Doppler radar radial wind assimilation using an hourly cycling 3D‐Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting

Simonin, David; Ballard, Su; Li, Z.

doi:10.1002/qj.2298

Cited by 63 publications

(87 citation statements)

References 58 publications

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“…Therefore, experiments were undertaken for a domain covering southern England and Wales with hourly‐cycling 3D‐Var (Ballard et al, , ) and then with 4D‐Var (Golding et al , ; Sun et al , ; Ballard et al , ). This system was extended to allow assimilation of radar Doppler radial winds (Simonin et al , ), which were then introduced operationally in the UKV model in 2011. Work is underway to develop direct assimilation of radar reflectivity (Hawkness‐Smith and Ballard, ) and ceilometer backscatter as well as radar refractivity (Nicol et al , ).…”

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

Convection‐permitting models: a step‐change in rainfall forecasting

Clark

Roberts

Lean

et al. 2016

Meteorological Applications

302

315

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Convection-permitting models (CPMs) have provided weather forecasting centres with a step-change in capabilities for forecasting rainfall. They are now used operationally to forecast precipitation in many parts of the world, including the UK. CPMs are models in which the dynamics of atmospheric convection is treated with sufficient accuracy in order to make it viable to switch off convection parametrization. This review describes the current state-of-the-art in operational CPM-based numerical weather prediction (NWP), primarily within the UK, and the historical development of CPMs. The characteristics of CPM systems and forecasts are highlighted and placed in an international context to recognize similar trends and highlight some differences. It is shown that the realism of CPM-based forecasts can provide improved subjective guidance on convection, and, when measured on appropriate scales, can improve rainfall forecasting skill compared to coarser-resolution NWP. Data assimilation techniques used with operational CPMs are reviewed and given historical context. Examples of new types of observations that may increase the skill of forecasts from improved initial conditions are discussed. CPM-based nowcasting systems are shown to provide considerable improvements in short-range forecasts of rapidly developing, intense systems. As a result, these CPM-based systems provide a new forecasting capability. Finally, the development of CPMs has also required new techniques to verify forecasts and define their skill. These have revealed that the lack of predictability of the smallest scales involving convection means that ensemble techniques are required to represent forecast uncertainty, resulting in a new capability to provide objective forecast probabilities of local precipitation.

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Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

Convection‐permitting models: a step‐change in rainfall forecasting

Clark

Roberts

Lean

et al. 2016

Meteorological Applications

302

315

View full text Add to dashboard Cite

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“…For the BoM's developmental ACCESS-City LAMs, only echoes classed as precipitation are assumed to be qualified for assimilation. The observations undergo extensive processing after ingestion in the assimilation system [the details of the quality control options are like those in Simonin et al (2014)], including observation-minusbackground checks, removal of isolated pixels, and comparison with neighbors, before spatial averaging to reduce data density. This means that classification is not the only mechanism for removing unreliable velocity observations.…”

Section: Application To Doppler Radar Data Assimilationmentioning

confidence: 99%

“…This means that classification is not the only mechanism for removing unreliable velocity observations. Note that for radial velocity, misclassification at long range may not be a large problem because observations far from the radar are not assimilated due to increasing error contributions (Fabry 2010;Simonin et al 2014). Currently, a range limit of 100 km is applied for assimilated observations.…”

Section: Application To Doppler Radar Data Assimilationmentioning

confidence: 99%

Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

Rennie

Curtis

Peter

et al. 2015

Journal of Atmospheric and Oceanic Technology

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The Australian Bureau of Meteorology's operational weather radar network comprises a heterogeneous radar collection covering diverse geography and climate. A naïve Bayes classifier has been developed to identify a range of common echo types observed with these radars. The success of the classifier has been evaluated against its training dataset and by routine monitoring. The training data indicate that more than 90% of precipitation may be identified correctly. The echo types most difficult to distinguish from rainfall are smoke, chaff, and anomalous propagation ground and sea clutter. Their impact depends on their climatological frequency. Small quantities of frequently misclassified persistent echo (like permanent ground clutter or insects) can also cause quality control issues. The Bayes classifier is demonstrated to perform better than a simple threshold method, particularly for reducing misclassification of clutter as precipitation. However, the result depends on finding a balance between excluding precipitation and including erroneous echo. Unlike many single-polarization classifiers that are only intended to extract precipitation echo, the Bayes classifier also discriminates types of nonprecipitation echo. Therefore, the classifier provides the means to utilize clear air echo for applications like data assimilation, and the class information will permit separate data handling of different echo types.

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“…Doppler capability also allows winds to be estimated in precipitation with similar spatial and temporal resolution, enabling the detection of hazards such as wind shear. The assimilation of these observations has been shown to improve rain and wind significantly in NWP‐based nowcasts (Simonin et al , ).…”

Section: Protecting Lives and Livelihoodsmentioning

confidence: 99%

From months to minutes – exploring the value of high‐resolution rainfall observation and prediction during the UK winter storms of 2013/2014

Lewis

Mittermaier

Mylne

et al. 2015

Meteorological Applications

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ABSTRACT:As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions that can represent the numerous feedbacks and linkages between sub-systems is greater than ever. This was well illustrated during winter 2013/2014 when a prolonged series of deep Atlantic depressions over a 3 month period resulted in damaging wind storms and exceptional rainfall accumulations. The impact on livelihoods and property from the resulting coastal surge and river and surface flooding was substantial. This study reviews the observational and modelling toolkit available to operational meteorologists during this period, which focusses on precipitation forecasting months, weeks, days and hours ahead of time. The routine availability of high-resolution (km scale) deterministic and ensemble rainfall predictions for short-range weather forecasting as well as weather-resolving seasonal prediction capability represent notable landmarks that have resulted from significant progress in research and development over the past decade. Latest results demonstrated that the suite of global and high-resolution UK numerical weather prediction models provided excellent guidance during this period, supported by high-resolution observations networks, such as weather radar, which proved resilient in difficult conditions. The specific challenges for demonstrating this performance for high-resolution precipitation forecasts are discussed. Despite their good operational performance, there remains a need to further develop the capability and skill of these tools to fully meet user needs and to increase the value that they deliver. These challenges are discussed, notably to accelerate the progress towards understanding the value that might be delivered through more integrated environmental prediction.

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Doppler radar radial wind assimilation using an hourly cycling 3D‐Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting

Cited by 63 publications

References 58 publications

Convection‐permitting models: a step‐change in rainfall forecasting

Convection‐permitting models: a step‐change in rainfall forecasting

Bayesian Echo Classification for Australian Single-Polarization Weather Radar with Application to Assimilation of Radial Velocity Observations

From months to minutes – exploring the value of high‐resolution rainfall observation and prediction during the UK winter storms of 2013/2014

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