This paper presents a new set of numerical simulations of two colliding density currents in a idealized framework, integrating the Boussinesq vorticity equation in a rectangular bounded domain. These simulations are used to examine the dynamical features of the collision, in the light of recent laboratory experiments. The collision dynamics present various interesting features. Here we have focused on the interface slope at the front of the two unequal density currents and on the maximum height reached by the fluid after the collision. For the secondary triggering of atmospheric convection by colliding cold pools from previous convective events, these may affect the positioning and the momentum of the collision uplift, respectively. The interface slope has been shown to be dependent on the currents' buoyancy ratio (i.e. the ratio between the density differences of the two fluids with the ambient fluid), whereas the maximum height has no strong dependence, for a given initial current depth. A theoretical model, based on an analogy with a vortex pair, has been proposed for the interface-slope dependence, taking as input the buoyancy ratio or the propagating speeds. This model agrees reasonably well with the observed numerical values.
Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the mid-latitudes for weather forecasting timescales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the UK Met Office over tropical East Africa, for 24 cases in the period April-May 2019. The CP-ENS, an ensemble with parametrized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skilful forecast for both 3-h and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighbourhood approach, show that the CP-ENS is skilful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the mid-latitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is under-spread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.
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.
Dynamical downscaling of ensemble forecasts to convection‐permitting resolutions aims to improve forecast skill by explicitly resolving mesoscale dynamical features. The success of this approach is dependent on the ability of the model to spin up smaller features embedded in the larger‐scale flow and provide more local information than could be inferred from knowledge of the climatological response to the large‐scale flow alone. Here we test whether such additional information is obtained from the Met Office Global and Regional Ensemble Prediction Systems (MOGREPS) for the sea‐breeze phenomenon which is resolved properly only at convection‐permitting resolutions but is driven by large‐sale conditions that are well represented in the global driving model. The sea breeze is detected using a new automatic tracking algorithm suitable for use in convective‐scale forecast data. The skill of probabilistic forecasts of sea‐breeze occurrence from the high‐resolution ensemble is compared to that of a Bayesian forecast trained on paired high/low‐resolution ensemble members. This creates a statistical forecast of the high‐resolution ensemble member given knowledge of the global forecast ensemble alone. The aim of this paper is twofold: firstly to assess whether the information about sea‐breeze occurrence is encoded in a few large‐scale parameters and can be forecast by a statistical method, and secondly to estimate what information is gained by running the high‐resolution forecast beyond that which is contained in these large‐scale flow parameters. Comparison of the two forecasting methods using a variety of verification methods all lead to the same conclusion: although both the Bayesian forecast and convection‐permitting ensemble provide information about sea‐breeze occurrence, the convection‐permitting ensemble is significantly more able to discriminate between sea‐breeze events and non‐events for all lead times.
Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realised. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia and the Philippines.Fractions Skill Score is used to assess the spatial scale-dependence of skill in forecasts of precipitation during October 2018 - March 2019. CP forecasts are skilful for 3-hour precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2, 4.5 and 8.8 km grid spacing and an 8.8km ensemble with parameterised convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterised ensemble is less skilful than CP ensembles over Malaysia and Indonesia and more skilful over the Philippines; however, the parameterised ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are under-spread indicating that future model development should focus on this issue.
<p>Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the<br>mid-latitudes for weather forecasting timescales over the past decade, enabled by the increase in<br>computational resources. Recently, efforts are being made to study the benefits of CP-ENS for<br>tropical regions. This study examines CP-ENS forecasts produced by the UK Met Office over<br>tropical East Africa, for 24 cases in the period April-May 2019. The CP-ENS, an ensemble with<br>parametrized convection (Glob-ENS), and their deterministic counterparts are evaluated against<br>rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have<br>the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated<br>compared to observations. Pairwise comparisons between the different configurations reveal that<br>the CP-ENS is generally the most skilful forecast for both 3-h and 24-h accumulations of heavy<br>rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic<br>forecasts of heavy rainfall, verified using a neighbourhood approach, show that the CP-ENS is<br>skilful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good<br>as found in the mid-latitudes. Skill decreases with lead time and varies diurnally, especially for<br>CP forecasts. The CP-ENS is under-spread both in terms of forecasting the locations of heavy<br>rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in<br>using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific<br>suggestions for further research and development, including probabilistic forecast guidance.</p>
Testbeds have become integral to advancing the transfer of knowledge and capabilities from research to operational weather forecasting in many parts of the world. The first high-impact weather testbed in tropical Africa was recently carried out through the African SWIFT program, with participation from researchers and forecasters from Senegal, Ghana, Nigeria, Kenya, the United Kingdom, and international and pan-African organizations. The testbed aims were to trial new forecasting and nowcasting products with operational forecasters, to inform future research, and to act as a template for future testbeds in the tropics. The African SWIFT testbed integrated users and researchers throughout the process to facilitate development of impact-based forecasting methods and new research ideas driven both by operations and user input. The new products are primarily satellite-based nowcasting systems and ensemble forecasts at global and regional convection-permitting scales. Neither of these was used operationally in the participating African countries prior to the testbed. The testbed received constructive, positive feedback via intense user interaction including fishery, agriculture, aviation, and electricity sectors. After the testbed, a final set of recommended standard operating procedures for satellite-based nowcasting in tropical Africa have been produced. The testbed brought the attention of funding agencies and organizational directors to the immediate benefit of improved forecasts. Delivering the testbed strengthened the partnership between each country’s participating university and weather forecasting agency and internationally, which is key to ensuring the longevity of the testbed outcomes.
The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio-economic consequences.Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small-scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high-impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio-temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100-300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill
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