THORPEX Interactive Grand Global Ensemble Research Articles

Towards the selection of a longer record of quantitative precipitation forecast dataset from TIGGE ensembles for India during the monsoon season

ABSTRACT A reliable forecast of summer monsoon precipitation is crucial in India. To select a longer record of forecast data from the THORPEX Interactive Grand Global Ensemble (TIGGE) archives, we applied two criteria: the length of the dataset should be at least 10 years of continuous record, and the forecast product should have at least 20 ensemble members for a lead time of minimum 5 days. We evaluate the ensemble quantitative precipitation forecasts (QPFs) obtained from the four agencies throughout the monsoon season (June to September) from 2011 to 2020 in 22 river basins of India in forecasting normal and extreme precipitation events. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is used as observation data. The proficiency of QPFs is evaluated using six criteria from deterministic, dichotomous, and probabilistic error statistics. The error values are classified into three categories – low, moderate, and high. A Forecast Reliability Index is formulated using the given four QPFs, and three categories for each of the six error statistics to answer (a) which QPF shows better performance in which river basin and (b) whether any conclusion can be made on the overall performance of a QPF for all the River Basins of India.

Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable?

Pakistan suffered from severe floods in the past, but in August 2022, the country experienced exceptional extreme rainfall events that caused widespread and catastrophic flooding. The 2022 flood affected all aspects of socio-economic lives including agriculture, infrastructure, and mortality of humans and livestock. The two-day accumulated extreme rainfall on 17–18 August was anomalous and contributed the most to the flood in the southern provinces of Pakistan. The damage caused by extreme rainfall and the subsequent flooding has raised questions regarding the predictability of extreme rainfall by the existing weather forecasting models. Here, we use ensemble forecasts from four numerical weather prediction models under THORPEX Interactive Grand Global Ensemble datasets to examine the predictability of extreme rainfall at a six-day lead. The extreme precipitation during 17–18 August 2022 was predictable a week before the event that contributed the most to the flooding. All the forecast models provided an early warning at a six-day lead time. UK Meteorological Office and European Centre for Medium-Range Weather Forecasts models produced comparable results to observations at all the lead times. Our findings highlight that an integrated framework of extended quantitative precipitation forecasts and hydrological modeling can help reduce the country’s flood vulnerability and risk associated with it.

Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.

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.

Data assimilation of adaptive observation and application for typhoon forecasts over the Western North Pacific

In this study, we added the post-processing module of the Ensemble Transform Sensitivity (ETS) to identify regions sensitive to adaptive observations of different variables at different levels for typhoon forecasts. We selected five cases between 2015 and 2020 over the Western North Pacific (WNP) using 50 ensemble forecasts from the European Center provided by the THORPEX Interactive Grand Global Ensemble (TIGGE) portal. Furthermore, intercomparison experiments were performed to explore the impact of adaptive observational data assimilation with conventional observations and Himawari-8 data over the sensitive (SEN), non-sensitive (NOSEN), and all studied regions (ALL) on typhoon forecasts using the Gridpoint Statistical Interpolation (GSI) assimilation system of the National Centers for Environmental Prediction (NCEP) of United States. The results show that the assimilation of less observational data in the SEN areas can achieve the forecast effect of assimilation in ALL regions, the intensity error can be reduced by approximately 15%, and the track error is reduced in some cases. Finally, detailed analysis of the circulation field and flux perspectives for Typhoon Lekima, which occurred in August 2019 over the WNP, confirmed our conclusion. At 1800 UTC on August 8, the data assimilation was performed over the SEN, NOSEN, and ALL areas. The initial circulation field, in which the vorticity increased in the southwest of the typhoon and decreased in the southeast, benefited the westward movement of the typhoon. The 18 h forecast indicated that the circulation in the SEN area assimilation experiment showed a negative vorticity increase in the southwest side of the typhoon and positive potential temperature as well as water vapor forecast differences on its north side, which contrasted with the results obtained in the NOSEN and ALL experiments. This provided favorable weather conditions for the typhoon to change direction and move northward. Targeted observation experiments investigating Typhoon Lekima indicated that assimilating observations of sensitive areas greatly improves typhoon forecasting and assimilation efficiency. However, the ALL experiment yielded the best forecast among the three cases.

Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013

Timely and reliable in-season forecasting of crop yield is crucial for regional and national agricultural management. To improve the winter wheat yield prediction accuracy at the regional scale, we developed a data assimilation scheme that assimilated the MODIS leaf area index (LAI) into the WOrld FOod STudies (WOFOST) model. The meteorological data of the WOFOST model include current weather data, 15-day THORPEX Interactive Grand Global Ensemble (TIGGE) forecast dataset and weather forecast data generated by TIGGE forecast and historical meteorological data (1979–2008) using the vectorial angle method. The WOFOST model was calibrated within 10 subregions of Hebei province based on field measured winter wheat growth data from each corresponding agrometeorological station to account for the spatial variability of crop and soil parameters to some extent, and the Savitzky-Golay (S-G) filtered MODIS LAI was then assimilated into the WOFOST model for regional winter wheat yield forecasting. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors from Feb. 10th to Apr. 30th, and the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was used to minimize the cost function by reinitializing three WOFOST parameters. The winter wheat yield forecasting date was started from Apr. 30th, and the results showed that assimilating MODIS LAI into the WOFOST model substantially improved the accuracy of regional wheat yield predictions (R = 0.60, CCC = 0.53, RMSE = 619.73 kg/ha) compared with the unassimilated results (R = 0.35, CCC = 0.24, RMSE = 857.32 kg/ha), and the relative error (RE) between the averaged predicted yield and official statistics for most cities decreased after data assimilation. This demonstrated that assimilating MODIS LAI can optimize the simulation of LAI in the WOFOST model, thereby further reduce the uncertainty of yield forecasting. These promising results highlighted the potential of integrating remotely sensed data, crop model and weather forecasts for in-season prediction of crop yield at the regional scale.

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.

Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

A new view on the risk of typhoon occurrence in the western North Pacific

Abstract. To study high-impact tropical cyclones (TCs) is of crucial importance due to their extraordinary destructive potential that leads to major losses in many coastal areas in the western North Pacific (WNP). Nevertheless, because of the rarity of high-impact TCs, it is difficult to construct a robust hazard assessment based on the historical best track records. This paper aims to address this issue by introducing a computationally simple and efficient approach to build a physically consistent high-impact TC event set with non-realised TC events in the THORPEX Interactive Grand Global Ensemble (TIGGE) archive. This event set contains more than 10 000 years of TC events. The temporal and spatial characteristics of the new event set are consistent with the historical TC climatology in the WNP. It is shown that this TC event set contains ∼100 and ∼77 times more very severe typhoons and violent typhoons than the historical records, respectively. Furthermore, this approach can be used to improve the return-period estimation of TC-associated extreme wind. Consequently, a robust extreme TC hazard assessment, reflective of the current long-term climate variability phase, can be achieved using this approach.

Bias correction of global ensemble precipitation forecasts by Random Forest method

One of the most important topics in operational applications of precipitation forecasts is their improvement by bias correction methods. In this study, the ensemble precipitation forecasts of six numerical models from the TIGGE (THORPEX Interactive Grand Global Ensemble) database, associated with four basins in Iran for 2008–2018, were extracted and bias-corrected by the Quantile Mapping (QM) and Random Forest (RF) methods. Random Forest is a supervised machine learning algorithm made of an ensemble of decision trees. The results in all four basins demonstrated that most models had better skills in forecasting precipitation depth after bias correction using the RF method, compared to using the QM method and raw forecasts. In the dichotomous evaluation for 5-mm and 25-mm precipitation thresholds, all models improved their performance after bias correction. However, the QM performed slightly better than the RF. In probabilistic evaluations, significant improvements were observed after bias correction using the RF method, compared to using the QM in the models, and the reliability diagrams of the bias-corrected forecasts by the RF concentrated around the 1:1 line in all four basins. In seasonal evaluation, models had better probabilistic forecasts in autumn and winter than in spring and summer, and showed better scores in the lower tercile category than in the middle and upper tercile categories. In general, the improvement of a model’s performance after bias correction with the Random Forest method shows the importance of this method for operational application.

Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.

Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts

Abstract. Reference evapotranspiration (ET0) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches, including the nonhomogeneous Gaussian regression (NGR), affine kernel dressing (AKD), and Bayesian model averaging (BMA) techniques, for improving daily and weekly ET0 forecasting based on single or multiple numerical weather predictions (NWPs) from the THORPEX Interactive Grand Global Ensemble (TIGGE), which includes the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and the United Kingdom Meteorological Office (UKMO) forecasts. The approaches were examined for the forecasting of summer ET0 at 101 US Regional Climate Reference Network stations distributed all over the contiguous United States (CONUS). We found that the NGR, AKD, and BMA methods greatly improved the skill and reliability of the ET0 forecasts compared with a linear regression bias correction method, due to the considerable adjustments in the spread of ensemble forecasts. The methods were especially effective when applied over the raw NCEP forecasts, followed by the raw UKMO forecasts, because of their low skill compared with that of the raw ECMWF forecasts. The post-processed weekly forecasts had much lower rRMSE values (between 8 % and 11 %) than the persistence-based weekly forecasts (22 %) and the post-processed daily forecasts (between 13 % and 20 %). Compared with the single-model ensemble, ET0 forecasts based on ECMWF multi-model ensemble ET0 forecasts showed higher skill at shorter lead times (1 or 2 d) and over the southern and western regions of the US. The improvement was higher at a daily timescale than at a weekly timescale. The NGR and AKD methods showed the best performance; however, unlike the AKD method, the NGR method can post-process multi-model forecasts and is easier to interpret than the other methods. In summary, this study demonstrated that the three probabilistic approaches generally outperform conventional procedures based on the simple bias correction of single-model forecasts, with the NGR post-processing of the ECMWF and ECMWF–UKMO forecasts providing the most cost-effective ET0 forecasting.

Performance evaluation of sub‐daily ensemble precipitation forecasts

AbstractNowadays, major advances have been made in meteorological forecasts. For instance, ensemble forecast systems have been developed to quantify prediction uncertainty. In this research, sub‐daily ensemble precipitation forecasts of five THORPEX Interactive Grand Global Ensemble (TIGGE) models from 2014 to 2018 were evaluated in 10 major basins located in north and west Iran. Furthermore, Bayesian model averaging (BMA) was used to combine five prediction models and construct a grand ensemble. The results indicate that the models had the best performance in the Karun and Western Border basins to the southwest and west, with average performance in the Sefidrood basin in the north. In terms of the prediction of precipitation depth, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO) models, and in terms of prediction of precipitation occurrence and non‐occurrence, the National Centers for Environmental Prediction (NCEP) model, performed best. Overall, the Japan Meteorological Agency (JMA) and the China Meteorological Administration (CMA) models acquired medium scores. The BMA technique greatly improved the probability forecasts, reducing uncertainties in the numerical models. Moreover, the models’ forecasts were weaker with a 6 hr lead time compared with those with 24 hr, which may be attributable to the inaccurate detection of the initiation time of precipitation by the models. In addition, the performance of the UKMO (ECMWF) models with increasing basin elevation increased (decreased), while all models better forecasted precipitation in wet years/seasons than they did in dry years/seasons. Overall, the evaluations showed that the ECMWF, UKMO and NCEP models performed well in the majority of the northern and western basins of Iran.

Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

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.

Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016

Recently, cyclones are quite active and dramatically influence the sea ice distribution in the Arctic region. Therefore, precise prediction of sea ice distribution during the cyclones is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice–ocean coupled model is developed for forecasting the short-term sea ice distribution along the Arctic sea routes. Since a commercial ship navigates to avoid the sea ice as much as possible, a factor to score the forecast skill is considered to be ice edge error which is an averaged distance between forecasted and observed ice edges. First, we discuss the influence of horizontal grid resolution on short-term sea ice predictions during May to November 2015. The model grid resolution is varied from 2.5 km, 5 km, 10 km and 15 km. This grid dependency study suggests that the 2.5 km resolution model predicts the ice edge accurately compared to the satellite observations. Second, the primary experiment was run from 2 August 2016 to 7 August 2016 under the extreme cyclone in the Laptev Sea using ensemble predictions. The ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project and European Center for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data. The ensemble ice edge errors are computed from 2 August 2016 to 7 August 2016. The maximum forecast skill of ensemble average ice edge error in the ice–ocean coupled model is 11.74 ± 0.54 km with the threshold of 15% ice concentration for the AMSR2 and model predicted ice edges. It can be said that the present model of 2.5 km grids satisfies the ship crew requirement of around 10 km ice edge error for 5-day forecast.

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.

Post-processing Multiensemble Temperature and Precipitation Forecasts Through an Exchangeable Normal-Gamma Model and Its Tobit Extension

Meteorological ensembles are a collection of scenarios for future weather delivered by a meteorological center. Such ensembles form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess estimate. Unfortunately, ensembles cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to calibrate the ensemble. Two main families of post-processing methods, either competing such as BMA or collaborative such as EMOS, can be found in the literature. This paper proposes a mixed effect model belonging to the collaborative family. The structure of the model is based on the hypothesis of invariance under the relabelling of the ensemble members. Its interesting specificities are as follows: 1) exchangeability, which contributes to parsimony, with a latent pivot variable synthesizing the essential meteorological features of the ensembles, 2) a multi-ensemble implementation, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto Normal statistical structures, first with a direct application for temperatures, then with its Tobit extension for precipitation. Inference is performed by EM algorithms with recourse made to stochastic conditional simulations in the precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation ensemble forecasts produced over five river basins managed by Hydro-Qu$\'e$bec. These ensemble forecasts were extracted from the THORPEX Interactive Grand Global Ensemble (TIGGE) database. The results indicate that post-processed ensemble are calibrated and generally sharper than the raw ensembles.

Ensemble Sensitivity Analysis of the Blocking System over Russia in Summer 2010

Abstract In summer 2010, the weather conditions in the Euro-Russian sector were affected by a long-lasting atmospheric block that led to a heat wave in Russia and floods in Pakistan. Following previous studies describing the block’s predictability, the present study aims to investigate uncertainties in the upper-level wave pattern and diabatic processes that were responsible for the block’s forecast variability during its onset, mature, and decay phases. With this aim, an ensemble sensitivity analysis (ESA) is performed for three medium-range THORPEX Interactive Grand Global Ensemble multimodel ensemble forecasts, one associated with each phase of the block’s life cycle. The ESA revealed that the block’s predictability was influenced by forecast uncertainties in the general wave pattern and in the vertically integrated water vapor transport (IVT), used here as a proxy for diabatic processes. These uncertainties are associated with spatial shifts and intensity changes of synoptic waves and IVT during the whole life cycle of the block. During the onset phase, specific features include an Atlantic precursor block and the occurrence of several cyclones. During the mature stage, the blocking ridge itself was highly predictable, while forecast uncertainties in the wave pattern and in IVT primarily were associated with uncertainties in the block’s western flank. During the decay phase, the ESA signals were less intense, but the forecast variability significantly depended on the transformation of the block into a high-over-low pattern. It can be concluded that ESA is suitable to investigate the block’s forecast variability in multimodel ensembles.