The Observing System Research And Predictability Experiment Research Articles

Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.

How reliable are TIGGE daily deterministic precipitation forecasts over different climate and topographic conditions of Iran?

AbstractAccurate forecasting of precipitation has been a very crucial issue for developing meteorological and hydrological early warning systems. Ensemble forecasts using numerical weather prediction models are promising for these purposes. This research addressed the reliability of global precipitation forecasts of the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive over distinct climate and topographic regions of Iran. Besides, post‐processing the raw forecasts and assessing TIGGE datasets' performance in predicting extreme rainfall events, at the thresholds of 10–20, 20–50, 50–100, and higher than 100 mm, are other objectives of this study. Results indicated that the European Centre for Medium‐Range Weather Forecasts (ECMWF) and Meteo France forecasts, with the mean correlation coefficients (CCs) of 0.64 and 0.61, were closer to the ground‐gauge observations, especially in different types of humid zones. On the contrary, in semi‐arid and extra‐arid climate zones, the CC value was relatively low. Furthermore, Meteo France, Korea Meteorological Administration, Japan Meteorological Agency and ECMWF outperformed other models in detecting rainy or non‐rainy days. Also, by increasing the elevation, the correlation between forecasts and in situ observations increased significantly, and the percentage increment of CC for ECMWF and Meteo France models is higher than 38%. Moreover, the skill of predictions for extreme events (higher than 50 mm) decreased meaningfully, while at the thresholds of 10–20 and 20–50 mm, ECMWF and China Meteorological Administration outperformed other models. Finally, post‐processing of raw forecasts using quantile mapping improved the CC and relative bias metrics up to 30% and 51%, respectively, especially in extra‐arid, semi‐arid and Mediterranean climate zones.

Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data

Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.

Evaluation of TIGGE Daily Accumulated Precipitation Forecasts over the Qu River Basin, China

Quantitative precipitation forecasts (QPFs) provided by three operational global ensemble prediction systems (EPSs) from the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive were evaluated over the Qu River basin, China during the plum rain and typhoon seasons of 2009–13. Two post-processing methods, the ensemble model output statistics based on censored shifted gamma distribution (CSGD-EMOS) and quantile mapping (QM), were used to reduce bias and to improve the QPFs. The results were evaluated by using three incremental precipitation thresholds and multiple verification metrics. It is demonstrated that QPFs from NCEP and ECMWF presented similarly skillful forecasts, although the ECMWF QPFs performed more satisfactorily in the typhoon season and the NCEP QPFs were better in the plum rain season. Most of the verification metrics showed evident seasonal discriminations, with more satisfactory behavior in the plum rain season. Lighter precipitation tended to be overestimated, but heavier precipitation was always underestimated. The post-processed QPFs showed a significant improvement from the raw forecasts and the effects of post-processing varied with the lead time, precipitation threshold, and EPS. Precipitation was better corrected at longer lead times and higher thresholds. CSGD-EMOS was more effective for probabilistic metrics and the root-mean-square error. QM had a greater effect on removing bias according to bias and categorical metrics, but was unable to warrant reliabilities. In general, raw forecasts can provide acceptable QPFs eight days in advance. After post-processing, the useful forecasts can be significantly extended beyond 10 days, showing promising prospects for flood forecasting.

Probabilistic Evaluation of Tibetan Plateau Mesoscale Vortex on 18 July 2013

Tibetan Plateau (TP) mesoscale vortex (TPMV) was regarded as one of the most important rain bearing systems in China. Previous studies focused on the mechanisms of the TPMV in the viewpoint of deterministic forecast; however, few studies investigate the predictability of the TPMV using the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) from the European Center for Medium Range Weather Forecasts (ECWMF). This paper investigates the location and the intensity of the larger-scale synoptic systems that influenced the development of the TPMV and its associated heavy rainfall by correlation and composite analysis. The case study on 18 July 2013 shows that stronger Balkhash Lake ridge, weaker Baikal Lake trough, and weaker western Pacific subtropical high (WPSH) are favorable to formation of TPMV over the Sichuan basin (SCB); otherwise, weaker Balkhash Lake ridge, stronger Baikal Lake trough, and stronger WPSH result in formation of TPMV to west of the SCB slightly. After the initial time, forecast for next 48 h of the geopotential height over the SCB can be viewed as a precursor of the subsequent time-averaged 90–108 h forecast of TPMV. TPMV had critical contributions to the heavy rainfall over the SCB on 18 July 2013.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007–2015. The comparison reveals that rainstorm events are predictable up to 5days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24hr forecast to 48hr forecast in all the three models.

Using the inverse of expected error variance to determine weights of individual ensemble members: Application to temperature prediction

The inverse of expected error variance is utilized to determine weights of individual ensemble members based on the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) forecast datasets. The weights of all ensemble members are thus calculated for summer 2012, with the NCEP final operational global analysis (FNL) data as the truth. Based on the weights of all ensemble members, the variable weighted ensemble mean (VWEM) of temperature of summer 2013 is derived and compared with that from the simple equally weighted ensemble mean. The results show that VWEM has lower root-mean-square error (RMSE) as well as absolute error, and has improved the temperature prediction accuracy. The improvements are quite notable over the Tibetan Plateau and its surrounding areas; specifically, a relative improvement rate of RMSE of more than 24% in 2-m temperature is demonstrated. Moreover, the improvement rates vary slightly with the prediction lead-time (24–96 h). It is suggested that the VWEM approach be employed in operational ensemble prediction to provide guidance for weather forecasting and climate prediction.

The Subseasonal to Seasonal (S2S) Prediction Project Database

Abstract Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

A Review of Targeted Observations

Abstract It has long been conceived that numerical weather forecasts will benefit from the assimilation of supplementary observations that augment the conventional observational network. In particular, the concept of “targeting” observations in selected regions to improve a forecast of a high-impact weather event had been promoted and tested prior to and during the World Meteorological Organization/World Weather Research Programme’s The Observing System Research and Predictability Experiment (THORPEX) era (2005–14), through field campaigns and assimilation experiments. The end of the THORPEX era provided an appropriate opportunity to review the outcomes and, in particular, the evaluations of the influence of assimilating targeted observations on numerical weather predictions. The main outcome in the extratropics was that the influence of the targeted observations was positive though small (typically an average forecast error reduction of less than 10%). In the tropics, the targeted observations usually improved tropical cyclone track forecasts. Significantly, the results from these and other experiments were found to be sensitive to the sample chosen, the method of verification, and the numerical weather prediction system including the data assimilation scheme and the treatment of observations. Recommendations for the future include innovations to optimize the use of the Global Observing System via better exploitation of routinely available resources together with new instrumentation; expanding into the convective scale and mesoscale; investing quantitative evaluations and improving our understanding of how observations affect forecasts; and assessing the socioeconomic value of improved forecasts. A comprehensive bibliography of approximately 200 papers is provided in the online supplement to this paper.

Analysis of Tropical Storm Formation Based on Ensemble Data Assimilation and High-Resolution Numerical Simulations of a Nondeveloping Disturbance

AbstractA nondeveloping tropical disturbance, identified as TCS025, was observed during three intensive observing periods during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS-08) field experiment. The low-level circulation of the disturbance was relatively weak, asymmetric, and displaced a considerable distance from the midlevel circulation. An ensemble of high-resolution numerical simulations initialized from global model analyses was used to further examine TCS025. These simulations tended to unrealistically overdevelop the TCS025 disturbance. This study extends that work by examining the impact of assimilating in situ observations of TCS025 and dual-Doppler radial velocities from the airborne Electra Doppler Radar (ELDORA) using the Data Assimilation Research Testbed (DART) ensemble data assimilation system.The assimilation of observations results in a more accurate vortex structure that is consistent with the observational analysis. In addition, forecasts initialized from the state of the ensemble after data assimilation exhibit less development than both the control simulation and an ensemble of forecasts without prior data assimilation.A composite analysis of developing and nondeveloping forecasts from the ensemble reveals that convection was more active in developing simulations, especially near the low-level circulation center. This led to larger diabatic heating rates, spinup of the low-level circulation from vorticity stretching, and greater alignment of the low- and midlevel vorticity centers. In contrast, nondeveloping simulations exhibited less convection, and the circulation was more heavily impacted by vertical wind shear.

Evaluation of NCEP TIGGE short-range forecast for Indian summer monsoon intraseasonal oscillation

This study focuses on the short-range prediction of Monsoon Intraseasonal Oscillations (MISOs) using the National Centers for Environmental Prediction(NCEP) Ensemble Prediction System (EPS) data from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) archive. The Indian Summer Monsoon Rainfall (ISMR), which plays an important role in the socio-economic growth of the country, is highly variable and is mostly governed by the MISOs. In addition to this, deterministic forecasts of ISMR are not very reliable. Hence, a probabilistic approach at daily scale is required. Keeping this in mind, the present analysis is done by using daily forecast data for up to 7-day lead time and compared with observations. The analysis shows that the ensemble forecast well captures the variability as compared to observations even up to 7 days. The spatial characteristics and the northward propagation of MISO are observed thoroughly in the EPS. The evolution of dynamical and thermodynamical parameters such as specific humidity, moist static energy, moisture divergence, and vorticity is also captured well but show deviation from the observation from 96 h lead time onwards. The tropospheric temperature forecast captures the observed gradient but with certain bias in magnitude whereas the wind shear is simulated quite well both in pattern and magnitude. These analyses bring out the biases in TIGGE EPS forecast and also point out the possible moist processes which needs to be improved.

Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin, China

Drought assessment is important for developing measures to reduce agricultural vulnerability and thereby secure the livelihoods of those who depend on agriculture. This study uses four global ensemble weather prediction systems: the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centres for Environmental Prediction (NCEP) in the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive from 2006 to 2010. Based on results from the XXT (the first X denotes Xinanjiang, the second X denotes hybrid, and the T denotes TOPMODEL) distributed hydrological model, as well as soil moisture observations and digital elevation model (DEM) data, synthesized drought grades were established in the Huaihe River Basin of China. To filter out the impact of short-term fluctuations on observed soil moisture, a 30-day moving average was calculated. Use of the moving average significantly improves the correlation between observed soil moisture and simulated soil water deficit depth. Finally, a linear regression model describing the relationship between observed soil moisture and simulated soil water deficit depth was constructed. The deterministic regression coefficient was 0.5872, the correlation coefficient was 0.77, and the regression coefficient was −154.23. The trends in drought grades calculated using soil moisture and soil water deficit depth were found to be the same, and the grades agreed to within one level. Our findings highlight the importance of synthesizing drought grading when assessing drought using different soil moisture indicators in order to obtain a more comprehensive forecast of drought conditions.

The TIGGE Project and Its Achievements

Abstract The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.

Satellite and Aircraft Observations of the Eyewall Replacement Cycle in Typhoon Sinlaku (2008)

Abstract Satellite and aircraft observations of the concurrent evolution of cloud-top brightness temperatures (BTs) and the surface and flight-level wind fields were examined before and during an eyewall replacement cycle (ERC) in Typhoon Sinlaku (2008) as part of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and the Tropical Cyclone Structure 2008 (TCS08) field campaign. The structural evolution of deep convection through the life cycle of the ERC was clearly evident in the radial variation of positive water vapor (WV) minus infrared (IR) brightness temperature differences over the 96-h period. Within this framework, the ERC was divided into six broadly defined stages, wherein convective processes (including eyewall development and decay) were analyzed and then validated using microwave data. Dual maxima in aircraft wind speeds and geostationary satellite BTs along flight transects through Sinlaku were used to document the temporal evolution of the ERC within the TC inner core. Negative correlations were found between IR BTs and surface wind speeds, indicating that colder cloud tops were associated with stronger surface winds. Spatial lags indicated that the strongest surface winds were located radially inward of both the flight-level winds and coldest cloud tops. Finally, timing of the ERC was observed equally in IR and WV minus IR (WVIR) BTs with one exception. Decay of the inner eyewall was detected earlier in the WVIR data. These findings highlight the potential utility of WVIR and IR BT radial profiles, particularly so for basins without active aircraft weather reconnaissance programs such as the western North Pacific.

Observations of a Nondeveloping Tropical Disturbance in the Western North Pacific during TCS-08 (2008)

Abstract Large uncertainty still remains in determining whether a tropical cloud cluster will develop into a tropical cyclone. During The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS-08) field experiment, over 50 tropical cloud clusters were monitored for development, but only 4 developed into a tropical cyclone. One nondeveloping tropical disturbance (TCS025) was closely observed for potential formation during five aircraft research missions, which provided an unprecedented set of observations pertaining to the large-scale and convective environments of a nondeveloping system. The TCS025 disturbance was comprised of episodic convection that occurred in relation to the diurnal cycle along the eastern extent of a broad low-level trough. The upper-level environment was dominated by two cyclonic cells in the tropical upper-tropospheric trough (TUTT) north of the low-level trough in which the TCS025 circulation was embedded. An in-depth examination of in situ observations revealed that the nondeveloping circulation was asymmetric and vertically misaligned, which led to larger system-relative flow on the mesoscale. Persistent environmental vertical wind shear and horizontal shearing deformation near the circulation kept the system from becoming better organized and appears to have allowed low equivalent potential temperature () air originating from one of the TUTT cells to the north (upshear) to impact the thermodynamic environment of TCS025. This in turn weakened subsequent convection that might otherwise have improved alignment and contributed to the transition of TCS025 to a tropical cyclone.

A study on the predictability of the transition day from the dry to the rainy season over South Korea

This study was conducted to evaluate the prediction accuracies of THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data at six operational forecast centers using the root-mean square difference (RMSD) and Brier score (BS) from April to July 2012. And it was performed to test the precipitation predictability of ensemble prediction systems (EPS) on the onset of the summer rainy season, the day of withdrawal in spring drought over South Korea on 29 June 2012 with use of the ensemble mean precipitation, ensemble probability precipitation, 10-day lag ensemble forecasts (ensemble mean and probability precipitation), and effective drought index (EDI). The RMSD analysis of atmospheric variables (geopotential-height at 500 hPa, temperature at 850 hPa, sea-level pressure and specific humidity at 850 hPa) showed that the prediction accuracies of the EPS at the Meteorological Service of Canada (CMC) and China Meteorological Administration (CMA) were poor and those at the European Center for Medium-Range Weather Forecasts (ECMWF) and Korea Meteorological Administration (KMA) were good. Also, ECMWF and KMA showed better results than other EPSs for predicting precipitation in the BS distributions. It is also evaluated that the onset of the summer rainy season could be predicted using ensemble-mean precipitation from 4-day leading time at all forecast centers. In addition, the spatial distributions of predicted precipitation of the EPS at KMA and the Met Office of the United Kingdom (UKMO) were similar to those of observed precipitation; thus, the predictability showed good performance. The precipitation probability forecasts of EPS at CMA, the National Centers for Environmental Prediction (NCEP), and UKMO (ECMWF and KMA) at 1-day lead time produced over-forecasting (under-forecasting) in the reliability diagram. And all the ones at 2∼4-day lead time showed under-forecasting. Also, the precipitation on onset day of the summer rainy season could be predicted from a 4-day lead time to initial time by using the 10-day lag ensemble mean and probability forecasts. Additionally, the predictability for withdrawal day of spring drought to be ended due to precipitation on onset day of summer rainy season was evaluated using Effective Drought Index (EDI) to be calculated by ensemble mean precipitation forecasts and spreads at five EPSs.

PEARP, the Météo‐France short‐range ensemble prediction system

Météo‐France has implemented a short‐range ensemble prediction system known as Prévision d'Ensemble ARPEGE (PEARP). This system is a global ensemble performing forecasts up to 4.5 days. It uses the operational global numerical weather prediction model Action de Recherche Petite Echelle Grande Echelle (ARPEGE) and benefits from variable horizontal resolution, so that it is comparable to some limited‐area mesoscale systems over France. Perturbations to the initial conditions are computed by combining an ensemble data assimilation system with singular vectors. Model uncertainties are represented through a ‘multiphysics’ approach with ten different physical parametrization sets. The article describes the set‐up of the system and provides an assessment of the approaches used to represent initial conditions and model uncertainties. The positive impact of the variable horizontal resolution of PEARP is also illustrated. As a global ensemble forecast system (EFS), PEARP is also used to forecast cyclone tracks. It is shown that it has correctly predicted the landfall of hurricane Sandy. The performance of PEARP as run operationally with these features in 2014 is assessed objectively and compared with that of four operational global EFSs using classical probabilistic scores. This comparison is based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This is one of the first evaluations of EFSs for short‐range forecasts. The reliability and global skill of the five EFSs are evaluated over a three‐month period with scores computed against observations. PEARP shows skill comparable to or better than the other EFSs.

Aspects of ECMWF model performance in polar areas

Global numerical weather prediction skill over polar areas is assessed, mostly based on the European Centre for Medium‐Range Weather Forecasts (ECMWF) system but also the Met Office, Japan Meteorological Agency (JMA), Environment Canada and National Centers for Environmental Prediction (NCEP) analysis data. Polar forecast verification against analyses shows a similar trend of forecast improvement over the past 12 years compared with improvements at lower latitudes. These improvements are presumably due to increased model resolution and model sophistication, improved data assimilation methods and increased observational data coverage and better data quality. By comparing ECMWF's real‐time forecast skill changes against those from reforecasts initialized from reanalyses, it is possible to quantify how much of the improvement is from system improvements and how much is attributable to weather variability. Ensemble skill also improved over time and, again, consistently across latitudes. The quality of analyses serving for forecast verification and initialization has been investigated further. An intercomparison of The Observing system Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) analyses and forecasts revealed substantial differences for surface parameters, but also at lower levels in the troposphere, where most of the physical processes relevant to weather in the short‐to‐medium range take place over the poles. The differences between the TIGGE analyses were generally much larger than differences between members of the ECMWF 4D‐Var ensemble of analyses generated internally at ECMWF. This suggests that neither the multi‐analysis approach nor ensemble data assimilation may represent polar analysis uncertainty properly. This is particularly visible at the surface and lower levels in the atmosphere. Forecast spread and error match much better north of 65∘N where less atmospheric variability prevails along the entire forecast range, while in areas of significant synoptic activity the spread also appears too low.

Observations of the Eyewall Structure of Typhoon Sinlaku (2008) during the Transformation Stage of Extratropical Transition

AbstractA unique dataset observing the life cycle of Typhoon Sinlaku was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in 2008. In this study observations of the transformation stage of the extratropical transition of Sinlaku are analyzed. Research flights with the Naval Research Laboratory P-3 and the U.S. Air Force WC-130 aircraft were conducted in the core region of Sinlaku. Data from the Electra Doppler Radar (ELDORA), dropsondes, aircraft flight level, and satellite atmospheric motion vectors were analyzed with the recently developed Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) software with a 1-km horizontal- and 0.5-km vertical-node spacing. The SAMURAI analysis shows marked asymmetries in the structure of the core region in the radar reflectivity and three-dimensional wind field. The highest radar reflectivities were found in the left of shear semicircle, and maximum ascent was found in the downshear left quadrant. Initial radar echos were found slightly upstream of the downshear direction and downdrafts were primarily located in the upshear semicircle, suggesting that individual cells in Sinlaku’s eyewall formed in the downshear region, matured as they traveled downstream, and decayed in the upshear region. The observed structure is consistent with previous studies of tropical cyclones in vertical wind shear, suggesting that the eyewall convection is primarily shaped by increased vertical wind shear during step 2 of the transformation stage, as was hypothesized by Klein et al. A transition from active convection upwind to stratiform precipitation downwind is similar to that found in the principal rainband of more intense tropical cyclones.

MEDEX: a general overview

Abstract. The general objective of the international MEDiterranean EXperiment (MEDEX) was the better understanding and forecasting of cyclones that produce high impact weather in the Mediterranean. This paper reviews the motivation and foundation of MEDEX, the gestation, history and organisation of the project, as well as the main products and scientific achievements obtained from it. MEDEX obtained the approval of World Meteorological Organisation (WMO) and can be considered as framed within other WMO actions, such as the ALPine EXperiment (ALPEX), the Mediterranean Cyclones Study Project (MCP) and, to a certain extent, THe Observing System Research and Predictability EXperiment (THORPEX) and the HYdrological cycle in Mediterranean EXperiment (HyMeX). Through two phases (2000–2005 and 2006–2010), MEDEX has produced a specific database, with information about cyclones and severe or high impact weather events, several main reports and a specific data targeting system field campaign (DTS-MEDEX-2009). The scientific achievements are significant in fields like climatology, dynamical understanding of the physical processes and social impact of cyclones, as well as in aspects related to the location of sensitive zones for individual cases, the climatology of sensitivity zones and the improvement of the forecasts through innovative methods like mesoscale ensemble prediction systems.