Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1

Operational forecasting of volcanic ash and SO2 clouds is challenging due to the large uncertainties that typically exist on the eruption source term and the mass removal mechanisms occurring downwind. Current operational forecast systems build on single-run deterministic scenarios that do not account for model input uncertainties and their propagation in time during transport. An ensemble-based forecast strategy has been implemented in the FALL3D-8.1 atmospheric dispersal model to configure, execute, and post-process an arbitrary number of ensemble members in a parallel workflow. In addition to intra-member model domain decomposition, a set of inter-member communicators defines a higher level of code parallelism to enable future incorporation of model data assimilation cycles. Two types of standard products are automatically generated by the ensemble post-process task. On one hand, deterministic forecast products result from some combination of the ensemble members (e.g., ensemble mean, ensemble median, etc.) with an associated quantification of forecast uncertainty given by the ensemble spread. On the other hand, probabilistic products can also be built based on the percentage of members that verify a certain threshold condition. The novel aspect of FALL3D-8.1 is the automatisation of the ensemble-based workflow, including an eventual model validation. To this purpose, novel categorical forecast diagnostic metrics, originally defined in deterministic forecast contexts, are generalised here to probabilistic forecasts in order to have a unique set of skill scores valid to both deterministic and probabilistic forecast contexts. Ensemble-based deterministic and probabilistic approaches are compared using different types of observation datasets (satellite cloud detection and retrieval and deposit thickness observations) for the July 2018 Ambae eruption in the Vanuatu archipelago and the April 2015 Calbuco eruption in Chile. Both ensemble-based approaches outperform single-run simulations in all categorical metrics but no clear conclusion can be extracted on which is the best option between these two.

Weather based pest forewarning models for mealybug infestation in Robusta coffee (Coffea canephora)

An investigation was carried out to study the effect of weather parameters on mealybug infestation in Robusta coffee and to develop forewarning model using 39 years (1977 to 2015) pest data on mealy bug damage in coffee plantations collected at Regional Coffee Research Station, Chundale, Wayanad district of Kerala were recorded at fortnightly intervals during 1977 to 2015 (39 years). The results revealed that mealybug infestation was found to vary with season and also year to year. Fruit setting and budding stages of coffee were severely damaged by mealy bug. Analysis indicated that annually, average damage due to mealy bug was 6.4 per cent and ranged between 5.4 per cent in 1984 to 9.7 per cent in 2011. The mealybug damage was maximum during summer season and lowest during South-West Monsoon (June-September) season. Season wise regression models were developed using data up to 2013 for forewarning per cent damage of mealy bug and validated for 2014 and 2015. The model for summer season mealybug damage has maximum R2=0.79 which can be used for operational forecasting of mealy bug damage.

Assessment of air pollution in cities by road transport in projects for managing the metropolis ecological state

The article is devoted to the study of the pollution level in the city streets by road transport. Purpose. The aim of the work is to operative assess the concentration of pollutants in street canyons in projects for managing the ecological state of the metropolis. Research Methodology. The article used statistical analysis, mathematical modeling. Scientific novelty. A model for assessing the level of air pollution in city street canyons is proposed. On the basis of this model, the concentration of pollutants in the street canyons of the Pechersky district of Kiev was determined, taking into account the daily dynamics of the traffic flows intensity. Conclusions. The research results can be used in the operational forecasting of the pollution level of the roadside space ecosystems, which will allow timely, by controlling the parameters of the traffic flow, to prevent critical situations in which the concentration of pollutants exceeds the maximum permissible values. Key words: management, transport, pollution assessment, traffic flow, design, modeling of pollution fields.

Introducing the MISR level 2 near real-time aerosol product

Atmospheric aerosols are an important element of Earth's climate system and have significant impacts on the environment and on human health. Global aerosol modeling has been increasingly used for operational forecasting and as support for decision making. For example, aerosol analyses and forecasts are routinely used to provide air quality information and alerts in both civilian and military applications. The growing demand for operational aerosol forecasting calls for additional observational data that can be assimilated into models to improve model accuracy and predictive skill. These factors have motivated the development, testing, and release of a new near real-time (NRT) level 2 (L2) aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform. The NRT product capitalizes on the unique attributes of the MISR aerosol retrieval approach and product contents, such as reliable aerosol optical depth as well as aerosol microphysical information. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition of the satellite observations. Implications for the product quality and consistency are discussed and compared to the current operational L2 MISR aerosol product. Several ways of implementing additional use-specific retrieval screenings are also highlighted.

A Statistical Analysis of High Frequency Track and Intensity Forecasts from NOAA’s Operational Hurricane Weather Research and Forecast (HWRF) Modeling System

A statistical analysis is performed on the high-frequency (3 1/3 s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) model for all tropical cyclones (TCs) in the North Atlantic basin over a 3-year period (2017-2019). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax with average values of 6-8 km. Vmax fluctuations were as high as 20 kt in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt for category 2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running mean windows of 3-9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

The African SWIFT project: growing science capability to bring about a revolution in weather prediction.

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps.Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment.