Ensemble prediction for nowcasting with a convection-permitting model - II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study short-range forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed

Optical and X-ray Variability in The Least Luminous AGN, NGC4395

We report the detection of optical and X-ray variability in the least luminous known Seyfert galaxy, NGC4395. The featureless continuum changed by a factor of 2 in 6 months, which is typical of more luminous AGN. The largest variation was seen at shorter wavelengths, so that the spectrum becomes `harder' during higher activity states. In a one week optical broad band monitoring program, a 20% change was seen between successive nights. In a 1 month period the spectral shape changed from a power law with spectral index alpha ~0 (characteristic of quasars) to a spectral index alpha ~2 (as observed in other dwarf AGN). ROSAT HRI and PSPC archive data show a variable X-ray source coincident with the galactic nucleus. A change in X-ray flux by a factor \~2 in 15 days has been observed. When compared with more luminous AGN, NGC4395 appears to be very X-ray quiet. The hardness ratio obtained from the PSPC data suggests that the spectrum could be absorbed. We also report the discovery of weak CaIIK absorption, suggesting the presence of a young stellar cluster providing of the order of 10% of the blue light. Using HST UV archive data, together with the optical and X-ray observations, we examine the spectral energy distribution for NGC4395 and discuss the physical conditions implied by the nuclear activity under the standard AGN model. The observations can be explained by either an accreting massive black hole emitting at about 10^(-3) L_(Edd) or by a single old compact SNR with an age of 50 to 500 yr generated by a small nuclear starburst.Comment: 19 pages, 9 figures, to appear in MNRA

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events

Dialysis residential care : a future dialysis service model

People with chronic kidney disease are ageing and have increasing co-morbidities. The current delivery of renal replacement therapy, dialysis and transplantation, needs to adjust to changing patient needs. This paper proposes a potential future service delivery model featuring a dialysis residential care facility and a care coordination focus. The residential care facility would be composed of four levels of care; high, hostel, independent and outpatient. The paper argues that this model may result in decreased morbidity, improved patient quality of life and may prove cost effective. Patients\u27 nutritional status, medication adherence and transport efficiency may be improved. We propose this model to stimulate further debate in order to meet the needs of current and future chronic kidney disease patients.<br /

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

While contemporary Numerical Weather Prediction models represent the large-scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small-scale features such as convective rainfall. Even though high-resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models’ initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water-related observations – especially at convective scales (also known as storm scales) – this paper discusses the observation and assimilation of water- related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective-scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water-related observations (e.g. from radar, microwave, and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud and precipitation affected observations), the need to account for the flow-dependent multivariate ‘balance’ between atmospheric water and both dynamical and mass fields, and the inherent non-Gaussian nature of atmospheric water variables

Integrated washland management for flood defence and biodiversity

A combination of reform of agricultural policy, changing priorities in the countryside, growing commitment to protect and enhance biodiversity, and concerns about increased flood risk in lowlands have drawn attention to the potential contribution that managed washlands can make to deliver benefits to biodiversity and flood management. In this context, and with funding from Defra and English Nature, the study reported here1 set out to determine the scope for simultaneously achieving flood management and biodiversity objectives, and how this might be achieved in practice. The broad purpose is to inform policy on washland creation and management, including mechanisms for implementation if deemed appropriate