Observing System Research Research Articles

Quantifying potential marine debris sources and potential threats to penguins on the West Antarctic Peninsula

Marine pollution is becoming ubiquitous in the environment. Observations of pollution on beaches, in the coastal ocean, and in organisms in the Antarctic are becoming distressingly common. Increasing human activity, growing tourism, and an expanding krill fishing industry along the West Antarctic Peninsula all represent potential sources of plastic pollution and other debris (collectively referred to as debris) to the region. However, the sources of these pollutants from point (pollutants released from discrete sources) versus non-point (pollutants from a large area rather than a specific source) sources are poorly understood. We used buoyant simulated particles released in a high-resolution physical ocean model to quantify pollutant loads throughout the region. We considered non-point sources of debris from the Antarctic Circumpolar Current, Bellingshausen Sea, Weddell Sea, and point source pollution from human activities including tourism, research, and fishing. We also determined possible origins for observed debris based on data from the Southern Ocean Observing System and Palmer Long-Term Ecological Research program. Our results indicate that point source pollution released in the coastal Antarctic is more likely to serve as a source for observed debris than non-point sources, and that the dominant source of pollution is region-specific. Penguin colonies in the South Shetland and Elephant Islands had the greatest debris load from point sources whereas loads from non-point sources were greatest around the southernmost colonies. Penguin colonies at Cornwallis Island and Fort Point were exposed to the highest theoretical debris loads. While these results do not include physical processes such as windage and Stokes Drift that are known to impact debris distributions and transport in the coastal ocean, these results provide critical insights to building an effective stratified sampling and monitoring effort to better understand debris distributions, concentrations, and origins throughout the West Antarctic Peninsula.

Evaluation of modelled climatologies of O3, CO, water vapour and NOy in the upper troposphere–lower stratosphere using regular in situ observations by passenger aircraft

Abstract. Evaluating global chemistry models in the upper troposphere–lower stratosphere (UTLS) is an important step toward an improved understanding of the chemical composition in this region. This composition is regularly sampled through in situ measurements based on passenger aircraft, in the framework of the In-service Aircraft for a Global Observing System (IAGOS) research infrastructure. This study focuses on the comparison of the IAGOS measurements in ozone, carbon monoxide (CO), nitrogen reactive species (NOy) and water vapour, with a 25-year simulation output from the LMDZ-OR-INCA chemistry–climate model. For this purpose, we present and apply an extension of the Interpol-IAGOS software that projects the IAGOS data onto any model grid, in order to derive a gridded IAGOS product and a masked (sub-sampled) model product that are directly comparable to one another. Climatologies are calculated in the upper troposphere (UT) and in the lower stratosphere (LS) separately but also in the UTLS as a whole, as a demonstration for the models that do not sort out the physical variables necessary to distinguish between the UT and the LS. In the northern extratropics, the comparison in the UTLS layer suggests that the geographical distribution in the tropopause height is well reproduced by the model. In the separated layers, the model simulates well the water vapour climatologies in the UT and the ozone climatologies in the LS. There are opposite biases in CO in both UT and LS, which suggests that the cross-tropopause transport is overestimated. The NOy observations highlight the difficulty of the model in parameterizing the lightning emissions. In the tropics, the upper-tropospheric climatologies are remarkably well simulated for water vapour. They also show realistic CO peaks due to biomass burning in the most convective systems, and the ozone latitudinal variations are well correlated between the observations and the model. Ozone is more sensitive to lightning emissions than to biomass burning emissions, whereas the CO sensitivity to biomass burning emissions strongly depends on location and season. The present study demonstrates that the Interpol-IAGOS software is a tool facilitating the assessment of global model simulations in the UTLS, which is potentially useful for any modelling experiment involving chemistry climate or chemistry transport models.

An Analysis of Prediction Skill of Heat Waves Over India Using TIGGE Ensemble Forecasts

AbstractHeat wave has become a great concern for India in the recent years due to its disastrous impact on various sectors including health. Thus, accurate forecasts of heat wave events well in advance are required for preparing adequate mitigation strategies. The present study assesses the prediction skill of numerical weather prediction models in The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) experiments for predicting heat waves over India up to 7 days in advance. The models considered for this analysis are; the European Centre for Medium‐Range Weather Forecasts (ECMWF), the UK Met Office (UKMO) and National Centre for Environmental Prediction (NCEP). The model forecast verifications have been carried out for the hot weather season (April–June) over India for the period of 2008–2013. Fourteen heat wave events were identified during the study period using gridded daily maximum temperature (Tmax). The study reveals that the spatial distribution of maximum Temperature is well predicted by the TIGGE models for a forecast lead time of 1–7 days. The analysis suggested that the magnitude of heat wave events, even with a 7 days lead time, can be correctly predicted by more than 80% of ensemble members in all the TIGGE models. The prediction skill of the maximum temperatures over heat wave prone area during heat wave events is higher for ECMWF, then UKMO and NCEP models. The forecast verification analysis thus indicates that the TIGGE models are able to provide early warnings of heat waves with at least 5 days lead time.

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.

Towards the new Thematic Core Service Tsunami within the EPOS Research Infrastructure

Tsunamis constitute a significant hazard for European coastal populations, and the impact of tsunami events worldwide can extend well beyond the coastal regions directly affected. Understanding the complex mechanisms of tsunami generation, propagation, and inundation, as well as managing the tsunami risk, requires multidisciplinary research and infrastructures that cross national boundaries. Recent decades have seen both great advances in tsunami science and consolidation of the European tsunami research community. A recurring theme has been the need for a sustainable platform for coordinated tsunami community activities and a hub for tsunami services. Following about three years of preparation, in July 2021, the European tsunami community attained the status of Candidate Thematic Core Service (cTCS) within the European Plate Observing System (EPOS) Research Infrastructure. Within a transition period of three years, the Tsunami candidate TCS is anticipated to develop into a fully operational EPOS TCS. We here outline the path taken to reach this point, and the envisaged form of the future EPOS TCS Tsunami. Our cTCS is planned to be organised within four thematic pillars: (1) Support to Tsunami Service Providers, (2) Tsunami Data, (3) Numerical Models, and (4) Hazard and Risk Products. We outline how identified needs in tsunami science and tsunami risk mitigation will be addressed within this structure and how participation within EPOS will become an integration point for community development.

Evaluating the accuracy of ARMA and multi-index methods for predicting winter wheat maturity date.

Accurate and timely prediction of regional winter wheat maturity date can provide essential information to improve the management of agriculture and avoid declines in the yield and quality of crops. In this paper, we propose the use of an autoregressive moving-average model to predict vegetation indices on 1, 9, and 17 May each year, and applied them to the methods of evaluating crop maturity based on vegetation indices. Growing degree days and a widely applied local empirical method were selected to explore and compare the feasibility of several methods. We analyzed winter wheat harvested from the Guanzhong Plain during 2003-2013 and used leave-one-out cross-validation to compare and verify the performance of the maturity prediction methods. The results demonstrated that (i) the vegetation index methods and growing degree days methods predicted maturity with higher accuracy than did the widely applied local empirical method, and (ii) the two-step filtering method based on future meteorological data from The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble exhibited the highest prediction accuracy on 1 May and had the lowest error fluctuation range on 17 May. These results provide new insights for predicting regional crop maturity, deploying agricultural harvesting equipment in various regions, and avoiding decreases in crop yields caused by adverse weather. © 2021 Society of Chemical Industry.

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.

The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China

Evaluating the adaptability of precipitation forecasting is of great importance for regional flood control and drought warnings. This study conducted evaluations using the 1–9 days cumulative precipitation forecast data of five typical operational global ensemble prediction systems (EPSs) from TIGGE (i.e., The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble) and the observed daily precipitation data of 40 meteorological stations over the Huaihe River basin (HB). A series of verification metrics is used to evaluate the performances of quantitative precipitation forecasts (QPFs) and probabilistic quantitative precipitation forecasts (PQPFs) from the five EPSs from April to December 2015 in terms of overall performance, different precipitation thresholds, different lead times and the spatial distribution over the HB. The adaptability of the multimodel superensemble integrated from the five EPSs by the Bayesian model average is also examined during the main flood season. The results show that (1) the forecast quality of the China Meteorological Administration EPS is the worst for all lead times, which may relate to its having the fewest ensemble members. The European Centre for Medium-Range Weather Forecasts (ECMWF) EPS performs the best in terms of QPF and PQPF qualities for longer lead times because ECMWF has the largest ensemble members. (2) All EPSs have better discrimination at low thresholds, indicating the reference value for drought warnings. ECMWF is expected to obtain the best PQPF skill for a large threshold through postprocessing; (3) due to the differences in climates in the North and South of the basin, QPF and PQPF qualities are better in the northern HB than in the southern HB; (4) except for climate, the PQPF skill is also influenced by precipitation type, while the QPF accuracy is affected by terrain. The PQPF is good at forecasting the precipitation caused by ocean effects but not by mountain topography. The QPF accuracy decreases in mountainous areas; and (5) the multimodel superensemble has little effect on PQPF skill improvement but can improve QPF accuracy when raw EPSs have significantly different QPF accuracies.

A Global Archive of Coseismic DInSAR Products Obtained Through Unsupervised Sentinel-1 Data Processing

We present an automatic and unsupervised tool for the systematic generation of Sentinel-1 (S1) differential synthetic aperture radar interferometry (DInSAR) coseismic products. In particular, the tool first retrieves the location, depth, and magnitude of every seismic event from interoperable online earthquake catalogs (e.g., the United States Geological Survey (USGS) and the Italian National Institute of Geophysics and Volcanology (INGV) and then, for significant (with respect to a set of selected thresholds) earthquakes, it automatically triggers the downloading of S1 data and their interferometric processing over the area affected by the earthquake. The automatic system we developed has also been implemented within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, with the aim of creating a global database of DInSAR S1 coseismic products, which consist of displacement maps and the associated wrapped interferograms and spatial coherences. This information will progressively be made freely available through the European Plate Observing System (EPOS) Research Infrastructure, thus providing the scientific community with a large catalog of DInSAR data that can be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. The developed tool can also support national and local authorities during seismic crises by quickly providing information on the surface deformation induced by earthquakes.

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.

Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

AbstractBased on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium‐Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April–June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013–2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light‐to‐heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm‐moist air into south China by the low‐level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30–36 h to 18–24 h to 6–12 h for the cases with large‐scale forcing, but not for the locally produced cases.

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.

THORPEX Research and the Science of Prediction

AbstractThe Observing System Research and Predictability Experiment (THORPEX) was a 10-yr, international research program organized by the World Meteorological Organization’s World Weather Research Program. THORPEX was motivated by the need to accelerate the rate of improvement in the accuracy of 1-day to 2-week forecasts of high-impact weather for the benefit of society, the economy, and the environment. THORPEX, which took place from 2005 to 2014, was the first major international program focusing on the advancement of global numerical weather prediction systems since the Global Atmospheric Research Program, which took place almost 40 years earlier, from 1967 through 1982. The scientific achievements of THORPEX were accomplished through bringing together scientists from operational centers, research laboratories, and the academic community to collaborate on research that would ultimately advance operational predictive skill. THORPEX included an unprecedented effort to make operational products readily accessible to the broader academic research community, with community efforts focused on problems where challenging science intersected with the potential to accelerate improvements in predictive skill. THORPEX also collaborated with other major programs to identify research areas of mutual interest, such as topics at the intersection of weather and climate. THORPEX research has 1) increased our knowledge of the global-to-regional influences on the initiation, evolution, and predictability of high-impact weather; 2) provided insight into how predictive skill depends on observing strategies and observing systems; 3) improved data assimilation and ensemble forecast systems; 4) advanced knowledge of high-impact weather associated with tropical and polar circulations and their interactions with midlatitude flows; and 5) expanded society’s use of weather information through applied and social science research.

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.