European Centre For Medium-Range Weather Forecasts Ensemble Research Articles

Predictability of convective precipitation for West Africa: verification of convection-permitting and global ensemble simulations

Within the framework of this investigation, convection-permitting (CP) ensemble forecasts were generated for West Africa by combining different initial and lateral boundary conditions (IBCs) with perturbations that address the uncertainty of land-surface atmosphere interactions (land-surface perturbations). For a multi-analysis setup, IBCs were taken from model analyses of different global models; for a single-model setup, they were selected from the ensemble system of the European Centre for Medium-range Weather Forecasts (ECMWF). The different ensemble setups were assessed using common probabilistic scores as well as by spatial forecast verification of precipitation generated mainly by convective systems during the West African monsoon season. Additionally, it was investigated whether the CP ensemble forecasts were superior to the ECMWF ensemble forecasts.Probabilistic scores were higher for the single-model ensemble than for the multi-analysis setup, but the latter displayed a larger dispersion and more extreme scenarios. From this, it is concluded that the different model analyses can differ strongly from each other. The land-surface perturbations were able to generate sufficient complementary spread. While the CP simulations showed a stronger negative precipitation bias in the southernmost region near the Guinean coast, the ECMWF simulations exhibited a negative bias further north in the Sahel region, where larger convective systems occur less frequently. Not in all cases did the CP ensemble versions produce better probabilistic scores than the global ensemble forecasts, but they yielded larger spread and less underdispersion. Rank histograms, though, were also influenced by the different structure of the precipitation patterns of the global and CP forecasts. Scores improved when using a later version of the CP model as well as with the skill of the global ensemble forecasts used as IBCs. Altogether, the proposed realization of CP ensemble forecasts is found to be suited for the prediction of convective precipitation in West Africa.

Selective ensemble‐mean technique for tropical cyclone track forecast by using time‐lagged ensemble and multi‐centre ensemble in the western North Pacific

This article investigates the impacts of both the time‐lagged ensemble technique and the multi‐centre ensemble prediction system (EPS) on tropical cyclone (TC) track forecasts. Four EPSs from European Centre for Medium‐range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP) and China Meteorological Administration (CMA) participating in the TIGGE project are considered, and 60 TCs in the western North Pacific from 2010 to 2012 are investigated. The time‐lagged ensemble uses single‐centre EPS (SCE) initialized at different times to increase ensemble members for specific forecast times. Both the means of selected EPS members (SEAV) method and the ensemble‐mean method are applied to the time‐lagged ensemble, respectively. Verification results show that the time‐lagged ensemble yields marginal changes in forecast position errors compared with its zero‐lag counterparts for all SCEs by applying the SEAV method. The mean forecast position errors by the ensemble‐mean method are greater when adding previous data compared with the SEAV method.A variety of multi‐centre EPSs (MCEs) composed of SEAV‐integrated SCEs are investigated. These MCEs, labelled according to their component SCEs, are EC&NCEP, EC&CMA, EC&JMA, EC&NCEP&CMA, EC&NCEP&JMA and EC&NCEP&CMA&JMA. The EC&NCEP MCE, for example, is a SEAV‐integrated combination of the ECMWF and NCEP ensemble TC forecast tracks. It is found that MCE track forecasts are more accurate than SCE track forecasts. EC&NCEP has the best performance among all MCEs. In addition, MCEs are compared with an equally weighted consensus of deterministic model forecasts (MCDs). The MCEs outperform their corresponding MCDs at 24 h. Sensitivity tests on the weights of component SCEs in SEAV‐derived MCEs are conducted. For lead times within 72 h, if the component SCEs in an MCE have comparable performances, the MCE with equal weights of SCEs will be more accurate than those of non‐equal weights of SCEs. Otherwise, non‐equal weights of SCEs are more suitable for MCEs. However, for lead times longer than 72 h, the above result is not convincing.

An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting

Solar energy production is steadily growing in several countries. Depending on meteorological variables such as solar irradiance, cloud cover and temperature, solar power production predictability is limited by the chaotic nature of these parameters. This work presents an application of the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) to produce short-term probabilistic solar power forecasts (SPF). The EPS is based on running a meteorological model multiple times, starting from slightly perturbed initial conditions. The distribution of these different runs allows estimating the prediction uncertainty. We use about two years of power data from three solar farms located in different parts of Italy, characterized by different climatic conditions. We retrieve ensemble members of meteorological variables to build a power probability density function (PDF) for 0–72h forecast horizons. A neural network (NN) is applied to reduce model bias and to generate a PDF of power starting from the ensemble of meteorological variables. Then two statistical methods are applied to calibrate further the ensemble of power predictions, which results in the final probabilistic power forecast. The first technique has been already tested for wind power forecasting (WPF), and it is based on estimating the variance deficit (VD) or “lack of spread” of the ensemble distribution. The second is a well-established method to calibrate probabilistic predictions based on Ensemble Model Output Statistics (EMOS). Also, a persistence ensemble (PE) is used as a baseline technique. PE builds the ensemble members from the most recent power measurements available at the same lead time. It is shown that EPS coupled with VD and EMOS is a valid method to produce skillful probabilistic SPF in different climatic conditions. VD and EMOS show a similar level of statistical consistency and forecast skill for each test case. Overall, the performances of VD and EMOS appear significantly better than those of PE.

The Prediction of Northern Hemisphere Tropical Cyclone Extended Life Cycles by the ECMWF Ensemble and Deterministic Prediction Systems. Part I: Tropical Cyclone Stage*

Abstract This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.

Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Abstract. We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

Skill and economic benefits of dynamical downscaling of ECMWF ENSEMBLE seasonal forecast over southern Africa with RegCM4

ABSTRACTThis study presents an assessment of the skill and economic benefits of a seasonal forecast carried out by nine‐member ensembles of regional and global models over southern Africa for the period of 1991–2002. The regional ensemble seasonal hindcasts were conducted at a resolution of 25 km by driving RegCM4 with the corresponding ensembles of the European Centre for Medium‐Range Weather Forecasts (ECMWF) seasonal hindcasts. Skill and cost/loss ratio analysis were carried out on the probabilistic hindcasts to assess how much added value a regional model would bring over the driving ECMWF forecasts. RegCM4 shows an added value over most areas of southeastern Africa where the skill of the global circulation model (GCM) is already positive. In terms of economic benefits, RegCM4 brings additional benefits for southeastern Africa either by benefiting more users with interest in larger cost/loss ratio or by providing a higher economic value for users with small cost/loss ratio. Additional RegCM4 simulations with the same resolution were performed using a near ‘perfect’ boundary conditions with ‘continuous (climate)’ and ‘reinitialized’ modes to test the impact of land surface initialization/ spin up time on the seasonal mean precipitation simulation. Evaluation of the ‘perfect’ boundary forced simulations reveals that the regional model not only generates finer spatial‐scale features that are missing in the ECMWF GCM, in terms of the December to April (DJFMA) climatology, but also reduced the wet bias of the driving GCM. Land surface initialization is found to have less impact on the DJFMA seasonal mean rainfall for forecasts initialized on 1 November, suggesting that 1 month is enough to recover from the shock of poor soil moisture initialization.

On the impact of re‐centring initial conditions for ensemble forecasts

Many weather forecasting centres generate the initial conditions for their ensemble prediction system by adding a set of perturbations to a single analysis. At the European Centre for Medium‐Range Weather Forecasts (ECMWF), initial conditions from an ensemble of data assimilations (EDA) are re‐centred on the deterministic high‐resolution analysis. This study assesses how the re‐centring step affects the ensemble forecasts and what benefits starting directly from the perturbed EDA members may bring. Numerical experiments are based on suitably modified configurations of the ECMWF medium‐range ensemble.Re‐centring leads to an artificial increase of the climatological variance of the initial conditions for variables and spatial scales that are not well constrained in the analysis. For those variables and spatial scales, the EDA variance can reach a similar magnitude as the climatological variance. Re‐centring on the unperturbed member of the EDA leads to a degradation of the ensemble skill in comparison to starting directly from the perturbed EDA members. Depending on the amplitude of the EDA spread and which scales are less constrained, different lead times are affected. Furthermore, the re‐centring step increases the jumpiness of the ensemble forecasts in general and alters the precipitation frequency during the first 12 h of the forecasts.It also becomes apparent from our results that the benefit for the ECMWF ensemble of starting directly from EDA initial conditions is sensitive to several factors: the difference in analysis accuracy between the unperturbed EDA control and the high‐resolution analysis, the number of EDA members, and the EDA spread. At present, the forecast skill still benefits from centring the ensemble initial conditions on the high‐resolution analysis, but future changes to the EDA may change the balance.

Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models.

The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.

Extended-Range Forecasts of Atlantic Tropical Cyclone Events during 2012 Using the ECMWF 32-Day Ensemble Predictions*

Abstract Previous studies have demonstrated the capability of the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member, 32-day ensemble to forecast tropical cyclone (TC) events (formation and tracks) in the western North Pacific on the extended range (5–30 days). In this study, the performance of the ECMWF ensemble in extended-range forecasting of Atlantic TCs during May–December 2012 is evaluated using similar approaches. The conclusion from this evaluation is that Atlantic TC events have lower forecastability using the ECMWF ensemble than in the western North Pacific. Hurricanes Kirk and Leslie and Tropical Storms (TSs) Joyce and Oscar were successfully forecast in weeks 1–4 and, thus, are labeled as highly forecastable. Somewhat forecastable storms that are only forecast in three of the four weeks include Hurricanes Ernesto, Isaac, Nadine, and Sandy plus TS Florence. The limited forecastable storms that were successful in only the first two weeks include Hurricanes Gordon and Rafael plus TS Debby. The surprising result was that two hurricanes (Chris and Michael) and three TSs (Helene, Patty, and Tony) were not even forecast in week 1 before the starting time in the National Hurricane Center working best track (WBT) for these storms. As was the case in the western North Pacific, a substantial number of false alarm storms (no matches with any WBT) are predicted, with about 35% occurring in the first week. Except for the African wave–type false alarms, three other false alarm types may be easily recognized. A larger sample will be required to statistically verify the reliability of the probabilistic forecasts for the African wave–type ensemble storms.

Ensemble Sensitivity Tools for Assessing Extratropical Cyclone Intensity and Track Predictability

Abstract This paper applies ensemble sensitivity analysis to a U.S. East Coast snowstorm on 26–28 December 2010 in a way that may be beneficial for an operational forecaster to better understand the forecast uncertainties. Sensitivity using the principal components of the leading empirical orthogonal functions (EOFs) on the 50-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble identifies the sensitive regions and weather systems at earlier times associated with the cyclone intensity and track uncertainty separately. The 5.5-day forecast cyclone intensity uncertainty in the ECMWF ensemble is associated with trough and ridge systems over the northeastern Pacific and central United States, respectively, while the track uncertainty is associated with a short-wave trough over the southern Great Plains. Sensitivity based on the ensemble mean sea level pressure difference between two run cycles also suggests that the track's shift between the two cycles is linked with the initial errors in the short-wave trough over the southern Great Plains. The sensitivity approach is run forward in time using forward ensemble regression based on short-range forecast errors, which further confirms that the short-term error over the southern plains trough was associated with the shift in cyclone position between the two forecast cycles. A coherent Rossby wave packet originated from the central North Pacific 6 days before this snowstorm event. The sensitivity signals behave like a wave packet and exhibit the same group velocity of ~29° longitude per day, indicating that Rossby wave packets may have also amplified uncertainty in both the cyclone amplitude and track forecast.

On the reliability of ensemble variance in subspaces defined by singular vectors

AbstractIn a statistically consistent ensemble forecast, ensemble variance agrees with the mean squared error of the ensemble mean when looking at a large sample of independent realizations. Here, the first goal is to introduce a new diagnostic that quantifies the reliability of ensemble variance in subspaces spanned by singular vectors of the model propagator. The second goal is to apply this technique to ensemble forecast experiments with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in order to gain new insight into essential approximations made when the sources of forecast uncertainties are represented with initial perturbations based on the leading initial singular vectors. Diagnostics are based on extratropical singular vectors optimized for growth of total energy over 48 hours. For the initial perturbations as well as the singular vector diagnostics, two versions of the tangent‐linear model are compared, which differ in terms of the accuracy of the tangent‐linear approximation.When model uncertainties are not represented, initial perturbations based solely on the leading singular vectors can achieve reliable ensemble variances in the subspace spanned by the singular vectors evolved to their optimization time. However, it is impossible to achieve at the same time reliable total variances with up to about 100 singular vectors in the extratropics. Far more singular vectors would be required to do so. Ensembles using initial perturbations based on up to 100 leading singular vectors with amplitudes tuned such that total variance matches total error suffer from overdispersion in the subspace spanned by the leading singular vectors. In the operational configuration of the ECMWF ensemble, the singular vector initial perturbations introduce overdispersion in the subspace of the leading 50 extratropical singular vectors.

Predictability of a Mediterranean Tropical-Like Storm Downstream of the Extratropical Transition of Hurricane Helene (2006)

AbstractThe extratropical transition (ET) of a tropical cyclone is known as a source of forecast uncertainty that can propagate far downstream. The present study focuses on the predictability of a Mediterranean tropical-like storm (Medicane) on 26 September 2006 downstream of the ET of Hurricane Helene from 22 to 25 September. While the development of the Medicane was missed in the deterministic forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) initialized before and during ET, it was contained in the ECMWF ensemble forecasts in more than 10% of the 50 members up to 108-h lead time. The 200 ensemble members initialized at 0000 UTC from 20 to 23 September were clustered into two nearly equiprobable scenarios after the synoptic situation over the Mediterranean. In the first and verifying scenario, Helene was steered northeastward by an upstream trough during ET and contributed to the building of a downstream ridge. A trough elongated farther downstream toward Italy and enabled the development of the Medicane in 9 of 102 members. In the second and nonverifying scenario, Helene turned southeastward during ET and the downstream ridge building was reduced. A large-scale low over the British Isles dominated the circulation in Europe and only 1 of 98 members forecasted the Medicane. The two scenarios resulted from a different phasing between Helene and the upstream trough. Sensitivity experiments performed with the Méso-NH model further revealed that initial perturbations targeted on Helene and the upstream trough were sufficient in forecasting the warm-core Medicane at 84- and 108-h lead time.

Comparing TIGGE multimodel forecasts with reforecast‐calibrated ECMWF ensemble forecasts

AbstractForecasts provided by the THORPEX Interactive Grand Global Ensemble (TIGGE) project were compared with reforecast‐calibrated ensemble predictions from the European Centre for Medium‐Range Weather Forecasts (ECMWF) in extratropical regions. Considering the statistical performance of global probabilistic forecasts of 850 hPa and 2 m temperatures, a multimodel ensemble containing nine ensemble prediction systems (EPS) from the TIGGE archive did not improve on the performance of the best single‐model, the ECMWF EPS. However, a reduced multimodel system, consisting of only the four best ensemble systems, provided by Canada, the USA, the United Kingdom and ECMWF, showed an improved performance. The multimodel ensemble provides a benchmark for the single‐model systems contributing to the multimodel. However, reforecast‐calibrated ECMWF EPS forecasts were of comparable or superior quality to the multimodel predictions, when verified against two different reanalyses or observations. This improved performance was achieved by using the ECMWF reforecast dataset to correct for systematic errors and spread deficiencies. The ECMWF EPS was the main contributor for the improved performance of the multimodel ensemble; that is, if the multimodel system did not include the ECMWF contribution, it was not able to improve on the performance of the ECMWF EPS alone. These results were shown to be only marginally sensitive to the choice of verification dataset. Copyright © 2012 Royal Meteorological Society

Preliminary comparison of the CMA, ECMWF, NCEP, and JMA ensemble prediction systems

Based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) dataset, using various verification methods, the performances of four typical ensemble prediction systems (EPSs) from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the US National Centers for Environmental Prediction (NCEP), and the Japan Meteorological Agency (JMA) are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and high-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better combination of the following: data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution (the two basic attributions of the forecast system) of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of EPS, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble members, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.

Application of a Medium-Range Global Hydrologic Probabilistic Forecast Scheme to the Ohio River Basin

Abstract A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003–07. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis temperatures and winds, and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF Ensemble Prediction System (EPS) 10-day ensemble forecast. A parallel setup was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effects of the initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The hydrologic prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. The initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

Application of a medium range global hydrologic probabilistic forecast scheme to the Ohio River Basin

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003–07. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis temperatures and winds, and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF Ensemble Prediction System (EPS) 10-day ensemble forecast. A parallel setup was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effects of the initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The hydrologic prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. The initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

Global Ensemble Predictions of 2009’s Tropical Cyclones Initialized with an Ensemble Kalman Filter

AbstractVerification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO).The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias.Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

The Central European limited‐area ensemble forecasting system: ALADIN‐LAEF

AbstractThe Central European limited‐area ensemble forecasting system ALADIN‐LAEF (Aire Limitée Adaptation Dynamique Développement InterNational—Limited‐Area Ensemble Forecasting) has been developed within the framework of ALADIN international cooperation and the Regional Cooperation for Limited‐Area modelling in Central Europe (RC LACE). It was put into pre‐operation in March 2007. The main feature of the pre‐operational ALADIN‐LAEF was the dynamical downscaling of the global ensemble forecast from the European Centre for Medium‐range Weather Forecasts (ECMWF). In 2009, ALADIN‐LAEF was upgraded with several methods for dealing with the forecast uncertainties to improve the forecast quality. These are: (1) the blending method, which combines the large‐scale uncertainty generated by ECMWF singular vectors with the small‐scale perturbations resolved by ALADIN breeding into atmospheric initial condition perturbations; (2) the multi‐physics approach, wherein different physics schemes are used for different forecast members to account for model uncertainties; and (3) the non‐cycling surface breeding technique, which generates surface initial condition perturbations.This article illustrates the technical details of the updated ALADIN‐LAEF and investigates its performance. Detailed verification of the upgraded ALADIN‐LAEF and a comparison with its first implementation (dynamical downscaling of ECMWF ensemble forecasts) are presented for a two‐month period in summer 2007. The results show better performance and skill for the upgraded system due to the better representation of forecast uncertainties. Copyright © 2011 Royal Meteorological Society

Impact of singular‐vector‐based satellite data thinning on NWP

AbstractSingular‐vector(SV)‐based selective satellite data thinning is applied to the Southern Hemisphere (SH) extratropics to reduce analysis uncertainty and forecast error. For two seasons, the European Centre for Medium‐Range Weather Forecasts (ECMWF) four‐dimensional variational data assimilation system has been run in five different configurations with different satellite data coverage: two reference experiments used low‐density and high‐density coverage over the globe; in the SH two SV‐based selective thinning experiments used low‐density data everywhere apart from targeted regions; and one random‐based thinning experiment used low‐density data everywhere apart from randomly defined regions. The SV‐based target regions have been defined either by daily operational SVs computed for the ECMWF Ensemble Prediction System, or by the previous year's mean seasonal distribution.Results indicate that the impact of the additional data largely depends on the season. Overall, forecast errors grow faster in the SH cold season than in the warm season. In the SH cold season, the general impact of adding data is smaller and the relative difference between the impact of the individual targeting experiments is smaller as well. In the cold season, the data assimilation system failed to extract the meteorological signal carried by the extra satellite data in sensitive regions. In the SH warm season, all experiments with more data produce a statistically more significant and longer‐lasting positive impact on forecast skill. In this season, the SV‐based targeting experiment performs best and as well as the reference experiment in which the data density is increased globally. Copyright © 2011 Royal Meteorological Society

On the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities

Abstract The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated. Ensemble forecasts of up to 5 days issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (UKMET) were evaluated for the 2008 Atlantic and western North Pacific seasons. In the Atlantic, the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models. Dynamic “probability circles” that contained 67% of the ECMWF ensemble captured the best track in ∼67% of all cases for 24–84-h forecasts, and were slightly underdispersive beyond 96 h. In contrast, the Goerss predicted consensus error (GPCE) was overdispersive. The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts. The ECMWF ensemble performed similarly when the size was reduced from 50 to 20. On average, it produced a lower measure of independence between its members than an ensemble comprising different deterministic models. The 67% circles normally captured the best track during straight-line motion, but less so for sharply turning tracks. In contrast to the Atlantic, the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific, in part because of a less skillful ensemble mean (and consensus). Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts, and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities.