in the spring and winter; the forecasts also have under-dispersion for all seasons, which implies that the ensemble spread of an ensemble is smaller than that of the corresponding observations. Additionally, the reliability curve in the associated reliability diagram indicates an over-forecasting of LLWS events bias. The selection of a forecast probability threshold from the LLWS ensemble forecast was confirmed to be one of the most important factors for issuing a severe LLWS warning. A simple method to select a forecast probability threshold without economic factors was conducted. The results showed that the selection of threshold is more useful for issuing a severe LLWS warning than none being selected.

Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

AbstractWe investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

Bias Correction of Short-Range Ensemble Forecasts of Daily Maximum Temperature Using Decaying Average

In this study, we assessed the performance of the decaying average bias correction method in removing the systematic error in daily maximum temperature (dTmax) ensemble forecasts. We applied the technique to a short-range high-resolution limited-area ensemble prediction system of the Korea Meteorological Administration, which shows under-predictive and under-dispersive characteristics for dTmax. The bias correction was applied to the grid of the model using spatial interpolation of the decaying average bias from surrounding reference points. The method was verified by evaluating the accuracy of the ensemble mean, spread-skill relationship, and the performance of the probabilistic forecasts. The results showed that the decaying average technique minimized the systematic error in the ensemble mean and improved the performance of the probabilistic forecasts. The overall mean absolute error of the ensemble mean was lowered from 2.2 to 1.2 ∘C and the root-mean-square error from 2.5 to 1.6 ∘C. The continuous ranked probability score decreased from 1.9 to 1.0 ∘C. The reliability of three dichotomous events also improved and the Brier skill scores increased. However, the bias correction only slightly affected the ensemble spread, and the system remained under-dispersive.

Effects of Resolution, Cumulus Parameterization Scheme, and Probability Forecasting on Precipitation Forecasts in a High-Resolution Limited-Area Ensemble Prediction System

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

Effect of Considering Sub-Grid Scale Uncertainties on the Forecasts of a High-Resolution Limited Area Ensemble Prediction System

The ensemble prediction system (EPS) is widely used in research and at operation center because it can represent the uncertainty of predicted atmospheric state and provide information of probabilities. The high-resolution (so-called “convection-permitting”) limited area EPS can represent the convection and turbulence related to precipitation phenomena in more detail, but it is also much sensitive to small-scale or sub-grid scale processes. The convection and turbulence are represented using physical processes in the model and model errors occur due to sub-grid scale processes that were not resolved. This study examined the effect of considering sub-grid scale uncertainties using the high-resolution limited area EPS of the Korea Meteorological Administration (KMA). The developed EPS has horizontal resolution of 3 km and 12 ensemble members. The initial and boundary conditions were provided by the global model. The Random Parameters (RP) scheme was used to represent sub-grid scale uncertainties. The EPSs with and without the RP scheme were developed and the results were compared. During the one month period of July, 2013, a significant difference was shown in the spread of 1.5 m temperature and the Root Mean Square Error and spread of 10 m zonal wind due to application of the RP scheme. For precipitation forecast, the precipitation tended to be overestimated relative to the observation when the RP scheme was applied. Moreover, the forecast became more accurate for heavy precipitations and the longer forecast lead times. For two heavy rainfall cases occurred during the research period, the higher Equitable Threat Score was observed for heavy precipitations in the system with the RP scheme compared to the one without, demonstrating consistency with the statistical results for the research period. Therefore, the predictability for heavy precipitation phenomena that affected the Korean Peninsula increases if the RP scheme is used to consider sub-grid scale uncertainties in forecasting precipitation phenomena using the high-resolution limited area EPS of KMA.

Ensemble Prediction with Different Spatial Resolutions for the 2014 Sochi Winter Olympic Games: The Effects of Calibration and Multimodel Approaches

Abstract Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place during the Winter Olympic Games in Sochi, Russia. The global, synoptic-scale ensemble system used is the IFS ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), and its performance is compared with both the operational pan-European Grand Limited Area Ensemble Prediction System (GLAMEPS) at 11-km horizontal resolution and the experimental regional convection-permitting HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) EPS (HarmonEPS) at 2.5 km. Both GLAMEPS and HarmonEPS are multimodel systems, and it is seen that a large part of the skill in these systems comes from the multimodel approach, as long as all subensembles are performing reasonably. The number of members has less impact on the overall skill measurement. The relative importance of resolution and calibration is also assessed. Statistical calibration was applied and evaluated. In contrast to what is seen for the raw ensembles, the number of members, as well as the number of subensembles, is important for the calibrated ensembles. HarmonEPS shows greater potential than GLAMEPS for predicting wintertime weather, and also has an advantage after calibration.

Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system

The increasing availability of operational limited area ensemble prediction systems (LEPS) opens up new opportunities for the application of weather forecasts in agriculture and water resource management. This study aims to evaluate the performances of probabilistic daily reference crop evapotranspiration (ET0) forecasts with lead times up to 5days and a spatial resolution of 7km, computed by using COSMO-LEPS outputs (provided by the European Consortium for small–scale modelling, COSMO), in a region of southern Italy known for its complex topography in proximity to the Mediterranean coastline. ET0 was estimated by means of three different estimation methods, i.e. the Hargreaves-Samani (HS), Priestley-Taylor (PT) and FAO Penman-Monteith (PM) equations, in order to assess the size of the weather forecast errors with models of different accuracies. Forecasts were verified with ground-based data from 18 automatic weather stations, and for two irrigation seasons. Performances were assessed with both deterministic indices, including BIAS, RMSE, correlation coefficients and coefficients of variation of the 16-member ensemble forecasts, and probabilistic metrics, such as the Brier skill score, reliability diagrams and relative operating characteristic. ET0 forecasts with PM equation were robust and reliable, with slight sensitivity to the forecast lead time. High performances were also achieved with HS and PT equations, except for locations close to the coastline, where large systematic errors affect the numerical weather forecasts.

기상청 고해상도 국지 앙상블 예측 시스템 구축 및 성능 검증

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

A novel application of an analog ensemble for short-term wind power forecasting

The efficient integration of wind in the energy market is limited by its natural variability and predictability. This limitation can be tackled by using the probabilistic predictions that provide accurate deterministic forecasts along with a quantification of their uncertainty. We propose as a novelty the application of an analog ensemble (AnEn) method to generate probabilistic wind power forecasts (WPF). The AnEn prediction of a given variable is constituted by a set of measurements of the past, concurrent to the past forecasts most similar to the current one. The AnEn performance for WPF is compared with three state-of-the-science methods for probabilistic predictions over a wind farm and a 505-day long period: a wind power prediction based on the ensemble wind forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (ECMWF-EPS), the Limited-area Ensemble Prediction System (LEPS) developed within the COnsortium for Small-scale MOdelling (COSMO-LEPS) and a quantile regression (QR) technique. The AnEn performs as well as ECMWF-EPS, COSMO-LEPS and QR for common events while it exhibits more skill for rare events. A comparison with the performances obtained with a deterministic forecasting method based on a Neural Network is also carried out showing the benefits of using AnEn.

The benefit of climatological and calibrated reforecast data for simulating hydrological droughts in Switzerland

Streamflow droughts are characterized by reduced runoff and can cause significant damage to nature and society. Forecasts of streamflow droughts could improve risk management and preparedness. How the quality of the meteorological forcing of a hydrological model affects the hydrological response during streamflow droughts was explored in case studies of two Swiss rivers: the Thur, which is mainly rain-fed and the Landquart, which is dominantly snow-fed. A fully distributed version of the PREcipitation-Runoff-EVApotranspiration HRU model (PREVAH) was used with input from: (1) meteorological observations, (2) reforecast from the probabilistic numerical weather prediction model Limited-area Ensemble Prediction System developed and run by the COnsortium for Small-scale MOdelling (COSMO-LEPS) and (3) COSMO-LEPS reforecasts that are calibrated with the bias correction method quantile mapping. The meteorological input variables such as precipitation, air temperature, wind speed, relative humidity, global radiation and sunshine duration are required to initialize PREVAH. Different combinations of the input variables, for example observed precipitation with the other variables from COSMO-LEPS, were used to assess the sensitivity of each input variable to the performance of the low-flow simulation (1981–2000). Streamflow droughts are defined by indices of duration, severity and magnitude, by applying a seasonally varying threshold. Simulations for low-flow and streamflow drought performed better when meteorological observations or calibrated COSMO-LEPS reforecasts were used as forcing instead of the COSMO-LEPS reforecast. It was sufficient to replace precipitation with observations or calibrated COSMO-LEPS values and keep the other variables from COSMO-LEPS to improve the low-flow simulations. Depending on the catchment characteristics, better quality of temperature and relative humidity may also be relevant.

Calibration of Limited-Area Ensemble Precipitation Forecasts for Hydrological Predictions

Abstract The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003–07 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The calibration provided a beneficial impact for the ensemble forecast over Switzerland and Germany; whereas, it resulted as less effective for Emilia-Romagna. The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast–analysis relation was not so strong for the available training dataset. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall–runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions

Abstract. Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km), and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system). The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno River (northern Italy), for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the involved meteorological models. Finally, a different behaviour comes out at different forecast ranges. For short ranges, the impact of boundary conditions is weaker and the spread can be mainly attributed to the different characteristics of the models. At longer forecast ranges, the similar behaviour of the multi-model members forced by the same large-scale conditions indicates that the systems are governed mainly by the boundary conditions, although the different limited area models' characteristics may still have a non-negligible impact.

Added economic value of limited area multi-EPS weather forecasting applications

ABSTRACTWe compare the GLAMEPS system, a pan-European limited area ensemble prediction system, with ECMWF's EPS over Belgium for an extended period from March 2010 until the end of December 2010. In agreement with a previous study, we find GLAMEPS scores considerably better than ECMWF's EPS. To compute the economic value, we introduce a new relative economic value score for continuous forecasts. The added value of combining the GLAMEPS system with the LAEF system over Belgium is studied. We conclude that adding LAEF to GLAMEPS increases the value, although the increase is small compared to the improvement of GLAMEPS to ECMWF's EPS. As an added benefit we find that the combined GLAMEPS-LAEF multi-EPS system is more robust, that is, it is less vulnerable to the (accidental) removal of one of its components.

Comparing different meteorological ensemble approaches: hydrological predictions for a flood episode in Northern Italy

Abstract. Within the framework of coupled meteorological-hydrological predictions, this study aims at comparing two high-resolution meteorological ensembles, covering short and medium range. The two modelling systems have similar characteristics, as almost the same number of members, the model resolution (about 7 km), the driving ECMWF global ensemble prediction system, but are obtained through different methodologies: the former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF), while the latter follows a single-model approach, based on COSMO-LEPS (Limited-area Ensemble Prediction System), the operational ensemble forecasting system developed within the COSMO consortium. Precipitation forecasts are evaluated in terms of hydrological response, after coupling the meteorological models with a distributed rainfall-runoff model (TOPKAPI) to simulate the discharge of the Reno river (Northern Italy), for a severe weather episode. Although a single case study does not allow for robust and definite conclusions, the comparison among different predictions points out a remarkably better performance of mesoscale model ensemble forecasts compared to global ones. Moreover, the multi-model ensemble outperforms the single model approach.

High-resolution ensemble prediction of a polar low development

Severe weather is frequently associated with polar lows over ice-free waters during Arctic winter. We propose a high-resolution, limited area ensemble prediction system (EPS) to enable early warnings of such events. The system (UMEPS) employs the UK Met Office non-hydrostatic Unified Model at 4-km resolution to downscale the 21 ensemble members of the HIRLAM-based LAMEPS run twice daily with 12-km resolution at met.no since February 2008. LAMEPS includes a 3DVar-based control forecast, although initial and boundary perturbations are taken from a version of EPS at ECMWF with perturbations targeted to Northern Europe (TEPS). The added value of UMEPS is evaluated for one polar low during the March 2008 IPY-THORPEX campaign. Forecast probabilities, pseudo-satellite pictures, polar low tracks and strike probability maps are compared with observational data. The forecast quality depends crucially on the size and location of the UMEPS domain. When sufficiently large, the influence from data imposed at the lateral boundaries can be reduced by a careful domain selection. The results are sensitive to the model’s parameterizations of physical processes. Although preliminary, this study indicates that with a short-range, high-resolution UMEPS, potentially valuable warnings of extreme weather can be given up to 2 days in advance.

Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges

In this work, the main characteristics of COSMO-LEPS, the Limited-area Ensemble Prediction System developed in the framework of the COnsortium for Small-scale MOdelling, are presented. The present status of the system is shown with the description of the methodology and of the main upgrades which took place during its years of activity. The performance of COSMO-LEPS for the probabilistic prediction of precipitation is assessed in terms of both time-series and seasonal scores over a 7-yr period. A fixed number of stations are selected and observations are compared to short and early medium-range forecasts. Different verification indices are used to assess the skill of COSMO-LEPS and to identify the impact of system modifications on forecast skill. The different system upgrades are found to impact positively on COSMO-LEPS performance, with a gain of 2 d of predictability in the last 4 yr of operational forecasts. This holds when the skill of the system is assessed both for single events (e.g. precipitation surpassing a fixed threshold) and for multi-event situations. Scores for fixed forecast ranges but varying thresholds confirm increasingly better performance of the system. For a few seasons, the performance of COSMO-LEPS is also assessed in terms of probabilistic prediction of some upper-air variables. Then, the skill of COSMO-LEPS is compared to that of the global-ensemble system providing the boundaries, to identify the extent to which skill improvements may relate to those of the driving ensemble. Finally, the main streams of development for COSMO-LEPS system are discussed with future possible upgrades and methodology modifications.