Performance of 4D‐Var <scp>NWP</scp>‐based nowcasting of precipitation at the Met Office for summer 2012

Ballard, Su; Li, Zhihong; Simonin, David; Caron, Jean‐François

doi:10.1002/qj.2665

Cited by 69 publications

(103 citation statements)

References 73 publications

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“…The quality of the reflectivity data is being improved by the roll‐out of dual‐polarization across the network. Furthermore, geostationary satellite radiances, wind profilers, global navigation satellite system (GNSS) integrated column water and surface observations as well as radar data are available sub‐hourly (Ballard et al, ) and MODE‐S aircraft temperatures and winds (de Haan ; de Haan and Stoffelen, ) are potentially available at high temporal resolution. Additional information can be extracted from the time variation of observations using 4D‐Var, by providing a constraint on the evolution of the forecast over a fixed time‐window.…”

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

“…Once operational use of CPMs was achieved, it was also a natural next step to test whether they could be used for nowcasting (6 h forecasts) using hourly as opposed to 3‐hourly cycling NWP. 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.…”

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

“…The trial of the 4D‐Var, hourly‐cycling NDP (see Section ) for the summer of 2012 showed the benefit of frequently updating forecasts using more recent observations within the NWP model in the prediction of convective storms causing flash flooding (Ballard et al, ). It showed some significant objective improvements over the latest available forecasts from the UKV model (updated only every 6 h) and the operational advection nowcasting system called the Short Term Ensemble Prediction System (STEPS) nowcast (Bowler et al, ; Seed et al , ).…”

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

See 2 more Smart Citations

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

Clark

Roberts

Lean

et al. 2016

Meteorological Applications

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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%

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

Section: Data Assimilation For Convection‐permitting Modelsmentioning

confidence: 99%

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

Clark

Roberts

Lean

et al. 2016

Meteorological Applications

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302

315

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“…The Met Office high resolution convection permitting model, the UKV, is a variable resolution model that covers the UK [22][23][24][25]. The horizontal grid has a 1.5 km fixed resolution on the interior surrounded by a variable resolution grid, which increases smoothly in size to 4 km.…”

Section: The Met Office Ukv Model and 3d Variational Assimilation Schemementioning

confidence: 99%

Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics

et al. 2016

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It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error correlations for Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations that are assimilated into the Met Office high-resolution model. The errors are calculated using a diagnostic that calculates statistical averages of observation-minus-background and observation-minus-analysis residuals. This diagnostic is sensitive to the background and observation error statistics used in the assimilation, although, with careful interpretation of the results, it can still provide useful information. We find that the diagnosed SEVIRI error variances are as low as one-tenth of those currently used in the operational system. The water vapour channels have significantly correlated inter-channel errors, as do the surface channels. The surface channels have larger observation error variances and inter-channel correlations in coastal areas of the domain; this is the result of assimilating mixed pixel (land-sea) observations. The horizontal observation error correlations range between 30 km and 80 km, which is larger than the operational thinning distance of 24 km. We also find that estimates from the diagnostics are unaffected by biased observations, provided that the observation-minus-background and observation-minus-analysis residual means are subtracted.

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“…Discuss., https://doi.org /10.5194/gmd-2017- The model used for this study is a 1.5 km grid-length version of the Met Office's Unified Model for a region over the southern UK (SUK-1.5 4 ). This model is no longer operational, but was used during the London 2012 Olympics Golding et al (2014); Ballard et al (2016). The model's initial conditions (ICs) and lateral boundary conditions (LBCs) were produced using a set The existing EPS used for this study was the 24-member MOGREPS-G system (the global configuration of the Met Office Global and Regional Ensemble Prediction System (Bowler et al, 2008)).…”

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confidence: 99%

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Bannister

Migliorini

Rudd

et al. 2017

Preprint

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Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is 'large enough' to 5 be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains.The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), 10 they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts.It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation 15 purposes.Copyright statement.

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Performance of 4D‐Var NWP‐based nowcasting of precipitation at the Met Office for summer 2012

Cited by 69 publications

References 73 publications

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

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

Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

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